diff --git a/.travis.yml b/.travis.yml
index 6a8cd47563..576dbbe64d 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -23,9 +23,9 @@ matrix:
   - os: linux
     env: DISTRIB="conda" DOCPUSH="true" PYTHON="3.7" SKIP_TESTS="true"
   - os: linux
-    env: DISTRIB="conda" RUN_FLAKE8="true" SKIP_TESTS="true"
+    env: DISTRIB="conda" PYTHON="3.8" RUN_FLAKE8="true" SKIP_TESTS="true"
   - os: linux
-    env: DISTRIB="conda" RUN_MYPY="true" SKIP_TESTS="true"
+    env: DISTRIB="conda" PYTHON="3.8" RUN_MYPY="true" SKIP_TESTS="true"
   - os: linux
     env: DISTRIB="conda" COVERAGE="true" PYTHON="3.6"
   - os: linux
diff --git a/autosklearn/__version__.py b/autosklearn/__version__.py
index fd6968a5d9..85dd3dc532 100644
--- a/autosklearn/__version__.py
+++ b/autosklearn/__version__.py
@@ -1,4 +1,4 @@
 """Version information."""
 
 # The following line *must* be the last in the module, exactly as formatted:
-__version__ = "0.10.0"
+__version__ = "0.11.0"
diff --git a/autosklearn/automl.py b/autosklearn/automl.py
index a0b88aeea6..59ee4c119c 100644
--- a/autosklearn/automl.py
+++ b/autosklearn/automl.py
@@ -1,13 +1,15 @@
 # -*- encoding: utf-8 -*-
+import copy
 import io
 import json
-import multiprocessing
 import platform
 import os
 import sys
-from typing import Optional, List, Union
+import time
+from typing import Any, Dict, Optional, List, Union
 import unittest.mock
 import warnings
+import tempfile
 
 from ConfigSpace.read_and_write import json as cs_json
 import dask.distributed
@@ -35,10 +37,11 @@
 from autosklearn.evaluation.abstract_evaluator import _fit_and_suppress_warnings
 from autosklearn.evaluation.train_evaluator import _fit_with_budget
 from autosklearn.metrics import calculate_score
+from autosklearn.util.backend import Backend
 from autosklearn.util.stopwatch import StopWatch
 from autosklearn.util.logging_ import get_logger, setup_logger
 from autosklearn.util import pipeline, RE_PATTERN
-from autosklearn.ensemble_builder import EnsembleBuilder
+from autosklearn.ensemble_builder import EnsembleBuilderManager
 from autosklearn.ensembles.singlebest_ensemble import SingleBest
 from autosklearn.smbo import AutoMLSMBO
 from autosklearn.util.hash import hash_array_or_matrix
@@ -93,16 +96,15 @@ def send_warnings_to_log(
 class AutoML(BaseEstimator):
 
     def __init__(self,
-                 backend,
+                 backend: Backend,
                  time_left_for_this_task,
                  per_run_time_limit,
                  initial_configurations_via_metalearning=25,
                  ensemble_size=1,
                  ensemble_nbest=1,
                  max_models_on_disc=1,
-                 ensemble_memory_limit: Optional[int] = 1024,
                  seed=1,
-                 ml_memory_limit=3072,
+                 memory_limit=3072,
                  metadata_directory=None,
                  debug_mode=False,
                  include_estimators=None,
@@ -131,9 +133,8 @@ def __init__(self,
         self._ensemble_size = ensemble_size
         self._ensemble_nbest = ensemble_nbest
         self._max_models_on_disc = max_models_on_disc
-        self._ensemble_memory_limit = ensemble_memory_limit
         self._seed = seed
-        self._ml_memory_limit = ml_memory_limit
+        self._memory_limit = memory_limit
         self._data_memory_limit = None
         self._metadata_directory = metadata_directory
         self._include_estimators = include_estimators
@@ -171,6 +172,7 @@ def __init__(self,
             self._resampling_strategy_arguments['folds'] = 5
         self._n_jobs = n_jobs
         self._dask_client = dask_client
+
         self.precision = precision
         self._disable_evaluator_output = disable_evaluator_output
         # Check arguments prior to doing anything!
@@ -178,7 +180,7 @@ def __init__(self,
             raise ValueError('disable_evaluator_output must be of type bool '
                              'or list.')
         if isinstance(self._disable_evaluator_output, list):
-            allowed_elements = ['model', 'y_optimization']
+            allowed_elements = ['model', 'cv_model', 'y_optimization', 'y_test', 'y_valid']
             for element in self._disable_evaluator_output:
                 if element not in allowed_elements:
                     raise ValueError("List member '%s' for argument "
@@ -207,8 +209,7 @@ def __init__(self,
 
         self.InputValidator = InputValidator()
 
-        # Place holder for the run history of the
-        # Ensemble building process
+        # The ensemble performance history through time
         self.ensemble_performance_history = []
 
         if not isinstance(self._time_for_task, int):
@@ -221,6 +222,40 @@ def __init__(self,
         # After assigning and checking variables...
         # self._backend = Backend(self._output_dir, self._tmp_dir)
 
+    def _create_dask_client(self):
+        self._is_dask_client_internally_created = True
+        processes = False
+        if self._n_jobs is not None and self._n_jobs > 1:
+            processes = True
+            dask.config.set({'distributed.worker.daemon': False})
+        self._dask_client = dask.distributed.Client(
+            dask.distributed.LocalCluster(
+                n_workers=self._n_jobs,
+                processes=processes,
+                threads_per_worker=1,
+                # We use the temporal directory to save the
+                # dask workers, because deleting workers
+                # more time than deleting backend directories
+                # This prevent an error saying that the worker
+                # file was deleted, so the client could not close
+                # the worker properly
+                local_directory=tempfile.gettempdir(),
+            )
+        )
+
+    def _close_dask_client(self):
+        if (
+            hasattr(self, '_is_dask_client_internally_created')
+            and self._is_dask_client_internally_created
+            and self._dask_client
+        ):
+            self._dask_client.shutdown()
+            self._dask_client.close()
+            del self._dask_client
+            self._dask_client = None
+            self._is_dask_client_internally_created = False
+            del self._is_dask_client_internally_created
+
     def _get_logger(self, name):
         logger_name = 'AutoML(%d):%s' % (self._seed, name)
         setup_logger(os.path.join(self._backend.temporary_directory,
@@ -256,7 +291,7 @@ def _do_dummy_prediction(self, datamanager, num_run):
 
         self._logger.info("Starting to create dummy predictions.")
 
-        memory_limit = self._ml_memory_limit
+        memory_limit = self._memory_limit
         if memory_limit is not None:
             memory_limit = int(memory_limit)
 
@@ -279,16 +314,39 @@ def _do_dummy_prediction(self, datamanager, num_run):
                                     cost_for_crash=get_cost_of_crash(self._metric),
                                     **self._resampling_strategy_arguments)
 
-        status, cost, runtime, additional_info = \
-            ta.run(1, cutoff=self._time_for_task)
+        status, cost, runtime, additional_info = ta.run(num_run, cutoff=self._time_for_task)
         if status == StatusType.SUCCESS:
             self._logger.info("Finished creating dummy predictions.")
         else:
-            self._logger.error('Error creating dummy predictions: %s ',
-                               str(additional_info))
-            # Fail if dummy prediction fails.
-            raise ValueError("Dummy prediction failed with run state %s and additional output: %s."
-                             % (str(status), str(additional_info)))
+            if additional_info.get('exitcode') == -6:
+                self._logger.error(
+                    "Dummy prediction failed with run state %s. "
+                    "The error suggests that the provided memory limits were too tight. Please "
+                    "increase the 'ml_memory_limit' and try again. If this does not solve your "
+                    "problem, please open an issue and paste the additional output. "
+                    "Additional output: %s.",
+                    str(status), str(additional_info),
+                )
+                # Fail if dummy prediction fails.
+                raise ValueError(
+                    "Dummy prediction failed with run state %s. "
+                    "The error suggests that the provided memory limits were too tight. Please "
+                    "increase the 'ml_memory_limit' and try again. If this does not solve your "
+                    "problem, please open an issue and paste the additional output. "
+                    "Additional output: %s." %
+                    (str(status), str(additional_info)),
+                )
+
+            else:
+                self._logger.error(
+                    "Dummy prediction failed with run state %s and additional output: %s.",
+                    str(status), str(additional_info),
+                )
+                # Fail if dummy prediction fails.
+                raise ValueError(
+                    "Dummy prediction failed with run state %s and additional output: %s."
+                    % (str(status), str(additional_info))
+                )
 
     def fit(
         self,
@@ -327,6 +385,16 @@ def fit(
             raise ValueError('Metric must be instance of '
                              'autosklearn.metrics.Scorer.')
 
+        # If no dask client was provided, we create one, so that we can
+        # start a ensemble process in parallel to smbo optimize
+        if (
+            self._dask_client is None and
+            (self._ensemble_size > 0 or self._n_jobs is not None and self._n_jobs > 1)
+        ):
+            self._create_dask_client()
+        else:
+            self._is_dask_client_internally_created = False
+
         if dataset_name is None:
             dataset_name = hash_array_or_matrix(X)
 
@@ -402,9 +470,8 @@ def fit(
         self._logger.debug('  ensemble_size: %d', self._ensemble_size)
         self._logger.debug('  ensemble_nbest: %f', self._ensemble_nbest)
         self._logger.debug('  max_models_on_disc: %s', str(self._max_models_on_disc))
-        self._logger.debug('  ensemble_memory_limit: %d', self._ensemble_memory_limit)
         self._logger.debug('  seed: %d', self._seed)
-        self._logger.debug('  ml_memory_limit: %d', self._ml_memory_limit)
+        self._logger.debug('  memory_limit: %s', str(self._memory_limit))
         self._logger.debug('  metadata_directory: %s', self._metadata_directory)
         self._logger.debug('  debug_mode: %s', self._debug_mode)
         self._logger.debug('  include_estimators: %s', str(self._include_estimators))
@@ -445,14 +512,6 @@ def fit(
         )
 
         self._backend._make_internals_directory()
-        try:
-            os.makedirs(self._backend.get_model_dir())
-        except (OSError, FileExistsError):
-            raise
-        try:
-            os.makedirs(self._backend.get_cv_model_dir())
-        except (OSError, FileExistsError):
-            raise
 
         self._task = datamanager.info['task']
         self._label_num = datamanager.info['label_num']
@@ -489,6 +548,7 @@ def fit(
             include_preprocessors=self._include_preprocessors,
             exclude_preprocessors=self._exclude_preprocessors)
         if only_return_configuration_space:
+            self._close_dask_client()
             return self.configuration_space
 
         # == RUN ensemble builder
@@ -499,8 +559,8 @@ def fit(
         self._stopwatch.start_task(ensemble_task_name)
         elapsed_time = self._stopwatch.wall_elapsed(self._dataset_name)
         time_left_for_ensembles = max(0, self._time_for_task - elapsed_time)
+        proc_ensemble = None
         if time_left_for_ensembles <= 0:
-            self._proc_ensemble = None
             # Fit only raises error when ensemble_size is not zero but
             # time_left_for_ensembles is zero.
             if self._ensemble_size > 0:
@@ -508,26 +568,30 @@ def fit(
                                  "is no time left. Try increasing the value "
                                  "of time_left_for_this_task.")
         elif self._ensemble_size <= 0:
-            self._proc_ensemble = None
             self._logger.info('Not starting ensemble builder because '
                               'ensemble size is <= 0.')
         else:
             self._logger.info(
                 'Start Ensemble with %5.2fsec time left' % time_left_for_ensembles)
 
-            # Create a queue to communicate with the ensemble process
-            # And get the run history
-            # Use a Manager as a workaround to memory errors cause
-            # by three subprocesses (Automl-ensemble_builder-pynisher)
-            mgr = multiprocessing.Manager()
-            mgr.Namespace()
-            queue = mgr.Queue()
-
-            self._proc_ensemble = self._get_ensemble_process(
-                time_left_for_ensembles,
-                queue=queue,
+            proc_ensemble = EnsembleBuilderManager(
+                start_time=time.time(),
+                time_left_for_ensembles=time_left_for_ensembles,
+                backend=copy.deepcopy(self._backend),
+                dataset_name=dataset_name,
+                task=task,
+                metric=self._metric,
+                ensemble_size=self._ensemble_size,
+                ensemble_nbest=self._ensemble_nbest,
+                max_models_on_disc=self._max_models_on_disc,
+                seed=self._seed,
+                precision=self.precision,
+                max_iterations=None,
+                read_at_most=np.inf,
+                ensemble_memory_limit=self._memory_limit,
+                logger_name=self._logger.name,
+                random_state=self._seed,
             )
-            self._proc_ensemble.start()
 
         self._stopwatch.stop_task(ensemble_task_name)
 
@@ -580,7 +644,7 @@ def fit(
                 backend=self._backend,
                 total_walltime_limit=time_left_for_smac,
                 func_eval_time_limit=per_run_time_limit,
-                memory_limit=self._ml_memory_limit,
+                memory_limit=self._memory_limit,
                 data_memory_limit=self._data_memory_limit,
                 watcher=self._stopwatch,
                 n_jobs=self._n_jobs,
@@ -600,6 +664,7 @@ def fit(
                 disable_file_output=self._disable_evaluator_output,
                 get_smac_object_callback=self._get_smac_object_callback,
                 smac_scenario_args=self._smac_scenario_args,
+                ensemble_callback=proc_ensemble,
             )
 
             try:
@@ -619,14 +684,15 @@ def fit(
 
         # Wait until the ensemble process is finished to avoid shutting down
         # while the ensemble builder tries to access the data
-        if self._proc_ensemble is not None and self._ensemble_size > 0:
-            self._proc_ensemble.join()
-            self.ensemble_performance_history = self._proc_ensemble.get_ensemble_history()
-
-        self._proc_ensemble = None
+        if proc_ensemble is not None:
+            self.ensemble_performance_history = list(proc_ensemble.history)
+            if len(proc_ensemble.futures) > 0:
+                future = proc_ensemble.futures.pop()
+                future.cancel()
 
         if load_models:
             self._load_models()
+        self._close_dask_client()
 
         return self
 
@@ -775,62 +841,45 @@ def fit_ensemble(self, y, task=None, precision=32,
         # Make sure that input is valid
         y = self.InputValidator.validate_target(y, is_classification=True)
 
-        self._proc_ensemble = self._get_ensemble_process(
-            1, task, precision, dataset_name, max_iterations=1,
-            ensemble_nbest=ensemble_nbest, ensemble_size=ensemble_size)
-        self._proc_ensemble.main()
-        self.ensemble_performance_history = self._proc_ensemble.get_ensemble_history()
-        self._proc_ensemble = None
-        self._load_models()
-        return self
-
-    def _get_ensemble_process(self, time_left_for_ensembles,
-                              task=None, precision=None,
-                              dataset_name=None, max_iterations=None,
-                              ensemble_nbest=None, ensemble_size=None, queue=None):
-
-        if task is None:
-            task = self._task
-        else:
-            self._task = task
-
-        if precision is None:
-            precision = self.precision
-        else:
-            self.precision = precision
-
-        if dataset_name is None:
-            dataset_name = self._dataset_name
-        else:
-            self._dataset_name = dataset_name
-
-        if ensemble_nbest is None:
-            ensemble_nbest = self._ensemble_nbest
-        else:
-            self._ensemble_nbest = ensemble_nbest
-
-        if ensemble_size is None:
-            ensemble_size = self._ensemble_size
+        # Create a client if needed
+        if self._dask_client is None:
+            self._create_dask_client()
         else:
-            self._ensemble_size = ensemble_size
-
-        return EnsembleBuilder(
-            backend=self._backend,
-            dataset_name=dataset_name,
-            task_type=task,
+            self._is_dask_client_internally_created = False
+
+        # Use the current thread to start the ensemble builder process
+        # The function ensemble_builder_process will internally create a ensemble
+        # builder in the provide dask client
+        manager = EnsembleBuilderManager(
+            start_time=time.time(),
+            time_left_for_ensembles=self._time_for_task,
+            backend=copy.deepcopy(self._backend),
+            dataset_name=dataset_name if dataset_name else self._dataset_name,
+            task=task if task else self._task,
             metric=self._metric,
-            limit=time_left_for_ensembles,
-            ensemble_size=ensemble_size,
-            ensemble_nbest=ensemble_nbest,
+            ensemble_size=ensemble_size if ensemble_size else self._ensemble_size,
+            ensemble_nbest=ensemble_nbest if ensemble_nbest else self._ensemble_nbest,
             max_models_on_disc=self._max_models_on_disc,
             seed=self._seed,
-            precision=precision,
-            max_iterations=max_iterations,
+            precision=precision if precision else self.precision,
+            max_iterations=1,
             read_at_most=np.inf,
-            memory_limit=self._ensemble_memory_limit,
+            ensemble_memory_limit=self._memory_limit,
             random_state=self._seed,
-            queue=queue,
+            logger_name=self._logger.name,
         )
+        manager.build_ensemble(self._dask_client)
+        future = manager.futures.pop()
+        dask.distributed.wait([future])  # wait for the ensemble process to finish
+        result = future.result()
+        if result is None:
+            raise ValueError("Error building the ensemble - please check the log file and command "
+                             "line output for error messages.")
+        self.ensemble_performance_history, _, _, _, _ = result
+
+        self._load_models()
+        self._close_dask_client()
+        return self
 
     def _load_models(self):
         self.ensemble_ = self._backend.load_ensemble(self._seed)
@@ -889,8 +938,9 @@ def _load_best_individual_model(self):
         # SingleBest contains the best model found by AutoML
         ensemble = SingleBest(
             metric=self._metric,
-            random_state=self._seed,
+            seed=self._seed,
             run_history=self.runhistory_,
+            backend=self._backend,
         )
         self._logger.warning(
             "No valid ensemble was created. Please check the log"
@@ -1104,6 +1154,19 @@ def _create_search_space(self, tmp_dir, backend, datamanager,
     def configuration_space_created_hook(self, datamanager, configuration_space):
         return configuration_space
 
+    def __getstate__(self) -> Dict[str, Any]:
+        # Cannot serialize a client!
+        self._dask_client = None
+        return self.__dict__
+
+    def __del__(self):
+        self._close_dask_client()
+
+        # When a multiprocessing work is done, the
+        # objects are deleted. We don't want to delete run areas
+        # until the estimator is deleted
+        self._backend.context.delete_directories(force=False)
+
 
 class AutoMLClassifier(AutoML):
     def __init__(self, *args, **kwargs):
diff --git a/autosklearn/data/abstract_data_manager.py b/autosklearn/data/abstract_data_manager.py
index 819db62793..c167bc95b2 100644
--- a/autosklearn/data/abstract_data_manager.py
+++ b/autosklearn/data/abstract_data_manager.py
@@ -75,34 +75,6 @@ def encoder(self) -> DataPreprocessor:
     def encoder(self, value: DataPreprocessor) -> DataPreprocessor:
         self._encoder = value
 
-    def perform1HotEncoding(self) -> None:
-        sparse = True if self.info['is_sparse'] == 1 else False
-        has_missing = True if self.info['has_missing'] else False
-        to_encode = ['categorical']
-        if has_missing:
-            to_encode += ['binary']
-        encoding_mask = [feat_type.lower() in to_encode
-                         for feat_type in self.feat_type]
-
-        data = [self.data['X_train']]
-        if 'X_valid' in self.data:
-            data.append(self.data['X_valid'])
-        if 'X_test' in self.data:
-            data.append(self.data['X_test'])
-        data, sparse = perform_one_hot_encoding(
-            sparse=sparse, categorical=encoding_mask,
-            data=data)
-
-        self.info['is_sparse'] = 1 if sparse else 0
-        self.data['X_train'] = data[0]
-        if 'X_valid' in self.data and 'X_test' in self.data:
-            self.data['X_valid'] = data[1]
-            self.data['X_test'] = data[2]
-        elif 'X_valid' in self.data:
-            self.data['X_valid'] = data[1]
-        elif 'X_test' in self.data:
-            self.data['X_test'] = data[1]
-
     def __repr__(self) -> str:
         return 'DataManager : ' + self.name
 
diff --git a/autosklearn/data/validation.py b/autosklearn/data/validation.py
index a72cabd69d..56d11b5084 100644
--- a/autosklearn/data/validation.py
+++ b/autosklearn/data/validation.py
@@ -1,5 +1,6 @@
 # -*- encoding: utf-8 -*-
 
+import functools
 import warnings
 from typing import List, Optional, Tuple, Union
 
@@ -365,6 +366,25 @@ def _check_and_encode_features(
                 assert self.feature_encoder is not None
                 self.feature_encoder.fit(X)
 
+                # The column transformer reoders the feature types - we therefore need to change
+                # it as well
+                def comparator(cmp1, cmp2):
+                    if (
+                        cmp1 == 'categorical' and cmp2 == 'categorical'
+                        or cmp1 == 'numerical' and cmp2 == 'numerical'
+                    ):
+                        return 0
+                    elif cmp1 == 'categorical' and cmp2 == 'numerical':
+                        return -1
+                    elif cmp1 == 'numerical' and cmp2 == 'categorical':
+                        return 1
+                    else:
+                        raise ValueError((cmp1, cmp2))
+                self.feature_types = sorted(
+                    self.feature_types,
+                    key=functools.cmp_to_key(comparator)
+                )
+
         if self.feature_encoder:
             try:
                 X = self.feature_encoder.transform(X)
diff --git a/autosklearn/ensemble_builder.py b/autosklearn/ensemble_builder.py
index d71ab09d25..c8b01070af 100644
--- a/autosklearn/ensemble_builder.py
+++ b/autosklearn/ensemble_builder.py
@@ -1,27 +1,33 @@
 # -*- encoding: utf-8 -*-
 import math
 import numbers
-import multiprocessing
 import glob
 import gzip
 import os
+import pickle
 import re
+import shutil
 import time
 import traceback
-from typing import Optional, Union
+from typing import List, Optional, Tuple, Union
+import zlib
+
+import dask.distributed
 
 import numpy as np
 import pandas as pd
 import pynisher
-import lockfile
 from sklearn.utils.validation import check_random_state
+from smac.callbacks import IncorporateRunResultCallback
+from smac.optimizer.smbo import SMBO
+from smac.runhistory.runhistory import RunInfo, RunValue
 
 from autosklearn.util.backend import Backend
 from autosklearn.constants import BINARY_CLASSIFICATION
 from autosklearn.metrics import calculate_score, Scorer
 from autosklearn.ensembles.ensemble_selection import EnsembleSelection
 from autosklearn.ensembles.abstract_ensemble import AbstractEnsemble
-from autosklearn.util.logging_ import get_logger
+from autosklearn.util.logging_ import get_logger, setup_logger
 
 Y_ENSEMBLE = 0
 Y_VALID = 1
@@ -30,26 +36,329 @@
 MODEL_FN_RE = r'_([0-9]*)_([0-9]*)_([0-9]{1,3}\.[0-9]*)\.npy'
 
 
-class EnsembleBuilder(multiprocessing.Process):
+class EnsembleBuilderManager(IncorporateRunResultCallback):
+    def __init__(
+        self,
+        start_time: float,
+        time_left_for_ensembles: float,
+        backend: Backend,
+        dataset_name: str,
+        task: int,
+        metric: Scorer,
+        ensemble_size: int,
+        ensemble_nbest: int,
+        max_models_on_disc: Union[float, int],
+        seed: int,
+        precision: int,
+        max_iterations: Optional[int],
+        read_at_most: int,
+        ensemble_memory_limit: Optional[int],
+        random_state: int,
+        logger_name: str,
+    ):
+        """ SMAC callback to handle ensemble building
+
+        Parameters
+        ----------
+        start_time: int
+            the time when this job was started, to account for any latency in job allocation
+        time_left_for_ensemble: int
+            How much time is left for the task. Job should finish within this allocated time
+        backend: util.backend.Backend
+            backend to write and read files
+        dataset_name: str
+            name of dataset
+        task_type: int
+            type of ML task
+        metric: str
+            name of metric to score predictions
+        ensemble_size: int
+            maximal size of ensemble (passed to autosklearn.ensemble.ensemble_selection)
+        ensemble_nbest: int/float
+            if int: consider only the n best prediction
+            if float: consider only this fraction of the best models
+            Both wrt to validation predictions
+            If performance_range_threshold > 0, might return less models
+        max_models_on_disc: int
+           Defines the maximum number of models that are kept in the disc.
+           If int, it must be greater or equal than 1, and dictates the max number of
+           models to keep.
+           If float, it will be interpreted as the max megabytes allowed of disc space. That
+           is, if the number of ensemble candidates require more disc space than this float
+           value, the worst models will be deleted to keep within this budget.
+           Models and predictions of the worst-performing models will be deleted then.
+           If None, the feature is disabled.
+           It defines an upper bound on the models that can be used in the ensemble.
+        seed: int
+            random seed
+        max_iterations: int
+            maximal number of iterations to run this script
+            (default None --> deactivated)
+        precision: [16,32,64,128]
+            precision of floats to read the predictions
+        memory_limit: Optional[int]
+            memory limit in mb. If ``None``, no memory limit is enforced.
+        read_at_most: int
+            read at most n new prediction files in each iteration
+        logger_name: str
+            Name of the logger where we are gonna write information
+
+    Returns
+    -------
+        List[Tuple[int, float, float, float]]:
+            A list with the performance history of this ensemble, of the form
+            [[pandas_timestamp, train_performance, val_performance, test_performance], ...]
+        """
+        self.start_time = start_time
+        self.time_left_for_ensembles = time_left_for_ensembles
+        self.backend = backend
+        self.dataset_name = dataset_name
+        self.task = task
+        self.metric = metric
+        self.ensemble_size = ensemble_size
+        self.ensemble_nbest = ensemble_nbest
+        self.max_models_on_disc = max_models_on_disc
+        self.seed = seed
+        self.precision = precision
+        self.max_iterations = max_iterations
+        self.read_at_most = read_at_most
+        self.ensemble_memory_limit = ensemble_memory_limit
+        self.random_state = random_state
+        self.logger_name = logger_name
+
+        # Store something similar to SMAC's runhistory
+        self.history = []
+
+        # We only submit new ensembles when there is not an active ensemble job
+        self.futures = []
+
+        # The last criteria is the number of iterations
+        self.iteration = 0
+
+        # Keep track of when we started to know when we need to finish!
+        self.start_time = time.time()
+
+    def __call__(
+        self,
+        smbo: 'SMBO',
+        run_info: RunInfo,
+        result: RunValue,
+        time_left: float,
+    ):
+        self.build_ensemble(smbo.tae_runner.client)
+
+    def build_ensemble(self, dask_client: dask.distributed.Client) -> None:
+
+        # The second criteria is elapsed time
+        elapsed_time = time.time() - self.start_time
+
+        logger = EnsembleBuilder._get_ensemble_logger(
+            self.logger_name,
+            self.backend.temporary_directory,
+            False
+        )
+
+        # First test for termination conditions
+        if self.time_left_for_ensembles < elapsed_time:
+            logger.info(
+                "Terminate ensemble building as not time is left (run for {}s)".format(
+                    elapsed_time
+                ),
+            )
+            return
+        if self.max_iterations is not None and self.max_iterations <= self.iteration:
+            logger.info(
+                "Terminate ensemble building because of max iterations: {} of {}".format(
+                    self.max_iterations,
+                    self.iteration
+                )
+            )
+            return
+
+        if len(self.futures) != 0:
+            if self.futures[0].done():
+                result = self.futures.pop().result()
+                if result:
+                    ensemble_history, self.ensemble_nbest, _, _, _ = result
+                    logger.debug("iteration={} @ elapsed_time={} has history={}".format(
+                        self.iteration,
+                        elapsed_time,
+                        ensemble_history,
+                    ))
+                    self.history.extend(ensemble_history)
+
+        # Only submit new jobs if the previous ensemble job finished
+        if len(self.futures) == 0:
+
+            # Add the result of the run
+            # On the next while iteration, no references to
+            # ensemble builder object, so it should be garbage collected to
+            # save memory while waiting for resources
+            # Also, notice how ensemble nbest is returned, so we don't waste
+            # iterations testing if the deterministic predictions size can
+            # be fitted in memory
+            try:
+                # Submit a Dask job from this job, to properly
+                # see it in the dask diagnostic dashboard
+                # Notice that the forked ensemble_builder_process will
+                # wait for the below function to be done
+                self.futures.append(dask_client.submit(
+                    fit_and_return_ensemble,
+                    backend=self.backend,
+                    dataset_name=self.dataset_name,
+                    task_type=self.task,
+                    metric=self.metric,
+                    ensemble_size=self.ensemble_size,
+                    ensemble_nbest=self.ensemble_nbest,
+                    max_models_on_disc=self.max_models_on_disc,
+                    seed=self.seed,
+                    precision=self.precision,
+                    memory_limit=self.ensemble_memory_limit,
+                    read_at_most=self.read_at_most,
+                    random_state=self.seed,
+                    logger_name=self.logger_name,
+                    end_at=self.start_time + self.time_left_for_ensembles,
+                    iteration=self.iteration,
+                    return_predictions=False,
+                    priority=100,
+                ))
+
+                logger.info(
+                    "{}/{} Started Ensemble builder job at {} for iteration {}.".format(
+                        # Log the client to make sure we
+                        # remain connected to the scheduler
+                        self.futures[0],
+                        dask_client,
+                        time.strftime("%Y.%m.%d-%H.%M.%S"),
+                        self.iteration,
+                    ),
+                )
+                self.iteration += 1
+            except Exception as e:
+                exception_traceback = traceback.format_exc()
+                error_message = repr(e)
+                logger.critical(exception_traceback)
+                logger.critical(error_message)
+
+
+def fit_and_return_ensemble(
+    backend: Backend,
+    dataset_name: str,
+    task_type: str,
+    metric: Scorer,
+    ensemble_size: int,
+    ensemble_nbest: int,
+    max_models_on_disc: Union[float, int],
+    seed: int,
+    precision: int,
+    memory_limit: Optional[int],
+    read_at_most: int,
+    random_state: int,
+    logger_name: str,
+    end_at: float,
+    iteration: int,
+    return_predictions: bool,
+) -> Tuple[
+        List[Tuple[int, float, float, float]],
+        int,
+        Optional[np.ndarray],
+        Optional[np.ndarray],
+        Optional[np.ndarray],
+]:
+    """
+
+    A short function to fit and create an ensemble. It is just a wrapper to easily send
+    a request to dask to create an ensemble and clean the memory when finished
+
+    Parameters
+    ----------
+        backend: util.backend.Backend
+            backend to write and read files
+        dataset_name: str
+            name of dataset
+        metric: str
+            name of metric to score predictions
+        task_type: int
+            type of ML task
+        ensemble_size: int
+            maximal size of ensemble (passed to autosklearn.ensemble.ensemble_selection)
+        ensemble_nbest: int/float
+            if int: consider only the n best prediction
+            if float: consider only this fraction of the best models
+            Both wrt to validation predictions
+            If performance_range_threshold > 0, might return less models
+        max_models_on_disc: int
+           Defines the maximum number of models that are kept in the disc.
+           If int, it must be greater or equal than 1, and dictates the max number of
+           models to keep.
+           If float, it will be interpreted as the max megabytes allowed of disc space. That
+           is, if the number of ensemble candidates require more disc space than this float
+           value, the worst models will be deleted to keep within this budget.
+           Models and predictions of the worst-performing models will be deleted then.
+           If None, the feature is disabled.
+           It defines an upper bound on the models that can be used in the ensemble.
+        seed: int
+            random seed
+        precision: [16,32,64,128]
+            precision of floats to read the predictions
+        memory_limit: Optional[int]
+            memory limit in mb. If ``None``, no memory limit is enforced.
+        read_at_most: int
+            read at most n new prediction files in each iteration
+        logger_name: str
+            Name of the logger where we are gonna write information
+        end_at: float
+            At what time the job must finish. Needs to be the endtime and not the time left
+            because we do not know when dask schedules the job.
+        iteration: int
+            The current iteration
+
+    Returns
+    -------
+        List[Tuple[int, float, float, float]]
+            A list with the performance history of this ensemble, of the form
+            [[pandas_timestamp, train_performance, val_performance, test_performance], ...]
+
+    """
+    result = EnsembleBuilder(
+        backend=backend,
+        dataset_name=dataset_name,
+        task_type=task_type,
+        metric=metric,
+        ensemble_size=ensemble_size,
+        ensemble_nbest=ensemble_nbest,
+        max_models_on_disc=max_models_on_disc,
+        seed=seed,
+        precision=precision,
+        memory_limit=memory_limit,
+        read_at_most=read_at_most,
+        random_state=random_state,
+        logger_name=logger_name,
+    ).run(
+        end_at=end_at,
+        iteration=iteration,
+        return_predictions=return_predictions,
+    )
+    return result
+
+
+class EnsembleBuilder(object):
     def __init__(
             self,
             backend: Backend,
             dataset_name: str,
             task_type: int,
             metric: Scorer,
-            limit: int,
             ensemble_size: int = 10,
             ensemble_nbest: int = 100,
             max_models_on_disc: int = 100,
             performance_range_threshold: float = 0,
             seed: int = 1,
-            max_iterations: int = None,
             precision: int = 32,
-            sleep_duration: int = 2,
             memory_limit: Optional[int] = 1024,
             read_at_most: int = 5,
             random_state: Optional[Union[int, np.random.RandomState]] = None,
-            queue: multiprocessing.Queue = None
+            logger_name: str = 'ensemble_builder',
     ):
         """
             Constructor
@@ -64,8 +373,6 @@ def __init__(
                 type of ML task
             metric: str
                 name of metric to score predictions
-            limit: int
-                time limit in sec
             ensemble_size: int
                 maximal size of ensemble (passed to autosklearn.ensemble.ensemble_selection)
             ensemble_nbest: int/float
@@ -91,17 +398,14 @@ def __init__(
                 and max_models_on_disc. Might return less
             seed: int
                 random seed
-            max_iterations: int
-                maximal number of iterations to run this script
-                (default None --> deactivated)
             precision: [16,32,64,128]
                 precision of floats to read the predictions
-            sleep_duration: int
-                duration of sleeping time between two iterations of this script (in sec)
             memory_limit: Optional[int]
                 memory limit in mb. If ``None``, no memory limit is enforced.
             read_at_most: int
                 read at most n new prediction files in each iteration
+            logger_name: str
+                Name of the logger where we are gonna write information
         """
 
         super(EnsembleBuilder, self).__init__()
@@ -110,10 +414,6 @@ def __init__(
         self.dataset_name = dataset_name
         self.task_type = task_type
         self.metric = metric
-        self.time_limit = limit  # time limit
-        # define time_left here so that it is defined in case the ensemble builder is called
-        # without starting a separate process
-        self.time_left = limit
         self.ensemble_size = ensemble_size
         self.performance_range_threshold = performance_range_threshold
 
@@ -140,39 +440,16 @@ def __init__(
         self.max_resident_models = None
 
         self.seed = seed
-        self.max_iterations = max_iterations
         self.precision = precision
-        self.sleep_duration = sleep_duration
         self.memory_limit = memory_limit
         self.read_at_most = read_at_most
         self.random_state = check_random_state(random_state)
 
-        # part of the original training set
-        # used to build the ensemble
-        self.dir_ensemble = os.path.join(
-            self.backend.temporary_directory,
-            '.auto-sklearn',
-            'predictions_ensemble',
-        )
-        # validation set (public test set) -- y_true not known
-        self.dir_valid = os.path.join(
-            self.backend.temporary_directory,
-            '.auto-sklearn',
-            'predictions_valid',
-        )
-        # test set (private test set) -- y_true not known
-        self.dir_test = os.path.join(
-            self.backend.temporary_directory,
-            '.auto-sklearn',
-            'predictions_test',
-        )
-        self.dir_models = os.path.join(
-            self.backend.temporary_directory,
-            '.auto-sklearn',
-            'models',
-        )
-        logger_name = 'EnsembleBuilder(%d):%s' % (self.seed, self.dataset_name)
-        self.logger = get_logger(logger_name)
+        # Setup the logger
+        self.logger_name = logger_name
+        self.logger = self._get_ensemble_logger(
+            self.logger_name, self.backend.temporary_directory, False)
+
         if ensemble_nbest == 1:
             self.logger.debug("Behaviour depends on int/float: %s, %s (ensemble_nbest, type)" %
                               (ensemble_nbest, type(ensemble_nbest)))
@@ -180,6 +457,10 @@ def __init__(
         self.start_time = 0
         self.model_fn_re = re.compile(MODEL_FN_RE)
 
+        self.last_hash = None  # hash of ensemble training data
+        self.y_true_ensemble = None
+        self.SAVE2DISC = True
+
         # already read prediction files
         # {"file name": {
         #    "ens_score": float
@@ -188,16 +469,57 @@ def __init__(
         #    "mtime_test": str,
         #    "seed": int,
         #    "num_run": int,
-        #    "deleted": bool,
+        # }}
+        self.read_scores = {}
+        # {"file_name": {
         #    Y_ENSEMBLE: np.ndarray
         #    Y_VALID: np.ndarray
         #    Y_TEST: np.ndarray
         #    }
         # }
         self.read_preds = {}
-        self.last_hash = None  # hash of ensemble training data
-        self.y_true_ensemble = None
-        self.SAVE2DISC = True
+
+        # Depending on the dataset dimensions,
+        # regenerating every iteration, the predictions
+        # scores for self.read_preds
+        # is too computationally expensive
+        # As the ensemble builder is stateless
+        # (every time the ensemble builder gets resources
+        # from dask, it builds this object from scratch)
+        # we save the state of this dictionary to memory
+        # and read it if available
+        self.ensemble_memory_file = os.path.join(
+            self.backend.internals_directory,
+            'ensemble_read_preds.pkl'
+        )
+        if os.path.exists(self.ensemble_memory_file):
+            try:
+                with (open(self.ensemble_memory_file, "rb")) as memory:
+                    self.read_preds, self.last_hash = pickle.load(memory)
+            except Exception as e:
+                self.logger.warning(
+                    "Could not load the previous iterations of ensemble_builder predictions."
+                    "This might impact the quality of the run. Exception={} {}".format(
+                        e,
+                        traceback.format_exc(),
+                    )
+                )
+        self.ensemble_score_file = os.path.join(
+            self.backend.internals_directory,
+            'ensemble_read_scores.pkl'
+        )
+        if os.path.exists(self.ensemble_score_file):
+            try:
+                with (open(self.ensemble_score_file, "rb")) as memory:
+                    self.read_scores = pickle.load(memory)
+            except Exception as e:
+                self.logger.warning(
+                    "Could not load the previous iterations of ensemble_builder scores."
+                    "This might impact the quality of the run. Exception={} {}".format(
+                        e,
+                        traceback.format_exc(),
+                    )
+                )
 
         # hidden feature which can be activated via an environment variable. This keeps all
         # models and predictions which have ever been a candidate. This is necessary to post-hoc
@@ -207,174 +529,234 @@ def __init__(
         self.validation_performance_ = np.inf
 
         # Track the ensemble performance
-        self.datamanager = self.backend.load_datamanager()
-        self.y_valid = self.datamanager.data.get('Y_valid')
-        self.y_test = self.datamanager.data.get('Y_test')
-
-        # Support for tracking the performance across time
-        # A Queue is needed to handle multiprocessing, not only
-        # internally for pynisher calls, but to return data
-        # to the main process
-        # Hence, because we are using three different processes,
-        # the below strategy prevents MemoryErrors. That is,
-        # without clearly isolating the queue with a manger,
-        # we run into a threading MemoryError
-        if queue is None:
-            mgr = multiprocessing.Manager()
-            mgr.Namespace()
-            self.queue = mgr.Queue()
-        else:
-            self.queue = queue
-        self.queue.put([])
-        self.queue.get()
+        datamanager = self.backend.load_datamanager()
+        self.y_valid = datamanager.data.get('Y_valid')
+        self.y_test = datamanager.data.get('Y_test')
+        del datamanager
+        self.ensemble_history = []
+
+    @classmethod
+    def _get_ensemble_logger(self, logger_name, dirname, setup):
+        """
+        Returns the logger of for the ensemble process.
+        A subprocess will require to set this up, for instance,
+        pynisher forks
+        """
+        if setup:
+            setup_logger(
+                os.path.join(
+                    dirname,
+                    '%s.log' % str(logger_name)
+                ),
+            )
+
+        return get_logger('EnsembleBuilder')
+
+    def run(
+        self,
+        iteration: int,
+        time_left: Optional[float] = None,
+        end_at: Optional[float] = None,
+        time_buffer=5,
+        return_predictions: bool = False,
+    ):
+
+        if time_left is None and end_at is None:
+            raise ValueError('Must provide either time_left or end_at.')
+        elif time_left is not None and end_at is not None:
+            raise ValueError('Cannot provide both time_left and end_at.')
+
+        self.logger = self._get_ensemble_logger(
+            self.logger_name, self.backend.temporary_directory, True)
 
-    def run(self):
-        buffer_time = 5  # TODO: Buffer time should also be used in main!?
         process_start_time = time.time()
         while True:
-            time_elapsed = time.time() - process_start_time
-            time_left = self.time_limit - buffer_time - time_elapsed
-            self.time_left = time_left
+
+            if time_left is not None:
+                time_elapsed = time.time() - process_start_time
+                time_left -= time_elapsed
+            else:
+                current_time = time.time()
+                if current_time > end_at:
+                    break
+                else:
+                    time_left = end_at - current_time
+
+            if time_left - time_buffer < 1:
+                break
             safe_ensemble_script = pynisher.enforce_limits(
-                wall_time_in_s=int(time_left),
+                wall_time_in_s=int(time_left - time_buffer),
                 mem_in_mb=self.memory_limit,
                 logger=self.logger
             )(self.main)
-            safe_ensemble_script()
+            safe_ensemble_script(time_left, iteration, return_predictions)
             if safe_ensemble_script.exit_status is pynisher.MemorylimitException:
                 # if ensemble script died because of memory error,
                 # reduce nbest to reduce memory consumption and try it again
-                if isinstance(self.ensemble_nbest, numbers.Integral) and \
-                        self.ensemble_nbest == 1:
-                    self.logger.critical(
-                        "Memory Exception -- Unable to further reduce the number of ensemble "
-                        "members -- please restart Auto-sklearn with a higher value for the "
-                        "argument 'ensemble_memory_limit' (current limit is {} MB)."
-                        "".format(self.memory_limit)
-                    )
+
+                # ATTENTION: main will start from scratch; # all data structures are empty again
+                try:
+                    os.remove(self.ensemble_memory_file)
+                except:  # noqa E722
+                    pass
+
+                if isinstance(self.ensemble_nbest, numbers.Integral) and self.ensemble_nbest <= 1:
+                    if self.read_at_most == 1:
+                        self.logger.error(
+                            "Memory Exception -- Unable to further reduce the number of ensemble "
+                            "members and can no further limit the number of ensemble members "
+                            "loaded per iteration -- please restart Auto-sklearn with a higher "
+                            "value for the argument `memory_limit` (current limit is %s MB). "
+                            "The ensemble builder will keep running to delete files from disk in "
+                            "case this was enabled.", self.memory_limit
+                        )
+                        self.ensemble_nbest = 0
+                    else:
+                        self.read_at_most = 1
+                        self.logger.warning(
+                            "Memory Exception -- Unable to further reduce the number of ensemble "
+                            "members -- Now reducing the number of predictions per call to read "
+                            "at most to 1."
+                        )
                 else:
                     if isinstance(self.ensemble_nbest, numbers.Integral):
-                        self.ensemble_nbest = int(self.ensemble_nbest / 2)
+                        self.ensemble_nbest = max(1, int(self.ensemble_nbest / 2))
                     else:
                         self.ensemble_nbest = self.ensemble_nbest / 2
                     self.logger.warning("Memory Exception -- restart with "
                                         "less ensemble_nbest: %d" % self.ensemble_nbest)
-                    # ATTENTION: main will start from scratch;
-                    # all data structures are empty again
-                    continue
-            break
+                    return [], self.ensemble_nbest, None, None, None
+            else:
+                return safe_ensemble_script.result
 
-    def main(self, return_pred=False):
-        """
+        return [], self.ensemble_nbest, None, None, None
+
+    def main(self, time_left, iteration, return_predictions):
+
+        # Pynisher jobs inside dask 'forget'
+        # the logger configuration. So we have to set it up
+        # accordingly
+        self.logger = self._get_ensemble_logger(
+            self.logger_name, self.backend.temporary_directory, False)
 
-        :param return_pred:
-            return tuple with last valid, test predictions
-        :return:
-        """
         self.start_time = time.time()
-        iteration = 0
-        valid_pred, test_pred = None, None
-        while True:
+        train_pred, valid_pred, test_pred = None, None, None
 
-            # maximal number of iterations
-            if (self.max_iterations is not None
-                    and 0 < self.max_iterations <= iteration):
-                self.logger.info("Terminate ensemble building because of max iterations: %d of %d",
-                                 self.max_iterations,
-                                 iteration)
-                break
-            iteration += 1
+        used_time = time.time() - self.start_time
+        self.logger.debug(
+            'Starting iteration %d, time left: %f',
+            iteration,
+            time_left - used_time,
+        )
 
-            used_time = time.time() - self.start_time
-            self.logger.debug(
-                'Starting iteration %d, time left: %f',
-                iteration,
-                self.time_left - used_time,
-            )
+        # populates self.read_preds and self.read_scores
+        if not self.score_ensemble_preds():
+            if return_predictions:
+                return self.ensemble_history, self.ensemble_nbest, train_pred, valid_pred, test_pred
+            else:
+                return self.ensemble_history, self.ensemble_nbest, None, None, None
+
+        # Only the models with the n_best predictions are candidates
+        # to be in the ensemble
+        candidate_models = self.get_n_best_preds()
+        if not candidate_models:  # no candidates yet
+            if return_predictions:
+                return self.ensemble_history, self.ensemble_nbest, train_pred, valid_pred, test_pred
+            else:
+                return self.ensemble_history, self.ensemble_nbest, None, None, None
+
+        # populates predictions in self.read_preds
+        # reduces selected models if file reading failed
+        n_sel_valid, n_sel_test = self. \
+            get_valid_test_preds(selected_keys=candidate_models)
+
+        # If valid/test predictions loaded, then reduce candidate models to this set
+        if len(n_sel_test) != 0 and len(n_sel_valid) != 0 \
+                and len(set(n_sel_valid).intersection(set(n_sel_test))) == 0:
+            # Both n_sel_* have entries, but there is no overlap, this is critical
+            self.logger.error("n_sel_valid and n_sel_test are not empty, but do not overlap")
+            if return_predictions:
+                return self.ensemble_history, self.ensemble_nbest, train_pred, valid_pred, test_pred
+            else:
+                return self.ensemble_history, self.ensemble_nbest, None, None, None
+
+        # If any of n_sel_* is not empty and overlaps with candidate_models,
+        # then ensure candidate_models AND n_sel_test are sorted the same
+        candidate_models_set = set(candidate_models)
+        if candidate_models_set.intersection(n_sel_valid).intersection(n_sel_test):
+            candidate_models = sorted(list(candidate_models_set.intersection(
+                n_sel_valid).intersection(n_sel_test)))
+            n_sel_test = candidate_models
+            n_sel_valid = candidate_models
+        elif candidate_models_set.intersection(n_sel_valid):
+            candidate_models = sorted(list(candidate_models_set.intersection(
+                n_sel_valid)))
+            n_sel_valid = candidate_models
+        elif candidate_models_set.intersection(n_sel_test):
+            candidate_models = sorted(list(candidate_models_set.intersection(
+                n_sel_test)))
+            n_sel_test = candidate_models
+        else:
+            # This has to be the case
+            n_sel_test = []
+            n_sel_valid = []
 
-            # populates self.read_preds
-            if not self.score_ensemble_preds():
-                time.sleep(self.sleep_duration)
-                continue
+        if os.environ.get('ENSEMBLE_KEEP_ALL_CANDIDATES'):
+            for candidate in candidate_models:
+                self._has_been_candidate.add(candidate)
 
-            # Only the models with the n_best predictions are candidates
-            # to be in the ensemble
-            candidate_models = self.get_n_best_preds()
-            if not candidate_models:  # no candidates yet
-                continue
+        # train ensemble
+        ensemble = self.fit_ensemble(selected_keys=candidate_models)
 
-            # populates predictions in self.read_preds
-            # reduces selected models if file reading failed
-            n_sel_valid, n_sel_test = self. \
-                get_valid_test_preds(selected_keys=candidate_models)
-
-            # If valid/test predictions loaded, then reduce candidate models to this set
-            if len(n_sel_test) != 0 and len(n_sel_valid) != 0 \
-                    and len(set(n_sel_valid).intersection(set(n_sel_test))) == 0:
-                # Both n_sel_* have entries, but there is no overlap, this is critical
-                self.logger.error("n_sel_valid and n_sel_test are not empty, but do "
-                                  "not overlap")
-                time.sleep(self.sleep_duration)
-                continue
+        # Save the ensemble for later use in the main auto-sklearn module!
+        if ensemble is not None and self.SAVE2DISC:
+            self.backend.save_ensemble(ensemble, iteration, self.seed)
 
-            # If any of n_sel_* is not empty and overlaps with candidate_models,
-            # then ensure candidate_models AND n_sel_test are sorted the same
-            candidate_models_set = set(candidate_models)
-            if candidate_models_set.intersection(n_sel_valid).intersection(n_sel_test):
-                candidate_models = sorted(list(candidate_models_set.intersection(
-                    n_sel_valid).intersection(n_sel_test)))
-                n_sel_test = candidate_models
-                n_sel_valid = candidate_models
-            elif candidate_models_set.intersection(n_sel_valid):
-                candidate_models = sorted(list(candidate_models_set.intersection(
-                    n_sel_valid)))
-                n_sel_valid = candidate_models
-            elif candidate_models_set.intersection(n_sel_test):
-                candidate_models = sorted(list(candidate_models_set.intersection(
-                    n_sel_test)))
-                n_sel_test = candidate_models
-            else:
-                # This has to be the case
-                n_sel_test = []
-                n_sel_valid = []
-
-            if os.environ.get('ENSEMBLE_KEEP_ALL_CANDIDATES'):
-                for candidate in candidate_models:
-                    self._has_been_candidate.add(candidate)
-
-            # train ensemble
-            ensemble = self.fit_ensemble(selected_keys=candidate_models)
-            if ensemble is not None:
-                train_pred = self.predict(set_="train",
-                                          ensemble=ensemble,
-                                          selected_keys=candidate_models,
-                                          n_preds=len(candidate_models),
-                                          index_run=iteration)
-                # We can't use candidate_models here, as n_sel_* might be empty
-                valid_pred = self.predict(set_="valid",
-                                          ensemble=ensemble,
-                                          selected_keys=n_sel_valid,
-                                          n_preds=len(candidate_models),
-                                          index_run=iteration)
-                # TODO if predictions fails, build the model again during the
-                #  next iteration!
-                test_pred = self.predict(set_="test",
-                                         ensemble=ensemble,
-                                         selected_keys=n_sel_test,
-                                         n_preds=len(candidate_models),
-                                         index_run=iteration)
-
-                # Add a score to run history to see ensemble progress
-                self._add_ensemble_trajectory(
-                    train_pred,
-                    valid_pred,
-                    test_pred
-                )
-            else:
-                time.sleep(self.sleep_duration)
+        # Delete files of non-candidate models - can only be done after fitting the ensemble and
+        # saving it to disc so we do not accidentally delete models in the previous ensemble
+        if self.max_resident_models is not None:
+            self._delete_excess_models(selected_keys=candidate_models)
+
+        # Save the read scores status for the next iteration
+        with open(self.ensemble_score_file, "wb") as memory:
+            pickle.dump(self.read_scores, memory)
+
+        if ensemble is not None:
+            train_pred = self.predict(set_="train",
+                                      ensemble=ensemble,
+                                      selected_keys=candidate_models,
+                                      n_preds=len(candidate_models),
+                                      index_run=iteration)
+            # We can't use candidate_models here, as n_sel_* might be empty
+            valid_pred = self.predict(set_="valid",
+                                      ensemble=ensemble,
+                                      selected_keys=n_sel_valid,
+                                      n_preds=len(candidate_models),
+                                      index_run=iteration)
+            # TODO if predictions fails, build the model again during the
+            #  next iteration!
+            test_pred = self.predict(set_="test",
+                                     ensemble=ensemble,
+                                     selected_keys=n_sel_test,
+                                     n_preds=len(candidate_models),
+                                     index_run=iteration)
+
+            # Add a score to run history to see ensemble progress
+            self._add_ensemble_trajectory(
+                train_pred,
+                valid_pred,
+                test_pred
+            )
 
-        if return_pred:
-            return valid_pred, test_pred
+        # The loaded predictions and the hash can only be saved after the ensemble has been
+        # built, because the hash is computed during the construction of the ensemble
+        with open(self.ensemble_memory_file, "wb") as memory:
+            pickle.dump((self.read_preds, self.last_hash), memory)
+
+        if return_predictions:
+            return self.ensemble_history, self.ensemble_nbest, train_pred, valid_pred, test_pred
+        else:
+            return self.ensemble_history, self.ensemble_nbest, None, None, None
 
     def get_disk_consumption(self, pred_path):
         """
@@ -384,28 +766,16 @@ def get_disk_consumption(self, pred_path):
         match = self.model_fn_re.search(pred_path)
         if not match:
             raise ValueError("Invalid path format %s" % pred_path)
-        _full_name = match.group(0)
-        _seed = match.group(1)
-        _num_run = match.group(2)
-        _budget = match.group(3)
-
-        # Besides the prediction, we have to take care of three other files: model,
-        # validation and test.
-        model_name = '%s.%s.%s.model' % (_seed, _num_run, _budget)
-        model_path = os.path.join(self.dir_models, model_name)
-        pred_valid_name = 'predictions_valid' + _full_name
-        pred_valid_path = os.path.join(self.dir_valid, pred_valid_name)
-        pred_test_name = 'predictions_test' + _full_name
-        pred_test_path = os.path.join(self.dir_test, pred_test_name)
-
-        paths = [pred_path]
-        if os.path.exists(model_path):
-            paths.append(model_path)
-        if os.path.exists(pred_valid_path):
-            paths.append(pred_valid_path)
-        if os.path.exists(pred_test_path):
-            paths.append(pred_test_path)
-        this_model_cost = sum([os.path.getsize(path) for path in paths])
+        _seed = int(match.group(1))
+        _num_run = int(match.group(2))
+        _budget = float(match.group(3))
+
+        stored_files_for_run = os.listdir(
+            self.backend.get_numrun_directory(_seed, _num_run, _budget))
+        stored_files_for_run = [
+            os.path.join(self.backend.get_numrun_directory(_seed, _num_run, _budget), file_name)
+            for file_name in stored_files_for_run]
+        this_model_cost = sum([os.path.getsize(path) for path in stored_files_for_run])
 
         # get the megabytes
         return round(this_model_cost / math.pow(1024, 2), 2)
@@ -413,7 +783,7 @@ def get_disk_consumption(self, pred_path):
     def score_ensemble_preds(self):
         """
             score predictions on ensemble building data set;
-            populates self.read_preds
+            populates self.read_preds and self.read_scores
         """
 
         self.logger.debug("Read ensemble data set predictions")
@@ -428,16 +798,11 @@ def score_ensemble_preds(self):
                 )
                 return False
 
-        # no validation predictions so far -- no dir
-        if not os.path.isdir(self.dir_ensemble):
-            self.logger.debug("No ensemble dataset prediction directory found")
-            return False
-
         pred_path = os.path.join(
-            glob.escape(self.dir_ensemble),
+            glob.escape(self.backend.get_runs_directory()),
+            '%d_*_*' % self.seed,
             'predictions_ensemble_%s_*_*.npy*' % self.seed,
         )
-
         y_ens_files = glob.glob(pred_path)
         y_ens_files = [y_ens_file for y_ens_file in y_ens_files
                        if y_ens_file.endswith('.npy') or y_ens_file.endswith('.npy.gz')]
@@ -448,12 +813,6 @@ def score_ensemble_preds(self):
                               " %s" % pred_path)
             return False
 
-        done_path = os.path.join(
-            glob.escape(self.backend.get_done_directory()), '%s_*' % self.seed
-        )
-        done = glob.glob(done_path)
-        done = [os.path.split(d)[1] for d in done]
-
         # First sort files chronologically
         to_read = []
         for y_ens_fn in self.y_ens_files:
@@ -462,8 +821,7 @@ def score_ensemble_preds(self):
             _num_run = int(match.group(2))
             _budget = float(match.group(3))
 
-            if '%s_%s' % (_seed, _num_run) in done:
-                to_read.append([y_ens_fn, match, _seed, _num_run, _budget])
+            to_read.append([y_ens_fn, match, _seed, _num_run, _budget])
 
         n_read_files = 0
         # Now read file wrt to num_run
@@ -478,9 +836,9 @@ def score_ensemble_preds(self):
                 self.logger.info('Error loading file (not .npy or .npy.gz): %s', y_ens_fn)
                 continue
 
-            if not self.read_preds.get(y_ens_fn):
-                self.read_preds[y_ens_fn] = {
-                    "ens_score": -1,
+            if not self.read_scores.get(y_ens_fn):
+                self.read_scores[y_ens_fn] = {
+                    "ens_score": -np.inf,
                     "mtime_ens": 0,
                     "mtime_valid": 0,
                     "mtime_test": 0,
@@ -488,17 +846,21 @@ def score_ensemble_preds(self):
                     "num_run": _num_run,
                     "budget": _budget,
                     "disc_space_cost_mb": None,
-                    Y_ENSEMBLE: None,
-                    Y_VALID: None,
-                    Y_TEST: None,
                     # Lazy keys so far:
                     # 0 - not loaded
                     # 1 - loaded and in memory
                     # 2 - loaded but dropped again
+                    # 3 - deleted from disk due to space constraints
                     "loaded": 0
                 }
+            if not self.read_preds.get(y_ens_fn):
+                self.read_preds[y_ens_fn] = {
+                    Y_ENSEMBLE: None,
+                    Y_VALID: None,
+                    Y_TEST: None,
+                }
 
-            if self.read_preds[y_ens_fn]["mtime_ens"] == os.path.getmtime(y_ens_fn):
+            if self.read_scores[y_ens_fn]["mtime_ens"] == os.path.getmtime(y_ens_fn):
                 # same time stamp; nothing changed;
                 continue
 
@@ -511,27 +873,24 @@ def score_ensemble_preds(self):
                                         metric=self.metric,
                                         all_scoring_functions=False)
 
-                if self.read_preds[y_ens_fn]["ens_score"] > -1:
+                if np.isfinite(self.read_scores[y_ens_fn]["ens_score"]):
                     self.logger.debug(
                         'Changing ensemble score for file %s from %f to %f '
                         'because file modification time changed? %f - %f',
                         y_ens_fn,
-                        self.read_preds[y_ens_fn]["ens_score"],
+                        self.read_scores[y_ens_fn]["ens_score"],
                         score,
-                        self.read_preds[y_ens_fn]["mtime_ens"],
+                        self.read_scores[y_ens_fn]["mtime_ens"],
                         os.path.getmtime(y_ens_fn),
                     )
 
-                self.read_preds[y_ens_fn]["ens_score"] = score
+                self.read_scores[y_ens_fn]["ens_score"] = score
 
                 # It is not needed to create the object here
                 # To save memory, we just score the object.
-                # self.read_preds[y_ens_fn][Y_ENSEMBLE] = y_ensemble
-                self.read_preds[y_ens_fn]["mtime_ens"] = os.path.getmtime(
-                    y_ens_fn
-                )
-                self.read_preds[y_ens_fn]["loaded"] = 2
-                self.read_preds[y_ens_fn]["disc_space_cost_mb"] = self.get_disk_consumption(
+                self.read_scores[y_ens_fn]["mtime_ens"] = os.path.getmtime(y_ens_fn)
+                self.read_scores[y_ens_fn]["loaded"] = 2
+                self.read_scores[y_ens_fn]["disc_space_cost_mb"] = self.get_disk_consumption(
                     y_ens_fn
                 )
 
@@ -543,19 +902,19 @@ def score_ensemble_preds(self):
                     y_ens_fn,
                     traceback.format_exc(),
                 )
-                self.read_preds[y_ens_fn]["ens_score"] = -1
+                self.read_scores[y_ens_fn]["ens_score"] = -np.inf
 
         self.logger.debug(
             'Done reading %d new prediction files. Loaded %d predictions in '
             'total.',
             n_read_files,
-            np.sum([pred["loaded"] > 0 for pred in self.read_preds.values()])
+            np.sum([pred["loaded"] > 0 for pred in self.read_scores.values()])
         )
         return True
 
     def get_n_best_preds(self):
         """
-            get best n predictions (i.e., keys of self.read_preds)
+            get best n predictions (i.e., keys of self.read_scores)
             according to score on "ensemble set"
             n: self.ensemble_nbest
 
@@ -590,7 +949,7 @@ def get_n_best_preds(self):
                                     num_keys - 1,
                                     num_dummy)
             sorted_keys = [
-                (k, v["ens_score"], v["num_run"]) for k, v in self.read_preds.items()
+                (k, v["ens_score"], v["num_run"]) for k, v in self.read_scores.items()
                 if v["seed"] == self.seed and v["num_run"] == 1
             ]
         # reload predictions if scores changed over time and a model is
@@ -618,20 +977,20 @@ def get_n_best_preds(self):
                     [
                         v["ens_score"],
                         v["disc_space_cost_mb"],
-                    ] for v in self.read_preds.values() if v["disc_space_cost_mb"] is not None
+                    ] for v in self.read_scores.values() if v["disc_space_cost_mb"] is not None
                 ]
-                max_consumption = max(i[1] for i in consumption)
+                max_consumption = max(c[1] for c in consumption)
 
                 # We are pessimistic with the consumption limit indicated by
                 # max_models_on_disc by 1 model. Such model is assumed to spend
                 # max_consumption megabytes
-                if (sum(i[1] for i in consumption) + max_consumption) > self.max_models_on_disc:
+                if (sum(c[1] for c in consumption) + max_consumption) > self.max_models_on_disc:
 
                     # just leave the best -- higher is better!
                     # This list is in descending order, to preserve the best models
                     sorted_cum_consumption = np.cumsum([
-                        i[1] for i in list(reversed(sorted(consumption)))
-                    ])
+                        c[1] for c in list(reversed(sorted(consumption)))
+                    ]) + max_consumption
                     max_models = np.argmax(sorted_cum_consumption > self.max_models_on_disc)
 
                     # Make sure that at least 1 model survives
@@ -640,11 +999,13 @@ def get_n_best_preds(self):
                         "Limiting num of models via float max_models_on_disc={}"
                         " as accumulated={} worst={} num_models={}".format(
                             self.max_models_on_disc,
-                            (sum(i[1] for i in consumption) + max_consumption),
+                            (sum(c[1] for c in consumption) + max_consumption),
                             max_consumption,
                             self.max_resident_models
                         )
                     )
+                else:
+                    self.max_resident_models = None
             else:
                 self.max_resident_models = self.max_models_on_disc
 
@@ -681,31 +1042,38 @@ def get_n_best_preds(self):
 
         # remove loaded predictions for non-winning models
         for k in sorted_keys[ensemble_n_best:]:
-            self.read_preds[k][Y_ENSEMBLE] = None
-            self.read_preds[k][Y_VALID] = None
-            self.read_preds[k][Y_TEST] = None
-            if self.read_preds[k]['loaded'] == 1:
+            if k in self.read_preds:
+                self.read_preds[k][Y_ENSEMBLE] = None
+                self.read_preds[k][Y_VALID] = None
+                self.read_preds[k][Y_TEST] = None
+            if self.read_scores[k]['loaded'] == 1:
                 self.logger.debug(
                     'Dropping model %s (%d,%d) with score %f.',
                     k,
-                    self.read_preds[k]['seed'],
-                    self.read_preds[k]['num_run'],
-                    self.read_preds[k]['ens_score'],
+                    self.read_scores[k]['seed'],
+                    self.read_scores[k]['num_run'],
+                    self.read_scores[k]['ens_score'],
                 )
-                self.read_preds[k]['loaded'] = 2
+                self.read_scores[k]['loaded'] = 2
 
         # Load the predictions for the winning
         for k in sorted_keys[:ensemble_n_best]:
-            if self.read_preds[k][Y_ENSEMBLE] is None:
+            if (
+                (
+                    k not in self.read_preds or
+                    self.read_preds[k][Y_ENSEMBLE] is None
+                )
+                and self.read_scores[k]['loaded'] != 3
+            ):
                 self.read_preds[k][Y_ENSEMBLE] = self._read_np_fn(k)
                 # No need to load valid and test here because they are loaded
                 #  only if the model ends up in the ensemble
-            self.read_preds[k]['loaded'] = 1
+                self.read_scores[k]['loaded'] = 1
 
-        # return best scored keys of self.read_preds
+        # return best scored keys of self.read_scores
         return sorted_keys[:ensemble_n_best]
 
-    def get_valid_test_preds(self, selected_keys: list):
+    def get_valid_test_preds(self, selected_keys: List[str]) -> Tuple[List[str], List[str]]:
         """
         get valid and test predictions from disc
         and store them in self.read_preds
@@ -727,28 +1095,37 @@ def get_valid_test_preds(self, selected_keys: list):
         for k in selected_keys:
             valid_fn = glob.glob(
                 os.path.join(
-                    glob.escape(self.dir_valid),
+                    glob.escape(self.backend.get_runs_directory()),
+                    '%d_%d_%s' % (
+                        self.read_scores[k]["seed"],
+                        self.read_scores[k]["num_run"],
+                        self.read_scores[k]["budget"],
+                    ),
                     'predictions_valid_%d_%d_%s.npy*' % (
-                        self.read_preds[k]["seed"],
-                        self.read_preds[k]["num_run"],
-                        self.read_preds[k]["budget"],
+                        self.read_scores[k]["seed"],
+                        self.read_scores[k]["num_run"],
+                        self.read_scores[k]["budget"],
                     )
                 )
             )
             valid_fn = [vfn for vfn in valid_fn if vfn.endswith('.npy') or vfn.endswith('.npy.gz')]
             test_fn = glob.glob(
                 os.path.join(
-                    glob.escape(self.dir_test),
+                    glob.escape(self.backend.get_runs_directory()),
+                    '%d_%d_%s' % (
+                        self.read_scores[k]["seed"],
+                        self.read_scores[k]["num_run"],
+                        self.read_scores[k]["budget"],
+                    ),
                     'predictions_test_%d_%d_%s.npy*' % (
-                        self.read_preds[k]["seed"],
-                        self.read_preds[k]["num_run"],
-                        self.read_preds[k]["budget"]
+                        self.read_scores[k]["seed"],
+                        self.read_scores[k]["num_run"],
+                        self.read_scores[k]["budget"]
                     )
                 )
             )
             test_fn = [tfn for tfn in test_fn if tfn.endswith('.npy') or tfn.endswith('.npy.gz')]
 
-            # TODO don't read valid and test if not changed
             if len(valid_fn) == 0:
                 # self.logger.debug("Not found validation prediction file "
                 #                   "(although ensemble predictions available): "
@@ -756,15 +1133,18 @@ def get_valid_test_preds(self, selected_keys: list):
                 pass
             else:
                 valid_fn = valid_fn[0]
-                if self.read_preds[k]["mtime_valid"] == os.path.getmtime(valid_fn) \
-                        and self.read_preds[k][Y_VALID] is not None:
+                if (
+                    self.read_scores[k]["mtime_valid"] == os.path.getmtime(valid_fn)
+                    and k in self.read_preds
+                    and self.read_preds[k][Y_VALID] is not None
+                ):
                     success_keys_valid.append(k)
                     continue
                 try:
                     y_valid = self._read_np_fn(valid_fn)
                     self.read_preds[k][Y_VALID] = y_valid
                     success_keys_valid.append(k)
-                    self.read_preds[k]["mtime_valid"] = os.path.getmtime(valid_fn)
+                    self.read_scores[k]["mtime_valid"] = os.path.getmtime(valid_fn)
                 except Exception:
                     self.logger.warning('Error loading %s: %s',
                                         valid_fn, traceback.format_exc())
@@ -776,16 +1156,18 @@ def get_valid_test_preds(self, selected_keys: list):
                 pass
             else:
                 test_fn = test_fn[0]
-                if self.read_preds[k]["mtime_test"] == \
-                        os.path.getmtime(test_fn) \
-                        and self.read_preds[k][Y_TEST] is not None:
+                if (
+                    self.read_scores[k]["mtime_test"] == os.path.getmtime(test_fn)
+                    and k in self.read_preds
+                    and self.read_preds[k][Y_TEST] is not None
+                ):
                     success_keys_test.append(k)
                     continue
                 try:
                     y_test = self._read_np_fn(test_fn)
                     self.read_preds[k][Y_TEST] = y_test
                     success_keys_test.append(k)
-                    self.read_preds[k]["mtime_test"] = os.path.getmtime(test_fn)
+                    self.read_scores[k]["mtime_test"] = os.path.getmtime(test_fn)
                 except Exception:
                     self.logger.warning('Error loading %s: %s',
                                         test_fn, traceback.format_exc())
@@ -799,24 +1181,28 @@ def fit_ensemble(self, selected_keys: list):
             Parameters
             ---------
             selected_keys: list
-                list of selected keys of self.read_preds
+                list of selected keys of self.read_scores
 
             Returns
             -------
             ensemble: EnsembleSelection
                 trained Ensemble
         """
-        predictions_train = np.array([self.read_preds[k][Y_ENSEMBLE] for k in selected_keys])
+
+        predictions_train = [self.read_preds[k][Y_ENSEMBLE] for k in selected_keys]
         include_num_runs = [
             (
-                self.read_preds[k]["seed"],
-                self.read_preds[k]["num_run"],
-                self.read_preds[k]["budget"],
+                self.read_scores[k]["seed"],
+                self.read_scores[k]["num_run"],
+                self.read_scores[k]["budget"],
             )
             for k in selected_keys]
 
         # check hash if ensemble training data changed
-        current_hash = hash(predictions_train.data.tobytes())
+        current_hash = "".join([
+            str(zlib.adler32(predictions_train[i].data.tobytes()))
+            for i in range(len(predictions_train))
+        ])
         if self.last_hash == current_hash:
             self.logger.debug(
                 "No new model predictions selected -- skip ensemble building "
@@ -824,10 +1210,6 @@ def fit_ensemble(self, selected_keys: list):
                 self.validation_performance_,
             )
 
-            # Delete files of non-candidate models
-            if self.max_resident_models is not None:
-                self._delete_excess_models()
-
             return None
         self.last_hash = current_hash
 
@@ -859,16 +1241,13 @@ def fit_ensemble(self, selected_keys: list):
 
         except ValueError:
             self.logger.error('Caught ValueError: %s', traceback.format_exc())
-            time.sleep(self.sleep_duration)
             return None
         except IndexError:
             self.logger.error('Caught IndexError: %s' + traceback.format_exc())
-            time.sleep(self.sleep_duration)
             return None
-
-        # Delete files of non-candidate models
-        if self.max_resident_models is not None:
-            self._delete_excess_models()
+        finally:
+            # Explicitly free memory
+            del predictions_train
 
         return ensemble
 
@@ -887,7 +1266,7 @@ def predict(self, set_: str,
             ensemble: EnsembleSelection
                 trained Ensemble
             selected_keys: list
-                list of selected keys of self.read_preds
+                list of selected keys of self.read_scores
             n_preds: int
                 number of prediction models used for ensemble building
                 same number of predictions on valid and test are necessary
@@ -900,22 +1279,15 @@ def predict(self, set_: str,
         """
         self.logger.debug("Predicting the %s set with the ensemble!", set_)
 
-        # Save the ensemble for later use in the main auto-sklearn module!
-        if self.SAVE2DISC:
-            self.backend.save_ensemble(ensemble, index_run, self.seed)
-
         if set_ == 'valid':
             pred_set = Y_VALID
         elif set_ == 'test':
             pred_set = Y_TEST
         else:
             pred_set = Y_ENSEMBLE
-        predictions = np.array([
-            self.read_preds[k][pred_set]
-            for k in selected_keys
-        ])
+        predictions = [self.read_preds[k][pred_set] for k in selected_keys]
 
-        if n_preds == predictions.shape[0]:
+        if n_preds == len(predictions):
             y = ensemble.predict(predictions)
             if self.task_type == BINARY_CLASSIFICATION:
                 y = y[:, 1]
@@ -932,28 +1304,11 @@ def predict(self, set_: str,
             self.logger.info(
                 "Found inconsistent number of predictions and models (%d vs "
                 "%d) for subset %s",
-                predictions.shape[0],
+                len(predictions),
                 n_preds,
                 set_,
             )
             return None
-        # TODO: ADD saving of predictions on "ensemble data"
-
-    def get_ensemble_history(self):
-        """
-        Getter method to obtain the performance of the ensemble
-        building process across time
-
-        Return
-        ----------
-        dict that tracks the performance of the ensemble
-        building process on testing/training sets
-
-        """
-        ensemble_history = []
-        while(self.queue.qsize()):
-            ensemble_history.append(self.queue.get())
-        return ensemble_history
 
     def _add_ensemble_trajectory(self, train_pred, valid_pred, test_pred):
         """
@@ -970,6 +1325,20 @@ def _add_ensemble_trajectory(self, train_pred, valid_pred, test_pred):
             The predictions on the test set using ensemble
 
         """
+        if self.task_type == BINARY_CLASSIFICATION:
+            if len(train_pred.shape) == 1 or train_pred.shape[1] == 1:
+                train_pred = np.vstack(
+                    ((1 - train_pred).reshape((1, -1)), train_pred.reshape((1, -1)))
+                ).transpose()
+            if valid_pred is not None and (len(valid_pred.shape) == 1 or valid_pred.shape[1] == 1):
+                valid_pred = np.vstack(
+                    ((1 - valid_pred).reshape((1, -1)), valid_pred.reshape((1, -1)))
+                ).transpose()
+            if test_pred is not None and (len(test_pred.shape) == 1 or test_pred.shape[1] == 1):
+                test_pred = np.vstack(
+                    ((1 - test_pred).reshape((1, -1)), test_pred.reshape((1, -1)))
+                ).transpose()
+
         performance_stamp = {
             'Timestamp': pd.Timestamp.now(),
             'ensemble_optimization_score': calculate_score(
@@ -1001,12 +1370,12 @@ def _add_ensemble_trajectory(self, train_pred, valid_pred, test_pred):
                 all_scoring_functions=False
             )
 
-        self.queue.put(performance_stamp)
+        self.ensemble_history.append(performance_stamp)
 
     def _get_list_of_sorted_preds(self):
         """
             Returns a list of sorted predictions in descending order
-            Predictions are taken from self.read_preds.
+            Scores are taken from self.read_scores.
 
             Parameters
             ----------
@@ -1021,7 +1390,7 @@ def _get_list_of_sorted_preds(self):
         sorted_keys = list(reversed(sorted(
             [
                 (k, v["ens_score"], v["num_run"])
-                for k, v in self.read_preds.items()
+                for k, v in self.read_scores.items()
             ],
             key=lambda x: x[2],
         )))
@@ -1029,7 +1398,7 @@ def _get_list_of_sorted_preds(self):
         sorted_keys = list(reversed(sorted(sorted_keys, key=lambda x: x[1])))
         return sorted_keys
 
-    def _delete_excess_models(self):
+    def _delete_excess_models(self, selected_keys: List[str]):
         """
             Deletes models excess models on disc. self.max_models_on_disc
             defines the upper limit on how many models to keep.
@@ -1062,77 +1431,27 @@ def _delete_excess_models(self):
                 continue
 
             match = self.model_fn_re.search(pred_path)
-            _full_name = match.group(0)
-            _seed = match.group(1)
-            _num_run = match.group(2)
-            _budget = match.group(3)
+            _seed = int(match.group(1))
+            _num_run = int(match.group(2))
+            _budget = float(match.group(3))
 
             # Do not delete the dummy prediction
-            if int(_num_run) == 1:
+            if _num_run == 1:
                 continue
 
-            # Besides the prediction, we have to take care of three other files: model,
-            # validation and test.
-            model_name = '%s.%s.%s.model' % (_seed, _num_run, _budget)
-            model_path = os.path.join(self.dir_models, model_name)
-            pred_valid_name = 'predictions_valid' + _full_name
-            pred_valid_path = os.path.join(self.dir_valid, pred_valid_name)
-            pred_test_name = 'predictions_test' + _full_name
-            pred_test_path = os.path.join(self.dir_test, pred_test_name)
-
-            paths = [pred_path]
-            if os.path.exists(model_path):
-                paths.append(model_path)
-            if os.path.exists(pred_valid_path):
-                paths.append(pred_valid_path)
-            if os.path.exists(pred_test_path):
-                paths.append(pred_test_path)
-
-            # Lets lock all the files "at once" to avoid weird race conditions. Also,
-            # we either delete all files of a model (model, prediction, validation
-            # and test), or delete none. This makes it easier to keep track of which
-            # models have indeed been correctly removed.
-            locks = [lockfile.LockFile(path) for path in paths]
+            numrun_dir = self.backend.get_numrun_directory(_seed, _num_run, _budget)
             try:
-                for lock in locks:
-                    lock.acquire()
+                os.rename(numrun_dir, numrun_dir + '.old')
+                shutil.rmtree(numrun_dir + '.old')
+                self.logger.info("Deleted files of non-candidate model %s", pred_path)
+                self.read_scores[pred_path]["disc_space_cost_mb"] = None
+                self.read_scores[pred_path]["loaded"] = 3
+                self.read_scores[pred_path]["ens_score"] = -np.inf
             except Exception as e:
-                if isinstance(e, lockfile.AlreadyLocked):
-                    # If the file is already locked, we deal with it later. Not a big deal
-                    self.logger.info(
-                        'Model %s is already locked. Skipping it for now.', model_name)
-                else:
-                    # Other exceptions, however, should not occur.
-                    # The message bellow is asserted in test_delete_excess_models()
-                    self.logger.error(
-                        'Failed to lock model %s files due to error %s', model_name, e)
-                for lock in locks:
-                    if lock.i_am_locking():
-                        lock.release()
-                continue
-
-            # Delete files if model is not a candidate AND prediction is old. We check if
-            # the prediction is old to avoid deleting a model that hasn't been appreciated
-            # by self.get_n_best_preds() yet.
-            original_timestamp = self.read_preds[pred_path]['mtime_ens']
-            current_timestamp = os.path.getmtime(pred_path)
-            if current_timestamp == original_timestamp:
-                # The messages logged here are asserted in
-                # test_delete_excess_models(). Edit with care.
-                try:
-                    for path in paths:
-                        os.remove(path)
-                    self.logger.info(
-                        "Deleted files of non-candidate model %s", model_name)
-                except Exception as e:
-                    self.logger.error(
-                        "Failed to delete files of non-candidate model %s due"
-                        " to error %s", model_name, e)
-
-            # If we reached this point, all locks were done by this thread. So no need
-            # to check "lock.i_am_locking()" here.
-            for lock in locks:
-                lock.release()
+                self.logger.error(
+                    "Failed to delete files of non-candidate model %s due"
+                    " to error %s", pred_path, e
+                )
 
     def _read_np_fn(self, path):
 
diff --git a/autosklearn/ensembles/abstract_ensemble.py b/autosklearn/ensembles/abstract_ensemble.py
index 65f3ba9809..8ab8563e25 100644
--- a/autosklearn/ensembles/abstract_ensemble.py
+++ b/autosklearn/ensembles/abstract_ensemble.py
@@ -1,5 +1,5 @@
 from abc import ABCMeta, abstractmethod
-from typing import Dict, List, Tuple
+from typing import Dict, List, Tuple, Union
 
 import numpy as np
 
@@ -40,7 +40,7 @@ def fit(
         pass
 
     @abstractmethod
-    def predict(self, base_models_predictions: np.ndarray) -> np.ndarray:
+    def predict(self, base_models_predictions: Union[np.ndarray, List[np.ndarray]]) -> np.ndarray:
         """Create ensemble predictions from the base model predictions.
 
         Parameters
diff --git a/autosklearn/ensembles/ensemble_selection.py b/autosklearn/ensembles/ensemble_selection.py
index 2c02b9263f..0b5648796c 100644
--- a/autosklearn/ensembles/ensemble_selection.py
+++ b/autosklearn/ensembles/ensemble_selection.py
@@ -1,6 +1,6 @@
 import random
 from collections import Counter
-from typing import List, Tuple, cast
+from typing import Any, Dict, List, Tuple, Union, cast
 
 import numpy as np
 
@@ -27,6 +27,17 @@ def __init__(
         self.mode = mode
         self.random_state = random_state
 
+    def __getstate__(self) -> Dict[str, Any]:
+        # Cannot serialize a metric if
+        # it is user defined.
+        # That is, if doing pickle dump
+        # the metric won't be the same as the
+        # one in __main__. we don't use the metric
+        # in the EnsembleSelection so this should
+        # be fine
+        self.metric = None  # type: ignore
+        return self.__dict__
+
     def fit(
         self,
         predictions: List[np.ndarray],
@@ -39,7 +50,11 @@ def fit(
         if self.task_type not in TASK_TYPES:
             raise ValueError('Unknown task type %s.' % self.task_type)
         if not isinstance(self.metric, Scorer):
-            raise ValueError('Metric must be of type scorer')
+            raise ValueError("The provided metric must be an instance of Scorer, "
+                             "nevertheless it is {}({})".format(
+                                 self.metric,
+                                 type(self.metric),
+                             ))
         if self.mode not in ('fast', 'slow'):
             raise ValueError('Unknown mode %s' % self.mode)
 
@@ -250,27 +265,32 @@ def _bagging(
             dtype=np.int64,
         )
 
-    def predict(self, predictions: np.ndarray) -> np.ndarray:
-        predictions = np.asarray(
-            predictions,
-            dtype=np.float64,
-        )
+    def predict(self, predictions: Union[np.ndarray, List[np.ndarray]]) -> np.ndarray:
+
+        average = np.zeros_like(predictions[0], dtype=np.float64)
+        tmp_predictions = np.empty_like(predictions[0], dtype=np.float64)
 
         # if predictions.shape[0] == len(self.weights_),
         # predictions include those of zero-weight models.
-        if predictions.shape[0] == len(self.weights_):
-            return np.average(predictions, axis=0, weights=self.weights_)
+        if len(predictions) == len(self.weights_):
+            for pred, weight in zip(predictions, self.weights_):
+                np.multiply(pred, weight, out=tmp_predictions)
+                np.add(average, tmp_predictions, out=average)
 
         # if prediction model.shape[0] == len(non_null_weights),
         # predictions do not include those of zero-weight models.
-        elif predictions.shape[0] == np.count_nonzero(self.weights_):
+        elif len(predictions) == np.count_nonzero(self.weights_):
             non_null_weights = [w for w in self.weights_ if w > 0]
-            return np.average(predictions, axis=0, weights=non_null_weights)
+            for pred, weight in zip(predictions, non_null_weights):
+                np.multiply(pred, weight, out=tmp_predictions)
+                np.add(average, tmp_predictions, out=average)
 
         # If none of the above applies, then something must have gone wrong.
         else:
             raise ValueError("The dimensions of ensemble predictions"
                              " and ensemble weights do not match!")
+        del tmp_predictions
+        return average
 
     def __str__(self) -> str:
         return 'Ensemble Selection:\n\tTrajectory: %s\n\tMembers: %s' \
diff --git a/autosklearn/ensembles/singlebest_ensemble.py b/autosklearn/ensembles/singlebest_ensemble.py
index 532a2a3c18..31a69ae904 100644
--- a/autosklearn/ensembles/singlebest_ensemble.py
+++ b/autosklearn/ensembles/singlebest_ensemble.py
@@ -1,4 +1,5 @@
-from typing import List, Tuple
+import os
+from typing import List, Tuple, Union
 
 import numpy as np
 
@@ -7,6 +8,7 @@
 from autosklearn.ensembles.abstract_ensemble import AbstractEnsemble
 from autosklearn.metrics import Scorer
 from autosklearn.pipeline.base import BasePipeline
+from autosklearn.util.backend import Backend
 
 
 class SingleBest(AbstractEnsemble):
@@ -22,10 +24,12 @@ def __init__(
         self,
         metric: Scorer,
         run_history: RunHistory,
-        random_state: np.random.RandomState,
+        seed: int,
+        backend: Backend,
     ):
         self.metric = metric
-        self.random_state = random_state
+        self.seed = seed
+        self.backend = backend
 
         # Add some default values -- at least 1 model in ensemble is assumed
         self.indices_ = [0]
@@ -47,11 +51,30 @@ def get_identifiers_from_run_history(self) -> List[Tuple[int, int, float]]:
         for run_key in self.run_history.data.keys():
             run_value = self.run_history.data[run_key]
             score = self.metric._optimum - (self.metric._sign * run_value.cost)
+
             if (score > best_model_score and self.metric._sign > 0) \
                     or (score < best_model_score and self.metric._sign < 0):
-                best_model_identifier = [
-                    (self.random_state, run_value.additional_info['num_run'], run_key.budget)
-                ]
+
+                # Make sure that the individual best model actually exists
+                model_dir = self.backend.get_numrun_directory(
+                    self.seed,
+                    run_value.additional_info['num_run'],
+                    run_key.budget,
+                )
+                model_file_name = self.backend.get_model_filename(
+                    self.seed,
+                    run_value.additional_info['num_run'],
+                    run_key.budget,
+                )
+                file_path = os.path.join(model_dir, model_file_name)
+                if not os.path.exists(file_path):
+                    continue
+
+                best_model_identifier = [(
+                    self.seed,
+                    run_value.additional_info['num_run'],
+                    run_key.budget,
+                )]
                 best_model_score = score
 
         if not best_model_identifier:
@@ -62,7 +85,7 @@ def get_identifiers_from_run_history(self) -> List[Tuple[int, int, float]]:
 
         return best_model_identifier
 
-    def predict(self, predictions: np.ndarray) -> np.ndarray:
+    def predict(self, predictions: Union[np.ndarray, List[np.ndarray]]) -> np.ndarray:
         return predictions[0]
 
     def __str__(self) -> str:
diff --git a/autosklearn/estimators.py b/autosklearn/estimators.py
index c20a7b5109..89e731a202 100644
--- a/autosklearn/estimators.py
+++ b/autosklearn/estimators.py
@@ -22,9 +22,8 @@ def __init__(
         ensemble_size: int = 50,
         ensemble_nbest=50,
         max_models_on_disc=50,
-        ensemble_memory_limit: Optional[int] = 1024,
         seed=1,
-        ml_memory_limit=3072,
+        memory_limit=3072,
         include_estimators=None,
         exclude_estimators=None,
         include_preprocessors=None,
@@ -43,6 +42,7 @@ def __init__(
         logging_config=None,
         metadata_directory=None,
         metric=None,
+        load_models: bool = True,
     ):
         """
         Parameters
@@ -82,21 +82,16 @@ def __init__(
             It must be an integer greater or equal than 1.
             If set to None, all models are kept on the disc.
 
-        ensemble_memory_limit : int, optional (1024)
-            Memory limit in MB for the ensemble building process.
-            `auto-sklearn` will reduce the number of considered models
-            (``ensemble_nbest``) if the memory limit is reached.
-            If ``None``, no memory limit is enforced.
-
         seed : int, optional (default=1)
             Used to seed SMAC. Will determine the output file names.
 
-        ml_memory_limit : int, optional (3072)
+        memory_limit : int, optional (3072)
             Memory limit in MB for the machine learning algorithm.
             `auto-sklearn` will stop fitting the machine learning algorithm if
-            it tries to allocate more than `ml_memory_limit` MB.
+            it tries to allocate more than `memory_limit` MB.
             If None is provided, no memory limit is set.
-            In case of multi-processing, `ml_memory_limit` will be per job.
+            In case of multi-processing, `memory_limit` will be per job.
+            This memory limit also applies to the ensemble creation process.
 
         include_estimators : list, optional (None)
             If None, all possible estimators are used. Otherwise specifies
@@ -159,7 +154,7 @@ def __init__(
 
         output_folder : string, optional (None)
             folder to store predictions for optional test set, if ``None``
-            automatically use ``/tmp/autosklearn_output_$pid_$random_number``
+            no output will be generated
 
         delete_tmp_folder_after_terminate: string, optional (True)
             remove tmp_folder, when finished. If tmp_folder is None
@@ -170,17 +165,17 @@ def __init__(
             output_dir will always be deleted
 
         n_jobs : int, optional, experimental
-            The number of jobs to run in parallel for ``fit()``. ``-1`` means 
-            using all processors. By default, Auto-sklearn uses a single core 
+            The number of jobs to run in parallel for ``fit()``. ``-1`` means
+            using all processors. By default, Auto-sklearn uses a single core
             for fitting the machine learning model and a single core for fitting
             an ensemble. Ensemble building is not affected by ``n_jobs`` but
             can be controlled by the number of models in the ensemble. In
             contrast to most scikit-learn models, ``n_jobs`` given in the
-            constructor is not applied to the ``predict()`` method. If 
+            constructor is not applied to the ``predict()`` method. If
             ``dask_client`` is None, a new dask client is created.
-            
+
         dask_client : dask.distributed.Client, optional
-            User-created dask client, can be used to start a dask cluster and then 
+            User-created dask client, can be used to start a dask cluster and then
             attach auto-sklearn to it.
 
         disable_evaluator_output: bool or list, optional (False)
@@ -222,6 +217,9 @@ def __init__(
             :meth:`autosklearn.metrics.make_scorer`. These are the `Built-in
             Metrics`_.
             If None is provided, a default metric is selected depending on the task.
+            
+        load_models : bool, optional (True)
+            Whether to load the models after fitting Auto-sklearn.
 
         Attributes
         ----------
@@ -243,9 +241,8 @@ def __init__(
         self.ensemble_size = ensemble_size
         self.ensemble_nbest = ensemble_nbest
         self.max_models_on_disc = max_models_on_disc
-        self.ensemble_memory_limit = ensemble_memory_limit
         self.seed = seed
-        self.ml_memory_limit = ml_memory_limit
+        self.memory_limit = memory_limit
         self.include_estimators = include_estimators
         self.exclude_estimators = exclude_estimators
         self.include_preprocessors = include_preprocessors
@@ -264,12 +261,18 @@ def __init__(
         self.logging_config = logging_config
         self.metadata_directory = metadata_directory
         self._metric = metric
+        self._load_models = load_models
 
         self.automl_ = None  # type: Optional[AutoML]
         # n_jobs after conversion to a number (b/c default is None)
         self._n_jobs = None
         super().__init__()
 
+    def __getstate__(self):
+        # Cannot serialize a client!
+        self.dask_client = None
+        return self.__dict__
+
     def build_automl(
         self,
         seed: int,
@@ -298,9 +301,8 @@ def build_automl(
             ensemble_size=ensemble_size,
             ensemble_nbest=self.ensemble_nbest,
             max_models_on_disc=self.max_models_on_disc,
-            ensemble_memory_limit=self.ensemble_memory_limit,
             seed=seed,
-            ml_memory_limit=self.ml_memory_limit,
+            memory_limit=self.memory_limit,
             include_estimators=self.include_estimators,
             exclude_estimators=self.exclude_estimators,
             include_preprocessors=self.include_preprocessors,
@@ -343,7 +345,7 @@ def fit(self, **kwargs):
             tmp_folder=self.tmp_folder,
             output_folder=self.output_folder,
         )
-        self.automl_.fit(load_models=True, **kwargs)
+        self.automl_.fit(load_models=self._load_models, **kwargs)
 
         return self
 
diff --git a/autosklearn/evaluation/__init__.py b/autosklearn/evaluation/__init__.py
index 548db789b2..2c369149fe 100644
--- a/autosklearn/evaluation/__init__.py
+++ b/autosklearn/evaluation/__init__.py
@@ -35,11 +35,31 @@ def fit_predict_try_except_decorator(ta, queue, cost_for_crash, **kwargs):
         exception_traceback = traceback.format_exc()
         error_message = repr(e)
 
+        # Printing stuff to stdout just in case the queue doesn't work, which happened with the
+        # following traceback:
+        #     File "auto-sklearn/autosklearn/evaluation/__init__.py", line 29, in fit_predict_try_except_decorator  # noqa E501
+        #     return ta(queue=queue, **kwargs)
+        #     File "auto-sklearn/autosklearn/evaluation/train_evaluator.py", line 1067, in eval_holdout  # noqa E501
+        #     evaluator.fit_predict_and_loss(iterative=iterative)
+        #     File "auto-sklearn/autosklearn/evaluation/train_evaluator.py", line 616, in fit_predict_and_loss,  # noqa E501
+        #     status=status
+        #     File "auto-sklearn/autosklearn/evaluation/abstract_evaluator.py", line 320, in finish_up  # noqa E501
+        #     self.queue.put(rval_dict)
+        #     File "miniconda/3-4.5.4/envs/autosklearn/lib/python3.7/multiprocessing/queues.py", line 87, in put  # noqa E501
+        #     self._start_thread()
+        #     File "miniconda/3-4.5.4/envs/autosklearn/lib/python3.7/multiprocessing/queues.py", line 170, in _start_thread  # noqa E501
+        #     self._thread.start()
+        #     File "miniconda/3-4.5.4/envs/autosklearn/lib/python3.7/threading.py", line 847, in start  # noqa E501
+        #     RuntimeError: can't start new thread
+        print("Exception handling in `fit_predict_try_except_decorator`: "
+              "traceback: %s \nerror message: %s" % (exception_traceback, error_message))
+
         queue.put({'loss': cost_for_crash,
                    'additional_run_info': {'traceback': exception_traceback,
                                            'error': error_message},
                    'status': StatusType.CRASHED,
-                   'final_queue_element': True})
+                   'final_queue_element': True}, block=True)
+        queue.close()
 
 
 def get_cost_of_crash(metric):
@@ -222,6 +242,7 @@ def run(self, config, instance=None,
             logger=autosklearn.util.logging_.get_logger("pynisher"),
             wall_time_in_s=cutoff,
             mem_in_mb=self.memory_limit,
+            capture_output=True,
         )
 
         if isinstance(config, int):
@@ -265,6 +286,11 @@ def run(self, config, instance=None,
                 status = info[-1]['status']
                 additional_run_info = info[-1]['additional_run_info']
 
+                if obj.stdout:
+                    additional_run_info['subprocess_stdout'] = obj.stdout
+                if obj.stderr:
+                    additional_run_info['subprocess_stderr'] = obj.stderr
+
                 if obj.exit_status is pynisher.TimeoutException:
                     additional_run_info['info'] = 'Run stopped because of timeout.'
                 elif obj.exit_status is pynisher.MemorylimitException:
@@ -295,7 +321,11 @@ def run(self, config, instance=None,
             status = StatusType.ABORT
             cost = self.worst_possible_result
             additional_run_info = {'error': 'Your configuration of '
-                                            'auto-sklearn does not work!'}
+                                            'auto-sklearn does not work!',
+                                   'exit_status': obj.exit_status,
+                                   'subprocess_stdout': obj.stdout,
+                                   'subprocess_stderr': obj.stderr,
+                                   }
 
         else:
             try:
@@ -313,9 +343,18 @@ def run(self, config, instance=None,
                                                   'because the pynisher exit ' \
                                                   'status %s is unknown.' % \
                                                   str(obj.exit_status)
+                    additional_run_info['exit_status'] = obj.exit_status
+                    additional_run_info['subprocess_stdout'] = obj.stdout
+                    additional_run_info['subprocess_stderr'] = obj.stderr
             except Empty:
                 info = None
-                additional_run_info = {'error': 'Result queue is empty'}
+                additional_run_info = {
+                    'error': 'Result queue is empty',
+                    'exit_status': obj.exit_status,
+                    'subprocess_stdout': obj.stdout,
+                    'subprocess_stderr': obj.stderr,
+                    'exitcode': obj.exitcode
+                }
                 status = StatusType.CRASHED
                 cost = self.worst_possible_result
 
diff --git a/autosklearn/evaluation/abstract_evaluator.py b/autosklearn/evaluation/abstract_evaluator.py
index 93b3bde49c..80d3594209 100644
--- a/autosklearn/evaluation/abstract_evaluator.py
+++ b/autosklearn/evaluation/abstract_evaluator.py
@@ -1,9 +1,6 @@
-import os
 import time
 import warnings
-from collections import namedtuple
 
-import lockfile
 import numpy as np
 from sklearn.dummy import DummyClassifier, DummyRegressor
 from sklearn.ensemble import VotingClassifier, VotingRegressor
@@ -30,8 +27,6 @@
     'AbstractEvaluator'
 ]
 
-WriteTask = namedtuple('WriteTask', ['lock', 'writer', 'args'])
-
 
 class MyDummyClassifier(DummyClassifier):
     def __init__(self, configuration, random_state, init_params=None):
@@ -403,63 +398,8 @@ def file_output(
         # This file can be written independently of the others down bellow
         if ('y_optimization' not in self.disable_file_output):
             if self.output_y_hat_optimization:
-                try:
-                    os.makedirs(self.backend.output_directory)
-                except OSError:
-                    pass
                 self.backend.save_targets_ensemble(self.Y_optimization)
 
-        # The other four files have to be written together, meaning we start
-        # writing them just after acquiring the locks for all of them.
-        # But first we have to check which files have to be written.
-        write_tasks = []
-
-        # File 1 of 5: model
-        if ('model' not in self.disable_file_output):
-            if os.path.exists(self.backend.get_model_dir()):
-                file_path = self.backend.get_model_path(
-                    self.seed, self.num_run, self.budget)
-                write_tasks.append(
-                    WriteTask(
-                        lock=lockfile.LockFile(file_path),
-                        writer=self.backend.save_model,
-                        args=(self.model, file_path)
-                        ))
-
-        # File 2 of 5: predictions
-        if ('y_optimization' not in self.disable_file_output):
-            file_path = self.backend.get_prediction_output_path(
-                'ensemble', self.seed, self.num_run, self.budget)
-            write_tasks.append(
-                WriteTask(
-                    lock=lockfile.LockFile(file_path),
-                    writer=self.backend.save_predictions_as_npy,
-                    args=(Y_optimization_pred, file_path)
-                    ))
-
-        # File 3 of 5: validation predictions
-        if Y_valid_pred is not None:
-            file_path = self.backend.get_prediction_output_path(
-                'valid', self.seed, self.num_run, self.budget)
-            write_tasks.append(
-                WriteTask(
-                    lock=lockfile.LockFile(file_path),
-                    writer=self.backend.save_predictions_as_npy,
-                    args=(Y_valid_pred, file_path)
-                    ))
-
-        # File 4 of 5: test predictions
-        if Y_test_pred is not None:
-            file_path = self.backend.get_prediction_output_path(
-                'test', self.seed, self.num_run, self.budget)
-            write_tasks.append(
-                WriteTask(
-                    lock=lockfile.LockFile(file_path),
-                    writer=self.backend.save_predictions_as_npy,
-                    args=(Y_test_pred, file_path)
-                    ))
-
-        # File 5 of 5: ensemble of models in case of cross-validation
         if hasattr(self, 'models') and len(self.models) > 0 and self.models[0] is not None:
             if ('models' not in self.disable_file_output):
 
@@ -468,42 +408,27 @@ def file_output(
                 else:
                     models = VotingRegressor(estimators=None)
                 models.estimators_ = self.models
-
-                if os.path.exists(self.backend.get_cv_model_dir()):
-                    file_path = self.backend.get_cv_model_path(
-                        self.seed, self.num_run, self.budget)
-                    write_tasks.append(
-                        WriteTask(
-                            lock=lockfile.LockFile(file_path),
-                            writer=self.backend.save_model,
-                            args=(models, file_path)
-                            ))
-
-        # We then acquire the locks one by one in a stubborn fashion, i.e. if a file is
-        # already locked, we keep probing it until it is unlocked. This will NOT create a
-        # race condition with _delete_non_candidate_models() since this function doesn't
-        # acquire the locks in this stubborn way. The delete function releases all the
-        # locks and aborts the acquision process as soon as it finds a locked file.
-        for wt in write_tasks:
-            while True:
-                try:
-                    wt.lock.acquire()
-                    break
-                except lockfile.AlreadyLocked:
-                    time.sleep(.1)
-                    continue
-                except Exception as e:
-                    raise RuntimeError('Failed to lock %s due to %s' % (wt.lock, e))
-
-        # At this point we are good to write the files
-        for wt in write_tasks:
-            wt.writer(*wt.args)
-
-        # And finally release the locks
-        for wt in write_tasks:
-            wt.lock.release()
-
-        self.backend.note_numrun_as_done(self.seed, self.num_run)
+            else:
+                models = None
+        else:
+            models = None
+
+        self.backend.save_numrun_to_dir(
+            seed=self.seed,
+            idx=self.num_run,
+            budget=self.budget,
+            model=self.model if 'model' not in self.disable_file_output else None,
+            cv_model=models if 'cv_model' not in self.disable_file_output else None,
+            ensemble_predictions=(
+                Y_optimization_pred if 'y_optimization' not in self.disable_file_output else None
+            ),
+            valid_predictions=(
+                Y_valid_pred if 'y_valid' not in self.disable_file_output else None
+            ),
+            test_predictions=(
+                Y_test_pred if 'y_test' not in self.disable_file_output else None
+            ),
+        )
 
         return None, {}
 
diff --git a/autosklearn/experimental/askl2.py b/autosklearn/experimental/askl2.py
index 9367b21189..93ca76afe4 100644
--- a/autosklearn/experimental/askl2.py
+++ b/autosklearn/experimental/askl2.py
@@ -1,27 +1,45 @@
+import hashlib
 import json
 import os
+import pathlib
 import pickle
 from typing import Any, Dict, Optional, Union
 
+import dask.distributed
+
 from ConfigSpace import Configuration
 import numpy as np
 import pandas as pd
+import sklearn
 
+import autosklearn
 from autosklearn.classification import AutoSklearnClassifier
 import autosklearn.experimental.selector
 from autosklearn.metrics import Scorer
 
-this_directory = os.path.abspath(os.path.dirname(__file__))
-selector_file = os.path.join(this_directory, 'selector.pkl')
-training_data_file = os.path.join(this_directory, 'askl2_training_data.json')
+this_directory = pathlib.Path(__file__).resolve().parent
+training_data_file = this_directory / 'askl2_training_data.json'
 with open(training_data_file) as fh:
     training_data = json.load(fh)
+    fh.seek(0)
+    m = hashlib.md5()
+    m.update(fh.read().encode('utf8'))
+training_data_hash = m.hexdigest()[:10]
+sklearn_version = sklearn.__version__
+autosklearn_version = autosklearn.__version__
+selector_file = pathlib.Path(
+    os.environ.get(
+        'XDG_CACHE_HOME',
+        '~/.cache/auto-sklearn/askl2_selector_%s_%s_%s.pkl'
+        % (autosklearn_version, sklearn_version, training_data_hash),
+    )
+).expanduser()
 metafeatures = pd.DataFrame(training_data['metafeatures'])
 y_values = np.array(training_data['y_values'])
 strategies = training_data['strategies']
 minima_for_methods = training_data['minima_for_methods']
 maxima_for_methods = training_data['maxima_for_methods']
-if not os.path.exists(selector_file):
+if not selector_file.exists():
     selector = autosklearn.experimental.selector.OneVSOneSelector(
         configuration=training_data['configuration'],
         default_strategy_idx=strategies.index('RF_SH-eta4-i_holdout_iterative_es_if'),
@@ -34,12 +52,17 @@
         minima=minima_for_methods,
         maxima=maxima_for_methods,
     )
+    selector_file.parent.mkdir(exist_ok=True, parents=True)
     with open(selector_file, 'wb') as fh:
         pickle.dump(selector, fh)
 
 
-def get_smac_object_callback(portfolio):
-    def get_smac_object(
+class SmacObjectCallback:
+    def __init__(self, portfolio):
+        self.portfolio = portfolio
+
+    def __call__(
+        self,
         scenario_dict,
         seed,
         ta,
@@ -51,12 +74,13 @@ def get_smac_object(
         from smac.facade.smac_ac_facade import SMAC4AC
         from smac.runhistory.runhistory2epm import RunHistory2EPM4LogCost
         from smac.scenario.scenario import Scenario
+        from smac.intensification.simple_intensifier import SimpleIntensifier
 
         scenario = Scenario(scenario_dict)
 
         initial_configurations = [
             Configuration(configuration_space=scenario.cs, values=member)
-            for member in portfolio.values()]
+            for member in self.portfolio.values()]
 
         rh2EPM = RunHistory2EPM4LogCost
         return SMAC4AC(
@@ -66,15 +90,22 @@ def get_smac_object(
             tae_runner=ta,
             tae_runner_kwargs=ta_kwargs,
             initial_configurations=initial_configurations,
+            intensifier=SimpleIntensifier,
             run_id=seed,
             n_jobs=n_jobs,
             dask_client=dask_client,
         )
-    return get_smac_object
 
 
-def get_sh_object_callback(budget_type, eta, initial_budget, portfolio):
-    def get_smac_object(
+class SHObjectCallback:
+    def __init__(self, budget_type, eta, initial_budget, portfolio):
+        self.budget_type = budget_type
+        self.eta = eta
+        self.initial_budget = initial_budget
+        self.portfolio = portfolio
+
+    def __call__(
+        self,
         scenario_dict,
         seed,
         ta,
@@ -91,10 +122,10 @@ def get_smac_object(
         scenario = Scenario(scenario_dict)
         initial_configurations = [
             Configuration(configuration_space=scenario.cs, values=member)
-            for member in portfolio.values()]
+            for member in self.portfolio.values()]
 
         rh2EPM = RunHistory2EPM4LogCost
-        ta_kwargs['budget_type'] = budget_type
+        ta_kwargs['budget_type'] = self.budget_type
 
         smac4ac = SMAC4AC(
             scenario=scenario,
@@ -106,9 +137,9 @@ def get_smac_object(
             run_id=seed,
             intensifier=SuccessiveHalving,
             intensifier_kwargs={
-                'initial_budget': initial_budget,
+                'initial_budget': self.initial_budget,
                 'max_budget': 100,
-                'eta': eta,
+                'eta': self.eta,
                 'min_chall': 1,
             },
             dask_client=dask_client,
@@ -118,7 +149,6 @@ def get_smac_object(
             len(scenario.cs.get_hyperparameters()) / 2
         )
         return smac4ac
-    return get_smac_object
 
 
 class AutoSklearn2Classifier(AutoSklearnClassifier):
@@ -129,14 +159,14 @@ def __init__(
         ensemble_size: int = 50,
         ensemble_nbest: Union[float, int] = 50,
         max_models_on_disc: int = 50,
-        ensemble_memory_limit: int = 1024,
         seed: int = 1,
-        ml_memory_limit: int = 3072,
+        memory_limit: int = 3072,
         tmp_folder: Optional[str] = None,
         output_folder: Optional[str] = None,
         delete_tmp_folder_after_terminate: bool = True,
         delete_output_folder_after_terminate: bool = True,
         n_jobs: Optional[int] = None,
+        dask_client: Optional[dask.distributed.Client] = None,
         disable_evaluator_output: bool = False,
         smac_scenario_args: Optional[Dict[str, Any]] = None,
         logging_config: Optional[Dict[str, Any]] = None,
@@ -153,9 +183,8 @@ def __init__(
             ensemble_size=ensemble_size,
             ensemble_nbest=ensemble_nbest,
             max_models_on_disc=max_models_on_disc,
-            ensemble_memory_limit=ensemble_memory_limit,
             seed=seed,
-            ml_memory_limit=ml_memory_limit,
+            memory_limit=memory_limit,
             include_estimators=include_estimators,
             exclude_estimators=None,
             include_preprocessors=include_preprocessors,
@@ -167,6 +196,7 @@ def __init__(
             delete_tmp_folder_after_terminate=delete_tmp_folder_after_terminate,
             delete_output_folder_after_terminate=delete_output_folder_after_terminate,
             n_jobs=n_jobs,
+            dask_client=dask_client,
             disable_evaluator_output=disable_evaluator_output,
             get_smac_object_callback=None,
             smac_scenario_args=smac_scenario_args,
@@ -236,15 +266,15 @@ def fit(self, X, y,
         else:
             resampling_strategy_kwargs = None
 
-        portfolio_file = os.path.join(this_directory, 'askl2_portfolios', '%s.json' % automl_policy)
+        portfolio_file = this_directory / 'askl2_portfolios' / ('%s.json' % automl_policy)
         with open(portfolio_file) as fh:
             portfolio_json = json.load(fh)
         portfolio = portfolio_json['portfolio']
 
         if setting['fidelity'] == 'SH':
-            smac_callback = get_sh_object_callback('iterations', 4, 5.0, portfolio)
+            smac_callback = SHObjectCallback('iterations', 4, 5.0, portfolio)
         else:
-            smac_callback = get_smac_object_callback(portfolio)
+            smac_callback = SmacObjectCallback(portfolio)
 
         self.resampling_strategy = resampling_strategy
         self.resampling_strategy_arguments = resampling_strategy_kwargs
diff --git a/autosklearn/metalearning/files/accuracy_binary.classification_dense/algorithm_runs.arff b/autosklearn/metalearning/files/accuracy_binary.classification_dense/algorithm_runs.arff
index f40651cccb..a9868a02da 100644
--- a/autosklearn/metalearning/files/accuracy_binary.classification_dense/algorithm_runs.arff
+++ b/autosklearn/metalearning/files/accuracy_binary.classification_dense/algorithm_runs.arff
@@ -7,130 +7,135 @@
 @ATTRIBUTE runstatus {ok, timeout, memout, not_applicable, crash, other}
 
 @DATA
-75119,1.0,1,0.03143418467583492,ok
-75212,1.0,2,0.2517482517482518,ok
-252,1.0,3,0.15000000000000002,ok
-246,1.0,4,0.007575757575757569,ok
-75178,1.0,5,0.7515251974123442,ok
-75239,1.0,6,0.0,ok
-75171,1.0,7,0.16426193118756938,ok
-75233,1.0,8,0.06548279689234182,ok
-248,1.0,9,0.2257575757575757,ok
-75231,1.0,10,0.19734345351043647,ok
-2123,1.0,11,0.1357466063348416,ok
-75196,1.0,12,0.005221932114882533,ok
-75188,1.0,13,0.19066147859922178,ok
-75092,1.0,14,0.15384615384615385,ok
-75248,1.0,15,0.26194331983805663,ok
-75126,1.0,16,0.037328094302554016,ok
-75234,1.0,17,0.02375102375102378,ok
-75150,1.0,18,0.27002967359050445,ok
-258,1.0,19,0.007011866235167252,ok
-75168,1.0,20,0.16532258064516125,ok
-75235,1.0,21,0.0011111111111110628,ok
-75159,1.0,22,0.126027397260274,ok
-244,1.0,23,0.10757575757575755,ok
-75221,1.0,24,0.4132804757185332,ok
-75219,1.0,25,0.03823588913615217,ok
-75202,1.0,26,0.14102564102564108,ok
-3043,1.0,27,0.020900321543408373,ok
-75205,1.0,28,0.17648655986967143,ok
-75174,1.0,29,0.12064378824155364,ok
-288,1.0,30,0.12242424242424244,ok
-75250,1.0,31,0.3552020130278618,ok
-275,1.0,32,0.034220532319391594,ok
-75142,1.0,33,0.07143388091875413,ok
-75213,1.0,34,0.07086614173228345,ok
-75099,1.0,35,0.18848920863309349,ok
-75243,1.0,36,0.0,ok
-75184,1.0,37,0.09804637575314956,ok
-75222,1.0,38,0.1018099547511312,ok
-75175,1.0,39,0.09778299809132285,ok
-233,1.0,40,0.002846299810246644,ok
-75161,1.0,41,0.0611759458856761,ok
-75176,1.0,42,0.01615034503009838,ok
-75090,1.0,43,0.05244755244755239,ok
-75114,1.0,44,0.025540275049115935,ok
-260,1.0,45,0.058693244739756345,ok
-236,1.0,46,0.029848484848484902,ok
-75141,1.0,47,0.05364409914909363,ok
-75107,1.0,48,0.055333333333333345,ok
-262,1.0,49,0.002481389578163795,ok
-75146,1.0,50,0.10954375137756223,ok
-75189,1.0,51,0.020232468708520912,ok
-2350,1.0,52,0.3998696839836604,ok
-253,1.0,53,0.43004115226337447,ok
-2122,1.0,54,0.09138042773817234,ok
-75110,1.0,55,0.11460358608770793,ok
-75249,1.0,56,0.002411575562700996,ok
-75108,1.0,57,0.0,ok
-242,1.0,58,0.007575757575757569,ok
-75117,1.0,59,0.0766208251473477,ok
-75191,1.0,60,0.1275450575136864,ok
-75226,1.0,61,0.0006084575600852071,ok
-261,1.0,62,0.2606060606060606,ok
-75236,1.0,63,0.040000000000000036,ok
-75095,1.0,64,0.018170426065162948,ok
-75148,1.0,65,0.12292682926829268,ok
-75093,1.0,66,0.21269487750556793,ok
-75223,1.0,67,0.09505292719809899,ok
-75244,1.0,68,0.23406442769019875,ok
-75109,1.0,69,0.3222836095764272,ok
-75197,1.0,70,0.13793103448275867,ok
-75127,1.0,71,0.332350165172251,ok
-75143,1.0,72,0.012715033657442087,ok
-75153,1.0,73,0.08176100628930816,ok
-75173,1.0,74,0.11783439490445857,ok
-75215,1.0,75,0.02604166666666663,ok
-75195,1.0,76,0.00014866572511706977,ok
-75207,1.0,77,0.1502890173410405,ok
-266,1.0,78,0.023622047244094446,ok
-75225,1.0,79,0.07296650717703346,ok
-75166,1.0,80,0.09137994820569739,ok
-75100,1.0,81,0.00379609544468551,ok
-75169,1.0,82,0.03420132141469101,ok
-75132,1.0,83,0.06512234487423274,ok
-273,1.0,84,0.040184453227931516,ok
-75121,1.0,85,0.0,ok
-75098,1.0,86,0.02359307359307361,ok
-75115,1.0,87,0.025540275049115935,ok
-75116,1.0,88,0.011787819253438081,ok
-75185,1.0,89,0.12309820193637622,ok
-2119,1.0,90,?,not_applicable
-75157,1.0,91,0.4164345403899722,ok
-75113,1.0,92,0.0063157894736841635,ok
-75134,1.0,93,0.005900409903865644,ok
-75096,1.0,94,0.011080496198100809,ok
-75203,1.0,95,0.10028382213812681,ok
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diff --git a/autosklearn/metalearning/files/accuracy_binary.classification_dense/configurations.csv b/autosklearn/metalearning/files/accuracy_binary.classification_dense/configurations.csv
index 14ccf74d94..85e767fd63 100644
--- a/autosklearn/metalearning/files/accuracy_binary.classification_dense/configurations.csv
+++ b/autosklearn/metalearning/files/accuracy_binary.classification_dense/configurations.csv
@@ -1,128 +1,133 @@
 idx,balancing:strategy,classifier:__choice__,classifier:adaboost:algorithm,classifier:adaboost:learning_rate,classifier:adaboost:max_depth,classifier:adaboost:n_estimators,classifier:bernoulli_nb:alpha,classifier:bernoulli_nb:fit_prior,classifier:decision_tree:criterion,classifier:decision_tree:max_depth_factor,classifier:decision_tree:max_features,classifier:decision_tree:max_leaf_nodes,classifier:decision_tree:min_impurity_decrease,classifier:decision_tree:min_samples_leaf,classifier:decision_tree:min_samples_split,classifier:decision_tree:min_weight_fraction_leaf,classifier:extra_trees:bootstrap,classifier:extra_trees:criterion,classifier:extra_trees:max_depth,classifier:extra_trees:max_features,classifier:extra_trees:max_leaf_nodes,classifier:extra_trees:min_impurity_decrease,classifier:extra_trees:min_samples_leaf,classifier:extra_trees:min_samples_split,classifier:extra_trees:min_weight_fraction_leaf,classifier:gradient_boosting:early_stop,classifier:gradient_boosting:l2_regularization,classifier:gradient_boosting:learning_rate,classifier:gradient_boosting:loss,classifier:gradient_boosting:max_bins,classifier:gradient_boosting:max_depth,classifier:gradient_boosting:max_leaf_nodes,classifier:gradient_boosting:min_samples_leaf,classifier:gradient_boosting:n_iter_no_change,classifier:gradient_boosting:scoring,classifier:gradient_boosting:tol,classifier:gradient_boosting:validation_fraction,classifier:k_nearest_neighbors:n_neighbors,classifier:k_nearest_neighbors:p,classifier:k_nearest_neighbors:weights,classifier:lda:n_components,classifier:lda:shrinkage,classifier:lda:shrinkage_factor,classifier:lda:tol,classifier:liblinear_svc:C,classifier:liblinear_svc:dual,classifier:liblinear_svc:fit_intercept,classifier:liblinear_svc:intercept_scaling,classifier:liblinear_svc:loss,classifier:liblinear_svc:multi_class,classifier:liblinear_svc:penalty,classifier:liblinear_svc:tol,classifier:libsvm_svc:C,classifier:libsvm_svc:coef0,classifier:libsvm_svc:degree,classifier:libsvm_svc:gamma,classifier:libsvm_svc:kernel,classifier:libsvm_svc:max_iter,classifier:libsvm_svc:shrinking,classifier:libsvm_svc:tol,classifier:multinomial_nb:alpha,classifier:multinomial_nb:fit_prior,classifier:passive_aggressive:C,classifier:passive_aggressive:average,classifier:passive_aggressive:fit_intercept,classifier:passive_aggressive:loss,classifier:passive_aggressive:tol,classifier:qda:reg_param,classifier:random_forest:bootstrap,classifier:random_forest:criterion,classifier:random_forest:max_depth,classifier:random_forest:max_features,classifier:random_forest:max_leaf_nodes,classifier:random_forest:min_impurity_decrease,classifier:random_forest:min_samples_leaf,classifier:random_forest:min_samples_split,classifier:random_forest:min_weight_fraction_leaf,classifier:sgd:alpha,classifier:sgd:average,classifier:sgd:epsilon,classifier:sgd:eta0,classifier:sgd:fit_intercept,classifier:sgd:l1_ratio,classifier:sgd:learning_rate,classifier:sgd:loss,classifier:sgd:penalty,classifier:sgd:power_t,classifier:sgd:tol,data_preprocessing:categorical_transformer:categorical_encoding:__choice__,data_preprocessing:categorical_transformer:category_coalescence:__choice__,data_preprocessing:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction,data_preprocessing:numerical_transformer:imputation:strategy,data_preprocessing:numerical_transformer:rescaling:__choice__,data_preprocessing:numerical_transformer:rescaling:quantile_transformer:n_quantiles,data_preprocessing:numerical_transformer:rescaling:quantile_transformer:output_distribution,data_preprocessing:numerical_transformer:rescaling:robust_scaler:q_max,data_preprocessing:numerical_transformer:rescaling:robust_scaler:q_min,feature_preprocessor:__choice__,feature_preprocessor:extra_trees_preproc_for_classification:bootstrap,feature_preprocessor:extra_trees_preproc_for_classification:criterion,feature_preprocessor:extra_trees_preproc_for_classification:max_depth,feature_preprocessor:extra_trees_preproc_for_classification:max_features,feature_preprocessor:extra_trees_preproc_for_classification:max_leaf_nodes,feature_preprocessor:extra_trees_preproc_for_classification:min_impurity_decrease,feature_preprocessor:extra_trees_preproc_for_classification:min_samples_leaf,feature_preprocessor:extra_trees_preproc_for_classification:min_samples_split,feature_preprocessor:extra_trees_preproc_for_classification:min_weight_fraction_leaf,feature_preprocessor:extra_trees_preproc_for_classification:n_estimators,feature_preprocessor:fast_ica:algorithm,feature_preprocessor:fast_ica:fun,feature_preprocessor:fast_ica:n_components,feature_preprocessor:fast_ica:whiten,feature_preprocessor:feature_agglomeration:affinity,feature_preprocessor:feature_agglomeration:linkage,feature_preprocessor:feature_agglomeration:n_clusters,feature_preprocessor:feature_agglomeration:pooling_func,feature_preprocessor:kernel_pca:coef0,feature_preprocessor:kernel_pca:degree,feature_preprocessor:kernel_pca:gamma,feature_preproce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-13,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.3652103526812861,None,0.0,6,8,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,quantile_transformer,79,uniform,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.17357047764133562,fwe,chi2
-14,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6185424163558029,False,True,1,squared_hinge,ovr,l2,4.385767395120581e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010397452590017174,median,quantile_transformer,961,normal,,,feature_agglomeration,,,,,,,,,,,,,,,cosine,average,25,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-15,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.643761610990255,None,0.0,20,6,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.039859381630488695,mean,normalize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,
-16,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7947177310891914,None,0.0,13,3,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,mean,robust_scaler,,,0.7681430822715739,0.23411421138225857,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,16.747604337291705,f_classif,,,
-17,none,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.1769332447629957,mean,minmax,,,,,fast_ica,,,,,,,,,,,deflation,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-18,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.07518858116896743,None,0.0,7,10,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0027133920607872518,mean,robust_scaler,,,0.7873452530915046,0.16215139072179952,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-19,none,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,200,None,,0.0017457216340699586,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,none,,,,,kernel_pca,,,,,,,,,,,,,,,,,,,0.0,4,0.0007494648939847078,poly,1996,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-20,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.6039096182414764,None,0.0,2,20,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.1939609823704447,most_frequent,robust_scaler,,,0.7331327008874781,0.29747488162877694,extra_trees_preproc_for_classification,True,entropy,None,0.33401603690569853,None,0.0,4,4,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-21,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.8054434875626825,None,0.0,1,20,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,median,standardize,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4241048505416849,fpr,f_classif
-22,none,adaboost,SAMME,0.011233995624432622,9,477,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.018370622484682127,mean,standardize,,,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6039710338898471,False,,,,,,,,,,,,,,,
-23,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7734.521233796252,,,2.6048661611376747,rbf,-1,True,4.68717607435371e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,none,,,,,feature_agglomeration,,,,,,,,,,,,,,,euclidean,average,25,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-24,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.8367655423352977,None,0.0,1,5,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.01866043949164283,median,standardize,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.3824115704370156,fpr,f_classif
-25,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5010952533165973,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.005838375247601407,mean,normalize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,
-26,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.7297122444166401,False,True,1,squared_hinge,ovr,l2,0.00023416745444541063,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.03941815152197094,most_frequent,normalize,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0973649546833947,fpr,chi2
-27,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.4426097579931897,None,0.0,16,4,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,mean,robust_scaler,,,0.7407323456920554,0.0804756921037867,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,
-28,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5607168729746175,None,0.0,2,3,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.009810589644392154,most_frequent,quantile_transformer,1132,normal,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.06211089008167909,fdr,chi2
-29,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9333237919291875,None,0.0,8,3,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,
-30,none,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.9728532763078892,None,0.0,13,9,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,none,,,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6651071190864879,False,,,,,,,,,,,,,,,
-31,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9561075301148197,None,0.0,6,2,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,
-32,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.557321265077371,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-33,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,1.153931482107762e-10,0.011666190464009397,auto,255,None,29,190,18,loss,1e-07,0.147868307124396,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,
-34,none,adaboost,SAMME,0.6255555220658239,6,446,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.008833097306364968,most_frequent,none,,,,,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,10,None,4,17,1.0,76,,,,,
-35,none,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.563056219822946,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.32793677336996485,most_frequent,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-36,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,3.6797768538445263e-09,0.05511004217375807,auto,255,None,72,62,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,robust_scaler,,,0.7241481576763391,0.0501033899901458,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,36,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-37,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.7521969592385672,None,0.0,9,5,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.020402264943872365,median,robust_scaler,,,0.737227213630259,0.20752747663727789,fast_ica,,,,,,,,,,,parallel,exp,312,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-38,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.887898394612674,None,0.0,10,19,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,robust_scaler,,,0.8350698288864961,0.2347163538066168,extra_trees_preproc_for_classification,False,entropy,None,0.49795108918064074,None,0.0,13,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-39,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,1.6343482806592892e-09,0.19537524572679268,auto,255,None,32,29,13,loss,1e-07,0.05061738224302791,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-40,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7088164344533493,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,mean,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,
-41,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,1.943186107984505e-07,0.08852455702036988,auto,255,None,5,72,19,loss,1e-07,0.039837460762271175,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.01591265419367627,median,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,
-42,weighting,adaboost,SAMME,0.5210651042094924,10,401,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,standardize,,,,,feature_agglomeration,,,,,,,,,,,,,,,euclidean,complete,89,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-43,none,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0686374607394679,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,minmax,,,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.8490260132946401,False,,,,,,,,,,,,,,,
-44,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.6931271368243409,None,0.0,11,10,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.362940004438799,most_frequent,robust_scaler,,,0.9177587796305063,0.17084038458597867,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,30.491514144623952,mutual_info,,,
-45,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.9398869869697849,None,0.0,20,15,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,normalize,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,269,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-46,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,92.66144306787051,,,0.04654639302091653,rbf,-1,True,0.0022231544280610005,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.005194838694455963,mean,none,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,15,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-47,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2681.998807317996,-0.9558745731731211,,0.0011299948421773457,sigmoid,-1,False,1.5292507354478086e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-48,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.8095522998548572,None,0.0,17,10,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.031940595071539304,mean,quantile_transformer,1563,normal,,,extra_trees_preproc_for_classification,False,entropy,None,0.9330145598603196,None,0.0,18,10,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-49,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,26.502259985828186,,,9.475662388405154e-05,rbf,-1,False,0.00019690635968217866,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-50,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,2.5360196477156387e-07,0.011018756408558218,auto,255,None,40,122,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.00011749164080669445,most_frequent,quantile_transformer,757,uniform,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,5.097834697156853,False,True,1,squared_hinge,ovr,l1,0.053877834919477424,,,,,,,,,,,,,,,,,,,,,,
-51,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5255991794296913,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-52,weighting,decision_tree,,,,,,,gini,1.7853447328017091,1.0,None,0.0,13,7,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0001265964225445088,median,normalize,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.26399878867042303,,mutual_info_classif
-53,weighting,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.7776312540090575,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0005253715056715056,mean,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,False,,,,,,,,,,,,
-54,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,1.488333818193668e-10,0.09526676471267805,auto,255,None,258,87,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,normalize,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.3561953264350478,fpr,f_classif
-55,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.9523435078770708,0.8748383232154038,4,0.2195992292488049,poly,-1,True,4.849685143365339e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,minmax,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-56,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.7610308078756552,None,0.0,1,2,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.010000000000000004,mean,robust_scaler,,,0.9205545465103644,0.20272761060743316,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-57,weighting,adaboost,SAMME.R,0.03127273017917347,10,496,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,standardize,,,,,fast_ica,,,,,,,,,,,deflation,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-58,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,151,None,,0.0003283777607180646,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.2510527431514227,mean,normalize,,,,,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,-0.96093182232312,,0.05762510964607661,sigmoid,2632,,,,,,,,,,,,,,,,,
-59,none,bernoulli_nb,,,,,0.6524090653939782,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.005164169126808402,most_frequent,quantile_transformer,16,normal,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.033082145443631535,False,True,1,squared_hinge,ovr,l1,0.002615020898325393,,,,,,,,,,,,,,,,,,,,,,
-60,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.6453555269772124,None,0.0,1,4,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,normalize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-61,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,940.9437776449834,,,1.5839338084625942,rbf,-1,False,0.023072781592565438,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,none,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,77.25763685070707,mutual_info,,,
-62,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,220,auto,,0.0013824567992319003,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00021328932075284388,mean,minmax,,,,,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.039972474835314076,,0.01017810882899714,sigmoid,295,,,,,,,,,,,,,,,,,
-63,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.7326858450533382,0.02513634776687268,2,0.8635510319471909,poly,-1,True,0.0014598272110141993,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.06447266871225255,most_frequent,quantile_transformer,1000,normal,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,79.01961823033078,f_classif,,,
-64,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,29.55731828912983,False,True,1,squared_hinge,ovr,l2,0.0011332486951954968,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,
-65,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,1.1387795876755026e-06,0.12245605190343567,auto,255,None,58,7,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,robust_scaler,,,0.79305915648885,0.1841487406876042,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-66,none,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.563056219822946,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.32793677336996485,most_frequent,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-67,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,0.2638627266631576,0.022360820165005443,auto,255,None,171,6,19,loss,1e-07,0.016209060012924756,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.008763072395677399,mean,standardize,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,78,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-68,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,925.3053743248586,False,True,1,squared_hinge,ovr,l2,0.02764481198395717,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.3850718018366796,most_frequent,quantile_transformer,786,uniform,,,feature_agglomeration,,,,,,,,,,,,,,,cosine,average,156,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-69,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7573078019937971,None,0.0,2,5,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,robust_scaler,,,0.7265312295801113,0.28332515882236425,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.09660564590146253,fpr,f_classif
-70,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.005753626505113041,False,True,squared_hinge,0.00924401505172238,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.3192271696445429,median,quantile_transformer,913,normal,,,kernel_pca,,,,,,,,,,,,,,,,,,,,,,cosine,1963,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-71,none,adaboost,SAMME.R,0.22802213935525836,6,284,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,robust_scaler,,,0.7468645381288924,0.22838821662367073,feature_agglomeration,,,,,,,,,,,,,,,cosine,average,128,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-72,none,adaboost,SAMME,0.1280458968526125,7,125,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,robust_scaler,,,0.7156460334356771,0.2981902862926971,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,65.16171885308417,chi2,,,
-73,none,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9931779675565967,None,0.0,18,17,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,robust_scaler,,,0.75,0.2945917010472818,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,
-74,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.08646118576686276,None,0.0,4,9,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,most_frequent,quantile_transformer,959,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-75,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,1.2430504242164082e-09,0.03359568307446533,auto,255,None,1609,29,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,standardize,,,,,feature_agglomeration,,,,,,,,,,,,,,,cosine,complete,40,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-76,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7.491039171224502,True,True,squared_hinge,2.8058138048327884e-05,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,minmax,,,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.7754372809893839,False,True,1,squared_hinge,ovr,l1,3.9643594596766314e-05,,,,,,,,,,,,,,,,,,,,,,
-77,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.34936025988397124,None,0.0,2,13,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00018601309647406987,median,robust_scaler,,,0.8634309348843472,0.26976989465367607,extra_trees_preproc_for_classification,False,entropy,None,0.48025175288975036,None,0.0,6,17,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-78,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,0.011547155223520492,0.13665554140837183,auto,255,None,13,5,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.1824584492028449,median,minmax,,,,,feature_agglomeration,,,,,,,,,,,,,,,euclidean,average,261,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-79,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.933900673392165,None,0.0,7,4,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,minmax,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-80,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1661.2274747409913,,,0.025821208852387936,rbf,-1,False,0.0017011435104921824,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,none,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,306,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-81,none,adaboost,SAMME,0.011233995624432622,9,477,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.018370622484682127,mean,standardize,,,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6039710338898471,False,,,,,,,,,,,,,,,
-82,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,21.85207494001697,0.18872999589786676,4,1.1863646021868761,poly,-1,False,0.0021338371901294443,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0034080852834770217,mean,normalize,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.11326005109886934,fdr,f_classif
-83,none,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.563056219822946,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.32793677336996485,most_frequent,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-84,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,0.037849720944694,0.1268815770019783,auto,255,None,11,99,11,loss,1e-07,0.10654523627313527,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,
-85,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.593728171200453,None,0.0,7,20,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.02830941310359363,mean,normalize,,,,,extra_trees_preproc_for_classification,False,entropy,None,0.7389446586281172,None,0.0,20,11,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-86,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,1,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.019797877010737826,mean,normalize,,,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.8239678741128165,False,,,,,,,,,,,,,,,
-87,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.46057831591617715,False,True,hinge,0.04557857428827514,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00027457445401600137,median,standardize,,,,,extra_trees_preproc_for_classification,True,gini,None,0.48190346970486964,None,0.0,17,18,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-88,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,38,auto,,0.00019398431171606024,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010000000000000004,median,quantile_transformer,1440,normal,,,extra_trees_preproc_for_classification,True,gini,None,0.5126639956845847,None,0.0,16,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-89,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5722917944959481,None,0.0,1,14,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,
-90,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.26356754187780895,None,0.0,4,3,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010966178401758774,mean,robust_scaler,,,0.7817043861798886,0.24371720112407158,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
-91,weighting,adaboost,SAMME.R,0.13547552520095074,4,62,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.011851011202809002,most_frequent,standardize,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4888776875314595,fpr,chi2
-92,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,80.0028902468169,False,True,1,squared_hinge,ovr,l2,0.0024481999829574722,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.15047970487774687,mean,quantile_transformer,1130,normal,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,1.1912646635544712,False,True,1,squared_hinge,ovr,l1,0.00014314728486450857,,,,,,,,,,,,,,,,,,,,,,
-93,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.22879311080499845,None,0.0,8,4,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.014320022547615783,median,minmax,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.238630703440461,mutual_info,,,
-94,weighting,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,2,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,none,,,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6342574285315175,False,,,,,,,,,,,,,,,
-95,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.107967527639527e-05,True,True,hinge,0.00448294745544775,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,quantile_transformer,953,normal,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,35.00114345405331,chi2,,,
-96,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.14148638702297855,None,0.0,2,6,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.0038499650338378643,most_frequent,quantile_transformer,1759,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-97,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,1.1522414705935405e-10,0.6132800183073315,auto,255,None,1607,67,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010096911249562407,mean,robust_scaler,,,0.7663493326967942,0.136094217337434,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-98,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2746221958279041,False,True,1,squared_hinge,ovr,l2,6.546077664669006e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,quantile_transformer,1054,normal,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,
-99,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.479405900046494,,,0.013340778632154543,rbf,-1,False,6.913586060890114e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,robust_scaler,,,0.9953232616818098,0.06450577886132274,fast_ica,,,,,,,,,,,deflation,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-100,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.825844582229201,None,0.0,12,9,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0008422508222149647,median,robust_scaler,,,0.8658803880857064,0.2528423686293341,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-101,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2574971638664341,True,True,hinge,1.8027411099296446e-05,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.00536396328125103,median,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,
-102,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.7033210835501171,None,0.0,3,19,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,robust_scaler,,,0.7699195245692886,0.29258311638476237,feature_agglomeration,,,,,,,,,,,,,,,euclidean,ward,143,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-103,none,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,128,manual,0.8574560771111307,0.010886139381599514,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.24115007944216788,most_frequent,normalize,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,62.26514170991452,mutual_info,,,
-104,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.6282663389679584,None,0.0,1,3,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,median,minmax,,,,,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-105,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,1.430083734915855e-09,0.08779621406981941,auto,255,None,17,20,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.29749837779115257,mean,robust_scaler,,,0.7236445277814435,0.25,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
-106,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.3294588572341214,None,0.0,1,3,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
-107,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,39.22980757485479,,,0.13597389869623427,rbf,-1,False,0.00011639060052687874,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.006288306814260224,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-108,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.7922837297504705,False,True,1,squared_hinge,ovr,l2,0.0005476597326342352,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.012674631437254894,most_frequent,none,,,,,extra_trees_preproc_for_classification,False,entropy,None,0.23145146971319638,None,0.0,7,20,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-109,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.18060139887649052,None,0.0,10,12,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.010000000000000004,most_frequent,robust_scaler,,,0.9298612094255864,0.1263597946722308,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
-110,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.4111348710613201,None,0.0,16,8,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.01224348527546641,most_frequent,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,
-111,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6427543483503397,True,True,hinge,0.00045318775930916944,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.009338484332306904,median,quantile_transformer,174,normal,,,kernel_pca,,,,,,,,,,,,,,,,,,,,,,cosine,1848,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-112,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.04981080944617379,True,,0.07309386939525211,True,,constant,log,l2,,0.0006721243213777075,no_encoding,no_coalescense,,mean,quantile_transformer,574,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-113,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,9.029473608568458e-09,0.15779990576964315,auto,255,None,37,71,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.018093177195644097,mean,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,False,,,,,,,,,,,,
-114,none,adaboost,SAMME,0.011233995624432622,9,477,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.018370622484682127,mean,standardize,,,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6039710338898471,False,,,,,,,,,,,,,,,
-115,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l2,2.9468189962573666e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.014870368357163507,most_frequent,quantile_transformer,1000,uniform,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,83.43414935094258,f_classif,,,
-116,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.4864284879550203,None,0.0,1,4,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.008665854799630503,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-117,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.43699642289829416,None,0.0,6,20,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,quantile_transformer,1604,uniform,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-118,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.6922752267318475,True,True,squared_hinge,0.06299408576730353,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,quantile_transformer,1742,uniform,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,107,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-119,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.44727252219812963,None,0.0,2,10,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,standardize,,,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l1,8.17533189014765e-05,,,,,,,,,,,,,,,,,,,,,,
-120,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.46057831591617715,False,True,hinge,0.04557857428827514,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00027457445401600137,median,standardize,,,,,extra_trees_preproc_for_classification,True,gini,None,0.48190346970486964,None,0.0,17,18,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-121,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.4210978663047845,None,0.0,5,6,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.006365988550833196,most_frequent,robust_scaler,,,0.8348891074186082,0.00974961635730845,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0695919708890599,fpr,f_classif
-122,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4424195769042466,False,True,hinge,0.02490432909709204,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,mean,quantile_transformer,1000,uniform,,,kernel_pca,,,,,,,,,,,,,,,,,,,,,,cosine,769,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-123,weighting,decision_tree,,,,,,,entropy,1.0435267397863155,1.0,None,0.0,15,3,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.36913659113454333,most_frequent,normalize,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4947786362821451,fpr,f_classif
-124,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1561.1002600303584,,,0.0024048551704194507,rbf,-1,False,0.0001936214667496851,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,minmax,,,,,fast_ica,,,,,,,,,,,parallel,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-125,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.087313242780732,0.0636709914987994,3,0.004481917125024585,poly,-1,False,0.01848880618941367,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.002136015035740733,mean,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-126,weighting,adaboost,SAMME.R,1.696588612003168,9,455,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,minmax,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,82.18726095926662,chi2,,,
-127,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.6466927589470762,None,0.0,11,2,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.009672465311659264,most_frequent,standardize,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,369,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+1,none,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1514.059969351301,False,True,1,squared_hinge,ovr,l2,0.002448814309966599,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,standardize,,,,,extra_trees_preproc_for_classification,True,entropy,None,0.8221481751476923,None,0.0,2,3,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+3,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5707458022222313,None,0.0,3,12,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,minmax,,,,,extra_trees_preproc_for_classification,False,gini,None,0.5651056128131684,None,0.0,9,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+6,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.7283683762613216,None,0.0,12,20,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,median,standardize,,,,,fast_ica,,,,,,,,,,,deflation,exp,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+8,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,114,manual,0.11778631707605758,0.0029112721339713465,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.015582195257240751,median,normalize,,,,,kernel_pca,,,,,,,,,,,,,,,,,,,0.42909623253327767,3,0.25407612000343777,poly,1689,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+11,weighting,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,2,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.3865962574104293,mean,normalize,,,,,fast_ica,,,,,,,,,,,deflation,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+13,weighting,adaboost,SAMME,0.17983222024694556,4,357,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0008898781197407956,mean,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+15,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.21772092350860472,None,0.0,14,17,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.0020488212891693275,median,standardize,,,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,33.74257606530722,False,True,1,squared_hinge,ovr,l1,8.740452966205111e-05,,,,,,,,,,,,,,,,,,,,,,
+18,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,2.4243922131268786e-05,0.07570328162448307,auto,255,None,71,42,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,robust_scaler,,,0.8898729094898769,0.12138152991327475,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,94.96086286583851,chi2,,,
+20,none,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.9866058571747695,None,0.0,16,9,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.1680676743605393,most_frequent,robust_scaler,,,0.8840072797399463,0.08535534311393947,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,False,,,,,,,,,,,,
+23,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,0.00022373024203887573,0.027149343206135476,auto,255,None,49,13,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.00777614320416325,most_frequent,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,
+27,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.41025844265119926,None,0.0,18,15,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,median,quantile_transformer,1911,normal,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,False,,,,,,,,,,,,
+29,none,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.563056219822946,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.32793677336996485,most_frequent,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+31,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,8.757412830636845e-06,0.10095376320667454,auto,255,None,3,2,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.015401766535365336,median,quantile_transformer,647,normal,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,
+32,none,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,174,manual,0.21501892897746921,2.0347110826262054e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.013754893527625522,most_frequent,normalize,,,,,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,-0.019221513778605592,3,0.10190948951339239,poly,2521,,,,,,,,,,,,,,,,,
+35,none,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9.608707084865884,False,True,1,squared_hinge,ovr,l2,0.008086910150491696,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.020901889243957545,median,minmax,,,,,kitchen_sinks,,,,,,,,,,,,,,,,,,,,,,,,0.5694399784333516,3230,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+39,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.6799215694343422,None,0.0,1,5,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.004724310659953132,most_frequent,quantile_transformer,1037,normal,,,fast_ica,,,,,,,,,,,parallel,exp,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+41,none,extra_trees,,,,,,,,,,,,,,,True,gini,None,0.5033866291997137,None,0.0,2,14,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,minmax,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+43,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.9018061166635376,None,0.0,7,9,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,quantile_transformer,1802,normal,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,84.54647012968938,chi2,,,
+44,none,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l2,2.858526169098402e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0007827505640681123,median,standardize,,,,,extra_trees_preproc_for_classification,False,entropy,None,0.3443310459451119,None,0.0,3,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+49,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.049504283446131e-05,False,,0.0004438773852753879,True,2.4110816673432424e-07,constant,log,elasticnet,,0.01321814853186568,one_hot_encoding,minority_coalescer,0.01510416593631535,most_frequent,none,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,66.65285268958851,chi2,,,
+52,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,6.421059381158897e-08,0.11261253191288709,auto,255,None,45,28,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,
+55,none,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.9822179211645446,None,0.0,14,8,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.013966871874533052,most_frequent,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,
+58,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5214197490559657,None,0.0,1,2,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.00826182798588248,mean,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,
+63,none,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,130,manual,0.5446276346297756,0.00846307015171434,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.005736348762561667,median,quantile_transformer,973,uniform,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.08483615095863663,fdr,chi2
+65,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,1.2822815440468518e-10,0.12922700208645135,auto,255,None,31,44,13,loss,1e-07,0.08789566421608311,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.012230423571314038,mean,robust_scaler,,,0.71734690066056,0.20283418288016852,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,
+68,weighting,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,minmax,,,,,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+75,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13.284077389890939,-0.2621334760621076,5,0.9060908941898536,poly,-1,False,0.06323853917206591,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0011743021592446861,most_frequent,normalize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,
+82,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,3.79982070767762e-10,0.10307173798074214,auto,255,None,30,25,6,loss,1e-07,0.09705336338563596,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.004889186641013006,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+84,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.5507560981489436,None,0.0,7,4,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,
+91,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,0.0006322356877253852,0.02155479872417127,auto,255,None,6,15,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
+93,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5012519528536651,None,0.0,1,8,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,mean,standardize,,,,,feature_agglomeration,,,,,,,,,,,,,,,cosine,complete,323,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+96,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5602498483292023,None,0.0,1,3,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.3195602228752535,mean,standardize,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.09782235573464702,fdr,chi2
+99,weighting,adaboost,SAMME,0.17983222024694556,4,357,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0008898781197407956,mean,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+105,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.268699692198443e-05,True,,4.1225573912733725e-06,True,,constant,log,l2,,4.729985534114764e-05,no_encoding,no_coalescense,,median,quantile_transformer,246,normal,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,20.73090458774975,chi2,,,
+106,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,2.047681354714616e-06,0.016073071519249763,auto,255,None,127,60,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.005262156195293648,median,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,
+110,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0021814768111094564,False,True,hinge,8.726644999887521e-05,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,none,,,,,kitchen_sinks,,,,,,,,,,,,,,,,,,,,,,,,0.0010909910768059797,970,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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diff --git a/autosklearn/metalearning/files/accuracy_binary.classification_dense/description.txt b/autosklearn/metalearning/files/accuracy_binary.classification_dense/description.txt
index 54e1a4d03d..324215bec2 100644
--- a/autosklearn/metalearning/files/accuracy_binary.classification_dense/description.txt
+++ b/autosklearn/metalearning/files/accuracy_binary.classification_dense/description.txt
@@ -1,8 +1,9 @@
 features_cutoff_time: 3600
 features_cutoff_memory: 3072
-number_of_feature_steps: 35
+number_of_feature_steps: 42
 feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances
 feature_step LogNumberOfInstances: LogNumberOfInstances
+feature_step NumberOfClasses: NumberOfClasses
 feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures
 feature_step LogNumberOfFeatures: LogNumberOfFeatures
 feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues
@@ -20,6 +21,11 @@ feature_step DatasetRatio: DatasetRatio, LogDatasetRatio
 feature_step LogDatasetRatio: LogDatasetRatio
 feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio
 feature_step LogInverseDatasetRatio: LogInverseDatasetRatio
+feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD
+feature_step ClassProbabilityMin: ClassProbabilityMin
+feature_step ClassProbabilityMax: ClassProbabilityMax
+feature_step ClassProbabilityMean: ClassProbabilityMean
+feature_step ClassProbabilitySTD: ClassProbabilitySTD
 feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum
 feature_step SymbolsMin: SymbolsMin
 feature_step SymbolsMax: SymbolsMax
@@ -36,11 +42,12 @@ feature_step SkewnessMin: SkewnessMin
 feature_step SkewnessMax: SkewnessMax
 feature_step SkewnessMean: SkewnessMean
 feature_step SkewnessSTD: SkewnessSTD
-features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
+feature_step ClassEntropy: ClassEntropy
+features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy
 features_stochastic: 
-default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
+default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy
 
-algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127
+algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235,236,237,238,239,240,241,242,243,244,245,246,247,248,249,250,251,252,253,254,255,256,257,258,259,260,261,262,263,264,265,266,267,268,269,270,271,272,273,274,275,276,277,278,279,280,281,282,283,284,285,286,287,288,289,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,312,313,314,315,316,317,318,319,320,321,322,323,324,325,326,327,328,329,330,331,332,333,334,335,336,337,338,339,340,341,342,343,344,345,346,347,348,349,350,351,352,353,354,355,356,357,358,359,360,361,362,363,364,365,366,367,368,369,370,371,372,373,374,375,376,377,378,379,380,381,382,383,384,385,386,387,388,389,390,391,392,393,394,395,396,397,398,399,400,401,402,403,404,405,406,407,408,409,410,411,412,413,414,415,416,417,418,419,420,421,422,423,424,425,426,427,428,429,430,431,432,433,434,435,436,437,438,439,440,441,442,443,444,445,446,447,448,449,450,451,452,453,454,455,456,457,458,459,460,461,462,463,464
 algorithms_stochastic: 
 performance_measures: accuracy
 performance_type: solution_quality
diff --git a/autosklearn/metalearning/files/accuracy_binary.classification_dense/feature_costs.arff b/autosklearn/metalearning/files/accuracy_binary.classification_dense/feature_costs.arff
index 0e17e20d22..8512fd9275 100644
--- a/autosklearn/metalearning/files/accuracy_binary.classification_dense/feature_costs.arff
+++ b/autosklearn/metalearning/files/accuracy_binary.classification_dense/feature_costs.arff
@@ -4,6 +4,7 @@
 @ATTRIBUTE repetition NUMERIC
 @ATTRIBUTE NumberOfInstances NUMERIC
 @ATTRIBUTE LogNumberOfInstances NUMERIC
+@ATTRIBUTE NumberOfClasses NUMERIC
 @ATTRIBUTE NumberOfFeatures NUMERIC
 @ATTRIBUTE LogNumberOfFeatures NUMERIC
 @ATTRIBUTE MissingValues NUMERIC
@@ -21,6 +22,11 @@
 @ATTRIBUTE LogDatasetRatio NUMERIC
 @ATTRIBUTE InverseDatasetRatio NUMERIC
 @ATTRIBUTE LogInverseDatasetRatio NUMERIC
+@ATTRIBUTE ClassOccurences NUMERIC
+@ATTRIBUTE ClassProbabilityMin NUMERIC
+@ATTRIBUTE ClassProbabilityMax NUMERIC
+@ATTRIBUTE ClassProbabilityMean NUMERIC
+@ATTRIBUTE ClassProbabilitySTD NUMERIC
 @ATTRIBUTE NumSymbols NUMERIC
 @ATTRIBUTE SymbolsMin NUMERIC
 @ATTRIBUTE SymbolsMax NUMERIC
@@ -37,162 +43,138 @@
 @ATTRIBUTE SkewnessMax NUMERIC
 @ATTRIBUTE SkewnessMean NUMERIC
 @ATTRIBUTE SkewnessSTD NUMERIC
+@ATTRIBUTE ClassEntropy NUMERIC
 
 @DATA
-75123,1.0,1e-05,1e-05,1e-05,1e-05,0.00253,0.00221,1e-05,0.00024,3e-05,0.00011,0.0,2e-05,2e-05,5e-05,8e-05,2e-05,1e-05,7e-05,1e-05,0.00072,1e-05,1e-05,0.00019,0.00041,0.00011,0.00061,0.00012,9e-05,0.00015,0.00025,0.00067,0.00014,0.00011,0.00016,0.00027
-75090,1.0,1e-05,1e-05,2e-05,2e-05,0.00815,0.00368,7e-05,0.00228,9e-05,0.00235,0.0,5e-05,5e-05,0.0001,0.00013,0.00013,1e-05,0.00021,2e-05,0.00064,0.0,0.0,0.00014,0.00038,0.00011,0.00066,0.00011,0.00014,0.00014,0.00026,0.00066,0.00011,0.00013,0.00015,0.00027
-2123,1.0,1e-05,1e-05,1e-05,1e-05,0.00097,0.00079,1e-05,0.00013,1e-05,7e-05,0.0,2e-05,2e-05,5e-05,8e-05,2e-05,1e-05,5e-05,1e-05,0.0007,1e-05,0.0,0.00017,0.00042,0.0001,0.00052,6e-05,8e-05,0.00012,0.00026,0.00049,6e-05,0.00011,9e-05,0.00023
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-75217,1.0,1e-05,1e-05,2e-05,1e-05,0.00196,0.00158,1e-05,0.00026,2e-05,0.00016,1e-05,3e-05,3e-05,7e-05,0.0001,3e-05,1e-05,7e-05,2e-05,0.00089,1e-05,1e-05,0.00022,0.00053,0.00013,0.00074,9e-05,0.0001,0.00019,0.00036,0.00065,0.00013,0.0001,0.00011,0.00031
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-2117,1.0,1e-05,1e-05,1e-05,1e-05,0.01735,0.0154,1e-05,0.00141,1e-05,0.00057,0.0,1e-05,1e-05,3e-05,6e-05,2e-05,1e-05,5e-05,1e-05,0.0004,1e-05,0.0,8e-05,0.00021,0.0001,0.00065,0.00012,0.00011,0.00015,0.00026,0.00055,7e-05,0.00014,9e-05,0.00025
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+75227,1.0,2e-05,1e-05,0.00015,2e-05,1e-05,0.00348,0.00318,2e-05,0.00027,2e-05,7e-05,1e-05,4e-05,4e-05,8e-05,0.0001,3e-05,1e-05,4e-05,1e-05,0.00023,3e-05,1e-05,6e-05,0.00013,0.0007,0.0,1e-05,0.00024,0.00036,8e-05,0.00073,0.00012,0.00011,0.00015,0.00035,0.00077,0.00013,0.00012,0.00019,0.00033,0.00191
+75230,1.0,1e-05,0.0,9e-05,1e-05,1e-05,0.00087,0.0006,1e-05,0.00018,1e-05,0.00011,0.0,2e-05,2e-05,4e-05,6e-05,2e-05,1e-05,3e-05,1e-05,0.00015,2e-05,1e-05,4e-05,8e-05,0.00038,0.0,1e-05,0.0001,0.00021,5e-05,0.00037,5e-05,6e-05,8e-05,0.00018,0.00033,5e-05,6e-05,7e-05,0.00015,0.00048
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+75233,1.0,1e-05,1e-05,0.00012,1e-05,0.0,0.00257,0.00222,1e-05,0.00024,1e-05,0.00014,0.0,1e-05,1e-05,3e-05,5e-05,3e-05,1e-05,2e-05,1e-05,9e-05,1e-05,0.0,2e-05,5e-05,0.0003,0.0,0.0,8e-05,0.00017,4e-05,0.00076,0.00012,0.00013,0.00014,0.00037,0.00058,0.00012,8e-05,0.00013,0.00025,0.00119
+75234,1.0,1e-05,1e-05,0.00015,1e-05,1e-05,0.0058,0.00534,1e-05,0.00033,1e-05,0.00016,0.0,2e-05,2e-05,7e-05,0.00204,3e-05,1e-05,5e-05,1e-05,0.00024,3e-05,2e-05,0.0001,9e-05,0.00043,0.0,0.0,0.00012,0.00024,6e-05,0.00156,6e-05,7e-05,0.00121,0.00021,0.00054,6e-05,7e-05,9e-05,0.00032,0.00501
+75235,1.0,1e-05,1e-05,0.0003,2e-05,1e-05,0.00461,0.00385,2e-05,0.00055,2e-05,0.00026,1e-05,3e-05,3e-05,0.0001,0.00011,3e-05,1e-05,5e-05,2e-05,0.00026,3e-05,1e-05,8e-05,0.00014,0.00075,1e-05,1e-05,0.0002,0.00043,0.00011,0.00127,0.00017,0.0002,0.00029,0.00061,0.0008,0.00015,0.00012,0.00016,0.00038,0.00242
+75236,1.0,1e-05,1e-05,0.0001,1e-05,1e-05,0.00218,0.00118,2e-05,0.00065,3e-05,0.00041,1e-05,3e-05,3e-05,9e-05,9e-05,4e-05,1e-05,7e-05,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.0006,1e-05,1e-05,0.00021,0.00032,6e-05,0.00043,7e-05,7e-05,9e-05,0.00019,0.00062,7e-05,8e-05,0.0001,0.00037,0.00048
+75237,1.0,2e-05,1e-05,0.00785,2e-05,1e-05,0.31031,0.27854,2e-05,0.01161,3e-05,0.02022,1e-05,4e-05,3e-05,9e-05,0.00024,6e-05,2e-05,9e-05,2e-05,0.00062,7e-05,3e-05,0.00017,0.00035,0.0008,1e-05,1e-05,0.00028,0.00043,8e-05,0.00043,8e-05,7e-05,9e-05,0.0002,0.00044,6e-05,8e-05,9e-05,0.00021,0.14028
+75239,1.0,1e-05,1e-05,7e-05,1e-05,1e-05,0.00105,0.00084,1e-05,0.00015,1e-05,8e-05,1e-05,2e-05,5e-05,4e-05,8e-05,2e-05,1e-05,3e-05,1e-05,0.00016,2e-05,1e-05,5e-05,9e-05,0.00049,0.0,0.0,0.00012,0.00026,0.00011,0.00046,0.00013,8e-05,8e-05,0.00018,0.00044,6e-05,9e-05,0.00011,0.00018,0.00065
+75240,1.0,2e-05,1e-05,0.00036,5e-05,4e-05,0.00845,0.00644,6e-05,0.00122,4e-05,0.00091,1e-05,4e-05,4e-05,9e-05,0.00013,5e-05,2e-05,9e-05,5e-05,0.00031,3e-05,1e-05,7e-05,0.0002,0.00073,3e-05,2e-05,0.00019,0.0003,0.00018,0.00055,9e-05,9e-05,0.00011,0.00025,0.0005,7e-05,8e-05,0.00015,0.0002,0.00365
+75243,1.0,1e-05,1e-05,0.00029,1e-05,1e-05,0.00461,0.00415,1e-05,0.00035,1e-05,0.00015,0.0,2e-05,2e-05,5e-05,7e-05,2e-05,1e-05,4e-05,1e-05,0.00018,2e-05,1e-05,4e-05,0.00011,0.00022,1e-05,1e-05,5e-05,0.00011,5e-05,0.0004,6e-05,6e-05,8e-05,0.0002,0.00039,6e-05,6e-05,8e-05,0.00019,0.00265
+75244,1.0,3e-05,2e-05,0.0004,3e-05,2e-05,0.0173,0.01298,4e-05,0.00267,4e-05,0.00174,1e-05,7e-05,9e-05,0.00024,0.00027,8e-05,2e-05,0.00013,3e-05,0.00045,7e-05,2e-05,0.00011,0.00025,0.00077,6e-05,2e-05,0.00021,0.00034,0.00013,0.00077,0.00014,0.00013,0.00026,0.00023,0.00044,7e-05,8e-05,0.0001,0.00019,0.00649
+75248,1.0,1e-05,1e-05,0.00017,1e-05,1e-05,0.00416,0.00305,1e-05,0.0006,2e-05,0.00054,1e-05,2e-05,2e-05,5e-05,7e-05,2e-05,1e-05,4e-05,1e-05,0.00017,2e-05,1e-05,4e-05,0.0001,0.00029,5e-05,1e-05,6e-05,0.00012,6e-05,0.00081,0.00011,0.00015,0.00016,0.00038,0.0005,7e-05,8e-05,0.00012,0.00024,0.00184
+75249,1.0,1e-05,1e-05,0.00011,1e-05,1e-05,0.00192,0.00153,1e-05,0.00025,1e-05,0.00017,1e-05,2e-05,2e-05,5e-05,7e-05,2e-05,1e-05,5e-05,3e-05,0.00014,2e-05,1e-05,3e-05,8e-05,0.00029,1e-05,3e-05,6e-05,0.00012,7e-05,0.00041,6e-05,8e-05,0.0001,0.00017,0.00043,6e-05,7e-05,9e-05,0.00019,0.00092
+75250,1.0,1e-05,1e-05,0.00375,1e-05,1e-05,0.04994,0.047,1e-05,0.00247,1e-05,0.00049,1e-05,2e-05,2e-05,6e-05,7e-05,2e-05,1e-05,3e-05,1e-05,0.00022,4e-05,1e-05,4e-05,0.00012,0.00041,0.0,1e-05,0.00011,0.00023,6e-05,0.00076,0.00012,0.00012,0.00019,0.00032,0.00086,0.00012,0.00013,0.00018,0.00043,0.04178
diff --git a/autosklearn/metalearning/files/accuracy_binary.classification_dense/feature_runstatus.arff b/autosklearn/metalearning/files/accuracy_binary.classification_dense/feature_runstatus.arff
index 0034ad2bd6..5fff2dfed3 100644
--- a/autosklearn/metalearning/files/accuracy_binary.classification_dense/feature_runstatus.arff
+++ b/autosklearn/metalearning/files/accuracy_binary.classification_dense/feature_runstatus.arff
@@ -4,6 +4,7 @@
 @ATTRIBUTE repetition NUMERIC
 @ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other}
@@ -21,6 +22,11 @@
 @ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other}
@@ -37,162 +43,138 @@
 @ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other}
 
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diff --git a/autosklearn/metalearning/files/accuracy_binary.classification_dense/feature_values.arff b/autosklearn/metalearning/files/accuracy_binary.classification_dense/feature_values.arff
index a60400e3ce..736dc64321 100644
--- a/autosklearn/metalearning/files/accuracy_binary.classification_dense/feature_values.arff
+++ b/autosklearn/metalearning/files/accuracy_binary.classification_dense/feature_values.arff
@@ -13,7 +13,6 @@
 @ATTRIBUTE KurtosisMean NUMERIC
 @ATTRIBUTE KurtosisMin NUMERIC
 @ATTRIBUTE KurtosisSTD NUMERIC
-@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC
 @ATTRIBUTE LogDatasetRatio NUMERIC
 @ATTRIBUTE LogInverseDatasetRatio NUMERIC
 @ATTRIBUTE LogNumberOfFeatures NUMERIC
@@ -42,160 +41,135 @@
 @ATTRIBUTE SymbolsSum NUMERIC
 
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diff --git a/autosklearn/metalearning/files/accuracy_binary.classification_sparse/algorithm_runs.arff b/autosklearn/metalearning/files/accuracy_binary.classification_sparse/algorithm_runs.arff
index 559ab420fb..218d3336a4 100644
--- a/autosklearn/metalearning/files/accuracy_binary.classification_sparse/algorithm_runs.arff
+++ b/autosklearn/metalearning/files/accuracy_binary.classification_sparse/algorithm_runs.arff
@@ -7,95 +7,133 @@
 @ATTRIBUTE runstatus {ok, timeout, memout, not_applicable, crash, other}
 
 @DATA
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diff --git a/autosklearn/metalearning/files/accuracy_binary.classification_sparse/configurations.csv b/autosklearn/metalearning/files/accuracy_binary.classification_sparse/configurations.csv
index 75662496d2..36fd991e13 100644
--- a/autosklearn/metalearning/files/accuracy_binary.classification_sparse/configurations.csv
+++ b/autosklearn/metalearning/files/accuracy_binary.classification_sparse/configurations.csv
@@ -1,93 +1,131 @@
 idx,balancing:strategy,classifier:__choice__,classifier:adaboost:algorithm,classifier:adaboost:learning_rate,classifier:adaboost:max_depth,classifier:adaboost:n_estimators,classifier:bernoulli_nb:alpha,classifier:bernoulli_nb:fit_prior,classifier:decision_tree:criterion,classifier:decision_tree:max_depth_factor,classifier:decision_tree:max_features,classifier:decision_tree:max_leaf_nodes,classifier:decision_tree:min_impurity_decrease,classifier:decision_tree:min_samples_leaf,classifier:decision_tree:min_samples_split,classifier:decision_tree:min_weight_fraction_leaf,classifier:extra_trees:bootstrap,classifier:extra_trees:criterion,classifier:extra_trees:max_depth,classifier:extra_trees:max_features,classifier:extra_trees:max_leaf_nodes,classifier:extra_trees:min_impurity_decrease,classifier:extra_trees:min_samples_leaf,classifier:extra_trees:min_samples_split,classifier:extra_trees:min_weight_fraction_leaf,classifier:gradient_boosting:early_stop,classifier:gradient_boosting:l2_regularization,classifier:gradient_boosting:learning_rate,classifier:gradient_boosting:loss,classifier:gradient_boosting:max_bins,classifier:gradient_boosting:max_depth,classifier:gradient_boosting:max_leaf_nodes,classifier:gradient_boosting:min_samples_leaf,classifier:gradient_boosting:n_iter_no_change,classifier:gradient_boosting:scoring,classifier:gradient_boosting:tol,classifier:gradient_boosting:validation_fraction,classifier:k_nearest_neighbors:n_neighbors,classifier:k_nearest_neighbors:p,classifier:k_nearest_neighbors:weights,classifier:lda:n_components,classifier:lda:shrinkage,classifier:lda:shrinkage_factor,classifier:lda:tol,classifier:liblinear_svc:C,classifier:liblinear_svc:dual,classifier:liblinear_svc:fit_intercept,classifier:liblinear_svc:intercept_scaling,classifier:liblinear_svc:loss,classifier:liblinear_svc:multi_class,classifier:liblinear_svc:penalty,classifier:liblinear_svc:tol,classifier:libsvm_svc:C,classifier:libsvm_svc:coef0,classifier:libsvm_svc:degree,classifier:libsvm_svc:gamma,classifier:libsvm_svc:kernel,classifier:libsvm_svc:max_iter,classifier:libsvm_svc:shrinking,classifier:libsvm_svc:tol,classifier:multinomial_nb:alpha,classifier:multinomial_nb:fit_prior,classifier:passive_aggressive:C,classifier:passive_aggressive:average,classifier:passive_aggressive:fit_intercept,classifier:passive_aggressive:loss,classifier:passive_aggressive:tol,classifier:qda:reg_param,classifier:random_forest:bootstrap,classifier:random_forest:criterion,classifier:random_forest:max_depth,classifier:random_forest:max_features,classifier:random_forest:max_leaf_nodes,classifier:random_forest:min_impurity_decrease,classifier:random_forest:min_samples_leaf,classifier:random_forest:min_samples_split,classifier:random_forest:min_weight_fraction_leaf,classifier:sgd:alpha,classifier:sgd:average,classifier:sgd:epsilon,classifier:sgd:eta0,classifier:sgd:fit_intercept,classifier:sgd:l1_ratio,classifier:sgd:learning_rate,classifier:sgd:loss,classifier:sgd:penalty,classifier:sgd:power_t,classifier:sgd:tol,data_preprocessing:categorical_transformer:categorical_encoding:__choice__,data_preprocessing:categorical_transformer:category_coalescence:__choice__,data_preprocessing:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction,data_preprocessing:numerical_transformer:imputation:strategy,data_preprocessing:numerical_transformer:rescaling:__choice__,data_preprocessing:numerical_transformer:rescaling:quantile_transformer:n_quantiles,data_preprocessing:numerical_transformer:rescaling:quantile_transformer:output_distribution,data_preprocessing:numerical_transformer:rescaling:robust_scaler:q_max,data_preprocessing:numerical_transformer:rescaling:robust_scaler:q_min,feature_preprocessor:__choice__,feature_preprocessor:extra_trees_preproc_for_classification:bootstrap,feature_preprocessor:extra_trees_preproc_for_classification:criterion,feature_preprocessor:extra_trees_preproc_for_classification:max_depth,feature_preprocessor:extra_trees_preproc_for_classification:max_features,feature_preprocessor:extra_trees_preproc_for_classification:max_leaf_nodes,feature_preprocessor:extra_trees_preproc_for_classification:min_impurity_decrease,feature_preprocessor:extra_trees_preproc_for_classification:min_samples_leaf,feature_preprocessor:extra_trees_preproc_for_classification:min_samples_split,feature_preprocessor:extra_trees_preproc_for_classification:min_weight_fraction_leaf,feature_preprocessor:extra_trees_preproc_for_classification:n_estimators,feature_preprocessor:kernel_pca:coef0,feature_preprocessor:kernel_pca:degree,feature_preprocessor:kernel_pca:gamma,feature_preprocessor:kernel_pca:kernel,feature_preprocessor:kernel_pca:n_components,feature_preprocessor:kitchen_sinks:gamma,feature_preprocessor:kitchen_sinks:n_components,feature_preprocessor:liblinear_svc_preprocessor:C,feature_preprocessor:liblinear_svc_preprocessor:dual,feature_preprocessor:liblinear_svc_preprocessor:fit_intercept,feature_preprocessor:liblinear_svc_preprocess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-10,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.8253905225980761,None,0.0,18,20,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.003725255016700434,mean,robust_scaler,,,0.7701705977765594,0.2396520157991559,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.3579038776690903,False,True,1,squared_hinge,ovr,l1,4.994232534734023e-05,,,,,,,,,,,,,,,,,,,,,
-11,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.06263739023781334,None,0.0,18,8,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.02206379295100344,most_frequent,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,,
-12,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.07518858116896743,None,0.0,7,10,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0027133920607872518,mean,robust_scaler,,,0.7873452530915046,0.16215139072179952,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-13,none,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,200,None,,0.0017457216340699586,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,none,,,,,kernel_pca,,,,,,,,,,,0.0,4,0.0007494648939847078,poly,1996,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-14,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.6039096182414764,None,0.0,2,20,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.1939609823704447,most_frequent,robust_scaler,,,0.7331327008874781,0.29747488162877694,extra_trees_preproc_for_classification,True,entropy,None,0.33401603690569853,None,0.0,4,4,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-15,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7865652301288171,None,0.0,1,4,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,median,robust_scaler,,,0.8623635641193496,0.13298297278730625,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-16,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.905401147837377,-0.32832122529048147,3,1.001896309040499,poly,-1,False,5.1523484258030785e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-17,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.3304182089730454,None,0.0,1,11,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.015634546384927752,median,quantile_transformer,332,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-18,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5010952533165973,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.005838375247601407,mean,normalize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,,
-19,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.7297122444166401,False,True,1,squared_hinge,ovr,l2,0.00023416745444541063,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.03941815152197094,most_frequent,normalize,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0973649546833947,fpr,chi2,
-20,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.4426097579931897,None,0.0,16,4,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,mean,robust_scaler,,,0.7407323456920554,0.0804756921037867,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,,
-21,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5607168729746175,None,0.0,2,3,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.009810589644392154,most_frequent,quantile_transformer,1132,normal,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.06211089008167909,fdr,chi2,
-22,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9333237919291875,None,0.0,8,3,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,,
-23,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.557321265077371,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-24,none,adaboost,SAMME,0.6255555220658239,6,446,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.008833097306364968,most_frequent,none,,,,,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,10,None,4,17,1.0,76,,,,,,
-25,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.7772914874049878,None,0.0,10,15,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00015285482513309234,mean,robust_scaler,,,0.8373404000074934,0.14303151080827242,extra_trees_preproc_for_classification,True,entropy,None,0.5021713909357703,None,0.0,13,16,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-26,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7422641456052917,None,0.0,8,15,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.005561351266169067,most_frequent,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-27,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.887898394612674,None,0.0,10,19,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,robust_scaler,,,0.8350698288864961,0.2347163538066168,extra_trees_preproc_for_classification,False,entropy,None,0.49795108918064074,None,0.0,13,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-28,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7088164344533493,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,mean,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,,
-29,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,10,1,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0022606253047811435,median,quantile_transformer,1247,normal,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,25.327793585266683,chi2,,,,
-30,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,24.786439230569382,,,0.03411197494635044,rbf,-1,True,0.003003351499585181,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0007994838629325874,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-31,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2681.998807317996,-0.9558745731731211,,0.0011299948421773457,sigmoid,-1,False,1.5292507354478086e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-32,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.8095522998548572,None,0.0,17,10,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.031940595071539304,mean,quantile_transformer,1563,normal,,,extra_trees_preproc_for_classification,False,entropy,None,0.9330145598603196,None,0.0,18,10,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-33,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,26.502259985828186,,,9.475662388405154e-05,rbf,-1,False,0.00019690635968217866,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-34,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.339600708038189,False,True,1,squared_hinge,ovr,l2,9.572641158475304e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,quantile_transformer,1193,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-35,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5255991794296913,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-36,weighting,decision_tree,,,,,,,gini,1.7853447328017091,1.0,None,0.0,13,7,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0001265964225445088,median,normalize,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.26399878867042303,,mutual_info_classif,
-37,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.7610308078756552,None,0.0,1,2,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.010000000000000004,mean,robust_scaler,,,0.9205545465103644,0.20272761060743316,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-38,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,249.12499213127998,,,0.0005283374995918348,rbf,-1,False,0.0016932707039261864,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.48910068367909115,most_frequent,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-39,none,bernoulli_nb,,,,,0.6524090653939782,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.005164169126808402,most_frequent,quantile_transformer,16,normal,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.033082145443631535,False,True,1,squared_hinge,ovr,l1,0.002615020898325393,,,,,,,,,,,,,,,,,,,,,
-40,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.6453555269772124,None,0.0,1,4,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,normalize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-41,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.20911875507660596,None,0.0,12,8,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,median,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,,
-42,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5138705053438934,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.14411802159506462,most_frequent,quantile_transformer,857,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-43,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,29.55731828912983,False,True,1,squared_hinge,ovr,l2,0.0011332486951954968,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,,
-44,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.37801720673725603,True,True,squared_hinge,0.0001255132393635067,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.025993178344125793,most_frequent,standardize,,,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,1.4421737618429475,False,True,1,squared_hinge,ovr,l1,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,
-45,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.10581257327556097,None,0.0,7,12,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.004219392676189224,median,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,,
-46,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.4188764793561401,None,0.0,5,5,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.010589444903605501,mean,robust_scaler,,,0.9796997860990453,0.27262832749694405,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-47,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.005753626505113041,False,True,squared_hinge,0.00924401505172238,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.3192271696445429,median,quantile_transformer,913,normal,,,kernel_pca,,,,,,,,,,,,,,cosine,1963,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-48,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.41019363453549706,None,0.0,15,2,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.0003507389224306018,median,none,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.20614098085343638,fdr,chi2,
-49,none,adaboost,SAMME,0.1280458968526125,7,125,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,robust_scaler,,,0.7156460334356771,0.2981902862926971,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,65.16171885308417,chi2,,,,
-50,none,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9931779675565967,None,0.0,18,17,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,robust_scaler,,,0.75,0.2945917010472818,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,,
-51,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.08646118576686276,None,0.0,4,9,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,most_frequent,quantile_transformer,959,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-52,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.6271213423927893,None,0.0,1,4,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.009403509495453675,mean,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,,
-53,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8.85547546146731,True,True,squared_hinge,4.0111304608748804e-05,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-54,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.34936025988397124,None,0.0,2,13,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00018601309647406987,median,robust_scaler,,,0.8634309348843472,0.26976989465367607,extra_trees_preproc_for_classification,False,entropy,None,0.48025175288975036,None,0.0,6,17,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-55,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.8793175987664956,None,0.0,10,5,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,robust_scaler,,,0.7645748130904642,0.2677156877155214,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,130.79089036625916,False,True,1,squared_hinge,ovr,l1,0.005538878421721295,,,,,,,,,,,,,,,,,,,,,
-56,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.014911374426433241,None,0.0,6,2,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,,
-57,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0016941428709087083,True,,,True,,optimal,squared_hinge,l1,,5.124265599423001e-05,no_encoding,no_coalescense,,most_frequent,quantile_transformer,461,uniform,,,extra_trees_preproc_for_classification,True,gini,None,0.31371431180257103,None,0.0,5,17,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-58,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.7591396576940418,-0.02447104417007273,2,0.7325180366342191,poly,-1,True,0.00045922794962860523,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,standardize,,,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,2.057731506094293,False,True,1,squared_hinge,ovr,l1,0.03575998247131604,,,,,,,,,,,,,,,,,,,,,
-59,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.593728171200453,None,0.0,7,20,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.02830941310359363,mean,normalize,,,,,extra_trees_preproc_for_classification,False,entropy,None,0.7389446586281172,None,0.0,20,11,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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+355,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,68.60833575985336,,,0.03905068757955671,rbf,-1,False,0.02846559747114773,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.04775815990561371,mean,standardize,,,,,extra_trees_preproc_for_classification,False,entropy,None,0.45727987956076366,None,0.0,13,11,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+357,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.3431705351883592,None,0.0,4,16,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.09444471061261189,most_frequent,normalize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+359,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.9282407375709691,None,0.0,16,3,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.015995805068365877,mean,standardize,,,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.10042232633120375,False,True,1,squared_hinge,ovr,l1,0.004451009526629745,,,,,,,,,,,,,,,,,,,,,
+367,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.938372431886818e-05,True,True,hinge,0.0016143408148969647,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00024949978971758427,median,quantile_transformer,754,uniform,,,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,-0.22339839702350717,2,0.5718982387915436,poly,5260,,,,,,,,,,,,,,,,
+371,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4,1,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.01020427226149318,median,none,,,,,extra_trees_preproc_for_classification,True,entropy,None,0.3663460889081009,None,0.0,2,7,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
diff --git a/autosklearn/metalearning/files/accuracy_binary.classification_sparse/description.txt b/autosklearn/metalearning/files/accuracy_binary.classification_sparse/description.txt
index 6f3582bc97..f38b7dedec 100644
--- a/autosklearn/metalearning/files/accuracy_binary.classification_sparse/description.txt
+++ b/autosklearn/metalearning/files/accuracy_binary.classification_sparse/description.txt
@@ -1,8 +1,9 @@
 features_cutoff_time: 3600
 features_cutoff_memory: 3072
-number_of_feature_steps: 35
+number_of_feature_steps: 42
 feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances
 feature_step LogNumberOfInstances: LogNumberOfInstances
+feature_step NumberOfClasses: NumberOfClasses
 feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures
 feature_step LogNumberOfFeatures: LogNumberOfFeatures
 feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues
@@ -20,6 +21,11 @@ feature_step DatasetRatio: DatasetRatio, LogDatasetRatio
 feature_step LogDatasetRatio: LogDatasetRatio
 feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio
 feature_step LogInverseDatasetRatio: LogInverseDatasetRatio
+feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD
+feature_step ClassProbabilityMin: ClassProbabilityMin
+feature_step ClassProbabilityMax: ClassProbabilityMax
+feature_step ClassProbabilityMean: ClassProbabilityMean
+feature_step ClassProbabilitySTD: ClassProbabilitySTD
 feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum
 feature_step SymbolsMin: SymbolsMin
 feature_step SymbolsMax: SymbolsMax
@@ -36,11 +42,12 @@ feature_step SkewnessMin: SkewnessMin
 feature_step SkewnessMax: SkewnessMax
 feature_step SkewnessMean: SkewnessMean
 feature_step SkewnessSTD: SkewnessSTD
-features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
+feature_step ClassEntropy: ClassEntropy
+features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy
 features_stochastic: 
-default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
+default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy
 
-algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92
+algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235,236,237,238,239,240,241,242,243,244,245,246,247,248,249,250,251,252,253,254,255,256,257,258,259,260,261,262,263,264,265,266,267,268,269,270,271,272,273,274,275,276,277,278,279,280,281,282,283,284,285,286,287,288,289,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,312,313,314,315,316,317,318,319,320,321,322,323,324,325,326,327,328,329,330,331,332,333,334,335,336,337,338,339,340,341,342,343,344,345,346,347,348,349,350,351,352,353,354,355,356,357,358,359,360,361,362,363,364,365,366,367,368,369,370,371,372
 algorithms_stochastic: 
 performance_measures: accuracy
 performance_type: solution_quality
diff --git a/autosklearn/metalearning/files/accuracy_binary.classification_sparse/feature_costs.arff b/autosklearn/metalearning/files/accuracy_binary.classification_sparse/feature_costs.arff
index 0e17e20d22..8512fd9275 100644
--- a/autosklearn/metalearning/files/accuracy_binary.classification_sparse/feature_costs.arff
+++ b/autosklearn/metalearning/files/accuracy_binary.classification_sparse/feature_costs.arff
@@ -4,6 +4,7 @@
 @ATTRIBUTE repetition NUMERIC
 @ATTRIBUTE NumberOfInstances NUMERIC
 @ATTRIBUTE LogNumberOfInstances NUMERIC
+@ATTRIBUTE NumberOfClasses NUMERIC
 @ATTRIBUTE NumberOfFeatures NUMERIC
 @ATTRIBUTE LogNumberOfFeatures NUMERIC
 @ATTRIBUTE MissingValues NUMERIC
@@ -21,6 +22,11 @@
 @ATTRIBUTE LogDatasetRatio NUMERIC
 @ATTRIBUTE InverseDatasetRatio NUMERIC
 @ATTRIBUTE LogInverseDatasetRatio NUMERIC
+@ATTRIBUTE ClassOccurences NUMERIC
+@ATTRIBUTE ClassProbabilityMin NUMERIC
+@ATTRIBUTE ClassProbabilityMax NUMERIC
+@ATTRIBUTE ClassProbabilityMean NUMERIC
+@ATTRIBUTE ClassProbabilitySTD NUMERIC
 @ATTRIBUTE NumSymbols NUMERIC
 @ATTRIBUTE SymbolsMin NUMERIC
 @ATTRIBUTE SymbolsMax NUMERIC
@@ -37,162 +43,138 @@
 @ATTRIBUTE SkewnessMax NUMERIC
 @ATTRIBUTE SkewnessMean NUMERIC
 @ATTRIBUTE SkewnessSTD NUMERIC
+@ATTRIBUTE ClassEntropy NUMERIC
 
 @DATA
-75123,1.0,1e-05,1e-05,1e-05,1e-05,0.00253,0.00221,1e-05,0.00024,3e-05,0.00011,0.0,2e-05,2e-05,5e-05,8e-05,2e-05,1e-05,7e-05,1e-05,0.00072,1e-05,1e-05,0.00019,0.00041,0.00011,0.00061,0.00012,9e-05,0.00015,0.00025,0.00067,0.00014,0.00011,0.00016,0.00027
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-75101,1.0,1e-05,1e-05,1e-05,1e-05,0.0418,0.03546,1e-05,0.00389,1e-05,0.00248,0.0,2e-05,2e-05,4e-05,6e-05,2e-05,1e-05,5e-05,2e-05,0.00045,1e-05,0.0,0.00011,0.00023,0.0001,0.00041,6e-05,8e-05,0.0001,0.00018,0.00047,6e-05,0.0001,8e-05,0.00023
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diff --git a/autosklearn/metalearning/files/accuracy_binary.classification_sparse/feature_runstatus.arff b/autosklearn/metalearning/files/accuracy_binary.classification_sparse/feature_runstatus.arff
index 0034ad2bd6..5fff2dfed3 100644
--- a/autosklearn/metalearning/files/accuracy_binary.classification_sparse/feature_runstatus.arff
+++ b/autosklearn/metalearning/files/accuracy_binary.classification_sparse/feature_runstatus.arff
@@ -4,6 +4,7 @@
 @ATTRIBUTE repetition NUMERIC
 @ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other}
@@ -21,6 +22,11 @@
 @ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other}
@@ -37,162 +43,138 @@
 @ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other}
 
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+75225,1.0,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,other,ok,ok,ok,ok,ok
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+75244,1.0,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,other,ok,ok,ok,ok,ok
+75248,1.0,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,other,ok,ok,ok,ok,ok
+75249,1.0,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,other,ok,ok,ok,ok,ok
+75250,1.0,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,other,ok,ok,ok,ok,ok
diff --git a/autosklearn/metalearning/files/accuracy_binary.classification_sparse/feature_values.arff b/autosklearn/metalearning/files/accuracy_binary.classification_sparse/feature_values.arff
index a60400e3ce..736dc64321 100644
--- a/autosklearn/metalearning/files/accuracy_binary.classification_sparse/feature_values.arff
+++ b/autosklearn/metalearning/files/accuracy_binary.classification_sparse/feature_values.arff
@@ -13,7 +13,6 @@
 @ATTRIBUTE KurtosisMean NUMERIC
 @ATTRIBUTE KurtosisMin NUMERIC
 @ATTRIBUTE KurtosisSTD NUMERIC
-@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC
 @ATTRIBUTE LogDatasetRatio NUMERIC
 @ATTRIBUTE LogInverseDatasetRatio NUMERIC
 @ATTRIBUTE LogNumberOfFeatures NUMERIC
@@ -42,160 +41,135 @@
 @ATTRIBUTE SymbolsSum NUMERIC
 
 @DATA
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diff --git a/autosklearn/metalearning/files/accuracy_multiclass.classification_dense/algorithm_runs.arff b/autosklearn/metalearning/files/accuracy_multiclass.classification_dense/algorithm_runs.arff
index f40651cccb..a9868a02da 100644
--- a/autosklearn/metalearning/files/accuracy_multiclass.classification_dense/algorithm_runs.arff
+++ b/autosklearn/metalearning/files/accuracy_multiclass.classification_dense/algorithm_runs.arff
@@ -7,130 +7,135 @@
 @ATTRIBUTE runstatus {ok, timeout, memout, not_applicable, crash, other}
 
 @DATA
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diff --git a/autosklearn/metalearning/files/accuracy_multiclass.classification_dense/configurations.csv b/autosklearn/metalearning/files/accuracy_multiclass.classification_dense/configurations.csv
index 14ccf74d94..85e767fd63 100644
--- a/autosklearn/metalearning/files/accuracy_multiclass.classification_dense/configurations.csv
+++ b/autosklearn/metalearning/files/accuracy_multiclass.classification_dense/configurations.csv
@@ -1,128 +1,133 @@
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-43,none,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0686374607394679,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,minmax,,,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.8490260132946401,False,,,,,,,,,,,,,,,
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-46,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,92.66144306787051,,,0.04654639302091653,rbf,-1,True,0.0022231544280610005,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.005194838694455963,mean,none,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,15,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-47,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2681.998807317996,-0.9558745731731211,,0.0011299948421773457,sigmoid,-1,False,1.5292507354478086e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-48,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.8095522998548572,None,0.0,17,10,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.031940595071539304,mean,quantile_transformer,1563,normal,,,extra_trees_preproc_for_classification,False,entropy,None,0.9330145598603196,None,0.0,18,10,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-49,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,26.502259985828186,,,9.475662388405154e-05,rbf,-1,False,0.00019690635968217866,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-50,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,2.5360196477156387e-07,0.011018756408558218,auto,255,None,40,122,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.00011749164080669445,most_frequent,quantile_transformer,757,uniform,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,5.097834697156853,False,True,1,squared_hinge,ovr,l1,0.053877834919477424,,,,,,,,,,,,,,,,,,,,,,
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-54,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,1.488333818193668e-10,0.09526676471267805,auto,255,None,258,87,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,normalize,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.3561953264350478,fpr,f_classif
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-58,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,151,None,,0.0003283777607180646,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.2510527431514227,mean,normalize,,,,,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,-0.96093182232312,,0.05762510964607661,sigmoid,2632,,,,,,,,,,,,,,,,,
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-64,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,29.55731828912983,False,True,1,squared_hinge,ovr,l2,0.0011332486951954968,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,
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-69,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7573078019937971,None,0.0,2,5,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,robust_scaler,,,0.7265312295801113,0.28332515882236425,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.09660564590146253,fpr,f_classif
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-72,none,adaboost,SAMME,0.1280458968526125,7,125,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,robust_scaler,,,0.7156460334356771,0.2981902862926971,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,65.16171885308417,chi2,,,
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-76,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7.491039171224502,True,True,squared_hinge,2.8058138048327884e-05,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,minmax,,,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.7754372809893839,False,True,1,squared_hinge,ovr,l1,3.9643594596766314e-05,,,,,,,,,,,,,,,,,,,,,,
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-78,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,0.011547155223520492,0.13665554140837183,auto,255,None,13,5,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.1824584492028449,median,minmax,,,,,feature_agglomeration,,,,,,,,,,,,,,,euclidean,average,261,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-79,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.933900673392165,None,0.0,7,4,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,minmax,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-80,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1661.2274747409913,,,0.025821208852387936,rbf,-1,False,0.0017011435104921824,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,none,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,306,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-81,none,adaboost,SAMME,0.011233995624432622,9,477,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.018370622484682127,mean,standardize,,,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6039710338898471,False,,,,,,,,,,,,,,,
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-84,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,0.037849720944694,0.1268815770019783,auto,255,None,11,99,11,loss,1e-07,0.10654523627313527,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,
-85,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.593728171200453,None,0.0,7,20,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.02830941310359363,mean,normalize,,,,,extra_trees_preproc_for_classification,False,entropy,None,0.7389446586281172,None,0.0,20,11,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-86,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,1,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.019797877010737826,mean,normalize,,,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.8239678741128165,False,,,,,,,,,,,,,,,
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-89,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5722917944959481,None,0.0,1,14,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,
-90,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.26356754187780895,None,0.0,4,3,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010966178401758774,mean,robust_scaler,,,0.7817043861798886,0.24371720112407158,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
-91,weighting,adaboost,SAMME.R,0.13547552520095074,4,62,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.011851011202809002,most_frequent,standardize,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4888776875314595,fpr,chi2
-92,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,80.0028902468169,False,True,1,squared_hinge,ovr,l2,0.0024481999829574722,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.15047970487774687,mean,quantile_transformer,1130,normal,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,1.1912646635544712,False,True,1,squared_hinge,ovr,l1,0.00014314728486450857,,,,,,,,,,,,,,,,,,,,,,
-93,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.22879311080499845,None,0.0,8,4,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.014320022547615783,median,minmax,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.238630703440461,mutual_info,,,
-94,weighting,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,2,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,none,,,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6342574285315175,False,,,,,,,,,,,,,,,
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-96,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.14148638702297855,None,0.0,2,6,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.0038499650338378643,most_frequent,quantile_transformer,1759,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-97,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,1.1522414705935405e-10,0.6132800183073315,auto,255,None,1607,67,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010096911249562407,mean,robust_scaler,,,0.7663493326967942,0.136094217337434,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-98,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2746221958279041,False,True,1,squared_hinge,ovr,l2,6.546077664669006e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,quantile_transformer,1054,normal,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,
-99,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.479405900046494,,,0.013340778632154543,rbf,-1,False,6.913586060890114e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,robust_scaler,,,0.9953232616818098,0.06450577886132274,fast_ica,,,,,,,,,,,deflation,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-100,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.825844582229201,None,0.0,12,9,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0008422508222149647,median,robust_scaler,,,0.8658803880857064,0.2528423686293341,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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-102,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.7033210835501171,None,0.0,3,19,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,robust_scaler,,,0.7699195245692886,0.29258311638476237,feature_agglomeration,,,,,,,,,,,,,,,euclidean,ward,143,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-103,none,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,128,manual,0.8574560771111307,0.010886139381599514,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.24115007944216788,most_frequent,normalize,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,62.26514170991452,mutual_info,,,
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-105,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,1.430083734915855e-09,0.08779621406981941,auto,255,None,17,20,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.29749837779115257,mean,robust_scaler,,,0.7236445277814435,0.25,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
-106,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.3294588572341214,None,0.0,1,3,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
-107,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,39.22980757485479,,,0.13597389869623427,rbf,-1,False,0.00011639060052687874,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.006288306814260224,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-108,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.7922837297504705,False,True,1,squared_hinge,ovr,l2,0.0005476597326342352,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.012674631437254894,most_frequent,none,,,,,extra_trees_preproc_for_classification,False,entropy,None,0.23145146971319638,None,0.0,7,20,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-109,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.18060139887649052,None,0.0,10,12,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.010000000000000004,most_frequent,robust_scaler,,,0.9298612094255864,0.1263597946722308,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
-110,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.4111348710613201,None,0.0,16,8,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.01224348527546641,most_frequent,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,
-111,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6427543483503397,True,True,hinge,0.00045318775930916944,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.009338484332306904,median,quantile_transformer,174,normal,,,kernel_pca,,,,,,,,,,,,,,,,,,,,,,cosine,1848,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-112,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.04981080944617379,True,,0.07309386939525211,True,,constant,log,l2,,0.0006721243213777075,no_encoding,no_coalescense,,mean,quantile_transformer,574,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-113,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,9.029473608568458e-09,0.15779990576964315,auto,255,None,37,71,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.018093177195644097,mean,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,False,,,,,,,,,,,,
-114,none,adaboost,SAMME,0.011233995624432622,9,477,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.018370622484682127,mean,standardize,,,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6039710338898471,False,,,,,,,,,,,,,,,
-115,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l2,2.9468189962573666e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.014870368357163507,most_frequent,quantile_transformer,1000,uniform,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,83.43414935094258,f_classif,,,
-116,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.4864284879550203,None,0.0,1,4,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.008665854799630503,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-117,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.43699642289829416,None,0.0,6,20,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,quantile_transformer,1604,uniform,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-118,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.6922752267318475,True,True,squared_hinge,0.06299408576730353,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,quantile_transformer,1742,uniform,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,107,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-119,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.44727252219812963,None,0.0,2,10,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,standardize,,,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l1,8.17533189014765e-05,,,,,,,,,,,,,,,,,,,,,,
-120,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.46057831591617715,False,True,hinge,0.04557857428827514,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00027457445401600137,median,standardize,,,,,extra_trees_preproc_for_classification,True,gini,None,0.48190346970486964,None,0.0,17,18,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-121,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.4210978663047845,None,0.0,5,6,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.006365988550833196,most_frequent,robust_scaler,,,0.8348891074186082,0.00974961635730845,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0695919708890599,fpr,f_classif
-122,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4424195769042466,False,True,hinge,0.02490432909709204,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,mean,quantile_transformer,1000,uniform,,,kernel_pca,,,,,,,,,,,,,,,,,,,,,,cosine,769,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-123,weighting,decision_tree,,,,,,,entropy,1.0435267397863155,1.0,None,0.0,15,3,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.36913659113454333,most_frequent,normalize,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4947786362821451,fpr,f_classif
-124,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1561.1002600303584,,,0.0024048551704194507,rbf,-1,False,0.0001936214667496851,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,minmax,,,,,fast_ica,,,,,,,,,,,parallel,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-125,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.087313242780732,0.0636709914987994,3,0.004481917125024585,poly,-1,False,0.01848880618941367,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.002136015035740733,mean,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-126,weighting,adaboost,SAMME.R,1.696588612003168,9,455,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,minmax,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,82.18726095926662,chi2,,,
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+1,none,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1514.059969351301,False,True,1,squared_hinge,ovr,l2,0.002448814309966599,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,standardize,,,,,extra_trees_preproc_for_classification,True,entropy,None,0.8221481751476923,None,0.0,2,3,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+3,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5707458022222313,None,0.0,3,12,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,minmax,,,,,extra_trees_preproc_for_classification,False,gini,None,0.5651056128131684,None,0.0,9,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+6,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.7283683762613216,None,0.0,12,20,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,median,standardize,,,,,fast_ica,,,,,,,,,,,deflation,exp,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+8,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,114,manual,0.11778631707605758,0.0029112721339713465,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.015582195257240751,median,normalize,,,,,kernel_pca,,,,,,,,,,,,,,,,,,,0.42909623253327767,3,0.25407612000343777,poly,1689,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+11,weighting,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,2,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.3865962574104293,mean,normalize,,,,,fast_ica,,,,,,,,,,,deflation,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+13,weighting,adaboost,SAMME,0.17983222024694556,4,357,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0008898781197407956,mean,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+15,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.21772092350860472,None,0.0,14,17,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.0020488212891693275,median,standardize,,,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,33.74257606530722,False,True,1,squared_hinge,ovr,l1,8.740452966205111e-05,,,,,,,,,,,,,,,,,,,,,,
+18,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,2.4243922131268786e-05,0.07570328162448307,auto,255,None,71,42,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,robust_scaler,,,0.8898729094898769,0.12138152991327475,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,94.96086286583851,chi2,,,
+20,none,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.9866058571747695,None,0.0,16,9,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.1680676743605393,most_frequent,robust_scaler,,,0.8840072797399463,0.08535534311393947,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,False,,,,,,,,,,,,
+23,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,0.00022373024203887573,0.027149343206135476,auto,255,None,49,13,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.00777614320416325,most_frequent,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,
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+379,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,1.2940289605295536e-09,0.15591343717459372,auto,255,None,7,59,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0005549095909238961,mean,quantile_transformer,297,uniform,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,58.10560700979111,chi2,,,
+388,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,53.67009896349757,,,0.03687753298495132,rbf,-1,False,0.028791286245157307,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.047859755696856916,mean,standardize,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,70,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+392,none,adaboost,SAMME,0.7387448781351068,8,450,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0473229636375385,median,standardize,,,,,feature_agglomeration,,,,,,,,,,,,,,,euclidean,ward,344,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+399,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.5864398404787432,None,0.0,1,14,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0070663750850608895,most_frequent,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
+403,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1866.9172613016979,,,0.012606336762631641,rbf,-1,True,0.000504574634300575,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,
+405,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0001329242404968632,False,True,squared_hinge,0.000416219546803132,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.007568009633214092,median,quantile_transformer,1126,normal,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,29.06367291960815,chi2,,,
+408,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.6642356723981304,False,True,1,squared_hinge,ovr,l2,7.912577426155621e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.08464913801800422,most_frequent,normalize,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,53.35800942135187,chi2,,,
+413,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,7.194851113809193e-10,0.013882834218943657,auto,255,None,18,2,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0005124238915083231,mean,quantile_transformer,948,normal,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,60.45885911036194,f_classif,,,
+415,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,0.00037966471646911845,0.016422945536911454,auto,255,None,10,56,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.002419439132687246,median,quantile_transformer,828,uniform,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+422,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.36374112587320206,None,0.0,2,10,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010870084126959173,most_frequent,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,
+424,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,2.344883102482138e-07,0.0328548789988159,auto,255,None,42,24,16,loss,1e-07,0.13015466346727866,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,most_frequent,robust_scaler,,,0.7010898774332888,0.2534280821273264,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,
+428,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,3.738096432152635e-10,0.04468500715913894,auto,255,None,31,156,20,loss,1e-07,0.10279783687118692,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010000000000000004,most_frequent,quantile_transformer,1132,normal,,,feature_agglomeration,,,,,,,,,,,,,,,euclidean,ward,43,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+430,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5321814610661173,None,0.0,1,3,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,standardize,,,,,extra_trees_preproc_for_classification,True,gini,None,0.5,None,0.0,1,15,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+435,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.44903177399085925,None,0.0,2,17,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,mean,robust_scaler,,,0.7812408516672696,0.12791233577400205,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,
+438,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,6.090138036399552e-10,0.017305568433437588,auto,255,None,202,188,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,robust_scaler,,,0.7526196011313747,0.1563616005702636,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+445,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,68.60833575985336,,,0.03905068757955671,rbf,-1,False,0.02846559747114773,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.04775815990561371,mean,standardize,,,,,extra_trees_preproc_for_classification,False,entropy,None,0.45727987956076366,None,0.0,13,11,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+447,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.3431705351883592,None,0.0,4,16,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.09444471061261189,most_frequent,normalize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+450,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,7.837701348094735e-09,0.05315388879642303,auto,255,None,67,2,10,loss,1e-07,0.11655723047843726,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.001857226018763921,median,none,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.014648191862400293,fdr,f_classif
+458,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,5.581658271288433e-10,0.06171988234311011,auto,255,None,25,6,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.014859405637537319,most_frequent,robust_scaler,,,0.7983900845251328,0.2545046978458648,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,1.380085213170737,False,True,1,squared_hinge,ovr,l1,0.0061450732015354784,,,,,,,,,,,,,,,,,,,,,,
+462,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6,1,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.017471096998391875,median,none,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,67.7801145492284,f_classif,,,
diff --git a/autosklearn/metalearning/files/accuracy_multiclass.classification_dense/description.txt b/autosklearn/metalearning/files/accuracy_multiclass.classification_dense/description.txt
index 54e1a4d03d..324215bec2 100644
--- a/autosklearn/metalearning/files/accuracy_multiclass.classification_dense/description.txt
+++ b/autosklearn/metalearning/files/accuracy_multiclass.classification_dense/description.txt
@@ -1,8 +1,9 @@
 features_cutoff_time: 3600
 features_cutoff_memory: 3072
-number_of_feature_steps: 35
+number_of_feature_steps: 42
 feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances
 feature_step LogNumberOfInstances: LogNumberOfInstances
+feature_step NumberOfClasses: NumberOfClasses
 feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures
 feature_step LogNumberOfFeatures: LogNumberOfFeatures
 feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues
@@ -20,6 +21,11 @@ feature_step DatasetRatio: DatasetRatio, LogDatasetRatio
 feature_step LogDatasetRatio: LogDatasetRatio
 feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio
 feature_step LogInverseDatasetRatio: LogInverseDatasetRatio
+feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD
+feature_step ClassProbabilityMin: ClassProbabilityMin
+feature_step ClassProbabilityMax: ClassProbabilityMax
+feature_step ClassProbabilityMean: ClassProbabilityMean
+feature_step ClassProbabilitySTD: ClassProbabilitySTD
 feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum
 feature_step SymbolsMin: SymbolsMin
 feature_step SymbolsMax: SymbolsMax
@@ -36,11 +42,12 @@ feature_step SkewnessMin: SkewnessMin
 feature_step SkewnessMax: SkewnessMax
 feature_step SkewnessMean: SkewnessMean
 feature_step SkewnessSTD: SkewnessSTD
-features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
+feature_step ClassEntropy: ClassEntropy
+features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy
 features_stochastic: 
-default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
+default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy
 
-algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127
+algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235,236,237,238,239,240,241,242,243,244,245,246,247,248,249,250,251,252,253,254,255,256,257,258,259,260,261,262,263,264,265,266,267,268,269,270,271,272,273,274,275,276,277,278,279,280,281,282,283,284,285,286,287,288,289,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,312,313,314,315,316,317,318,319,320,321,322,323,324,325,326,327,328,329,330,331,332,333,334,335,336,337,338,339,340,341,342,343,344,345,346,347,348,349,350,351,352,353,354,355,356,357,358,359,360,361,362,363,364,365,366,367,368,369,370,371,372,373,374,375,376,377,378,379,380,381,382,383,384,385,386,387,388,389,390,391,392,393,394,395,396,397,398,399,400,401,402,403,404,405,406,407,408,409,410,411,412,413,414,415,416,417,418,419,420,421,422,423,424,425,426,427,428,429,430,431,432,433,434,435,436,437,438,439,440,441,442,443,444,445,446,447,448,449,450,451,452,453,454,455,456,457,458,459,460,461,462,463,464
 algorithms_stochastic: 
 performance_measures: accuracy
 performance_type: solution_quality
diff --git a/autosklearn/metalearning/files/accuracy_multiclass.classification_dense/feature_costs.arff b/autosklearn/metalearning/files/accuracy_multiclass.classification_dense/feature_costs.arff
index 0e17e20d22..8512fd9275 100644
--- a/autosklearn/metalearning/files/accuracy_multiclass.classification_dense/feature_costs.arff
+++ b/autosklearn/metalearning/files/accuracy_multiclass.classification_dense/feature_costs.arff
@@ -4,6 +4,7 @@
 @ATTRIBUTE repetition NUMERIC
 @ATTRIBUTE NumberOfInstances NUMERIC
 @ATTRIBUTE LogNumberOfInstances NUMERIC
+@ATTRIBUTE NumberOfClasses NUMERIC
 @ATTRIBUTE NumberOfFeatures NUMERIC
 @ATTRIBUTE LogNumberOfFeatures NUMERIC
 @ATTRIBUTE MissingValues NUMERIC
@@ -21,6 +22,11 @@
 @ATTRIBUTE LogDatasetRatio NUMERIC
 @ATTRIBUTE InverseDatasetRatio NUMERIC
 @ATTRIBUTE LogInverseDatasetRatio NUMERIC
+@ATTRIBUTE ClassOccurences NUMERIC
+@ATTRIBUTE ClassProbabilityMin NUMERIC
+@ATTRIBUTE ClassProbabilityMax NUMERIC
+@ATTRIBUTE ClassProbabilityMean NUMERIC
+@ATTRIBUTE ClassProbabilitySTD NUMERIC
 @ATTRIBUTE NumSymbols NUMERIC
 @ATTRIBUTE SymbolsMin NUMERIC
 @ATTRIBUTE SymbolsMax NUMERIC
@@ -37,162 +43,138 @@
 @ATTRIBUTE SkewnessMax NUMERIC
 @ATTRIBUTE SkewnessMean NUMERIC
 @ATTRIBUTE SkewnessSTD NUMERIC
+@ATTRIBUTE ClassEntropy NUMERIC
 
 @DATA
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-75101,1.0,1e-05,1e-05,1e-05,1e-05,0.0418,0.03546,1e-05,0.00389,1e-05,0.00248,0.0,2e-05,2e-05,4e-05,6e-05,2e-05,1e-05,5e-05,2e-05,0.00045,1e-05,0.0,0.00011,0.00023,0.0001,0.00041,6e-05,8e-05,0.0001,0.00018,0.00047,6e-05,0.0001,8e-05,0.00023
-75191,1.0,1e-05,1e-05,3e-05,1e-05,0.53436,0.40777,2e-05,0.09655,3e-05,0.0301,1e-05,2e-05,3e-05,5e-05,0.00011,7e-05,1e-05,0.00011,3e-05,0.00084,5e-05,7e-05,0.00017,0.00033,0.00023,0.00088,0.00012,0.00017,0.00032,0.00026,0.00068,9e-05,0.00012,0.00013,0.00035
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+75240,1.0,2e-05,1e-05,0.00036,5e-05,4e-05,0.00845,0.00644,6e-05,0.00122,4e-05,0.00091,1e-05,4e-05,4e-05,9e-05,0.00013,5e-05,2e-05,9e-05,5e-05,0.00031,3e-05,1e-05,7e-05,0.0002,0.00073,3e-05,2e-05,0.00019,0.0003,0.00018,0.00055,9e-05,9e-05,0.00011,0.00025,0.0005,7e-05,8e-05,0.00015,0.0002,0.00365
+75243,1.0,1e-05,1e-05,0.00029,1e-05,1e-05,0.00461,0.00415,1e-05,0.00035,1e-05,0.00015,0.0,2e-05,2e-05,5e-05,7e-05,2e-05,1e-05,4e-05,1e-05,0.00018,2e-05,1e-05,4e-05,0.00011,0.00022,1e-05,1e-05,5e-05,0.00011,5e-05,0.0004,6e-05,6e-05,8e-05,0.0002,0.00039,6e-05,6e-05,8e-05,0.00019,0.00265
+75244,1.0,3e-05,2e-05,0.0004,3e-05,2e-05,0.0173,0.01298,4e-05,0.00267,4e-05,0.00174,1e-05,7e-05,9e-05,0.00024,0.00027,8e-05,2e-05,0.00013,3e-05,0.00045,7e-05,2e-05,0.00011,0.00025,0.00077,6e-05,2e-05,0.00021,0.00034,0.00013,0.00077,0.00014,0.00013,0.00026,0.00023,0.00044,7e-05,8e-05,0.0001,0.00019,0.00649
+75248,1.0,1e-05,1e-05,0.00017,1e-05,1e-05,0.00416,0.00305,1e-05,0.0006,2e-05,0.00054,1e-05,2e-05,2e-05,5e-05,7e-05,2e-05,1e-05,4e-05,1e-05,0.00017,2e-05,1e-05,4e-05,0.0001,0.00029,5e-05,1e-05,6e-05,0.00012,6e-05,0.00081,0.00011,0.00015,0.00016,0.00038,0.0005,7e-05,8e-05,0.00012,0.00024,0.00184
+75249,1.0,1e-05,1e-05,0.00011,1e-05,1e-05,0.00192,0.00153,1e-05,0.00025,1e-05,0.00017,1e-05,2e-05,2e-05,5e-05,7e-05,2e-05,1e-05,5e-05,3e-05,0.00014,2e-05,1e-05,3e-05,8e-05,0.00029,1e-05,3e-05,6e-05,0.00012,7e-05,0.00041,6e-05,8e-05,0.0001,0.00017,0.00043,6e-05,7e-05,9e-05,0.00019,0.00092
+75250,1.0,1e-05,1e-05,0.00375,1e-05,1e-05,0.04994,0.047,1e-05,0.00247,1e-05,0.00049,1e-05,2e-05,2e-05,6e-05,7e-05,2e-05,1e-05,3e-05,1e-05,0.00022,4e-05,1e-05,4e-05,0.00012,0.00041,0.0,1e-05,0.00011,0.00023,6e-05,0.00076,0.00012,0.00012,0.00019,0.00032,0.00086,0.00012,0.00013,0.00018,0.00043,0.04178
diff --git a/autosklearn/metalearning/files/accuracy_multiclass.classification_dense/feature_runstatus.arff b/autosklearn/metalearning/files/accuracy_multiclass.classification_dense/feature_runstatus.arff
index 0034ad2bd6..5fff2dfed3 100644
--- a/autosklearn/metalearning/files/accuracy_multiclass.classification_dense/feature_runstatus.arff
+++ b/autosklearn/metalearning/files/accuracy_multiclass.classification_dense/feature_runstatus.arff
@@ -4,6 +4,7 @@
 @ATTRIBUTE repetition NUMERIC
 @ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other}
@@ -21,6 +22,11 @@
 @ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other}
@@ -37,162 +43,138 @@
 @ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other}
 
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diff --git a/autosklearn/metalearning/files/accuracy_multiclass.classification_dense/feature_values.arff b/autosklearn/metalearning/files/accuracy_multiclass.classification_dense/feature_values.arff
index a60400e3ce..736dc64321 100644
--- a/autosklearn/metalearning/files/accuracy_multiclass.classification_dense/feature_values.arff
+++ b/autosklearn/metalearning/files/accuracy_multiclass.classification_dense/feature_values.arff
@@ -13,7 +13,6 @@
 @ATTRIBUTE KurtosisMean NUMERIC
 @ATTRIBUTE KurtosisMin NUMERIC
 @ATTRIBUTE KurtosisSTD NUMERIC
-@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC
 @ATTRIBUTE LogDatasetRatio NUMERIC
 @ATTRIBUTE LogInverseDatasetRatio NUMERIC
 @ATTRIBUTE LogNumberOfFeatures NUMERIC
@@ -42,160 +41,135 @@
 @ATTRIBUTE SymbolsSum NUMERIC
 
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diff --git a/autosklearn/metalearning/files/accuracy_multiclass.classification_sparse/algorithm_runs.arff b/autosklearn/metalearning/files/accuracy_multiclass.classification_sparse/algorithm_runs.arff
index 559ab420fb..218d3336a4 100644
--- a/autosklearn/metalearning/files/accuracy_multiclass.classification_sparse/algorithm_runs.arff
+++ b/autosklearn/metalearning/files/accuracy_multiclass.classification_sparse/algorithm_runs.arff
@@ -7,95 +7,133 @@
 @ATTRIBUTE runstatus {ok, timeout, memout, not_applicable, crash, other}
 
 @DATA
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diff --git a/autosklearn/metalearning/files/accuracy_multiclass.classification_sparse/configurations.csv b/autosklearn/metalearning/files/accuracy_multiclass.classification_sparse/configurations.csv
index 75662496d2..36fd991e13 100644
--- a/autosklearn/metalearning/files/accuracy_multiclass.classification_sparse/configurations.csv
+++ b/autosklearn/metalearning/files/accuracy_multiclass.classification_sparse/configurations.csv
@@ -1,93 +1,131 @@
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-89,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4424195769042466,False,True,hinge,0.02490432909709204,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,mean,quantile_transformer,1000,uniform,,,kernel_pca,,,,,,,,,,,,,,cosine,769,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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+278,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5878222472475889,None,0.0,6,15,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,robust_scaler,,,0.7419183699029103,0.22028507583137247,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.21256588660504683,fdr,chi2,
+282,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.002017899394165588,False,,0.0005911937660372945,True,0.17801822098174286,constant,log,elasticnet,,0.00041682529601986016,no_encoding,no_coalescense,,median,quantile_transformer,1335,normal,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0642825062494559,fpr,chi2,
+284,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5609113236355627,None,0.0,2,2,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.47917908217902894,mean,quantile_transformer,673,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+286,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,33.440072270881174,-0.22811742088286835,3,0.05010299776905627,poly,-1,False,2.275627187537573e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,normalize,,,,,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,2,None,5,14,1.0,52,,,,,,
+287,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.3850609070926397,None,0.0,1,2,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.013992309764275707,median,normalize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,,
+289,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.818514683854827,None,0.0,15,20,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.4928285185491301,most_frequent,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,,
+292,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.0479982468066316,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,quantile_transformer,1097,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+293,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7040496889211508,None,0.0,17,19,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.016450784709133564,mean,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+300,none,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.7010137052424128,None,0.0,7,10,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.2761813291805052,most_frequent,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,,
+306,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2677516008848513,True,True,hinge,0.05752557410901496,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0010152857217605206,median,quantile_transformer,1000,uniform,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,51.60974796845029,chi2,,,,
+307,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,33,None,,0.006687546229372618,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.06231343712708962,mean,none,,,,,kernel_pca,,,,,,,,,,,-0.7000777299811654,3,0.008780219231661734,poly,498,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+312,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,60.6761602925822,,,0.038224035513907226,rbf,-1,False,0.0018214343969776353,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.421025538999516,median,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+313,weighting,adaboost,SAMME,1.2423888874490505,5,284,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.01582563952987033,most_frequent,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+316,weighting,adaboost,SAMME,1.2615483379313783,8,190,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.00017789451703497617,mean,robust_scaler,,,0.7345811776810758,0.054715769043996304,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+317,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.4572679693563934,None,0.0,1,11,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,mean,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,,
+318,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.843098530645432,None,0.0,1,4,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,mean,quantile_transformer,930,normal,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,,
+321,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.5864398404787432,None,0.0,1,14,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0070663750850608895,most_frequent,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,,
+326,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0001329242404968632,False,True,squared_hinge,0.000416219546803132,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.007568009633214092,median,quantile_transformer,1126,normal,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,29.06367291960815,chi2,,,,
+329,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.6642356723981304,False,True,1,squared_hinge,ovr,l2,7.912577426155621e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.08464913801800422,most_frequent,normalize,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,53.35800942135187,chi2,,,,
+331,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.5,None,0.0,1,5,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.15244281596303316,mean,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+332,none,adaboost,SAMME,0.22805500898886877,8,172,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.019649593891454008,mean,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+334,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7516795829719571,None,0.0,14,7,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0010013681780844724,mean,none,,,,,extra_trees_preproc_for_classification,True,entropy,None,0.6123323873969666,None,0.0,17,7,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+339,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.36374112587320206,None,0.0,2,10,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010870084126959173,most_frequent,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,,
+343,none,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9062834897256731,None,0.0,20,9,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.005187379382274718,mean,quantile_transformer,349,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+345,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5321814610661173,None,0.0,1,3,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,standardize,,,,,extra_trees_preproc_for_classification,True,gini,None,0.5,None,0.0,1,15,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+350,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.44903177399085925,None,0.0,2,17,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,mean,robust_scaler,,,0.7812408516672696,0.12791233577400205,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,,
+352,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5988524108452234,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,mean,robust_scaler,,,0.75,0.26961884796358077,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+355,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,68.60833575985336,,,0.03905068757955671,rbf,-1,False,0.02846559747114773,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.04775815990561371,mean,standardize,,,,,extra_trees_preproc_for_classification,False,entropy,None,0.45727987956076366,None,0.0,13,11,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+357,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.3431705351883592,None,0.0,4,16,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.09444471061261189,most_frequent,normalize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+359,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.9282407375709691,None,0.0,16,3,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.015995805068365877,mean,standardize,,,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.10042232633120375,False,True,1,squared_hinge,ovr,l1,0.004451009526629745,,,,,,,,,,,,,,,,,,,,,
+367,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.938372431886818e-05,True,True,hinge,0.0016143408148969647,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00024949978971758427,median,quantile_transformer,754,uniform,,,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,-0.22339839702350717,2,0.5718982387915436,poly,5260,,,,,,,,,,,,,,,,
+371,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4,1,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.01020427226149318,median,none,,,,,extra_trees_preproc_for_classification,True,entropy,None,0.3663460889081009,None,0.0,2,7,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
diff --git a/autosklearn/metalearning/files/accuracy_multiclass.classification_sparse/description.txt b/autosklearn/metalearning/files/accuracy_multiclass.classification_sparse/description.txt
index 6f3582bc97..f38b7dedec 100644
--- a/autosklearn/metalearning/files/accuracy_multiclass.classification_sparse/description.txt
+++ b/autosklearn/metalearning/files/accuracy_multiclass.classification_sparse/description.txt
@@ -1,8 +1,9 @@
 features_cutoff_time: 3600
 features_cutoff_memory: 3072
-number_of_feature_steps: 35
+number_of_feature_steps: 42
 feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances
 feature_step LogNumberOfInstances: LogNumberOfInstances
+feature_step NumberOfClasses: NumberOfClasses
 feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures
 feature_step LogNumberOfFeatures: LogNumberOfFeatures
 feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues
@@ -20,6 +21,11 @@ feature_step DatasetRatio: DatasetRatio, LogDatasetRatio
 feature_step LogDatasetRatio: LogDatasetRatio
 feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio
 feature_step LogInverseDatasetRatio: LogInverseDatasetRatio
+feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD
+feature_step ClassProbabilityMin: ClassProbabilityMin
+feature_step ClassProbabilityMax: ClassProbabilityMax
+feature_step ClassProbabilityMean: ClassProbabilityMean
+feature_step ClassProbabilitySTD: ClassProbabilitySTD
 feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum
 feature_step SymbolsMin: SymbolsMin
 feature_step SymbolsMax: SymbolsMax
@@ -36,11 +42,12 @@ feature_step SkewnessMin: SkewnessMin
 feature_step SkewnessMax: SkewnessMax
 feature_step SkewnessMean: SkewnessMean
 feature_step SkewnessSTD: SkewnessSTD
-features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
+feature_step ClassEntropy: ClassEntropy
+features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy
 features_stochastic: 
-default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
+default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy
 
-algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92
+algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235,236,237,238,239,240,241,242,243,244,245,246,247,248,249,250,251,252,253,254,255,256,257,258,259,260,261,262,263,264,265,266,267,268,269,270,271,272,273,274,275,276,277,278,279,280,281,282,283,284,285,286,287,288,289,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,312,313,314,315,316,317,318,319,320,321,322,323,324,325,326,327,328,329,330,331,332,333,334,335,336,337,338,339,340,341,342,343,344,345,346,347,348,349,350,351,352,353,354,355,356,357,358,359,360,361,362,363,364,365,366,367,368,369,370,371,372
 algorithms_stochastic: 
 performance_measures: accuracy
 performance_type: solution_quality
diff --git a/autosklearn/metalearning/files/accuracy_multiclass.classification_sparse/feature_costs.arff b/autosklearn/metalearning/files/accuracy_multiclass.classification_sparse/feature_costs.arff
index 0e17e20d22..8512fd9275 100644
--- a/autosklearn/metalearning/files/accuracy_multiclass.classification_sparse/feature_costs.arff
+++ b/autosklearn/metalearning/files/accuracy_multiclass.classification_sparse/feature_costs.arff
@@ -4,6 +4,7 @@
 @ATTRIBUTE repetition NUMERIC
 @ATTRIBUTE NumberOfInstances NUMERIC
 @ATTRIBUTE LogNumberOfInstances NUMERIC
+@ATTRIBUTE NumberOfClasses NUMERIC
 @ATTRIBUTE NumberOfFeatures NUMERIC
 @ATTRIBUTE LogNumberOfFeatures NUMERIC
 @ATTRIBUTE MissingValues NUMERIC
@@ -21,6 +22,11 @@
 @ATTRIBUTE LogDatasetRatio NUMERIC
 @ATTRIBUTE InverseDatasetRatio NUMERIC
 @ATTRIBUTE LogInverseDatasetRatio NUMERIC
+@ATTRIBUTE ClassOccurences NUMERIC
+@ATTRIBUTE ClassProbabilityMin NUMERIC
+@ATTRIBUTE ClassProbabilityMax NUMERIC
+@ATTRIBUTE ClassProbabilityMean NUMERIC
+@ATTRIBUTE ClassProbabilitySTD NUMERIC
 @ATTRIBUTE NumSymbols NUMERIC
 @ATTRIBUTE SymbolsMin NUMERIC
 @ATTRIBUTE SymbolsMax NUMERIC
@@ -37,162 +43,138 @@
 @ATTRIBUTE SkewnessMax NUMERIC
 @ATTRIBUTE SkewnessMean NUMERIC
 @ATTRIBUTE SkewnessSTD NUMERIC
+@ATTRIBUTE ClassEntropy NUMERIC
 
 @DATA
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+75234,1.0,1e-05,1e-05,0.00015,1e-05,1e-05,0.0058,0.00534,1e-05,0.00033,1e-05,0.00016,0.0,2e-05,2e-05,7e-05,0.00204,3e-05,1e-05,5e-05,1e-05,0.00024,3e-05,2e-05,0.0001,9e-05,0.00043,0.0,0.0,0.00012,0.00024,6e-05,0.00156,6e-05,7e-05,0.00121,0.00021,0.00054,6e-05,7e-05,9e-05,0.00032,0.00501
+75235,1.0,1e-05,1e-05,0.0003,2e-05,1e-05,0.00461,0.00385,2e-05,0.00055,2e-05,0.00026,1e-05,3e-05,3e-05,0.0001,0.00011,3e-05,1e-05,5e-05,2e-05,0.00026,3e-05,1e-05,8e-05,0.00014,0.00075,1e-05,1e-05,0.0002,0.00043,0.00011,0.00127,0.00017,0.0002,0.00029,0.00061,0.0008,0.00015,0.00012,0.00016,0.00038,0.00242
+75236,1.0,1e-05,1e-05,0.0001,1e-05,1e-05,0.00218,0.00118,2e-05,0.00065,3e-05,0.00041,1e-05,3e-05,3e-05,9e-05,9e-05,4e-05,1e-05,7e-05,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.0006,1e-05,1e-05,0.00021,0.00032,6e-05,0.00043,7e-05,7e-05,9e-05,0.00019,0.00062,7e-05,8e-05,0.0001,0.00037,0.00048
+75237,1.0,2e-05,1e-05,0.00785,2e-05,1e-05,0.31031,0.27854,2e-05,0.01161,3e-05,0.02022,1e-05,4e-05,3e-05,9e-05,0.00024,6e-05,2e-05,9e-05,2e-05,0.00062,7e-05,3e-05,0.00017,0.00035,0.0008,1e-05,1e-05,0.00028,0.00043,8e-05,0.00043,8e-05,7e-05,9e-05,0.0002,0.00044,6e-05,8e-05,9e-05,0.00021,0.14028
+75239,1.0,1e-05,1e-05,7e-05,1e-05,1e-05,0.00105,0.00084,1e-05,0.00015,1e-05,8e-05,1e-05,2e-05,5e-05,4e-05,8e-05,2e-05,1e-05,3e-05,1e-05,0.00016,2e-05,1e-05,5e-05,9e-05,0.00049,0.0,0.0,0.00012,0.00026,0.00011,0.00046,0.00013,8e-05,8e-05,0.00018,0.00044,6e-05,9e-05,0.00011,0.00018,0.00065
+75240,1.0,2e-05,1e-05,0.00036,5e-05,4e-05,0.00845,0.00644,6e-05,0.00122,4e-05,0.00091,1e-05,4e-05,4e-05,9e-05,0.00013,5e-05,2e-05,9e-05,5e-05,0.00031,3e-05,1e-05,7e-05,0.0002,0.00073,3e-05,2e-05,0.00019,0.0003,0.00018,0.00055,9e-05,9e-05,0.00011,0.00025,0.0005,7e-05,8e-05,0.00015,0.0002,0.00365
+75243,1.0,1e-05,1e-05,0.00029,1e-05,1e-05,0.00461,0.00415,1e-05,0.00035,1e-05,0.00015,0.0,2e-05,2e-05,5e-05,7e-05,2e-05,1e-05,4e-05,1e-05,0.00018,2e-05,1e-05,4e-05,0.00011,0.00022,1e-05,1e-05,5e-05,0.00011,5e-05,0.0004,6e-05,6e-05,8e-05,0.0002,0.00039,6e-05,6e-05,8e-05,0.00019,0.00265
+75244,1.0,3e-05,2e-05,0.0004,3e-05,2e-05,0.0173,0.01298,4e-05,0.00267,4e-05,0.00174,1e-05,7e-05,9e-05,0.00024,0.00027,8e-05,2e-05,0.00013,3e-05,0.00045,7e-05,2e-05,0.00011,0.00025,0.00077,6e-05,2e-05,0.00021,0.00034,0.00013,0.00077,0.00014,0.00013,0.00026,0.00023,0.00044,7e-05,8e-05,0.0001,0.00019,0.00649
+75248,1.0,1e-05,1e-05,0.00017,1e-05,1e-05,0.00416,0.00305,1e-05,0.0006,2e-05,0.00054,1e-05,2e-05,2e-05,5e-05,7e-05,2e-05,1e-05,4e-05,1e-05,0.00017,2e-05,1e-05,4e-05,0.0001,0.00029,5e-05,1e-05,6e-05,0.00012,6e-05,0.00081,0.00011,0.00015,0.00016,0.00038,0.0005,7e-05,8e-05,0.00012,0.00024,0.00184
+75249,1.0,1e-05,1e-05,0.00011,1e-05,1e-05,0.00192,0.00153,1e-05,0.00025,1e-05,0.00017,1e-05,2e-05,2e-05,5e-05,7e-05,2e-05,1e-05,5e-05,3e-05,0.00014,2e-05,1e-05,3e-05,8e-05,0.00029,1e-05,3e-05,6e-05,0.00012,7e-05,0.00041,6e-05,8e-05,0.0001,0.00017,0.00043,6e-05,7e-05,9e-05,0.00019,0.00092
+75250,1.0,1e-05,1e-05,0.00375,1e-05,1e-05,0.04994,0.047,1e-05,0.00247,1e-05,0.00049,1e-05,2e-05,2e-05,6e-05,7e-05,2e-05,1e-05,3e-05,1e-05,0.00022,4e-05,1e-05,4e-05,0.00012,0.00041,0.0,1e-05,0.00011,0.00023,6e-05,0.00076,0.00012,0.00012,0.00019,0.00032,0.00086,0.00012,0.00013,0.00018,0.00043,0.04178
diff --git a/autosklearn/metalearning/files/accuracy_multiclass.classification_sparse/feature_runstatus.arff b/autosklearn/metalearning/files/accuracy_multiclass.classification_sparse/feature_runstatus.arff
index 0034ad2bd6..5fff2dfed3 100644
--- a/autosklearn/metalearning/files/accuracy_multiclass.classification_sparse/feature_runstatus.arff
+++ b/autosklearn/metalearning/files/accuracy_multiclass.classification_sparse/feature_runstatus.arff
@@ -4,6 +4,7 @@
 @ATTRIBUTE repetition NUMERIC
 @ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other}
@@ -21,6 +22,11 @@
 @ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other}
@@ -37,162 +43,138 @@
 @ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other}
 
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diff --git a/autosklearn/metalearning/files/accuracy_multiclass.classification_sparse/feature_values.arff b/autosklearn/metalearning/files/accuracy_multiclass.classification_sparse/feature_values.arff
index a60400e3ce..736dc64321 100644
--- a/autosklearn/metalearning/files/accuracy_multiclass.classification_sparse/feature_values.arff
+++ b/autosklearn/metalearning/files/accuracy_multiclass.classification_sparse/feature_values.arff
@@ -13,7 +13,6 @@
 @ATTRIBUTE KurtosisMean NUMERIC
 @ATTRIBUTE KurtosisMin NUMERIC
 @ATTRIBUTE KurtosisSTD NUMERIC
-@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC
 @ATTRIBUTE LogDatasetRatio NUMERIC
 @ATTRIBUTE LogInverseDatasetRatio NUMERIC
 @ATTRIBUTE LogNumberOfFeatures NUMERIC
@@ -42,160 +41,135 @@
 @ATTRIBUTE SymbolsSum NUMERIC
 
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+75230,1.0,6.61834612757962,0.014925373134328358,0.01,0.005597014925373134,0.0018660447388134309,0.05970149253731343,16.75,8.500021934509277,3.735363759100437,0.01982259750366211,2.361585237546769,-2.8183982582710754,2.8183982582710754,4.1588830833596715,6.977281341630747,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.0,0.0,0.0,0.0,0.0,2.3237264156341553,1.4173028748482466,0.3571271300315857,0.6533592091248933,0.0,0.0,0.0,0.0,0.0
+75231,1.0,6.620420668885037,0.013059701492537313,0.01,0.005597014925373134,0.0017703248006382775,0.05970149253731343,16.75,115.78911590576172,15.754332467913628,0.9003109931945801,18.061812803398542,-2.8183982582710754,2.8183982582710754,4.1588830833596715,6.977281341630747,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.0,0.0,0.0,0.0,0.0,8.885228157043457,3.1392695950344205,0.9206903576850891,1.4797961507751884,0.0,0.0,0.0,0.0,0.0
+75232,1.0,0.9201702027239149,0.6647807637906648,0.5,0.3352192362093352,0.16478076379066478,0.05799151343705799,17.24390243902439,585.4510498046875,41.48030315044416,-0.2529125213623047,95.17568446650067,-2.847458599192265,2.8474585991922647,3.713572066704308,6.561030665896573,0.0,2.0,41.0,0.0,707.0,0.0,0.0,41.0,0.0,0.0,0.0,0.0,0.0,23.23834228515625,3.832647103316537,-1.5003634691238403,4.508387764393873,0.0,0.0,0.0,0.0,0.0
+75233,1.0,0.8824680010722367,0.6990344325013663,0.5,0.3009655674986336,0.19903443250136635,0.0038258334851521223,261.3809523809524,619.9618265719739,76.9042121753925,1.1050496101379395,139.01256776590665,-5.565978930826466,5.565978930826466,3.044522437723423,8.610501368549889,0.0,2.0,21.0,0.0,5489.0,0.0,0.0,21.0,0.0,0.0,0.0,0.0,0.0,21.77604866027832,5.3457508484522505,-0.7721167802810669,4.832918145056104,0.0,0.0,0.0,0.0,0.0
+75234,1.0,0.9998393017810485,0.5074626865671642,0.5,0.4925373134328358,0.007462686567164201,0.004033884630899556,247.9,1.9309582710266113,1.5332300662994385,1.3253521919250488,0.13647900129618118,-5.513025439041145,5.513025439041145,2.995732273553991,8.508757712595136,0.0,2.0,20.0,0.0,4958.0,0.0,0.0,20.0,0.0,0.0,0.0,0.0,0.0,-0.1076231524348259,-0.23315090499818325,-0.33683285117149353,0.052567253463639066,0.0,0.0,0.0,0.0,0.0
+75235,1.0,1.7067919449664923,0.4105579868708972,0.25,0.05579868708971553,0.1492903085996621,0.006564551422319475,152.33333333333334,13.76956558227539,2.346758103856697,-1.6145207422400454,4.524616106763882,-5.026071102226095,5.026071102226095,3.1780538303479458,8.204124932574041,0.0,4.0,24.0,0.0,3656.0,0.0,0.0,24.0,0.0,0.0,0.0,0.0,0.0,3.779214382171631,1.515954251983203,0.004355928860604763,1.0657977665170062,0.0,0.0,0.0,0.0,0.0
+75236,1.0,3.3208751934243548,0.10486891385767791,0.1,0.09363295880149813,0.003810297743328803,0.2397003745318352,4.171875,22.422517776489258,-0.9225467168815645,-1.9997756481170654,1.9352856443240884,-1.4283655750405777,1.4283655750405777,5.545177444479562,6.97354301952014,0.0,10.0,256.0,0.0,1068.0,0.0,0.0,256.0,0.0,0.0,0.0,0.0,0.0,4.94191312789917,0.8186543448100565,-0.6194983720779419,0.6381679017395948,0.0,0.0,0.0,0.0,0.0
+75237,1.0,0.7378736147990802,0.7918800894091566,0.5,0.20811991059084348,0.29188008940915655,1.8271625991997027e-05,54729.666666666664,-0.5975548261535093,-0.7926032911766772,-0.9140236377716064,0.13929334047064063,-10.910161193614513,10.910161193614513,1.0986122886681098,12.008773482282624,0.0,2.0,3.0,0.0,164189.0,0.0,0.0,3.0,0.0,0.0,0.0,0.0,0.0,-0.039879728108644485,-0.3348021370669206,-0.6223916411399841,0.2378660138627058,0.0,0.0,0.0,0.0,0.0
+75239,1.0,0.9443547030267275,0.6379707916986933,0.5,0.3620292083013067,0.1379707916986933,0.02536510376633359,39.42424242424242,30.96304702758789,5.229958094222913,-1.8830041885375977,9.159290860687747,-3.674380917046025,3.674380917046025,3.4965075614664802,7.170888478512505,0.0,2.0,33.0,0.0,1301.0,0.0,0.0,33.0,0.0,0.0,0.0,0.0,0.0,5.591684341430664,1.5151810998266393,-0.8763390183448792,1.68213778251707,0.0,0.0,0.0,0.0,0.0
+75240,1.0,0.9473321982231081,0.6342759940463534,0.5,0.3657240059536466,0.13427599404635338,0.01275781416117372,78.38333333333334,1911.103482572295,38.512800597597035,-3.0,145.76440092342054,-4.361611319722948,4.361611319722948,4.0943445622221,8.455955881945048,27.0,2.0,60.0,17.0,4703.0,4703.0,29447.0,33.0,0.2833333333333333,1.0,0.10435537600113402,0.8181818181818182,1.2222222222222223,43.144344696960424,3.913912678196276,-22.79380338813253,4.943976022970944,173.0,22.59259259259259,1.0,43.77997111758117,610.0
+75243,1.0,1.7118844784475225,0.33529140750979036,0.25,0.024648698456576825,0.13045500689907696,0.0009214466712739,1085.25,0.37898291200086787,-0.9656219973073414,-1.9999312246360998,0.7132002663612025,-6.989565654179703,6.989565654179703,2.0794415416798357,9.06900719585954,8.0,4.0,8.0,0.0,8682.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.5423951867147632,0.9342552225820131,-0.008293091335592482,0.40192683634075954,5.0,3.375,2.0,0.8569568250501305,27.0
+75244,1.0,0.33901826511567296,0.9370570367870401,0.5,0.06294296321295983,0.43705703678704017,0.009449881876476544,105.82142857142857,1180.200844451951,40.51648623113333,-3.0,132.43484378998235,-4.661753037398361,4.661753037398361,4.02535169073515,8.68710472813351,41.0,2.0,56.0,14.0,5926.0,5926.0,23694.0,15.0,0.25,1.0,0.0713984378766694,2.7333333333333334,0.36585365853658536,34.38314768097812,3.7769009011075547,-8.659484722748674,5.307279668054912,16.0,5.975609756097561,1.0,3.692363938923924,245.0
+75248,1.0,0.4971128374263667,0.8909272183449651,0.5,0.1090727816550349,0.39092721834496513,0.010967098703888335,91.18181818181819,5010.000199441565,104.23108191482348,-3.0,468.735807382562,-4.512855515163565,4.512855515163565,4.007333185232471,8.520188700396035,39.0,2.0,55.0,12.0,5015.0,4938.0,21515.0,16.0,0.21818181818181817,0.9846460618145564,0.07800235656666364,2.4375,0.41025641025641024,70.79548149028699,4.06998328231248,-50.04498375424548,9.467860234489164,14.0,5.769230769230769,1.0,3.407845687351005,225.0
+75249,1.0,0.3891675492147647,0.9236550632911392,0.5,0.07634493670886076,0.4236550632911392,0.011471518987341773,87.17241379310344,416.3357123975682,46.57141260912883,-3.0,84.69777944086748,-4.467887925280274,4.467887925280274,3.367295829986474,7.8351837552667485,22.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,7.0,0.2413793103448276,1.0,0.056048123090353556,3.142857142857143,0.3181818181818182,20.453256767506936,0.8884855226401125,-20.453256767506936,6.7466910388456185,5.0,2.0454545454545454,1.0,0.7056443043754556,45.0
+75250,1.0,3.992354626842552,0.146342438507591,0.04545454545454545,0.005796927628356971,0.03949259311161838,3.997881123004808e-05,25013.25,2.1476065760596077,1.4719570787187288,0.3045189380645752,0.6973345081454226,-10.127160963449944,10.127160963449944,1.3862943611198906,11.513455324569835,0.0,22.0,4.0,0.0,100053.0,0.0,0.0,4.0,0.0,0.0,0.0,0.0,0.0,1.2135857343673706,0.3996680751442909,-0.3896799087524414,0.7616029272290122,0.0,0.0,0.0,0.0,0.0
diff --git a/autosklearn/metalearning/files/average_precision_binary.classification_dense/algorithm_runs.arff b/autosklearn/metalearning/files/average_precision_binary.classification_dense/algorithm_runs.arff
index 0bf786ac75..918f05b453 100644
--- a/autosklearn/metalearning/files/average_precision_binary.classification_dense/algorithm_runs.arff
+++ b/autosklearn/metalearning/files/average_precision_binary.classification_dense/algorithm_runs.arff
@@ -7,130 +7,135 @@
 @ATTRIBUTE runstatus {ok, timeout, memout, not_applicable, crash, other}
 
 @DATA
-75119,1.0,1,0.004231944238182073,ok
-75212,1.0,2,0.1521360793759462,ok
-252,1.0,3,?,not_applicable
-246,1.0,4,?,not_applicable
-75178,1.0,5,?,not_applicable
-75239,1.0,6,0.0,ok
-75171,1.0,7,0.08666662514386947,ok
-75233,1.0,8,0.008129429494274976,ok
-248,1.0,9,?,not_applicable
-75231,1.0,10,?,not_applicable
-2123,1.0,11,?,not_applicable
-75196,1.0,12,0.0002747755999267243,ok
-75188,1.0,13,?,not_applicable
-75092,1.0,14,0.35563709986626435,ok
-75248,1.0,15,0.6810602093884777,ok
-75126,1.0,16,0.003701142785307976,ok
-75234,1.0,17,0.0025447045930394596,ok
-75150,1.0,18,0.21720753099838463,ok
-258,1.0,19,?,not_applicable
-75168,1.0,20,?,not_applicable
-75235,1.0,21,?,not_applicable
-75159,1.0,22,0.5473461247136038,ok
-244,1.0,23,?,not_applicable
-75221,1.0,24,?,not_applicable
-75219,1.0,25,0.006712691671677318,ok
-75202,1.0,26,?,not_applicable
-3043,1.0,27,0.05506375879821879,ok
-75205,1.0,28,?,not_applicable
-75174,1.0,29,0.10531801123409645,ok
-288,1.0,30,?,not_applicable
-75250,1.0,31,?,not_applicable
-275,1.0,32,?,not_applicable
-75142,1.0,33,0.014381056931335068,ok
-75213,1.0,34,0.041936349169275466,ok
-75099,1.0,35,0.4678374883252291,ok
-75243,1.0,36,?,not_applicable
-75184,1.0,37,0.07469764361674125,ok
-75222,1.0,38,0.21559326045474714,ok
-75175,1.0,39,0.044140533128435244,ok
-233,1.0,40,5.842925670007659e-05,ok
-75161,1.0,41,0.011037903669269156,ok
-75176,1.0,42,0.0006938651513165306,ok
-75090,1.0,43,?,not_applicable
-75114,1.0,44,0.004350183875990177,ok
-260,1.0,45,?,not_applicable
-236,1.0,46,?,not_applicable
-75141,1.0,47,0.011628188830952624,ok
-75107,1.0,48,0.4583775449502874,ok
-262,1.0,49,?,not_applicable
-75146,1.0,50,0.027314797507292266,ok
-75189,1.0,51,0.0014471736348927733,ok
-2350,1.0,52,0.5707237410842824,ok
-253,1.0,53,?,not_applicable
-2122,1.0,54,?,not_applicable
-75110,1.0,55,?,not_applicable
-75249,1.0,56,0.0029122637768774773,ok
-75108,1.0,57,0.0,ok
-242,1.0,58,?,not_applicable
-75117,1.0,59,0.008280862635277386,ok
-75191,1.0,60,0.057807890742150425,ok
-75226,1.0,61,6.481298084892728e-07,ok
-261,1.0,62,0.34655969840600265,ok
-75236,1.0,63,?,not_applicable
-75095,1.0,64,0.05557088536188293,ok
-75148,1.0,65,0.05374729572069947,ok
-75093,1.0,66,0.5728463360258527,ok
-75223,1.0,67,?,not_applicable
-75244,1.0,68,0.6590615414961503,ok
-75109,1.0,69,?,not_applicable
-75197,1.0,70,?,not_applicable
-75127,1.0,71,0.3152529081908554,ok
-75143,1.0,72,0.0028502200148861068,ok
-75153,1.0,73,0.01920221368233055,ok
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-75215,1.0,75,0.002629240748430073,ok
-75195,1.0,76,0.0,ok
-75207,1.0,77,?,not_applicable
-266,1.0,78,?,not_applicable
-75225,1.0,79,0.5209808779102566,ok
-75166,1.0,80,0.03269537102137032,ok
-75100,1.0,81,0.8342571973375668,ok
-75169,1.0,82,?,not_applicable
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-273,1.0,84,0.014785628938213002,ok
-75121,1.0,85,0.0,ok
-75098,1.0,86,?,not_applicable
-75115,1.0,87,0.011733935395969564,ok
-75116,1.0,88,0.0008296925826821733,ok
-75185,1.0,89,0.052504213743351236,ok
-2119,1.0,90,?,not_applicable
-75157,1.0,91,0.4866314915324267,ok
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-75134,1.0,93,?,not_applicable
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-75182,1.0,96,0.09984152302439242,ok
-251,1.0,97,?,not_applicable
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-75237,1.0,99,8.19202841673139e-07,ok
-75125,1.0,100,0.008449900024096846,ok
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-75230,1.0,104,?,not_applicable
-75139,1.0,105,0.0011780620103367667,ok
-75227,1.0,106,0.0870759275296118,ok
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-75124,1.0,108,0.4676991332383039,ok
-75240,1.0,109,0.021029116580781215,ok
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-75112,1.0,113,0.08377189935980578,ok
-75133,1.0,114,0.46574825307214884,ok
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-75154,1.0,116,?,not_applicable
-75177,1.0,117,0.04366392008249165,ok
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-75172,1.0,122,?,not_applicable
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-75187,1.0,125,0.0012821066451785823,ok
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-75193,1.0,127,?,not_applicable
+75192,1.0,1,0.4878671463781179,ok
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+246,1.0,8,?,not_applicable
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+75177,1.0,13,0.010583210410810229,ok
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+75239,1.0,15,0.0,ok
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+75171,1.0,21,0.08332235273571365,ok
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+75182,1.0,138,0.09558610812790125,ok
+253,1.0,27,?,not_applicable
+75157,1.0,29,0.4866314915324267,ok
+75187,1.0,267,0.0012256656184541637,ok
+75124,1.0,31,0.4642760381864932,ok
+75090,1.0,32,?,not_applicable
+75222,1.0,285,0.18411818014888381,ok
+75231,1.0,34,?,not_applicable
+75185,1.0,37,0.051632868284900324,ok
+2123,1.0,39,?,not_applicable
+75150,1.0,234,0.20737529848879477,ok
+75143,1.0,43,0.0003380689531951031,ok
+75196,1.0,46,0.0002747755999267243,ok
+75188,1.0,49,?,not_applicable
+75248,1.0,54,0.6619346271836501,ok
+75249,1.0,204,0.002653934516532064,ok
+75113,1.0,59,0.006215426914405242,ok
+75126,1.0,63,0.002676573380457681,ok
+288,1.0,64,?,not_applicable
+251,1.0,65,?,not_applicable
+75184,1.0,156,0.07476766843004146,ok
+75234,1.0,68,0.0025570500491878256,ok
+258,1.0,73,?,not_applicable
+75166,1.0,281,0.02779561860055213,ok
+75168,1.0,77,?,not_applicable
+75148,1.0,276,0.0560527025009423,ok
+75235,1.0,81,?,not_applicable
+75159,1.0,85,0.42859893550912664,ok
+75146,1.0,88,0.026973436894305536,ok
+244,1.0,89,?,not_applicable
+75141,1.0,163,0.01173804467069206,ok
+75221,1.0,92,?,not_applicable
+75219,1.0,101,0.003957289838288358,ok
+75202,1.0,96,?,not_applicable
+3043,1.0,99,0.010220794182362214,ok
+75205,1.0,103,?,not_applicable
+75174,1.0,106,0.09374742289809834,ok
+75250,1.0,111,?,not_applicable
+75179,1.0,238,0.21841087762761613,ok
+275,1.0,115,?,not_applicable
+242,1.0,116,?,not_applicable
+75207,1.0,117,?,not_applicable
+75142,1.0,118,0.014259133477716013,ok
+75099,1.0,171,0.4137854912995014,ok
+75243,1.0,126,?,not_applicable
+75175,1.0,253,0.04341182208035166,ok
+233,1.0,137,8.366996616537836e-05,ok
+75161,1.0,139,0.010310568908566298,ok
+75176,1.0,143,0.0006257932468407557,ok
+262,1.0,146,?,not_applicable
+75129,1.0,402,0.5738839791179444,ok
+261,1.0,221,0.3342676421661406,ok
+75114,1.0,152,0.0038194827767713546,ok
+75093,1.0,230,0.542497148196514,ok
+260,1.0,158,?,not_applicable
+236,1.0,160,?,not_applicable
+254,1.0,166,0.0,ok
+75107,1.0,167,0.44862438395455684,ok
+75181,1.0,175,?,not_applicable
+75189,1.0,186,0.001708447332475549,ok
+75163,1.0,189,0.02685795440651706,ok
+2350,1.0,191,0.5668173788275173,ok
+2122,1.0,195,?,not_applicable
+75110,1.0,198,?,not_applicable
+75213,1.0,394,0.033809555590903706,ok
+75095,1.0,284,0.05533956012799779,ok
+75108,1.0,207,0.0,ok
+75117,1.0,211,0.005428243341756178,ok
+75191,1.0,218,0.05749081881675733,ok
+75226,1.0,219,1.4184231685643311e-05,ok
+75244,1.0,372,0.6238946964248455,ok
+75236,1.0,223,?,not_applicable
+75169,1.0,227,?,not_applicable
+75116,1.0,319,0.0005163333961275551,ok
+75223,1.0,235,?,not_applicable
+75109,1.0,242,?,not_applicable
+75197,1.0,245,?,not_applicable
+75237,1.0,364,1.0920271134029491e-06,ok
+248,1.0,250,?,not_applicable
+2119,1.0,254,?,not_applicable
+75127,1.0,255,0.3143148012874739,ok
+75153,1.0,263,0.0211371390556796,ok
+75195,1.0,271,1.56543381590879e-08,ok
+266,1.0,277,?,not_applicable
+75225,1.0,280,0.5115141783633365,ok
+75100,1.0,288,0.8230078173288692,ok
+75132,1.0,294,0.850465089313205,ok
+75210,1.0,298,0.0,ok
+273,1.0,301,0.014844523360765693,ok
+75133,1.0,413,0.4929884165869626,ok
+75121,1.0,304,0.0,ok
+75098,1.0,310,?,not_applicable
+75115,1.0,315,0.002899511502766572,ok
+75217,1.0,337,?,not_applicable
+75134,1.0,341,?,not_applicable
+75096,1.0,346,?,not_applicable
+75203,1.0,350,?,not_applicable
+75123,1.0,360,?,not_applicable
+75125,1.0,369,0.005869457471176065,ok
+2120,1.0,371,?,not_applicable
+75232,1.0,374,0.11110189077492749,ok
+75103,1.0,379,0.02047713329298051,ok
+75230,1.0,386,?,not_applicable
+75240,1.0,399,0.017915489066529955,ok
+75198,1.0,404,?,not_applicable
+75201,1.0,407,?,not_applicable
+75105,1.0,415,0.8760240593052668,ok
+75154,1.0,420,?,not_applicable
+2117,1.0,429,0.02913958847349507,ok
+75156,1.0,430,0.11592045149703145,ok
+75097,1.0,434,0.014519472944802159,ok
+75101,1.0,438,0.17587098260204748,ok
+75172,1.0,443,?,not_applicable
+75106,1.0,449,0.7856870980692269,ok
+75120,1.0,456,0.002216917908264948,ok
+75193,1.0,461,?,not_applicable
diff --git a/autosklearn/metalearning/files/average_precision_binary.classification_dense/configurations.csv b/autosklearn/metalearning/files/average_precision_binary.classification_dense/configurations.csv
index 5644caa737..cc2f52cdc5 100644
--- a/autosklearn/metalearning/files/average_precision_binary.classification_dense/configurations.csv
+++ b/autosklearn/metalearning/files/average_precision_binary.classification_dense/configurations.csv
@@ -1,128 +1,133 @@
 idx,balancing:strategy,classifier:__choice__,classifier:adaboost:algorithm,classifier:adaboost:learning_rate,classifier:adaboost:max_depth,classifier:adaboost:n_estimators,classifier:bernoulli_nb:alpha,classifier:bernoulli_nb:fit_prior,classifier:decision_tree:criterion,classifier:decision_tree:max_depth_factor,classifier:decision_tree:max_features,classifier:decision_tree:max_leaf_nodes,classifier:decision_tree:min_impurity_decrease,classifier:decision_tree:min_samples_leaf,classifier:decision_tree:min_samples_split,classifier:decision_tree:min_weight_fraction_leaf,classifier:extra_trees:bootstrap,classifier:extra_trees:criterion,classifier:extra_trees:max_depth,classifier:extra_trees:max_features,classifier:extra_trees:max_leaf_nodes,classifier:extra_trees:min_impurity_decrease,classifier:extra_trees:min_samples_leaf,classifier:extra_trees:min_samples_split,classifier:extra_trees:min_weight_fraction_leaf,classifier:gradient_boosting:early_stop,classifier:gradient_boosting:l2_regularization,classifier:gradient_boosting:learning_rate,classifier:gradient_boosting:loss,classifier:gradient_boosting:max_bins,classifier:gradient_boosting:max_depth,classifier:gradient_boosting:max_leaf_nodes,classifier:gradient_boosting:min_samples_leaf,classifier:gradient_boosting:n_iter_no_change,classifier:gradient_boosting:scoring,classifier:gradient_boosting:tol,classifier:gradient_boosting:validation_fraction,classifier:k_nearest_neighbors:n_neighbors,classifier:k_nearest_neighbors:p,classifier:k_nearest_neighbors:weights,classifier:lda:n_components,classifier:lda:shrinkage,classifier:lda:shrinkage_factor,classifier:lda:tol,classifier:liblinear_svc:C,classifier:liblinear_svc:dual,classifier:liblinear_svc:fit_intercept,classifier:liblinear_svc:intercept_scaling,classifier:liblinear_svc:loss,classifier:liblinear_svc:multi_class,classifier:liblinear_svc:penalty,classifier:liblinear_svc:tol,classifier:libsvm_svc:C,classifier:libsvm_svc:coef0,classifier:libsvm_svc:degree,classifier:libsvm_svc:gamma,classifier:libsvm_svc:kernel,classifier:libsvm_svc:max_iter,classifier:libsvm_svc:shrinking,classifier:libsvm_svc:tol,classifier:multinomial_nb:alpha,classifier:multinomial_nb:fit_prior,classifier:passive_aggressive:C,classifier:passive_aggressive:average,classifier:passive_aggressive:fit_intercept,classifier:passive_aggressive:loss,classifier:passive_aggressive:tol,classifier:qda:reg_param,classifier:random_forest:bootstrap,classifier:random_forest:criterion,classifier:random_forest:max_depth,classifier:random_forest:max_features,classifier:random_forest:max_leaf_nodes,classifier:random_forest:min_impurity_decrease,classifier:random_forest:min_samples_leaf,classifier:random_forest:min_samples_split,classifier:random_forest:min_weight_fraction_leaf,classifier:sgd:alpha,classifier:sgd:average,classifier:sgd:epsilon,classifier:sgd:eta0,classifier:sgd:fit_intercept,classifier:sgd:l1_ratio,classifier:sgd:learning_rate,classifier:sgd:loss,classifier:sgd:penalty,classifier:sgd:power_t,classifier:sgd:tol,data_preprocessing:categorical_transformer:categorical_encoding:__choice__,data_preprocessing:categorical_transformer:category_coalescence:__choice__,data_preprocessing:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction,data_preprocessing:numerical_transformer:imputation:strategy,data_preprocessing:numerical_transformer:rescaling:__choice__,data_preprocessing:numerical_transformer:rescaling:quantile_transformer:n_quantiles,data_preprocessing:numerical_transformer:rescaling:quantile_transformer:output_distribution,data_preprocessing:numerical_transformer:rescaling:robust_scaler:q_max,data_preprocessing:numerical_transformer:rescaling:robust_scaler:q_min,feature_preprocessor:__choice__,feature_preprocessor:extra_trees_preproc_for_classification:bootstrap,feature_preprocessor:extra_trees_preproc_for_classification:criterion,feature_preprocessor:extra_trees_preproc_for_classification:max_depth,feature_preprocessor:extra_trees_preproc_for_classification:max_features,feature_preprocessor:extra_trees_preproc_for_classification:max_leaf_nodes,feature_preprocessor:extra_trees_preproc_for_classification:min_impurity_decrease,feature_preprocessor:extra_trees_preproc_for_classification:min_samples_leaf,feature_preprocessor:extra_trees_preproc_for_classification:min_samples_split,feature_preprocessor:extra_trees_preproc_for_classification:min_weight_fraction_leaf,feature_preprocessor:extra_trees_preproc_for_classification:n_estimators,feature_preprocessor:fast_ica:algorithm,feature_preprocessor:fast_ica:fun,feature_preprocessor:fast_ica:n_components,feature_preprocessor:fast_ica:whiten,feature_preprocessor:feature_agglomeration:affinity,feature_preprocessor:feature_agglomeration:linkage,feature_preprocessor:feature_agglomeration:n_clusters,feature_preprocessor:feature_agglomeration:pooling_func,feature_preprocessor:kernel_pca:coef0,feature_preprocessor:kernel_pca:degree,feature_preprocessor:kernel_pca:gamma,feature_preprocessor:kernel_pca:kernel,feature_preprocessor:kernel_pca:n_components,feature_preprocessor:kitchen_sinks:gamma,feature_preprocessor:kitchen_sinks:n_components,feature_preprocessor:liblinear_svc_preprocessor:C,feature_preprocessor:liblinear_svc_preprocessor:dual,feature_preprocessor:liblinear_svc_preprocessor:fit_intercept,feature_preprocessor:liblinear_svc_preprocessor:intercept_scaling,feature_preprocessor:liblinear_svc_preprocessor:loss,feature_preprocessor:liblinear_svc_preprocessor:multi_class,feature_preprocessor:liblinear_svc_preprocessor:penalty,feature_preprocessor:liblinear_svc_preprocessor:tol,feature_preprocessor:nystroem_sampler:coef0,feature_preprocessor:nystroem_sampler:degree,feature_preprocessor:nystroem_sampler:gamma,feature_preprocessor:nystroem_sampler:kernel,feature_preprocessor:nystroem_sampler:n_components,feature_preprocessor:pca:keep_variance,feature_preprocessor:pca:whiten,feature_preprocessor:polynomial:degree,feature_preprocessor:polynomial:include_bias,feature_preprocessor:polynomial:interaction_only,feature_preprocessor:random_trees_embedding:bootstrap,feature_preprocessor:random_trees_embedding:max_depth,feature_preprocessor:random_trees_embedding:max_leaf_nodes,feature_preprocessor:random_trees_embedding:min_samples_leaf,feature_preprocessor:random_trees_embedding:min_samples_split,feature_preprocessor:random_trees_embedding:min_weight_fraction_leaf,feature_preprocessor:random_trees_embedding:n_estimators,feature_preprocessor:select_percentile_classification:percentile,feature_preprocessor:select_percentile_classification:score_func,feature_preprocessor:select_rates_classification:alpha,feature_preprocessor:select_rates_classification:mode,feature_preprocessor:select_rates_classification:score_func
-1,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,154.65010711000676,-0.7535496516458353,2,4.566684304178357,poly,-1,False,1.895280127876398e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,minmax,,,,,extra_trees_preproc_for_classification,True,gini,None,0.698818742714691,None,0.0,20,12,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-2,none,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.9525269450493825,None,0.0,4,20,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,standardize,,,,,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-3,none,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,188,None,,0.09976330267940166,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,none,,,,,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,-0.05439674489937307,2,0.051865468486041774,poly,727,,,,,,,,,,,,,,,,,
-4,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,23916.78290516781,False,True,1,squared_hinge,ovr,l2,0.0006655607726658531,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.008808715242823137,median,none,,,,,kernel_pca,,,,,,,,,,,,,,,,,,,0.2633153190620261,2,0.004770332714291669,poly,1483,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-5,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,2,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010091246322497477,median,none,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,95.37976833605768,mutual_info,,,
-6,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.933900673392165,None,0.0,7,4,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,minmax,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-7,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,0.9455840414651977,0.035051597802663065,auto,255,None,6,39,20,loss,1e-07,0.03762410007700571,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,robust_scaler,,,0.8083718526277422,0.24604827304064597,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,1377.6677844231401,False,True,1,squared_hinge,ovr,l1,0.0019190146350085739,,,,,,,,,,,,,,,,,,,,,,
-8,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5238814331135578,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,median,none,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,28,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-9,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1292.7448076998514,0.004489932344506231,2,1.3088036376373702,poly,-1,False,1.7943262139196537e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,minmax,,,,,feature_agglomeration,,,,,,,,,,,,,,,cosine,average,386,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-10,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.5244313211416313,None,0.0,1,2,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.454300236339658,most_frequent,robust_scaler,,,0.7858830918128734,0.2705174720960696,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-11,weighting,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0006294780694492238,most_frequent,standardize,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.063715750591364,fdr,chi2
-12,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.541827838541001,True,True,squared_hinge,0.00015757576796329985,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,standardize,,,,,extra_trees_preproc_for_classification,False,entropy,None,0.5282111930398031,None,0.0,17,4,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-13,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.25657910619102237,None,0.0,6,6,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.021086303324492208,most_frequent,quantile_transformer,1247,uniform,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.16021572815319468,fpr,chi2
-14,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6185424163558029,False,True,1,squared_hinge,ovr,l2,4.385767395120581e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010397452590017174,median,quantile_transformer,961,normal,,,feature_agglomeration,,,,,,,,,,,,,,,cosine,average,25,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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-16,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.6931271368243409,None,0.0,11,10,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.362940004438799,most_frequent,robust_scaler,,,0.9177587796305063,0.17084038458597867,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,30.491514144623952,mutual_info,,,
-17,none,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.1769332447629957,mean,minmax,,,,,fast_ica,,,,,,,,,,,deflation,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-18,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00335225820194075,True,True,squared_hinge,2.9136221277715572e-05,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.00021598139496857644,median,minmax,,,,,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,4,None,4,9,1.0,14,,,,,
-19,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,199,None,,0.003239674765949011,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,robust_scaler,,,0.7228447795482008,0.25,kernel_pca,,,,,,,,,,,,,,,,,,,0.09440148939740256,3,0.0008114095405154861,poly,1996,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-20,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.6039096182414764,None,0.0,2,20,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.1939609823704447,most_frequent,robust_scaler,,,0.7331327008874781,0.29747488162877694,extra_trees_preproc_for_classification,True,entropy,None,0.33401603690569853,None,0.0,4,4,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-21,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,2.5882079595129472e-05,0.36125906161027654,auto,255,None,341,119,16,loss,1e-07,0.2924550184445239,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.02338478303505337,most_frequent,robust_scaler,,,0.7866731278723753,0.29543475006822734,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,68,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-22,weighting,adaboost,SAMME.R,0.2127290016566289,1,57,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.012688742338096554,median,quantile_transformer,1048,uniform,,,feature_agglomeration,,,,,,,,,,,,,,,euclidean,average,24,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-23,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7725.740943204492,,,3.122840783722913,rbf,-1,True,1.4116065061807418e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.14280178218510184,most_frequent,none,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,27,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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-25,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5010952533165973,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.005838375247601407,mean,normalize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,
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-34,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,22194.242946902177,0.45210849669021247,,0.0005669713702171556,sigmoid,-1,False,0.03542142266081451,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,quantile_transformer,291,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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-64,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,29.55731828912983,False,True,1,squared_hinge,ovr,l2,0.0011332486951954968,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,
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-75,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,1.2430504242164082e-09,0.03359568307446533,auto,255,None,1609,29,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,standardize,,,,,feature_agglomeration,,,,,,,,,,,,,,,cosine,complete,40,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-76,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7.975058275181761,True,True,squared_hinge,1.4887256000993565e-05,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,minmax,,,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.7747665215782399,False,True,1,squared_hinge,ovr,l1,3.926086263836518e-05,,,,,,,,,,,,,,,,,,,,,,
-77,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.34936025988397124,None,0.0,2,13,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00018601309647406987,median,robust_scaler,,,0.8634309348843472,0.26976989465367607,extra_trees_preproc_for_classification,False,entropy,None,0.48025175288975036,None,0.0,6,17,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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-79,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.933900673392165,None,0.0,7,4,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,minmax,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-80,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1661.2274747409913,,,0.025821208852387936,rbf,-1,False,0.0017011435104921824,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,none,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,306,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-81,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.1355242495213339,None,0.0,13,5,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010389218573530956,most_frequent,standardize,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.13381345070408926,fdr,f_classif
-82,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,21.85207494001697,0.18872999589786676,4,1.1863646021868761,poly,-1,False,0.0021338371901294443,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0034080852834770217,mean,normalize,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.11326005109886934,fdr,f_classif
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-84,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,0.0009192613701152634,0.12918951989377014,auto,255,None,9,150,17,loss,1e-07,0.08906232732168995,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010078479963961122,median,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,
-85,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.9499894634982698,None,0.0,20,15,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.01161399161402058,mean,normalize,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,42.76696665279698,f_classif,,,
-86,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,1,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.019797877010737826,mean,normalize,,,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.8239678741128165,False,,,,,,,,,,,,,,,
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-88,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.46057831591617715,False,True,hinge,0.04557857428827514,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00027457445401600137,median,standardize,,,,,extra_trees_preproc_for_classification,True,gini,None,0.48190346970486964,None,0.0,17,18,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-89,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,64,auto,,0.00021332620906594865,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.026032882800756493,median,standardize,,,,,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,-0.9513223792709335,,0.00029881582960753004,sigmoid,543,,,,,,,,,,,,,,,,,
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-91,none,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.563056219822946,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.32793677336996485,most_frequent,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-92,weighting,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.9990376970503236,None,0.0,11,8,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,normalize,,,,,feature_agglomeration,,,,,,,,,,,,,,,cosine,complete,109,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-93,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.22879311080499845,None,0.0,8,4,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.014320022547615783,median,minmax,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.238630703440461,mutual_info,,,
-94,weighting,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,2,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0073667222552306465,median,robust_scaler,,,0.701595034693991,0.29514640404971515,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6066051105210388,False,,,,,,,,,,,,,,,
-95,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.107967527639527e-05,True,True,hinge,0.00448294745544775,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,quantile_transformer,953,normal,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,35.00114345405331,chi2,,,
-96,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.457928416564218,None,0.0,12,2,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,
-97,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,1.1522414705935405e-10,0.6132800183073315,auto,255,None,1607,67,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010096911249562407,mean,robust_scaler,,,0.7663493326967942,0.136094217337434,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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-115,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l2,2.9468189962573666e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.014870368357163507,most_frequent,quantile_transformer,1000,uniform,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,83.43414935094258,f_classif,,,
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-123,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4834106677905965,False,True,1,squared_hinge,ovr,l2,0.00017625027586147415,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0009157492873444966,most_frequent,quantile_transformer,1150,normal,,,extra_trees_preproc_for_classification,False,entropy,None,0.4981700453792278,None,0.0,9,15,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-124,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1561.1002600303584,,,0.0024048551704194507,rbf,-1,False,0.0001936214667496851,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,minmax,,,,,fast_ica,,,,,,,,,,,parallel,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-125,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.087313242780732,0.0636709914987994,3,0.004481917125024585,poly,-1,False,0.01848880618941367,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.002136015035740733,mean,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-126,weighting,adaboost,SAMME.R,1.696588612003168,9,455,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,minmax,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,82.18726095926662,chi2,,,
-127,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.6466927589470762,None,0.0,11,2,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.009672465311659264,most_frequent,standardize,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,369,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+1,none,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1514.059969351301,False,True,1,squared_hinge,ovr,l2,0.002448814309966599,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,standardize,,,,,extra_trees_preproc_for_classification,True,entropy,None,0.8221481751476923,None,0.0,2,3,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+2,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.8739761373635383,None,0.0,5,9,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,quantile_transformer,1135,uniform,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.06130526963953546,fdr,f_classif
+7,none,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.1956599819716578,None,0.0,1,7,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,robust_scaler,,,0.92941901988866,0.07591140934892966,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,87.6629358232908,mutual_info,,,
+8,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,114,manual,0.11778631707605758,0.0029112721339713465,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.015582195257240751,median,normalize,,,,,kernel_pca,,,,,,,,,,,,,,,,,,,0.42909623253327767,3,0.25407612000343777,poly,1689,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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+10,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.7396493120555564,None,0.0,1,18,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010000000000000004,median,robust_scaler,,,0.8908334215036855,0.08666907249785906,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,
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+14,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,4.629265598705481e-10,0.03894546521791516,auto,255,None,5,22,18,loss,1e-07,0.0837137336215462,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.008397749525484112,mean,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
+15,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.21772092350860472,None,0.0,14,17,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.0020488212891693275,median,standardize,,,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,33.74257606530722,False,True,1,squared_hinge,ovr,l1,8.740452966205111e-05,,,,,,,,,,,,,,,,,,,,,,
+21,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,378.090682455998,,,1.5153998334214391,rbf,-1,False,4.744451572262126e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,minmax,,,,,extra_trees_preproc_for_classification,False,gini,None,0.4326333081739829,None,0.0,13,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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+24,weighting,adaboost,SAMME.R,0.460612885139835,9,500,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0033638377904512094,mean,quantile_transformer,1611,uniform,,,feature_agglomeration,,,,,,,,,,,,,,,euclidean,complete,201,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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+29,none,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.563056219822946,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.32793677336996485,most_frequent,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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+32,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,101,auto,,1.553086088086431e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.01137059177001425,most_frequent,normalize,,,,,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,-0.021621751597165284,4,0.7468597264810062,poly,5078,,,,,,,,,,,,,,,,,
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+37,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,161,auto,,0.005011915149502186,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.02713788966372092,median,minmax,,,,,kitchen_sinks,,,,,,,,,,,,,,,,,,,,,,,,0.07896167589979168,2189,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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+49,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00010123414317660679,False,,0.04011567850168966,True,,constant,log,l1,,0.00014179758438189507,one_hot_encoding,minority_coalescer,0.008920898694379564,mean,robust_scaler,,,0.7712963178784032,0.2702398922671455,kernel_pca,,,,,,,,,,,,,,,,,,,,,,cosine,1090,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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+59,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.15115687152536414,True,True,hinge,0.009954631623104506,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0013410356664535208,most_frequent,quantile_transformer,1293,uniform,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+63,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.957787813103507e-05,False,True,squared_hinge,0.046147943721042806,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.4778210360579751,mean,quantile_transformer,315,uniform,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,1315.2870319104447,False,True,1,squared_hinge,ovr,l1,0.030203490583956487,,,,,,,,,,,,,,,,,,,,,,
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+68,weighting,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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+346,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,0.15762740504817374,0.17692452709577644,auto,255,None,282,9,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.005207945885722629,most_frequent,robust_scaler,,,0.75,0.27100271805667314,feature_agglomeration,,,,,,,,,,,,,,,euclidean,complete,106,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+350,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.2318705830210126,True,True,hinge,0.0002994421772278671,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.13310408167721913,most_frequent,quantile_transformer,1036,uniform,,,kernel_pca,,,,,,,,,,,,,,,,,,,,,,cosine,703,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+360,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.5720469164289109,None,0.0,1,17,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.03310727058091163,median,robust_scaler,,,0.8680123006808074,0.024825227678742904,fast_ica,,,,,,,,,,,parallel,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+364,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.11789257899273609,None,0.0,2,3,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.4082847014868829,median,robust_scaler,,,0.9073691418507908,0.1954896244813517,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,
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+371,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,4.198700105243736e-09,0.011070860038925674,auto,255,None,9,116,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010247554402833578,median,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,
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+394,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0025736560290786094,False,True,squared_hinge,0.0006196872282311157,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.042446796272035237,most_frequent,quantile_transformer,773,normal,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,89.84127958910369,f_classif,,,
+399,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.5840717059726437,None,0.0,1,5,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.010000000000000004,mean,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,
+402,none,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.6918041257923966,None,0.0,1,4,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,quantile_transformer,776,uniform,,,fast_ica,,,,,,,,,,,parallel,cube,13,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+404,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8.380057363340162,False,True,hinge,0.0020653831067503525,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.05020677662128942,median,quantile_transformer,560,normal,,,kernel_pca,,,,,,,,,,,,,,,,,,,,,,cosine,891,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+407,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.7292011996224884,True,True,hinge,8.162763086383311e-05,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010337761588362885,median,quantile_transformer,877,uniform,,,kernel_pca,,,,,,,,,,,,,,,,,,,,,,cosine,1317,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+413,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,7.194851113809193e-10,0.013882834218943657,auto,255,None,18,2,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0005124238915083231,mean,quantile_transformer,948,normal,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,60.45885911036194,f_classif,,,
+415,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,0.00037966471646911845,0.016422945536911454,auto,255,None,10,56,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.002419439132687246,median,quantile_transformer,828,uniform,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+420,weighting,adaboost,SAMME.R,0.05920795019389743,8,313,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,standardize,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,104,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+429,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0002272680572529066,True,True,squared_hinge,0.02605444434617768,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
+430,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7236748113015686,None,0.0,1,8,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.25238593745609994,most_frequent,robust_scaler,,,0.7549699159713029,0.2143583239849504,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,60.05843686145916,f_classif,,,
+434,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.4438263804770306,None,0.0,2,10,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,mean,robust_scaler,,,0.9824945141547571,0.24967364012300372,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+438,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,6.090138036399552e-10,0.017305568433437588,auto,255,None,202,188,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,robust_scaler,,,0.7526196011313747,0.1563616005702636,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+443,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.6269076084367177,None,0.0,1,7,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.12943250859943486,most_frequent,standardize,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,62.84526554323969,chi2,,,
+449,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,2.074711735072034e-07,0.01106799034691714,auto,255,None,13,24,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.020949205931423413,mean,standardize,,,,,extra_trees_preproc_for_classification,True,gini,None,0.43505400875624267,None,0.0,20,8,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+456,none,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.772617001926524e-05,False,,0.00531060153046999,True,,constant,log,l1,,0.00016225369247634786,one_hot_encoding,no_coalescense,,median,quantile_transformer,745,uniform,,,extra_trees_preproc_for_classification,False,entropy,None,0.6017654648980271,None,0.0,17,9,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+461,none,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.9796199105973851,None,0.0,2,13,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.00845950033601661,mean,robust_scaler,,,0.8275228560550734,0.23814615163106348,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
diff --git a/autosklearn/metalearning/files/average_precision_binary.classification_dense/description.txt b/autosklearn/metalearning/files/average_precision_binary.classification_dense/description.txt
index 939543f7d1..94854e9f8b 100644
--- a/autosklearn/metalearning/files/average_precision_binary.classification_dense/description.txt
+++ b/autosklearn/metalearning/files/average_precision_binary.classification_dense/description.txt
@@ -1,8 +1,9 @@
 features_cutoff_time: 3600
 features_cutoff_memory: 3072
-number_of_feature_steps: 35
+number_of_feature_steps: 42
 feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances
 feature_step LogNumberOfInstances: LogNumberOfInstances
+feature_step NumberOfClasses: NumberOfClasses
 feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures
 feature_step LogNumberOfFeatures: LogNumberOfFeatures
 feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues
@@ -20,6 +21,11 @@ feature_step DatasetRatio: DatasetRatio, LogDatasetRatio
 feature_step LogDatasetRatio: LogDatasetRatio
 feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio
 feature_step LogInverseDatasetRatio: LogInverseDatasetRatio
+feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD
+feature_step ClassProbabilityMin: ClassProbabilityMin
+feature_step ClassProbabilityMax: ClassProbabilityMax
+feature_step ClassProbabilityMean: ClassProbabilityMean
+feature_step ClassProbabilitySTD: ClassProbabilitySTD
 feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum
 feature_step SymbolsMin: SymbolsMin
 feature_step SymbolsMax: SymbolsMax
@@ -36,11 +42,12 @@ feature_step SkewnessMin: SkewnessMin
 feature_step SkewnessMax: SkewnessMax
 feature_step SkewnessMean: SkewnessMean
 feature_step SkewnessSTD: SkewnessSTD
-features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
+feature_step ClassEntropy: ClassEntropy
+features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy
 features_stochastic: 
-default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
+default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy
 
-algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127
+algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235,236,237,238,239,240,241,242,243,244,245,246,247,248,249,250,251,252,253,254,255,256,257,258,259,260,261,262,263,264,265,266,267,268,269,270,271,272,273,274,275,276,277,278,279,280,281,282,283,284,285,286,287,288,289,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,312,313,314,315,316,317,318,319,320,321,322,323,324,325,326,327,328,329,330,331,332,333,334,335,336,337,338,339,340,341,342,343,344,345,346,347,348,349,350,351,352,353,354,355,356,357,358,359,360,361,362,363,364,365,366,367,368,369,370,371,372,373,374,375,376,377,378,379,380,381,382,383,384,385,386,387,388,389,390,391,392,393,394,395,396,397,398,399,400,401,402,403,404,405,406,407,408,409,410,411,412,413,414,415,416,417,418,419,420,421,422,423,424,425,426,427,428,429,430,431,432,433,434,435,436,437,438,439,440,441,442,443,444,445,446,447,448,449,450,451,452,453,454,455,456,457,458,459,460,461,462,463,464
 algorithms_stochastic: 
 performance_measures: average_precision
 performance_type: solution_quality
diff --git a/autosklearn/metalearning/files/average_precision_binary.classification_dense/feature_costs.arff b/autosklearn/metalearning/files/average_precision_binary.classification_dense/feature_costs.arff
index 0e17e20d22..8512fd9275 100644
--- a/autosklearn/metalearning/files/average_precision_binary.classification_dense/feature_costs.arff
+++ b/autosklearn/metalearning/files/average_precision_binary.classification_dense/feature_costs.arff
@@ -4,6 +4,7 @@
 @ATTRIBUTE repetition NUMERIC
 @ATTRIBUTE NumberOfInstances NUMERIC
 @ATTRIBUTE LogNumberOfInstances NUMERIC
+@ATTRIBUTE NumberOfClasses NUMERIC
 @ATTRIBUTE NumberOfFeatures NUMERIC
 @ATTRIBUTE LogNumberOfFeatures NUMERIC
 @ATTRIBUTE MissingValues NUMERIC
@@ -21,6 +22,11 @@
 @ATTRIBUTE LogDatasetRatio NUMERIC
 @ATTRIBUTE InverseDatasetRatio NUMERIC
 @ATTRIBUTE LogInverseDatasetRatio NUMERIC
+@ATTRIBUTE ClassOccurences NUMERIC
+@ATTRIBUTE ClassProbabilityMin NUMERIC
+@ATTRIBUTE ClassProbabilityMax NUMERIC
+@ATTRIBUTE ClassProbabilityMean NUMERIC
+@ATTRIBUTE ClassProbabilitySTD NUMERIC
 @ATTRIBUTE NumSymbols NUMERIC
 @ATTRIBUTE SymbolsMin NUMERIC
 @ATTRIBUTE SymbolsMax NUMERIC
@@ -37,162 +43,138 @@
 @ATTRIBUTE SkewnessMax NUMERIC
 @ATTRIBUTE SkewnessMean NUMERIC
 @ATTRIBUTE SkewnessSTD NUMERIC
+@ATTRIBUTE ClassEntropy NUMERIC
 
 @DATA
-75123,1.0,1e-05,1e-05,1e-05,1e-05,0.00253,0.00221,1e-05,0.00024,3e-05,0.00011,0.0,2e-05,2e-05,5e-05,8e-05,2e-05,1e-05,7e-05,1e-05,0.00072,1e-05,1e-05,0.00019,0.00041,0.00011,0.00061,0.00012,9e-05,0.00015,0.00025,0.00067,0.00014,0.00011,0.00016,0.00027
-75090,1.0,1e-05,1e-05,2e-05,2e-05,0.00815,0.00368,7e-05,0.00228,9e-05,0.00235,0.0,5e-05,5e-05,0.0001,0.00013,0.00013,1e-05,0.00021,2e-05,0.00064,0.0,0.0,0.00014,0.00038,0.00011,0.00066,0.00011,0.00014,0.00014,0.00026,0.00066,0.00011,0.00013,0.00015,0.00027
-2123,1.0,1e-05,1e-05,1e-05,1e-05,0.00097,0.00079,1e-05,0.00013,1e-05,7e-05,0.0,2e-05,2e-05,5e-05,8e-05,2e-05,1e-05,5e-05,1e-05,0.0007,1e-05,0.0,0.00017,0.00042,0.0001,0.00052,6e-05,8e-05,0.00012,0.00026,0.00049,6e-05,0.00011,9e-05,0.00023
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-75217,1.0,1e-05,1e-05,2e-05,1e-05,0.00196,0.00158,1e-05,0.00026,2e-05,0.00016,1e-05,3e-05,3e-05,7e-05,0.0001,3e-05,1e-05,7e-05,2e-05,0.00089,1e-05,1e-05,0.00022,0.00053,0.00013,0.00074,9e-05,0.0001,0.00019,0.00036,0.00065,0.00013,0.0001,0.00011,0.00031
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+75235,1.0,1e-05,1e-05,0.0003,2e-05,1e-05,0.00461,0.00385,2e-05,0.00055,2e-05,0.00026,1e-05,3e-05,3e-05,0.0001,0.00011,3e-05,1e-05,5e-05,2e-05,0.00026,3e-05,1e-05,8e-05,0.00014,0.00075,1e-05,1e-05,0.0002,0.00043,0.00011,0.00127,0.00017,0.0002,0.00029,0.00061,0.0008,0.00015,0.00012,0.00016,0.00038,0.00242
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diff --git a/autosklearn/metalearning/files/average_precision_binary.classification_dense/feature_runstatus.arff b/autosklearn/metalearning/files/average_precision_binary.classification_dense/feature_runstatus.arff
index 0034ad2bd6..5fff2dfed3 100644
--- a/autosklearn/metalearning/files/average_precision_binary.classification_dense/feature_runstatus.arff
+++ b/autosklearn/metalearning/files/average_precision_binary.classification_dense/feature_runstatus.arff
@@ -4,6 +4,7 @@
 @ATTRIBUTE repetition NUMERIC
 @ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other}
@@ -21,6 +22,11 @@
 @ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other}
@@ -37,162 +43,138 @@
 @ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other}
 
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diff --git a/autosklearn/metalearning/files/average_precision_binary.classification_dense/feature_values.arff b/autosklearn/metalearning/files/average_precision_binary.classification_dense/feature_values.arff
index a60400e3ce..736dc64321 100644
--- a/autosklearn/metalearning/files/average_precision_binary.classification_dense/feature_values.arff
+++ b/autosklearn/metalearning/files/average_precision_binary.classification_dense/feature_values.arff
@@ -13,7 +13,6 @@
 @ATTRIBUTE KurtosisMean NUMERIC
 @ATTRIBUTE KurtosisMin NUMERIC
 @ATTRIBUTE KurtosisSTD NUMERIC
-@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC
 @ATTRIBUTE LogDatasetRatio NUMERIC
 @ATTRIBUTE LogInverseDatasetRatio NUMERIC
 @ATTRIBUTE LogNumberOfFeatures NUMERIC
@@ -42,160 +41,135 @@
 @ATTRIBUTE SymbolsSum NUMERIC
 
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diff --git a/autosklearn/metalearning/files/average_precision_binary.classification_sparse/algorithm_runs.arff b/autosklearn/metalearning/files/average_precision_binary.classification_sparse/algorithm_runs.arff
index 928accbecb..6e80d914a1 100644
--- a/autosklearn/metalearning/files/average_precision_binary.classification_sparse/algorithm_runs.arff
+++ b/autosklearn/metalearning/files/average_precision_binary.classification_sparse/algorithm_runs.arff
@@ -7,95 +7,133 @@
 @ATTRIBUTE runstatus {ok, timeout, memout, not_applicable, crash, other}
 
 @DATA
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-246,1.0,2,?,not_applicable
-75178,1.0,3,?,not_applicable
-75171,1.0,4,0.0887550636869866,ok
-248,1.0,5,?,not_applicable
-75231,1.0,6,?,not_applicable
-75196,1.0,7,0.0002747755999267243,ok
-75188,1.0,8,?,not_applicable
-75248,1.0,9,0.6810602093884777,ok
-75126,1.0,10,0.006674311646996611,ok
-75234,1.0,11,0.006899933268162961,ok
-75150,1.0,12,0.23399578496169404,ok
-258,1.0,13,?,not_applicable
-75168,1.0,14,?,not_applicable
-75235,1.0,15,?,not_applicable
-244,1.0,16,?,not_applicable
-75221,1.0,17,?,not_applicable
-75219,1.0,18,0.006712691671677318,ok
-75202,1.0,19,?,not_applicable
-3043,1.0,20,0.05506375879821879,ok
-75205,1.0,21,?,not_applicable
-75174,1.0,22,0.10531801123409645,ok
-275,1.0,23,?,not_applicable
-75213,1.0,24,0.041936349169275466,ok
-75099,1.0,25,0.5022333187437142,ok
-75184,1.0,26,0.12129861132577913,ok
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-233,1.0,28,5.842925670007659e-05,ok
-75114,1.0,29,0.013783901023106226,ok
-236,1.0,30,?,not_applicable
-75141,1.0,31,0.012157825315366244,ok
-75107,1.0,32,0.4583775449502874,ok
-262,1.0,33,?,not_applicable
-75146,1.0,34,0.03235300923459594,ok
-75189,1.0,35,0.0014471736348927733,ok
-2350,1.0,36,0.5707237410842824,ok
-75249,1.0,37,0.0029122637768774773,ok
-242,1.0,38,?,not_applicable
-75117,1.0,39,0.008280862635277386,ok
-75191,1.0,40,0.057807890742150425,ok
-261,1.0,41,0.34655969840600265,ok
-75236,1.0,42,?,not_applicable
-75095,1.0,43,0.05557088536188293,ok
-75093,1.0,44,0.6232770240174773,ok
-75223,1.0,45,?,not_applicable
-75109,1.0,46,?,not_applicable
-75197,1.0,47,?,not_applicable
-75127,1.0,48,0.3152529081908554,ok
-75143,1.0,49,0.0035025340846421837,ok
-75153,1.0,50,0.01920221368233055,ok
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-75215,1.0,52,0.004897009985816103,ok
-75195,1.0,53,1.0801385901260119e-06,ok
-75207,1.0,54,?,not_applicable
-75225,1.0,55,0.5940959194743385,ok
-75166,1.0,56,0.06500637875421555,ok
-75100,1.0,57,0.983500701962392,ok
-75169,1.0,58,?,not_applicable
-75121,1.0,59,0.0,ok
-75098,1.0,60,?,not_applicable
-75115,1.0,61,0.011733935395969564,ok
-75116,1.0,62,0.0008296925826821733,ok
-75185,1.0,63,0.05923391940768985,ok
-2119,1.0,64,?,not_applicable
-75157,1.0,65,0.5107743543247277,ok
-75113,1.0,66,0.01014718506549861,ok
-75203,1.0,67,?,not_applicable
-75182,1.0,68,0.10060431919463964,ok
-251,1.0,69,?,not_applicable
-75123,1.0,70,?,not_applicable
-75125,1.0,71,0.009583682216643852,ok
-75232,1.0,72,0.11706237289853583,ok
-75103,1.0,73,0.031775122370021114,ok
-75192,1.0,74,0.5033097381704522,ok
-75230,1.0,75,?,not_applicable
-75139,1.0,76,0.0019167649076011761,ok
-75227,1.0,77,0.0870759275296118,ok
-2120,1.0,78,?,not_applicable
-75124,1.0,79,0.4676991332383039,ok
-75240,1.0,80,0.021029116580781215,ok
-75198,1.0,81,?,not_applicable
-75201,1.0,82,?,not_applicable
-75133,1.0,83,0.46574825307214884,ok
-75154,1.0,84,?,not_applicable
-75177,1.0,85,0.08262533543110306,ok
-2117,1.0,86,0.03275769843221743,ok
-75156,1.0,87,0.12925560311437256,ok
-75097,1.0,88,0.016742399578129286,ok
-75172,1.0,89,?,not_applicable
-75106,1.0,90,0.8354934073272688,ok
-75187,1.0,91,0.0012821066451785823,ok
-75120,1.0,92,0.01217949409305763,ok
+75192,1.0,1,0.4878671463781179,ok
+75119,1.0,3,0.0023574252624086744,ok
+75212,1.0,58,0.1531038958200216,ok
+246,1.0,6,?,not_applicable
+252,1.0,7,?,not_applicable
+75178,1.0,8,?,not_applicable
+75177,1.0,11,0.010583210410810229,ok
+75092,1.0,31,0.34536251585048616,ok
+75239,1.0,13,0.0,ok
+75215,1.0,220,0.0027691909122957714,ok
+75171,1.0,19,0.08580090152984066,ok
+75227,1.0,20,0.0864017835414308,ok
+75233,1.0,73,0.007850102029398953,ok
+75182,1.0,22,0.09992542504725932,ok
+253,1.0,23,?,not_applicable
+75157,1.0,172,0.4951030769313238,ok
+75124,1.0,317,0.47192427029766393,ok
+75222,1.0,27,0.18758049365145113,ok
+75231,1.0,28,?,not_applicable
+75185,1.0,30,0.0523509570028029,ok
+2123,1.0,32,?,not_applicable
+75150,1.0,59,0.21579019519283416,ok
+75143,1.0,273,0.0003608598387342399,ok
+75196,1.0,37,0.0002747755999267243,ok
+75188,1.0,40,?,not_applicable
+75248,1.0,45,0.6619346271836501,ok
+75249,1.0,164,0.002653934516532064,ok
+75113,1.0,50,0.006215426914405242,ok
+75126,1.0,52,0.002676573380457681,ok
+251,1.0,53,?,not_applicable
+75184,1.0,122,0.07528745452759777,ok
+75234,1.0,56,0.004936877014319552,ok
+258,1.0,60,?,not_applicable
+75166,1.0,227,0.02779561860055213,ok
+75168,1.0,63,?,not_applicable
+75148,1.0,183,0.07293643503720026,ok
+75235,1.0,67,?,not_applicable
+75159,1.0,68,0.5605685730424698,ok
+244,1.0,71,?,not_applicable
+75141,1.0,129,0.01173804467069206,ok
+75221,1.0,74,?,not_applicable
+75219,1.0,82,0.006191375107805208,ok
+75202,1.0,77,?,not_applicable
+3043,1.0,80,0.010785170617967688,ok
+75205,1.0,84,?,not_applicable
+75174,1.0,88,0.10410861094426194,ok
+288,1.0,89,?,not_applicable
+75250,1.0,90,?,not_applicable
+75179,1.0,363,0.22481062993849077,ok
+275,1.0,92,?,not_applicable
+75207,1.0,93,?,not_applicable
+75142,1.0,95,0.014340735646710567,ok
+75099,1.0,136,0.5162235680666997,ok
+75243,1.0,99,?,not_applicable
+75175,1.0,211,0.05232648789248073,ok
+233,1.0,107,8.366996616537836e-05,ok
+75161,1.0,109,0.011200609583141508,ok
+75176,1.0,110,0.0006257932468407557,ok
+262,1.0,113,?,not_applicable
+75129,1.0,272,0.5805996295606374,ok
+261,1.0,141,0.3381950434129559,ok
+75090,1.0,116,?,not_applicable
+75114,1.0,118,0.0038194827767713546,ok
+75093,1.0,187,0.5754045178901972,ok
+260,1.0,124,?,not_applicable
+236,1.0,126,?,not_applicable
+254,1.0,132,0.0,ok
+75107,1.0,133,0.44862438395455684,ok
+75139,1.0,137,0.001190801337554337,ok
+75146,1.0,140,0.028488273885671878,ok
+75189,1.0,145,0.001112662584136892,ok
+75163,1.0,150,0.02685795440651706,ok
+2350,1.0,152,0.5668173788275173,ok
+2122,1.0,156,?,not_applicable
+75110,1.0,159,?,not_applicable
+75213,1.0,162,0.06131091254116239,ok
+75095,1.0,230,0.05533956012799779,ok
+75108,1.0,167,0.0,ok
+75117,1.0,169,0.005428243341756178,ok
+75191,1.0,176,0.05749081881675733,ok
+75226,1.0,177,2.369743054941864e-05,ok
+75244,1.0,298,0.6266299217591191,ok
+75236,1.0,181,?,not_applicable
+75169,1.0,184,?,not_applicable
+75116,1.0,256,0.0006359144097769498,ok
+75223,1.0,189,?,not_applicable
+75109,1.0,196,?,not_applicable
+75197,1.0,199,?,not_applicable
+248,1.0,203,?,not_applicable
+2119,1.0,206,?,not_applicable
+75127,1.0,207,0.3143148012874739,ok
+75153,1.0,215,0.0211371390556796,ok
+75173,1.0,217,0.04617966821935626,ok
+75187,1.0,218,0.0012256656184541637,ok
+75195,1.0,223,2.662413856047152e-07,ok
+75225,1.0,226,0.5154066456573045,ok
+75100,1.0,356,0.9598316865324259,ok
+75132,1.0,237,0.891944235816124,ok
+75210,1.0,239,0.0,ok
+273,1.0,241,0.01506789853315349,ok
+75133,1.0,332,0.5995165540719386,ok
+75121,1.0,244,0.0,ok
+75098,1.0,248,?,not_applicable
+75115,1.0,253,0.004229694217922297,ok
+266,1.0,258,?,not_applicable
+75134,1.0,274,?,not_applicable
+75096,1.0,277,?,not_applicable
+75203,1.0,280,?,not_applicable
+75123,1.0,288,?,not_applicable
+75237,1.0,291,1.0920271134029491e-06,ok
+75125,1.0,294,0.00926416480533232,ok
+2120,1.0,297,?,not_applicable
+75232,1.0,300,0.11285722810988297,ok
+75103,1.0,372,0.024583847751503685,ok
+242,1.0,307,?,not_applicable
+75230,1.0,312,?,not_applicable
+75240,1.0,321,0.018974692437447427,ok
+75198,1.0,325,?,not_applicable
+75201,1.0,328,?,not_applicable
+75112,1.0,331,0.08596797767191378,ok
+75105,1.0,336,0.890912864676346,ok
+75154,1.0,338,?,not_applicable
+2117,1.0,344,0.029195020984121633,ok
+75156,1.0,348,0.11814063159869614,ok
+75097,1.0,349,0.014519472944802159,ok
+75101,1.0,352,0.18566222232178675,ok
+75172,1.0,353,?,not_applicable
+75106,1.0,359,0.8044503421707234,ok
+75120,1.0,365,0.002216917908264948,ok
+75193,1.0,369,?,not_applicable
diff --git a/autosklearn/metalearning/files/average_precision_binary.classification_sparse/configurations.csv b/autosklearn/metalearning/files/average_precision_binary.classification_sparse/configurations.csv
index ec2d231333..5748989e7b 100644
--- a/autosklearn/metalearning/files/average_precision_binary.classification_sparse/configurations.csv
+++ b/autosklearn/metalearning/files/average_precision_binary.classification_sparse/configurations.csv
@@ -1,93 +1,131 @@
 idx,balancing:strategy,classifier:__choice__,classifier:adaboost:algorithm,classifier:adaboost:learning_rate,classifier:adaboost:max_depth,classifier:adaboost:n_estimators,classifier:bernoulli_nb:alpha,classifier:bernoulli_nb:fit_prior,classifier:decision_tree:criterion,classifier:decision_tree:max_depth_factor,classifier:decision_tree:max_features,classifier:decision_tree:max_leaf_nodes,classifier:decision_tree:min_impurity_decrease,classifier:decision_tree:min_samples_leaf,classifier:decision_tree:min_samples_split,classifier:decision_tree:min_weight_fraction_leaf,classifier:extra_trees:bootstrap,classifier:extra_trees:criterion,classifier:extra_trees:max_depth,classifier:extra_trees:max_features,classifier:extra_trees:max_leaf_nodes,classifier:extra_trees:min_impurity_decrease,classifier:extra_trees:min_samples_leaf,classifier:extra_trees:min_samples_split,classifier:extra_trees:min_weight_fraction_leaf,classifier:gradient_boosting:early_stop,classifier:gradient_boosting:l2_regularization,classifier:gradient_boosting:learning_rate,classifier:gradient_boosting:loss,classifier:gradient_boosting:max_bins,classifier:gradient_boosting:max_depth,classifier:gradient_boosting:max_leaf_nodes,classifier:gradient_boosting:min_samples_leaf,classifier:gradient_boosting:n_iter_no_change,classifier:gradient_boosting:scoring,classifier:gradient_boosting:tol,classifier:gradient_boosting:validation_fraction,classifier:k_nearest_neighbors:n_neighbors,classifier:k_nearest_neighbors:p,classifier:k_nearest_neighbors:weights,classifier:lda:n_components,classifier:lda:shrinkage,classifier:lda:shrinkage_factor,classifier:lda:tol,classifier:liblinear_svc:C,classifier:liblinear_svc:dual,classifier:liblinear_svc:fit_intercept,classifier:liblinear_svc:intercept_scaling,classifier:liblinear_svc:loss,classifier:liblinear_svc:multi_class,classifier:liblinear_svc:penalty,classifier:liblinear_svc:tol,classifier:libsvm_svc:C,classifier:libsvm_svc:coef0,classifier:libsvm_svc:degree,classifier:libsvm_svc:gamma,classifier:libsvm_svc:kernel,classifier:libsvm_svc:max_iter,classifier:libsvm_svc:shrinking,classifier:libsvm_svc:tol,classifier:multinomial_nb:alpha,classifier:multinomial_nb:fit_prior,classifier:passive_aggressive:C,classifier:passive_aggressive:average,classifier:passive_aggressive:fit_intercept,classifier:passive_aggressive:loss,classifier:passive_aggressive:tol,classifier:qda:reg_param,classifier:random_forest:bootstrap,classifier:random_forest:criterion,classifier:random_forest:max_depth,classifier:random_forest:max_features,classifier:random_forest:max_leaf_nodes,classifier:random_forest:min_impurity_decrease,classifier:random_forest:min_samples_leaf,classifier:random_forest:min_samples_split,classifier:random_forest:min_weight_fraction_leaf,classifier:sgd:alpha,classifier:sgd:average,classifier:sgd:epsilon,classifier:sgd:eta0,classifier:sgd:fit_intercept,classifier:sgd:l1_ratio,classifier:sgd:learning_rate,classifier:sgd:loss,classifier:sgd:penalty,classifier:sgd:power_t,classifier:sgd:tol,data_preprocessing:categorical_transformer:categorical_encoding:__choice__,data_preprocessing:categorical_transformer:category_coalescence:__choice__,data_preprocessing:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction,data_preprocessing:numerical_transformer:imputation:strategy,data_preprocessing:numerical_transformer:rescaling:__choice__,data_preprocessing:numerical_transformer:rescaling:quantile_transformer:n_quantiles,data_preprocessing:numerical_transformer:rescaling:quantile_transformer:output_distribution,data_preprocessing:numerical_transformer:rescaling:robust_scaler:q_max,data_preprocessing:numerical_transformer:rescaling:robust_scaler:q_min,feature_preprocessor:__choice__,feature_preprocessor:extra_trees_preproc_for_classification:bootstrap,feature_preprocessor:extra_trees_preproc_for_classification:criterion,feature_preprocessor:extra_trees_preproc_for_classification:max_depth,feature_preprocessor:extra_trees_preproc_for_classification:max_features,feature_preprocessor:extra_trees_preproc_for_classification:max_leaf_nodes,feature_preprocessor:extra_trees_preproc_for_classification:min_impurity_decrease,feature_preprocessor:extra_trees_preproc_for_classification:min_samples_leaf,feature_preprocessor:extra_trees_preproc_for_classification:min_samples_split,feature_preprocessor:extra_trees_preproc_for_classification:min_weight_fraction_leaf,feature_preprocessor:extra_trees_preproc_for_classification:n_estimators,feature_preprocessor:kernel_pca:coef0,feature_preprocessor:kernel_pca:degree,feature_preprocessor:kernel_pca:gamma,feature_preprocessor:kernel_pca:kernel,feature_preprocessor:kernel_pca:n_components,feature_preprocessor:kitchen_sinks:gamma,feature_preprocessor:kitchen_sinks:n_components,feature_preprocessor:liblinear_svc_preprocessor:C,feature_preprocessor:liblinear_svc_preprocessor:dual,feature_preprocessor:liblinear_svc_preprocessor:fit_intercept,feature_preprocessor:liblinear_svc_preprocessor:intercept_scaling,feature_preprocessor:liblinear_svc_preprocessor:loss,feature_preprocessor:liblinear_svc_preprocessor:multi_class,feature_preprocessor:liblinear_svc_preprocessor:penalty,feature_preprocessor:liblinear_svc_preprocessor:tol,feature_preprocessor:nystroem_sampler:coef0,feature_preprocessor:nystroem_sampler:degree,feature_preprocessor:nystroem_sampler:gamma,feature_preprocessor:nystroem_sampler:kernel,feature_preprocessor:nystroem_sampler:n_components,feature_preprocessor:polynomial:degree,feature_preprocessor:polynomial:include_bias,feature_preprocessor:polynomial:interaction_only,feature_preprocessor:random_trees_embedding:bootstrap,feature_preprocessor:random_trees_embedding:max_depth,feature_preprocessor:random_trees_embedding:max_leaf_nodes,feature_preprocessor:random_trees_embedding:min_samples_leaf,feature_preprocessor:random_trees_embedding:min_samples_split,feature_preprocessor:random_trees_embedding:min_weight_fraction_leaf,feature_preprocessor:random_trees_embedding:n_estimators,feature_preprocessor:select_percentile_classification:percentile,feature_preprocessor:select_percentile_classification:score_func,feature_preprocessor:select_rates_classification:alpha,feature_preprocessor:select_rates_classification:mode,feature_preprocessor:select_rates_classification:score_func,feature_preprocessor:truncatedSVD:target_dim
-1,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.3163020577549344,None,0.0,15,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.01322981552715978,most_frequent,robust_scaler,,,0.9746667009556722,0.1974876581478311,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-2,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,23916.78290516781,False,True,1,squared_hinge,ovr,l2,0.0006655607726658531,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.008808715242823137,median,none,,,,,kernel_pca,,,,,,,,,,,0.2633153190620261,2,0.004770332714291669,poly,1483,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-3,weighting,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,88,2,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.254263872179305,median,normalize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-4,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.8295237320761981,None,0.0,14,13,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.4912899096849949,most_frequent,robust_scaler,,,0.8602962469260254,0.1681311280324052,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l1,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,
-5,none,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00011009762574324903,True,,0.031641726094190054,True,,invscaling,hinge,l2,0.2208821470267501,0.007895311091089785,one_hot_encoding,minority_coalescer,0.13807502937372682,mean,quantile_transformer,1340,normal,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,,
-6,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.5244313211416313,None,0.0,1,2,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.454300236339658,most_frequent,robust_scaler,,,0.7858830918128734,0.2705174720960696,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-7,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.541827838541001,True,True,squared_hinge,0.00015757576796329985,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,standardize,,,,,extra_trees_preproc_for_classification,False,entropy,None,0.5282111930398031,None,0.0,17,4,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-8,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.25657910619102237,None,0.0,6,6,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.021086303324492208,most_frequent,quantile_transformer,1247,uniform,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.16021572815319468,fpr,chi2,
-9,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.643761610990255,None,0.0,20,6,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.039859381630488695,mean,normalize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,,
-10,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.8253905225980761,None,0.0,18,20,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.003725255016700434,mean,robust_scaler,,,0.7701705977765594,0.2396520157991559,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.3579038776690903,False,True,1,squared_hinge,ovr,l1,4.994232534734023e-05,,,,,,,,,,,,,,,,,,,,,
-11,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.06263739023781334,None,0.0,18,8,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.02206379295100344,most_frequent,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,,
-12,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.07518858116896743,None,0.0,7,10,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0027133920607872518,mean,robust_scaler,,,0.7873452530915046,0.16215139072179952,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-13,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,199,None,,0.003239674765949011,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,robust_scaler,,,0.7228447795482008,0.25,kernel_pca,,,,,,,,,,,0.09440148939740256,3,0.0008114095405154861,poly,1996,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-14,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.6039096182414764,None,0.0,2,20,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.1939609823704447,most_frequent,robust_scaler,,,0.7331327008874781,0.29747488162877694,extra_trees_preproc_for_classification,True,entropy,None,0.33401603690569853,None,0.0,4,4,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-15,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7865652301288171,None,0.0,1,4,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,median,robust_scaler,,,0.8623635641193496,0.13298297278730625,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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-17,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.21794354428393548,None,0.0,2,16,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0025451910134387575,median,quantile_transformer,1477,uniform,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,,
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diff --git a/autosklearn/metalearning/files/average_precision_binary.classification_sparse/description.txt b/autosklearn/metalearning/files/average_precision_binary.classification_sparse/description.txt
index 0d89c72314..5a82016de7 100644
--- a/autosklearn/metalearning/files/average_precision_binary.classification_sparse/description.txt
+++ b/autosklearn/metalearning/files/average_precision_binary.classification_sparse/description.txt
@@ -1,8 +1,9 @@
 features_cutoff_time: 3600
 features_cutoff_memory: 3072
-number_of_feature_steps: 35
+number_of_feature_steps: 42
 feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances
 feature_step LogNumberOfInstances: LogNumberOfInstances
+feature_step NumberOfClasses: NumberOfClasses
 feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures
 feature_step LogNumberOfFeatures: LogNumberOfFeatures
 feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues
@@ -20,6 +21,11 @@ feature_step DatasetRatio: DatasetRatio, LogDatasetRatio
 feature_step LogDatasetRatio: LogDatasetRatio
 feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio
 feature_step LogInverseDatasetRatio: LogInverseDatasetRatio
+feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD
+feature_step ClassProbabilityMin: ClassProbabilityMin
+feature_step ClassProbabilityMax: ClassProbabilityMax
+feature_step ClassProbabilityMean: ClassProbabilityMean
+feature_step ClassProbabilitySTD: ClassProbabilitySTD
 feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum
 feature_step SymbolsMin: SymbolsMin
 feature_step SymbolsMax: SymbolsMax
@@ -36,11 +42,12 @@ feature_step SkewnessMin: SkewnessMin
 feature_step SkewnessMax: SkewnessMax
 feature_step SkewnessMean: SkewnessMean
 feature_step SkewnessSTD: SkewnessSTD
-features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
+feature_step ClassEntropy: ClassEntropy
+features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy
 features_stochastic: 
-default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
+default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy
 
-algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92
+algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235,236,237,238,239,240,241,242,243,244,245,246,247,248,249,250,251,252,253,254,255,256,257,258,259,260,261,262,263,264,265,266,267,268,269,270,271,272,273,274,275,276,277,278,279,280,281,282,283,284,285,286,287,288,289,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,312,313,314,315,316,317,318,319,320,321,322,323,324,325,326,327,328,329,330,331,332,333,334,335,336,337,338,339,340,341,342,343,344,345,346,347,348,349,350,351,352,353,354,355,356,357,358,359,360,361,362,363,364,365,366,367,368,369,370,371,372
 algorithms_stochastic: 
 performance_measures: average_precision
 performance_type: solution_quality
diff --git a/autosklearn/metalearning/files/average_precision_binary.classification_sparse/feature_costs.arff b/autosklearn/metalearning/files/average_precision_binary.classification_sparse/feature_costs.arff
index 0e17e20d22..8512fd9275 100644
--- a/autosklearn/metalearning/files/average_precision_binary.classification_sparse/feature_costs.arff
+++ b/autosklearn/metalearning/files/average_precision_binary.classification_sparse/feature_costs.arff
@@ -4,6 +4,7 @@
 @ATTRIBUTE repetition NUMERIC
 @ATTRIBUTE NumberOfInstances NUMERIC
 @ATTRIBUTE LogNumberOfInstances NUMERIC
+@ATTRIBUTE NumberOfClasses NUMERIC
 @ATTRIBUTE NumberOfFeatures NUMERIC
 @ATTRIBUTE LogNumberOfFeatures NUMERIC
 @ATTRIBUTE MissingValues NUMERIC
@@ -21,6 +22,11 @@
 @ATTRIBUTE LogDatasetRatio NUMERIC
 @ATTRIBUTE InverseDatasetRatio NUMERIC
 @ATTRIBUTE LogInverseDatasetRatio NUMERIC
+@ATTRIBUTE ClassOccurences NUMERIC
+@ATTRIBUTE ClassProbabilityMin NUMERIC
+@ATTRIBUTE ClassProbabilityMax NUMERIC
+@ATTRIBUTE ClassProbabilityMean NUMERIC
+@ATTRIBUTE ClassProbabilitySTD NUMERIC
 @ATTRIBUTE NumSymbols NUMERIC
 @ATTRIBUTE SymbolsMin NUMERIC
 @ATTRIBUTE SymbolsMax NUMERIC
@@ -37,162 +43,138 @@
 @ATTRIBUTE SkewnessMax NUMERIC
 @ATTRIBUTE SkewnessMean NUMERIC
 @ATTRIBUTE SkewnessSTD NUMERIC
+@ATTRIBUTE ClassEntropy NUMERIC
 
 @DATA
-75123,1.0,1e-05,1e-05,1e-05,1e-05,0.00253,0.00221,1e-05,0.00024,3e-05,0.00011,0.0,2e-05,2e-05,5e-05,8e-05,2e-05,1e-05,7e-05,1e-05,0.00072,1e-05,1e-05,0.00019,0.00041,0.00011,0.00061,0.00012,9e-05,0.00015,0.00025,0.00067,0.00014,0.00011,0.00016,0.00027
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+75234,1.0,1e-05,1e-05,0.00015,1e-05,1e-05,0.0058,0.00534,1e-05,0.00033,1e-05,0.00016,0.0,2e-05,2e-05,7e-05,0.00204,3e-05,1e-05,5e-05,1e-05,0.00024,3e-05,2e-05,0.0001,9e-05,0.00043,0.0,0.0,0.00012,0.00024,6e-05,0.00156,6e-05,7e-05,0.00121,0.00021,0.00054,6e-05,7e-05,9e-05,0.00032,0.00501
+75235,1.0,1e-05,1e-05,0.0003,2e-05,1e-05,0.00461,0.00385,2e-05,0.00055,2e-05,0.00026,1e-05,3e-05,3e-05,0.0001,0.00011,3e-05,1e-05,5e-05,2e-05,0.00026,3e-05,1e-05,8e-05,0.00014,0.00075,1e-05,1e-05,0.0002,0.00043,0.00011,0.00127,0.00017,0.0002,0.00029,0.00061,0.0008,0.00015,0.00012,0.00016,0.00038,0.00242
+75236,1.0,1e-05,1e-05,0.0001,1e-05,1e-05,0.00218,0.00118,2e-05,0.00065,3e-05,0.00041,1e-05,3e-05,3e-05,9e-05,9e-05,4e-05,1e-05,7e-05,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.0006,1e-05,1e-05,0.00021,0.00032,6e-05,0.00043,7e-05,7e-05,9e-05,0.00019,0.00062,7e-05,8e-05,0.0001,0.00037,0.00048
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+75243,1.0,1e-05,1e-05,0.00029,1e-05,1e-05,0.00461,0.00415,1e-05,0.00035,1e-05,0.00015,0.0,2e-05,2e-05,5e-05,7e-05,2e-05,1e-05,4e-05,1e-05,0.00018,2e-05,1e-05,4e-05,0.00011,0.00022,1e-05,1e-05,5e-05,0.00011,5e-05,0.0004,6e-05,6e-05,8e-05,0.0002,0.00039,6e-05,6e-05,8e-05,0.00019,0.00265
+75244,1.0,3e-05,2e-05,0.0004,3e-05,2e-05,0.0173,0.01298,4e-05,0.00267,4e-05,0.00174,1e-05,7e-05,9e-05,0.00024,0.00027,8e-05,2e-05,0.00013,3e-05,0.00045,7e-05,2e-05,0.00011,0.00025,0.00077,6e-05,2e-05,0.00021,0.00034,0.00013,0.00077,0.00014,0.00013,0.00026,0.00023,0.00044,7e-05,8e-05,0.0001,0.00019,0.00649
+75248,1.0,1e-05,1e-05,0.00017,1e-05,1e-05,0.00416,0.00305,1e-05,0.0006,2e-05,0.00054,1e-05,2e-05,2e-05,5e-05,7e-05,2e-05,1e-05,4e-05,1e-05,0.00017,2e-05,1e-05,4e-05,0.0001,0.00029,5e-05,1e-05,6e-05,0.00012,6e-05,0.00081,0.00011,0.00015,0.00016,0.00038,0.0005,7e-05,8e-05,0.00012,0.00024,0.00184
+75249,1.0,1e-05,1e-05,0.00011,1e-05,1e-05,0.00192,0.00153,1e-05,0.00025,1e-05,0.00017,1e-05,2e-05,2e-05,5e-05,7e-05,2e-05,1e-05,5e-05,3e-05,0.00014,2e-05,1e-05,3e-05,8e-05,0.00029,1e-05,3e-05,6e-05,0.00012,7e-05,0.00041,6e-05,8e-05,0.0001,0.00017,0.00043,6e-05,7e-05,9e-05,0.00019,0.00092
+75250,1.0,1e-05,1e-05,0.00375,1e-05,1e-05,0.04994,0.047,1e-05,0.00247,1e-05,0.00049,1e-05,2e-05,2e-05,6e-05,7e-05,2e-05,1e-05,3e-05,1e-05,0.00022,4e-05,1e-05,4e-05,0.00012,0.00041,0.0,1e-05,0.00011,0.00023,6e-05,0.00076,0.00012,0.00012,0.00019,0.00032,0.00086,0.00012,0.00013,0.00018,0.00043,0.04178
diff --git a/autosklearn/metalearning/files/average_precision_binary.classification_sparse/feature_runstatus.arff b/autosklearn/metalearning/files/average_precision_binary.classification_sparse/feature_runstatus.arff
index 0034ad2bd6..5fff2dfed3 100644
--- a/autosklearn/metalearning/files/average_precision_binary.classification_sparse/feature_runstatus.arff
+++ b/autosklearn/metalearning/files/average_precision_binary.classification_sparse/feature_runstatus.arff
@@ -4,6 +4,7 @@
 @ATTRIBUTE repetition NUMERIC
 @ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other}
@@ -21,6 +22,11 @@
 @ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other}
@@ -37,162 +43,138 @@
 @ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other}
 
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diff --git a/autosklearn/metalearning/files/average_precision_binary.classification_sparse/feature_values.arff b/autosklearn/metalearning/files/average_precision_binary.classification_sparse/feature_values.arff
index a60400e3ce..736dc64321 100644
--- a/autosklearn/metalearning/files/average_precision_binary.classification_sparse/feature_values.arff
+++ b/autosklearn/metalearning/files/average_precision_binary.classification_sparse/feature_values.arff
@@ -13,7 +13,6 @@
 @ATTRIBUTE KurtosisMean NUMERIC
 @ATTRIBUTE KurtosisMin NUMERIC
 @ATTRIBUTE KurtosisSTD NUMERIC
-@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC
 @ATTRIBUTE LogDatasetRatio NUMERIC
 @ATTRIBUTE LogInverseDatasetRatio NUMERIC
 @ATTRIBUTE LogNumberOfFeatures NUMERIC
@@ -42,160 +41,135 @@
 @ATTRIBUTE SymbolsSum NUMERIC
 
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diff --git a/autosklearn/metalearning/files/average_precision_multiclass.classification_dense/algorithm_runs.arff b/autosklearn/metalearning/files/average_precision_multiclass.classification_dense/algorithm_runs.arff
index 0bf786ac75..918f05b453 100644
--- a/autosklearn/metalearning/files/average_precision_multiclass.classification_dense/algorithm_runs.arff
+++ b/autosklearn/metalearning/files/average_precision_multiclass.classification_dense/algorithm_runs.arff
@@ -7,130 +7,135 @@
 @ATTRIBUTE runstatus {ok, timeout, memout, not_applicable, crash, other}
 
 @DATA
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-75212,1.0,2,0.1521360793759462,ok
-252,1.0,3,?,not_applicable
-246,1.0,4,?,not_applicable
-75178,1.0,5,?,not_applicable
-75239,1.0,6,0.0,ok
-75171,1.0,7,0.08666662514386947,ok
-75233,1.0,8,0.008129429494274976,ok
-248,1.0,9,?,not_applicable
-75231,1.0,10,?,not_applicable
-2123,1.0,11,?,not_applicable
-75196,1.0,12,0.0002747755999267243,ok
-75188,1.0,13,?,not_applicable
-75092,1.0,14,0.35563709986626435,ok
-75248,1.0,15,0.6810602093884777,ok
-75126,1.0,16,0.003701142785307976,ok
-75234,1.0,17,0.0025447045930394596,ok
-75150,1.0,18,0.21720753099838463,ok
-258,1.0,19,?,not_applicable
-75168,1.0,20,?,not_applicable
-75235,1.0,21,?,not_applicable
-75159,1.0,22,0.5473461247136038,ok
-244,1.0,23,?,not_applicable
-75221,1.0,24,?,not_applicable
-75219,1.0,25,0.006712691671677318,ok
-75202,1.0,26,?,not_applicable
-3043,1.0,27,0.05506375879821879,ok
-75205,1.0,28,?,not_applicable
-75174,1.0,29,0.10531801123409645,ok
-288,1.0,30,?,not_applicable
-75250,1.0,31,?,not_applicable
-275,1.0,32,?,not_applicable
-75142,1.0,33,0.014381056931335068,ok
-75213,1.0,34,0.041936349169275466,ok
-75099,1.0,35,0.4678374883252291,ok
-75243,1.0,36,?,not_applicable
-75184,1.0,37,0.07469764361674125,ok
-75222,1.0,38,0.21559326045474714,ok
-75175,1.0,39,0.044140533128435244,ok
-233,1.0,40,5.842925670007659e-05,ok
-75161,1.0,41,0.011037903669269156,ok
-75176,1.0,42,0.0006938651513165306,ok
-75090,1.0,43,?,not_applicable
-75114,1.0,44,0.004350183875990177,ok
-260,1.0,45,?,not_applicable
-236,1.0,46,?,not_applicable
-75141,1.0,47,0.011628188830952624,ok
-75107,1.0,48,0.4583775449502874,ok
-262,1.0,49,?,not_applicable
-75146,1.0,50,0.027314797507292266,ok
-75189,1.0,51,0.0014471736348927733,ok
-2350,1.0,52,0.5707237410842824,ok
-253,1.0,53,?,not_applicable
-2122,1.0,54,?,not_applicable
-75110,1.0,55,?,not_applicable
-75249,1.0,56,0.0029122637768774773,ok
-75108,1.0,57,0.0,ok
-242,1.0,58,?,not_applicable
-75117,1.0,59,0.008280862635277386,ok
-75191,1.0,60,0.057807890742150425,ok
-75226,1.0,61,6.481298084892728e-07,ok
-261,1.0,62,0.34655969840600265,ok
-75236,1.0,63,?,not_applicable
-75095,1.0,64,0.05557088536188293,ok
-75148,1.0,65,0.05374729572069947,ok
-75093,1.0,66,0.5728463360258527,ok
-75223,1.0,67,?,not_applicable
-75244,1.0,68,0.6590615414961503,ok
-75109,1.0,69,?,not_applicable
-75197,1.0,70,?,not_applicable
-75127,1.0,71,0.3152529081908554,ok
-75143,1.0,72,0.0028502200148861068,ok
-75153,1.0,73,0.01920221368233055,ok
-75173,1.0,74,0.045918714790693316,ok
-75215,1.0,75,0.002629240748430073,ok
-75195,1.0,76,0.0,ok
-75207,1.0,77,?,not_applicable
-266,1.0,78,?,not_applicable
-75225,1.0,79,0.5209808779102566,ok
-75166,1.0,80,0.03269537102137032,ok
-75100,1.0,81,0.8342571973375668,ok
-75169,1.0,82,?,not_applicable
-75132,1.0,83,0.8701802278884528,ok
-273,1.0,84,0.014785628938213002,ok
-75121,1.0,85,0.0,ok
-75098,1.0,86,?,not_applicable
-75115,1.0,87,0.011733935395969564,ok
-75116,1.0,88,0.0008296925826821733,ok
-75185,1.0,89,0.052504213743351236,ok
-2119,1.0,90,?,not_applicable
-75157,1.0,91,0.4866314915324267,ok
-75113,1.0,92,0.009714673071875124,ok
-75134,1.0,93,?,not_applicable
-75096,1.0,94,?,not_applicable
-75203,1.0,95,?,not_applicable
-75182,1.0,96,0.09984152302439242,ok
-251,1.0,97,?,not_applicable
-75123,1.0,98,?,not_applicable
-75237,1.0,99,8.19202841673139e-07,ok
-75125,1.0,100,0.008449900024096846,ok
-75232,1.0,101,0.11706237289853583,ok
-75103,1.0,102,0.029330892328217617,ok
-75192,1.0,103,0.4894107526437548,ok
-75230,1.0,104,?,not_applicable
-75139,1.0,105,0.0011780620103367667,ok
-75227,1.0,106,0.0870759275296118,ok
-2120,1.0,107,?,not_applicable
-75124,1.0,108,0.4676991332383039,ok
-75240,1.0,109,0.021029116580781215,ok
-75129,1.0,110,0.632260520966013,ok
-75198,1.0,111,?,not_applicable
-75201,1.0,112,?,not_applicable
-75112,1.0,113,0.08377189935980578,ok
-75133,1.0,114,0.46574825307214884,ok
-75105,1.0,115,0.927782202400772,ok
-75154,1.0,116,?,not_applicable
-75177,1.0,117,0.04366392008249165,ok
-2117,1.0,118,0.030101388707305388,ok
-75156,1.0,119,0.12785728733246993,ok
-75097,1.0,120,0.016261008313539493,ok
-75101,1.0,121,0.18253090770820612,ok
-75172,1.0,122,?,not_applicable
-75106,1.0,123,0.8354934073272688,ok
-75179,1.0,124,0.21620021154356506,ok
-75187,1.0,125,0.0012821066451785823,ok
-75120,1.0,126,0.0035852132645233237,ok
-75193,1.0,127,?,not_applicable
+75192,1.0,1,0.4878671463781179,ok
+75119,1.0,2,0.002137350729978782,ok
+75139,1.0,321,0.0011032597395757016,ok
+75212,1.0,7,0.15256933986822485,ok
+246,1.0,8,?,not_applicable
+252,1.0,9,?,not_applicable
+75178,1.0,10,?,not_applicable
+75177,1.0,13,0.010583210410810229,ok
+75092,1.0,14,0.30710749896123346,ok
+75239,1.0,15,0.0,ok
+75173,1.0,264,0.04498004745710049,ok
+75215,1.0,269,0.002476203635612073,ok
+75171,1.0,21,0.08332235273571365,ok
+75112,1.0,23,0.08193498734798665,ok
+75227,1.0,24,0.08588152611624555,ok
+75233,1.0,102,0.0064702521306301275,ok
+75182,1.0,138,0.09558610812790125,ok
+253,1.0,27,?,not_applicable
+75157,1.0,29,0.4866314915324267,ok
+75187,1.0,267,0.0012256656184541637,ok
+75124,1.0,31,0.4642760381864932,ok
+75090,1.0,32,?,not_applicable
+75222,1.0,285,0.18411818014888381,ok
+75231,1.0,34,?,not_applicable
+75185,1.0,37,0.051632868284900324,ok
+2123,1.0,39,?,not_applicable
+75150,1.0,234,0.20737529848879477,ok
+75143,1.0,43,0.0003380689531951031,ok
+75196,1.0,46,0.0002747755999267243,ok
+75188,1.0,49,?,not_applicable
+75248,1.0,54,0.6619346271836501,ok
+75249,1.0,204,0.002653934516532064,ok
+75113,1.0,59,0.006215426914405242,ok
+75126,1.0,63,0.002676573380457681,ok
+288,1.0,64,?,not_applicable
+251,1.0,65,?,not_applicable
+75184,1.0,156,0.07476766843004146,ok
+75234,1.0,68,0.0025570500491878256,ok
+258,1.0,73,?,not_applicable
+75166,1.0,281,0.02779561860055213,ok
+75168,1.0,77,?,not_applicable
+75148,1.0,276,0.0560527025009423,ok
+75235,1.0,81,?,not_applicable
+75159,1.0,85,0.42859893550912664,ok
+75146,1.0,88,0.026973436894305536,ok
+244,1.0,89,?,not_applicable
+75141,1.0,163,0.01173804467069206,ok
+75221,1.0,92,?,not_applicable
+75219,1.0,101,0.003957289838288358,ok
+75202,1.0,96,?,not_applicable
+3043,1.0,99,0.010220794182362214,ok
+75205,1.0,103,?,not_applicable
+75174,1.0,106,0.09374742289809834,ok
+75250,1.0,111,?,not_applicable
+75179,1.0,238,0.21841087762761613,ok
+275,1.0,115,?,not_applicable
+242,1.0,116,?,not_applicable
+75207,1.0,117,?,not_applicable
+75142,1.0,118,0.014259133477716013,ok
+75099,1.0,171,0.4137854912995014,ok
+75243,1.0,126,?,not_applicable
+75175,1.0,253,0.04341182208035166,ok
+233,1.0,137,8.366996616537836e-05,ok
+75161,1.0,139,0.010310568908566298,ok
+75176,1.0,143,0.0006257932468407557,ok
+262,1.0,146,?,not_applicable
+75129,1.0,402,0.5738839791179444,ok
+261,1.0,221,0.3342676421661406,ok
+75114,1.0,152,0.0038194827767713546,ok
+75093,1.0,230,0.542497148196514,ok
+260,1.0,158,?,not_applicable
+236,1.0,160,?,not_applicable
+254,1.0,166,0.0,ok
+75107,1.0,167,0.44862438395455684,ok
+75181,1.0,175,?,not_applicable
+75189,1.0,186,0.001708447332475549,ok
+75163,1.0,189,0.02685795440651706,ok
+2350,1.0,191,0.5668173788275173,ok
+2122,1.0,195,?,not_applicable
+75110,1.0,198,?,not_applicable
+75213,1.0,394,0.033809555590903706,ok
+75095,1.0,284,0.05533956012799779,ok
+75108,1.0,207,0.0,ok
+75117,1.0,211,0.005428243341756178,ok
+75191,1.0,218,0.05749081881675733,ok
+75226,1.0,219,1.4184231685643311e-05,ok
+75244,1.0,372,0.6238946964248455,ok
+75236,1.0,223,?,not_applicable
+75169,1.0,227,?,not_applicable
+75116,1.0,319,0.0005163333961275551,ok
+75223,1.0,235,?,not_applicable
+75109,1.0,242,?,not_applicable
+75197,1.0,245,?,not_applicable
+75237,1.0,364,1.0920271134029491e-06,ok
+248,1.0,250,?,not_applicable
+2119,1.0,254,?,not_applicable
+75127,1.0,255,0.3143148012874739,ok
+75153,1.0,263,0.0211371390556796,ok
+75195,1.0,271,1.56543381590879e-08,ok
+266,1.0,277,?,not_applicable
+75225,1.0,280,0.5115141783633365,ok
+75100,1.0,288,0.8230078173288692,ok
+75132,1.0,294,0.850465089313205,ok
+75210,1.0,298,0.0,ok
+273,1.0,301,0.014844523360765693,ok
+75133,1.0,413,0.4929884165869626,ok
+75121,1.0,304,0.0,ok
+75098,1.0,310,?,not_applicable
+75115,1.0,315,0.002899511502766572,ok
+75217,1.0,337,?,not_applicable
+75134,1.0,341,?,not_applicable
+75096,1.0,346,?,not_applicable
+75203,1.0,350,?,not_applicable
+75123,1.0,360,?,not_applicable
+75125,1.0,369,0.005869457471176065,ok
+2120,1.0,371,?,not_applicable
+75232,1.0,374,0.11110189077492749,ok
+75103,1.0,379,0.02047713329298051,ok
+75230,1.0,386,?,not_applicable
+75240,1.0,399,0.017915489066529955,ok
+75198,1.0,404,?,not_applicable
+75201,1.0,407,?,not_applicable
+75105,1.0,415,0.8760240593052668,ok
+75154,1.0,420,?,not_applicable
+2117,1.0,429,0.02913958847349507,ok
+75156,1.0,430,0.11592045149703145,ok
+75097,1.0,434,0.014519472944802159,ok
+75101,1.0,438,0.17587098260204748,ok
+75172,1.0,443,?,not_applicable
+75106,1.0,449,0.7856870980692269,ok
+75120,1.0,456,0.002216917908264948,ok
+75193,1.0,461,?,not_applicable
diff --git a/autosklearn/metalearning/files/average_precision_multiclass.classification_dense/configurations.csv b/autosklearn/metalearning/files/average_precision_multiclass.classification_dense/configurations.csv
index 5644caa737..cc2f52cdc5 100644
--- a/autosklearn/metalearning/files/average_precision_multiclass.classification_dense/configurations.csv
+++ b/autosklearn/metalearning/files/average_precision_multiclass.classification_dense/configurations.csv
@@ -1,128 +1,133 @@
 idx,balancing:strategy,classifier:__choice__,classifier:adaboost:algorithm,classifier:adaboost:learning_rate,classifier:adaboost:max_depth,classifier:adaboost:n_estimators,classifier:bernoulli_nb:alpha,classifier:bernoulli_nb:fit_prior,classifier:decision_tree:criterion,classifier:decision_tree:max_depth_factor,classifier:decision_tree:max_features,classifier:decision_tree:max_leaf_nodes,classifier:decision_tree:min_impurity_decrease,classifier:decision_tree:min_samples_leaf,classifier:decision_tree:min_samples_split,classifier:decision_tree:min_weight_fraction_leaf,classifier:extra_trees:bootstrap,classifier:extra_trees:criterion,classifier:extra_trees:max_depth,classifier:extra_trees:max_features,classifier:extra_trees:max_leaf_nodes,classifier:extra_trees:min_impurity_decrease,classifier:extra_trees:min_samples_leaf,classifier:extra_trees:min_samples_split,classifier:extra_trees:min_weight_fraction_leaf,classifier:gradient_boosting:early_stop,classifier:gradient_boosting:l2_regularization,classifier:gradient_boosting:learning_rate,classifier:gradient_boosting:loss,classifier:gradient_boosting:max_bins,classifier:gradient_boosting:max_depth,classifier:gradient_boosting:max_leaf_nodes,classifier:gradient_boosting:min_samples_leaf,classifier:gradient_boosting:n_iter_no_change,classifier:gradient_boosting:scoring,classifier:gradient_boosting:tol,classifier:gradient_boosting:validation_fraction,classifier:k_nearest_neighbors:n_neighbors,classifier:k_nearest_neighbors:p,classifier:k_nearest_neighbors:weights,classifier:lda:n_components,classifier:lda:shrinkage,classifier:lda:shrinkage_factor,classifier:lda:tol,classifier:liblinear_svc:C,classifier:liblinear_svc:dual,classifier:liblinear_svc:fit_intercept,classifier:liblinear_svc:intercept_scaling,classifier:liblinear_svc:loss,classifier:liblinear_svc:multi_class,classifier:liblinear_svc:penalty,classifier:liblinear_svc:tol,classifier:libsvm_svc:C,classifier:libsvm_svc:coef0,classifier:libsvm_svc:degree,classifier:libsvm_svc:gamma,classifier:libsvm_svc:kernel,classifier:libsvm_svc:max_iter,classifier:libsvm_svc:shrinking,classifier:libsvm_svc:tol,classifier:multinomial_nb:alpha,classifier:multinomial_nb:fit_prior,classifier:passive_aggressive:C,classifier:passive_aggressive:average,classifier:passive_aggressive:fit_intercept,classifier:passive_aggressive:loss,classifier:passive_aggressive:tol,classifier:qda:reg_param,classifier:random_forest:bootstrap,classifier:random_forest:criterion,classifier:random_forest:max_depth,classifier:random_forest:max_features,classifier:random_forest:max_leaf_nodes,classifier:random_forest:min_impurity_decrease,classifier:random_forest:min_samples_leaf,classifier:random_forest:min_samples_split,classifier:random_forest:min_weight_fraction_leaf,classifier:sgd:alpha,classifier:sgd:average,classifier:sgd:epsilon,classifier:sgd:eta0,classifier:sgd:fit_intercept,classifier:sgd:l1_ratio,classifier:sgd:learning_rate,classifier:sgd:loss,classifier:sgd:penalty,classifier:sgd:power_t,classifier:sgd:tol,data_preprocessing:categorical_transformer:categorical_encoding:__choice__,data_preprocessing:categorical_transformer:category_coalescence:__choice__,data_preprocessing:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction,data_preprocessing:numerical_transformer:imputation:strategy,data_preprocessing:numerical_transformer:rescaling:__choice__,data_preprocessing:numerical_transformer:rescaling:quantile_transformer:n_quantiles,data_preprocessing:numerical_transformer:rescaling:quantile_transformer:output_distribution,data_preprocessing:numerical_transformer:rescaling:robust_scaler:q_max,data_preprocessing:numerical_transformer:rescaling:robust_scaler:q_min,feature_preprocessor:__choice__,feature_preprocessor:extra_trees_preproc_for_classification:bootstrap,feature_preprocessor:extra_trees_preproc_for_classification:criterion,feature_preprocessor:extra_trees_preproc_for_classification:max_depth,feature_preprocessor:extra_trees_preproc_for_classification:max_features,feature_preprocessor:extra_trees_preproc_for_classification:max_leaf_nodes,feature_preprocessor:extra_trees_preproc_for_classification:min_impurity_decrease,feature_preprocessor:extra_trees_preproc_for_classification:min_samples_leaf,feature_preprocessor:extra_trees_preproc_for_classification:min_samples_split,feature_preprocessor:extra_trees_preproc_for_classification:min_weight_fraction_leaf,feature_preprocessor:extra_trees_preproc_for_classification:n_estimators,feature_preprocessor:fast_ica:algorithm,feature_preprocessor:fast_ica:fun,feature_preprocessor:fast_ica:n_components,feature_preprocessor:fast_ica:whiten,feature_preprocessor:feature_agglomeration:affinity,feature_preprocessor:feature_agglomeration:linkage,feature_preprocessor:feature_agglomeration:n_clusters,feature_preprocessor:feature_agglomeration:pooling_func,feature_preprocessor:kernel_pca:coef0,feature_preprocessor:kernel_pca:degree,feature_preprocessor:kernel_pca:gamma,feature_preprocessor:kernel_pca:kernel,feature_preprocessor:kernel_pca:n_components,feature_preprocessor:kitchen_sinks:gamma,feature_preprocessor:kitchen_sinks:n_components,feature_preprocessor:liblinear_svc_preprocessor:C,feature_preprocessor:liblinear_svc_preprocessor:dual,feature_preprocessor:liblinear_svc_preprocessor:fit_intercept,feature_preprocessor:liblinear_svc_preprocessor:intercept_scaling,feature_preprocessor:liblinear_svc_preprocessor:loss,feature_preprocessor:liblinear_svc_preprocessor:multi_class,feature_preprocessor:liblinear_svc_preprocessor:penalty,feature_preprocessor:liblinear_svc_preprocessor:tol,feature_preprocessor:nystroem_sampler:coef0,feature_preprocessor:nystroem_sampler:degree,feature_preprocessor:nystroem_sampler:gamma,feature_preprocessor:nystroem_sampler:kernel,feature_preprocessor:nystroem_sampler:n_components,feature_preprocessor:pca:keep_variance,feature_preprocessor:pca:whiten,feature_preprocessor:polynomial:degree,feature_preprocessor:polynomial:include_bias,feature_preprocessor:polynomial:interaction_only,feature_preprocessor:random_trees_embedding:bootstrap,feature_preprocessor:random_trees_embedding:max_depth,feature_preprocessor:random_trees_embedding:max_leaf_nodes,feature_preprocessor:random_trees_embedding:min_samples_leaf,feature_preprocessor:random_trees_embedding:min_samples_split,feature_preprocessor:random_trees_embedding:min_weight_fraction_leaf,feature_preprocessor:random_trees_embedding:n_estimators,feature_preprocessor:select_percentile_classification:percentile,feature_preprocessor:select_percentile_classification:score_func,feature_preprocessor:select_rates_classification:alpha,feature_preprocessor:select_rates_classification:mode,feature_preprocessor:select_rates_classification:score_func
-1,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,154.65010711000676,-0.7535496516458353,2,4.566684304178357,poly,-1,False,1.895280127876398e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,minmax,,,,,extra_trees_preproc_for_classification,True,gini,None,0.698818742714691,None,0.0,20,12,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-2,none,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.9525269450493825,None,0.0,4,20,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,standardize,,,,,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-3,none,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,188,None,,0.09976330267940166,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,none,,,,,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,-0.05439674489937307,2,0.051865468486041774,poly,727,,,,,,,,,,,,,,,,,
-4,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,23916.78290516781,False,True,1,squared_hinge,ovr,l2,0.0006655607726658531,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.008808715242823137,median,none,,,,,kernel_pca,,,,,,,,,,,,,,,,,,,0.2633153190620261,2,0.004770332714291669,poly,1483,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-5,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,2,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010091246322497477,median,none,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,95.37976833605768,mutual_info,,,
-6,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.933900673392165,None,0.0,7,4,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,minmax,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-7,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,0.9455840414651977,0.035051597802663065,auto,255,None,6,39,20,loss,1e-07,0.03762410007700571,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,robust_scaler,,,0.8083718526277422,0.24604827304064597,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,1377.6677844231401,False,True,1,squared_hinge,ovr,l1,0.0019190146350085739,,,,,,,,,,,,,,,,,,,,,,
-8,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5238814331135578,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,median,none,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,28,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-9,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1292.7448076998514,0.004489932344506231,2,1.3088036376373702,poly,-1,False,1.7943262139196537e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,minmax,,,,,feature_agglomeration,,,,,,,,,,,,,,,cosine,average,386,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-10,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.5244313211416313,None,0.0,1,2,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.454300236339658,most_frequent,robust_scaler,,,0.7858830918128734,0.2705174720960696,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-11,weighting,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0006294780694492238,most_frequent,standardize,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.063715750591364,fdr,chi2
-12,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.541827838541001,True,True,squared_hinge,0.00015757576796329985,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,standardize,,,,,extra_trees_preproc_for_classification,False,entropy,None,0.5282111930398031,None,0.0,17,4,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-13,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.25657910619102237,None,0.0,6,6,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.021086303324492208,most_frequent,quantile_transformer,1247,uniform,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.16021572815319468,fpr,chi2
-14,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6185424163558029,False,True,1,squared_hinge,ovr,l2,4.385767395120581e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010397452590017174,median,quantile_transformer,961,normal,,,feature_agglomeration,,,,,,,,,,,,,,,cosine,average,25,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-15,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.643761610990255,None,0.0,20,6,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.039859381630488695,mean,normalize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,
-16,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.6931271368243409,None,0.0,11,10,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.362940004438799,most_frequent,robust_scaler,,,0.9177587796305063,0.17084038458597867,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,30.491514144623952,mutual_info,,,
-17,none,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.1769332447629957,mean,minmax,,,,,fast_ica,,,,,,,,,,,deflation,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-18,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00335225820194075,True,True,squared_hinge,2.9136221277715572e-05,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.00021598139496857644,median,minmax,,,,,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,4,None,4,9,1.0,14,,,,,
-19,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,199,None,,0.003239674765949011,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,robust_scaler,,,0.7228447795482008,0.25,kernel_pca,,,,,,,,,,,,,,,,,,,0.09440148939740256,3,0.0008114095405154861,poly,1996,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-20,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.6039096182414764,None,0.0,2,20,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.1939609823704447,most_frequent,robust_scaler,,,0.7331327008874781,0.29747488162877694,extra_trees_preproc_for_classification,True,entropy,None,0.33401603690569853,None,0.0,4,4,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-21,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,2.5882079595129472e-05,0.36125906161027654,auto,255,None,341,119,16,loss,1e-07,0.2924550184445239,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.02338478303505337,most_frequent,robust_scaler,,,0.7866731278723753,0.29543475006822734,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,68,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-22,weighting,adaboost,SAMME.R,0.2127290016566289,1,57,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.012688742338096554,median,quantile_transformer,1048,uniform,,,feature_agglomeration,,,,,,,,,,,,,,,euclidean,average,24,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-23,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7725.740943204492,,,3.122840783722913,rbf,-1,True,1.4116065061807418e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.14280178218510184,most_frequent,none,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,27,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-24,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.21794354428393548,None,0.0,2,16,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0025451910134387575,median,quantile_transformer,1477,uniform,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
-25,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5010952533165973,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.005838375247601407,mean,normalize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,
-26,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5514127836113225,None,0.0,4,20,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.010750756228608195,most_frequent,robust_scaler,,,0.7471420685032172,0.2916152663822578,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.22557315057515023,fwe,chi2
-27,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.9663114854040722,None,0.0,11,12,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.26932389682904634,median,quantile_transformer,23,uniform,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l1,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,
-28,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.506204385946851,None,0.0,2,8,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.03433007982751011,mean,none,,,,,extra_trees_preproc_for_classification,False,gini,None,0.32676415738837183,None,0.0,5,6,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-29,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9333237919291875,None,0.0,8,3,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,
-30,none,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.9728532763078892,None,0.0,13,9,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,none,,,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6651071190864879,False,,,,,,,,,,,,,,,
-31,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9561075301148197,None,0.0,6,2,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,
-32,none,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,184,manual,0.5022041786628263,2.3064966255468173e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,most_frequent,standardize,,,,,kernel_pca,,,,,,,,,,,,,,,,,,,-0.32023224593255817,2,0.09479590655633498,poly,1382,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-33,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.8832772200272849,None,0.0,17,3,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,quantile_transformer,1733,normal,,,feature_agglomeration,,,,,,,,,,,,,,,euclidean,complete,262,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-34,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,22194.242946902177,0.45210849669021247,,0.0005669713702171556,sigmoid,-1,False,0.03542142266081451,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,quantile_transformer,291,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-35,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.47147939823444396,None,0.0,18,13,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,robust_scaler,,,0.9414880591550729,0.19219631546028484,fast_ica,,,,,,,,,,,parallel,logcosh,1978,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-36,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,0.032732094595234795,0.09918078053098744,auto,255,None,97,54,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.019135985037746533,median,quantile_transformer,1000,uniform,,,feature_agglomeration,,,,,,,,,,,,,,,euclidean,average,37,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-37,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.7521969592385672,None,0.0,9,5,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.020402264943872365,median,robust_scaler,,,0.737227213630259,0.20752747663727789,fast_ica,,,,,,,,,,,parallel,exp,312,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-38,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.887898394612674,None,0.0,10,19,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,robust_scaler,,,0.8350698288864961,0.2347163538066168,extra_trees_preproc_for_classification,False,entropy,None,0.49795108918064074,None,0.0,13,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-39,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,3.6796787331285204e-07,0.14399102828508656,auto,255,None,18,12,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,quantile_transformer,965,normal,,,feature_agglomeration,,,,,,,,,,,,,,,cosine,average,365,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-40,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7088164344533493,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,mean,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,
-41,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,1.943186107984505e-07,0.08852455702036988,auto,255,None,5,72,19,loss,1e-07,0.039837460762271175,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.01591265419367627,median,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,
-42,weighting,adaboost,SAMME,0.5210651042094924,10,401,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,standardize,,,,,feature_agglomeration,,,,,,,,,,,,,,,euclidean,complete,89,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-43,none,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0686374607394679,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,minmax,,,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.8490260132946401,False,,,,,,,,,,,,,,,
-44,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.6931271368243409,None,0.0,11,10,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.362940004438799,most_frequent,robust_scaler,,,0.9177587796305063,0.17084038458597867,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,30.491514144623952,mutual_info,,,
-45,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.3663040729445102,None,0.0,20,19,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.010000000000000004,median,robust_scaler,,,0.7338553275791654,0.25,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,331,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-46,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,92.66144306787051,,,0.04654639302091653,rbf,-1,True,0.0022231544280610005,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.005194838694455963,mean,none,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,15,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-47,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0663383838629283,,,0.06227886986638805,rbf,-1,False,5.002833516846625e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0005633176938817242,mean,standardize,,,,,feature_agglomeration,,,,,,,,,,,,,,,cosine,complete,64,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-48,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.8095522998548572,None,0.0,17,10,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.031940595071539304,mean,quantile_transformer,1563,normal,,,extra_trees_preproc_for_classification,False,entropy,None,0.9330145598603196,None,0.0,18,10,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-49,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,26.502259985828186,,,9.475662388405154e-05,rbf,-1,False,0.00019690635968217866,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-50,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,2.5360196477156387e-07,0.011018756408558218,auto,255,None,40,122,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.00011749164080669445,most_frequent,quantile_transformer,757,uniform,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,5.097834697156853,False,True,1,squared_hinge,ovr,l1,0.053877834919477424,,,,,,,,,,,,,,,,,,,,,,
-51,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5255991794296913,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-52,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.34697723213923637,None,0.0,15,18,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.1923594385988973,mean,normalize,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,25.77499302945827,chi2,,,
-53,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1252.7997374154156,0.20811431041897355,5,0.0018140593074227453,poly,-1,False,0.03149782358621565,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.001930822730474197,most_frequent,quantile_transformer,233,normal,,,feature_agglomeration,,,,,,,,,,,,,,,euclidean,ward,29,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-54,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,1.5661823810486637e-10,0.08996863554015262,auto,255,None,156,89,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.009331521286159971,median,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,
-55,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.6109468076475573,0.8748383232154038,4,0.41333070422336576,poly,-1,False,3.591268778393482e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.011804925819789203,median,minmax,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-56,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.7610308078756552,None,0.0,1,2,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.010000000000000004,mean,robust_scaler,,,0.9205545465103644,0.20272761060743316,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-57,weighting,adaboost,SAMME.R,0.03127273017917347,10,496,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,standardize,,,,,fast_ica,,,,,,,,,,,deflation,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-58,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,151,None,,0.0003283777607180646,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.2510527431514227,mean,normalize,,,,,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,-0.96093182232312,,0.05762510964607661,sigmoid,2632,,,,,,,,,,,,,,,,,
-59,none,bernoulli_nb,,,,,0.6524090653939782,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.005164169126808402,most_frequent,quantile_transformer,16,normal,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.033082145443631535,False,True,1,squared_hinge,ovr,l1,0.002615020898325393,,,,,,,,,,,,,,,,,,,,,,
-60,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.6453555269772124,None,0.0,1,4,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,normalize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-61,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,202.1500193823052,,,1.4635959201092044,rbf,-1,True,0.02257461509273525,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,none,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,75.98663128595764,mutual_info,,,
-62,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.20911875507660596,None,0.0,12,8,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,median,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,
-63,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.34534888916828577,0.02513634776687268,2,1.0733091880109409,poly,-1,True,0.0006974263047918307,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,quantile_transformer,1000,normal,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,77.99934218022861,mutual_info,,,
-64,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,29.55731828912983,False,True,1,squared_hinge,ovr,l2,0.0011332486951954968,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,
-65,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,1.2189763538537644e-08,0.11802527280286065,auto,255,None,139,29,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.01616335803518846,median,robust_scaler,,,0.8117197935318263,0.1817743361759944,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-66,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.8425560317492612,None,0.0,8,15,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.015276988502366128,median,none,,,,,fast_ica,,,,,,,,,,,parallel,cube,17,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-67,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,0.2638627266631576,0.022360820165005443,auto,255,None,171,6,19,loss,1e-07,0.016209060012924756,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.008763072395677399,mean,standardize,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,78,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-68,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,925.3053743248586,False,True,1,squared_hinge,ovr,l2,0.02764481198395717,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.3850718018366796,most_frequent,quantile_transformer,786,uniform,,,feature_agglomeration,,,,,,,,,,,,,,,cosine,average,156,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-69,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.8278163313806293,None,0.0,3,5,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,robust_scaler,,,0.725835793765774,0.2571431961341018,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.07784020380291123,fdr,f_classif
-70,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0023391956112474004,False,True,squared_hinge,0.011792446147470903,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.08295440990272292,most_frequent,quantile_transformer,962,normal,,,kernel_pca,,,,,,,,,,,,,,,,,,,,,,cosine,1778,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-71,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.41019363453549706,None,0.0,15,2,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.0003507389224306018,median,none,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.20614098085343638,fdr,chi2
-72,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.2080334109699375,None,0.0,15,13,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.010000000000000004,median,minmax,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-73,none,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9931779675565967,None,0.0,18,17,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,robust_scaler,,,0.75,0.2945917010472818,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,
-74,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.01084024283715146,None,0.0,12,3,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,False,,,,,,,,,,,,
-75,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,1.2430504242164082e-09,0.03359568307446533,auto,255,None,1609,29,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,standardize,,,,,feature_agglomeration,,,,,,,,,,,,,,,cosine,complete,40,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-76,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7.975058275181761,True,True,squared_hinge,1.4887256000993565e-05,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,minmax,,,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.7747665215782399,False,True,1,squared_hinge,ovr,l1,3.926086263836518e-05,,,,,,,,,,,,,,,,,,,,,,
-77,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.34936025988397124,None,0.0,2,13,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00018601309647406987,median,robust_scaler,,,0.8634309348843472,0.26976989465367607,extra_trees_preproc_for_classification,False,entropy,None,0.48025175288975036,None,0.0,6,17,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-78,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,2.925599188129995e-05,0.12560285030651847,auto,255,None,300,193,17,loss,1e-07,0.039442997915546514,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00299400754997757,mean,robust_scaler,,,0.7093292916146515,0.009366983030047793,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-79,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.933900673392165,None,0.0,7,4,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,minmax,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-80,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1661.2274747409913,,,0.025821208852387936,rbf,-1,False,0.0017011435104921824,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,none,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,306,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-81,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.1355242495213339,None,0.0,13,5,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010389218573530956,most_frequent,standardize,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.13381345070408926,fdr,f_classif
-82,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,21.85207494001697,0.18872999589786676,4,1.1863646021868761,poly,-1,False,0.0021338371901294443,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0034080852834770217,mean,normalize,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.11326005109886934,fdr,f_classif
-83,weighting,adaboost,SAMME,0.38341570193537416,9,250,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,minmax,,,,,feature_agglomeration,,,,,,,,,,,,,,,euclidean,average,13,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-84,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,0.0009192613701152634,0.12918951989377014,auto,255,None,9,150,17,loss,1e-07,0.08906232732168995,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010078479963961122,median,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,
-85,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.9499894634982698,None,0.0,20,15,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.01161399161402058,mean,normalize,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,42.76696665279698,f_classif,,,
-86,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,1,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.019797877010737826,mean,normalize,,,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.8239678741128165,False,,,,,,,,,,,,,,,
-87,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.46057831591617715,False,True,hinge,0.04557857428827514,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00027457445401600137,median,standardize,,,,,extra_trees_preproc_for_classification,True,gini,None,0.48190346970486964,None,0.0,17,18,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-88,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.46057831591617715,False,True,hinge,0.04557857428827514,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00027457445401600137,median,standardize,,,,,extra_trees_preproc_for_classification,True,gini,None,0.48190346970486964,None,0.0,17,18,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-89,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,64,auto,,0.00021332620906594865,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.026032882800756493,median,standardize,,,,,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,-0.9513223792709335,,0.00029881582960753004,sigmoid,543,,,,,,,,,,,,,,,,,
-90,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.26356754187780895,None,0.0,4,3,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010966178401758774,mean,robust_scaler,,,0.7817043861798886,0.24371720112407158,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
-91,none,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.563056219822946,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.32793677336996485,most_frequent,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-92,weighting,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.9990376970503236,None,0.0,11,8,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,normalize,,,,,feature_agglomeration,,,,,,,,,,,,,,,cosine,complete,109,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-93,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.22879311080499845,None,0.0,8,4,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.014320022547615783,median,minmax,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.238630703440461,mutual_info,,,
-94,weighting,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,2,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0073667222552306465,median,robust_scaler,,,0.701595034693991,0.29514640404971515,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6066051105210388,False,,,,,,,,,,,,,,,
-95,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.107967527639527e-05,True,True,hinge,0.00448294745544775,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,quantile_transformer,953,normal,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,35.00114345405331,chi2,,,
-96,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.457928416564218,None,0.0,12,2,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,
-97,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,1.1522414705935405e-10,0.6132800183073315,auto,255,None,1607,67,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010096911249562407,mean,robust_scaler,,,0.7663493326967942,0.136094217337434,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-98,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1228.3897711257332,0.6862670093933454,5,1.113863025113022,poly,-1,True,1.9271116749907707e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.018161834200128672,mean,normalize,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.06278549919408385,fpr,chi2
-99,none,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.4093238036872129,None,0.0,1,9,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.008254106845490612,most_frequent,robust_scaler,,,0.7823045687170416,0.23189540562406433,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,6,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-100,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.9815182201168626,None,0.0,15,19,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,robust_scaler,,,0.8878662977383482,0.25666364929970126,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,44.15748219826322,mutual_info,,,
-101,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.011049308179256012,True,True,squared_hinge,0.00873440005439477,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.014991197704970448,mean,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,
-102,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.7426893291639965,None,0.0,4,19,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010127721455862085,median,quantile_transformer,1108,uniform,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,263,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-103,none,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,128,manual,0.8574560771111307,0.010886139381599514,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.24115007944216788,most_frequent,normalize,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,62.26514170991452,mutual_info,,,
-104,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.6282663389679584,None,0.0,1,3,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,median,minmax,,,,,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-105,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,8.14394583618027e-09,0.1314274861878701,auto,255,None,27,15,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.3598067419611515,mean,robust_scaler,,,0.7494615433236279,0.260649229829574,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
-106,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.3294588572341214,None,0.0,1,3,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
-107,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,11.046854203258679,,,0.07809571976769347,rbf,-1,False,0.00036193159909053734,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.006445557635410205,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-108,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.7922837297504705,False,True,1,squared_hinge,ovr,l2,0.0005476597326342352,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.012674631437254894,most_frequent,none,,,,,extra_trees_preproc_for_classification,False,entropy,None,0.23145146971319638,None,0.0,7,20,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-109,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.18060139887649052,None,0.0,10,12,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.010000000000000004,most_frequent,robust_scaler,,,0.9298612094255864,0.1263597946722308,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
-110,none,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.563056219822946,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.32793677336996485,most_frequent,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-111,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6427543483503397,True,True,hinge,0.00045318775930916944,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.009338484332306904,median,quantile_transformer,174,normal,,,kernel_pca,,,,,,,,,,,,,,,,,,,,,,cosine,1848,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-112,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.02021502857757827,True,,0.055848937053280076,True,,constant,log,l2,,0.00016356162584832537,no_encoding,no_coalescense,,median,quantile_transformer,27,normal,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.11873149622606233,fwe,chi2
-113,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,9.029473608568458e-09,0.15779990576964315,auto,255,None,37,71,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.018093177195644097,mean,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,False,,,,,,,,,,,,
-114,weighting,adaboost,SAMME.R,0.23766684501868082,10,97,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.004737885672001703,median,none,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50.0,chi2,,,
-115,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l2,2.9468189962573666e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.014870368357163507,most_frequent,quantile_transformer,1000,uniform,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,83.43414935094258,f_classif,,,
-116,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.4864284879550203,None,0.0,1,4,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.008665854799630503,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-117,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.8851946632713493,None,0.0,2,20,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.006095662608922393,median,quantile_transformer,1861,normal,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.10486328449423876,fdr,f_classif
-118,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.6922752267318475,True,True,squared_hinge,0.06299408576730353,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,quantile_transformer,1742,uniform,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,107,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-119,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5508478903876926,None,0.0,5,3,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0020120736976747405,most_frequent,minmax,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-120,weighting,extra_trees,,,,,,,,,,,,,,,True,gini,None,0.9999521514529074,None,0.0,3,10,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,minmax,,,,,extra_trees_preproc_for_classification,False,gini,None,0.24745225001074223,None,0.0,14,18,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-121,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.4210978663047845,None,0.0,5,6,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.006365988550833196,most_frequent,robust_scaler,,,0.8348891074186082,0.00974961635730845,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0695919708890599,fpr,f_classif
-122,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.922871100218626,False,True,squared_hinge,0.09957180737451943,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0016563186856411274,median,quantile_transformer,850,uniform,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.09123695282772847,fpr,chi2
-123,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4834106677905965,False,True,1,squared_hinge,ovr,l2,0.00017625027586147415,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0009157492873444966,most_frequent,quantile_transformer,1150,normal,,,extra_trees_preproc_for_classification,False,entropy,None,0.4981700453792278,None,0.0,9,15,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-124,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1561.1002600303584,,,0.0024048551704194507,rbf,-1,False,0.0001936214667496851,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,minmax,,,,,fast_ica,,,,,,,,,,,parallel,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-125,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.087313242780732,0.0636709914987994,3,0.004481917125024585,poly,-1,False,0.01848880618941367,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.002136015035740733,mean,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-126,weighting,adaboost,SAMME.R,1.696588612003168,9,455,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,minmax,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,82.18726095926662,chi2,,,
-127,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.6466927589470762,None,0.0,11,2,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.009672465311659264,most_frequent,standardize,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,369,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+1,none,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1514.059969351301,False,True,1,squared_hinge,ovr,l2,0.002448814309966599,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,standardize,,,,,extra_trees_preproc_for_classification,True,entropy,None,0.8221481751476923,None,0.0,2,3,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+2,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.8739761373635383,None,0.0,5,9,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,quantile_transformer,1135,uniform,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.06130526963953546,fdr,f_classif
+7,none,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.1956599819716578,None,0.0,1,7,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,robust_scaler,,,0.92941901988866,0.07591140934892966,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,87.6629358232908,mutual_info,,,
+8,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,114,manual,0.11778631707605758,0.0029112721339713465,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.015582195257240751,median,normalize,,,,,kernel_pca,,,,,,,,,,,,,,,,,,,0.42909623253327767,3,0.25407612000343777,poly,1689,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+9,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.685918004243461,True,True,squared_hinge,1.7784504877380735e-05,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010335606354842895,most_frequent,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,
+10,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.7396493120555564,None,0.0,1,18,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010000000000000004,median,robust_scaler,,,0.8908334215036855,0.08666907249785906,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,
+13,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.704884397561306,None,0.0,1,5,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.009223082116761061,median,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
+14,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,4.629265598705481e-10,0.03894546521791516,auto,255,None,5,22,18,loss,1e-07,0.0837137336215462,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.008397749525484112,mean,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
+15,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.21772092350860472,None,0.0,14,17,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.0020488212891693275,median,standardize,,,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,33.74257606530722,False,True,1,squared_hinge,ovr,l1,8.740452966205111e-05,,,,,,,,,,,,,,,,,,,,,,
+21,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,378.090682455998,,,1.5153998334214391,rbf,-1,False,4.744451572262126e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,minmax,,,,,extra_trees_preproc_for_classification,False,gini,None,0.4326333081739829,None,0.0,13,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+23,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,0.00022373024203887573,0.027149343206135476,auto,255,None,49,13,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.00777614320416325,most_frequent,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,
+24,weighting,adaboost,SAMME.R,0.460612885139835,9,500,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0033638377904512094,mean,quantile_transformer,1611,uniform,,,feature_agglomeration,,,,,,,,,,,,,,,euclidean,complete,201,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+27,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.41025844265119926,None,0.0,18,15,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,median,quantile_transformer,1911,normal,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,False,,,,,,,,,,,,
+29,none,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.563056219822946,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.32793677336996485,most_frequent,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+31,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,8.307537134194023e-06,0.11450329729882897,auto,255,None,13,12,20,loss,1e-07,0.04822990410659657,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.022490730066866103,median,quantile_transformer,1230,normal,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
+32,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,101,auto,,1.553086088086431e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.01137059177001425,most_frequent,normalize,,,,,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,-0.021621751597165284,4,0.7468597264810062,poly,5078,,,,,,,,,,,,,,,,,
+34,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,0.06671977293714751,0.04456236025167353,auto,255,None,3,163,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.4333293305857194,mean,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+37,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,161,auto,,0.005011915149502186,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.02713788966372092,median,minmax,,,,,kitchen_sinks,,,,,,,,,,,,,,,,,,,,,,,,0.07896167589979168,2189,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+39,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.6799215694343422,None,0.0,1,5,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.004724310659953132,most_frequent,quantile_transformer,1037,normal,,,fast_ica,,,,,,,,,,,parallel,exp,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+43,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.9479098792950027,None,0.0,12,14,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,quantile_transformer,1758,normal,,,feature_agglomeration,,,,,,,,,,,,,,,cosine,complete,244,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+46,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.4758419143997852,True,True,hinge,0.00010443177354434714,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0002760017168128505,median,standardize,,,,,extra_trees_preproc_for_classification,True,gini,None,0.6518873041225323,None,0.0,15,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+49,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00010123414317660679,False,,0.04011567850168966,True,,constant,log,l1,,0.00014179758438189507,one_hot_encoding,minority_coalescer,0.008920898694379564,mean,robust_scaler,,,0.7712963178784032,0.2702398922671455,kernel_pca,,,,,,,,,,,,,,,,,,,,,,cosine,1090,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+54,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5507274069498738,None,0.0,9,9,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.008412121763959297,most_frequent,quantile_transformer,1034,uniform,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+59,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.15115687152536414,True,True,hinge,0.009954631623104506,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0013410356664535208,most_frequent,quantile_transformer,1293,uniform,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+63,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.957787813103507e-05,False,True,squared_hinge,0.046147943721042806,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.4778210360579751,mean,quantile_transformer,315,uniform,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,1315.2870319104447,False,True,1,squared_hinge,ovr,l1,0.030203490583956487,,,,,,,,,,,,,,,,,,,,,,
+64,weighting,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.5927254703947399,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.004193053494360822,median,none,,,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.5779022723643873,False,,,,,,,,,,,,,,,
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+449,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,2.074711735072034e-07,0.01106799034691714,auto,255,None,13,24,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.020949205931423413,mean,standardize,,,,,extra_trees_preproc_for_classification,True,gini,None,0.43505400875624267,None,0.0,20,8,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+456,none,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.772617001926524e-05,False,,0.00531060153046999,True,,constant,log,l1,,0.00016225369247634786,one_hot_encoding,no_coalescense,,median,quantile_transformer,745,uniform,,,extra_trees_preproc_for_classification,False,entropy,None,0.6017654648980271,None,0.0,17,9,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+461,none,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.9796199105973851,None,0.0,2,13,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.00845950033601661,mean,robust_scaler,,,0.8275228560550734,0.23814615163106348,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
diff --git a/autosklearn/metalearning/files/average_precision_multiclass.classification_dense/description.txt b/autosklearn/metalearning/files/average_precision_multiclass.classification_dense/description.txt
index 939543f7d1..94854e9f8b 100644
--- a/autosklearn/metalearning/files/average_precision_multiclass.classification_dense/description.txt
+++ b/autosklearn/metalearning/files/average_precision_multiclass.classification_dense/description.txt
@@ -1,8 +1,9 @@
 features_cutoff_time: 3600
 features_cutoff_memory: 3072
-number_of_feature_steps: 35
+number_of_feature_steps: 42
 feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances
 feature_step LogNumberOfInstances: LogNumberOfInstances
+feature_step NumberOfClasses: NumberOfClasses
 feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures
 feature_step LogNumberOfFeatures: LogNumberOfFeatures
 feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues
@@ -20,6 +21,11 @@ feature_step DatasetRatio: DatasetRatio, LogDatasetRatio
 feature_step LogDatasetRatio: LogDatasetRatio
 feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio
 feature_step LogInverseDatasetRatio: LogInverseDatasetRatio
+feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD
+feature_step ClassProbabilityMin: ClassProbabilityMin
+feature_step ClassProbabilityMax: ClassProbabilityMax
+feature_step ClassProbabilityMean: ClassProbabilityMean
+feature_step ClassProbabilitySTD: ClassProbabilitySTD
 feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum
 feature_step SymbolsMin: SymbolsMin
 feature_step SymbolsMax: SymbolsMax
@@ -36,11 +42,12 @@ feature_step SkewnessMin: SkewnessMin
 feature_step SkewnessMax: SkewnessMax
 feature_step SkewnessMean: SkewnessMean
 feature_step SkewnessSTD: SkewnessSTD
-features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
+feature_step ClassEntropy: ClassEntropy
+features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy
 features_stochastic: 
-default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
+default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy
 
-algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127
+algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235,236,237,238,239,240,241,242,243,244,245,246,247,248,249,250,251,252,253,254,255,256,257,258,259,260,261,262,263,264,265,266,267,268,269,270,271,272,273,274,275,276,277,278,279,280,281,282,283,284,285,286,287,288,289,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,312,313,314,315,316,317,318,319,320,321,322,323,324,325,326,327,328,329,330,331,332,333,334,335,336,337,338,339,340,341,342,343,344,345,346,347,348,349,350,351,352,353,354,355,356,357,358,359,360,361,362,363,364,365,366,367,368,369,370,371,372,373,374,375,376,377,378,379,380,381,382,383,384,385,386,387,388,389,390,391,392,393,394,395,396,397,398,399,400,401,402,403,404,405,406,407,408,409,410,411,412,413,414,415,416,417,418,419,420,421,422,423,424,425,426,427,428,429,430,431,432,433,434,435,436,437,438,439,440,441,442,443,444,445,446,447,448,449,450,451,452,453,454,455,456,457,458,459,460,461,462,463,464
 algorithms_stochastic: 
 performance_measures: average_precision
 performance_type: solution_quality
diff --git a/autosklearn/metalearning/files/average_precision_multiclass.classification_dense/feature_costs.arff b/autosklearn/metalearning/files/average_precision_multiclass.classification_dense/feature_costs.arff
index 0e17e20d22..8512fd9275 100644
--- a/autosklearn/metalearning/files/average_precision_multiclass.classification_dense/feature_costs.arff
+++ b/autosklearn/metalearning/files/average_precision_multiclass.classification_dense/feature_costs.arff
@@ -4,6 +4,7 @@
 @ATTRIBUTE repetition NUMERIC
 @ATTRIBUTE NumberOfInstances NUMERIC
 @ATTRIBUTE LogNumberOfInstances NUMERIC
+@ATTRIBUTE NumberOfClasses NUMERIC
 @ATTRIBUTE NumberOfFeatures NUMERIC
 @ATTRIBUTE LogNumberOfFeatures NUMERIC
 @ATTRIBUTE MissingValues NUMERIC
@@ -21,6 +22,11 @@
 @ATTRIBUTE LogDatasetRatio NUMERIC
 @ATTRIBUTE InverseDatasetRatio NUMERIC
 @ATTRIBUTE LogInverseDatasetRatio NUMERIC
+@ATTRIBUTE ClassOccurences NUMERIC
+@ATTRIBUTE ClassProbabilityMin NUMERIC
+@ATTRIBUTE ClassProbabilityMax NUMERIC
+@ATTRIBUTE ClassProbabilityMean NUMERIC
+@ATTRIBUTE ClassProbabilitySTD NUMERIC
 @ATTRIBUTE NumSymbols NUMERIC
 @ATTRIBUTE SymbolsMin NUMERIC
 @ATTRIBUTE SymbolsMax NUMERIC
@@ -37,162 +43,138 @@
 @ATTRIBUTE SkewnessMax NUMERIC
 @ATTRIBUTE SkewnessMean NUMERIC
 @ATTRIBUTE SkewnessSTD NUMERIC
+@ATTRIBUTE ClassEntropy NUMERIC
 
 @DATA
-75123,1.0,1e-05,1e-05,1e-05,1e-05,0.00253,0.00221,1e-05,0.00024,3e-05,0.00011,0.0,2e-05,2e-05,5e-05,8e-05,2e-05,1e-05,7e-05,1e-05,0.00072,1e-05,1e-05,0.00019,0.00041,0.00011,0.00061,0.00012,9e-05,0.00015,0.00025,0.00067,0.00014,0.00011,0.00016,0.00027
-75090,1.0,1e-05,1e-05,2e-05,2e-05,0.00815,0.00368,7e-05,0.00228,9e-05,0.00235,0.0,5e-05,5e-05,0.0001,0.00013,0.00013,1e-05,0.00021,2e-05,0.00064,0.0,0.0,0.00014,0.00038,0.00011,0.00066,0.00011,0.00014,0.00014,0.00026,0.00066,0.00011,0.00013,0.00015,0.00027
-2123,1.0,1e-05,1e-05,1e-05,1e-05,0.00097,0.00079,1e-05,0.00013,1e-05,7e-05,0.0,2e-05,2e-05,5e-05,8e-05,2e-05,1e-05,5e-05,1e-05,0.0007,1e-05,0.0,0.00017,0.00042,0.0001,0.00052,6e-05,8e-05,0.00012,0.00026,0.00049,6e-05,0.00011,9e-05,0.00023
-248,1.0,1e-05,1e-05,1e-05,1e-05,0.00088,0.00077,1e-05,0.0001,1e-05,4e-05,0.0,1e-05,1e-05,4e-05,5e-05,2e-05,1e-05,4e-05,1e-05,0.00041,1e-05,0.0,9e-05,0.00022,9e-05,0.00046,6e-05,8e-05,9e-05,0.00023,0.00049,7e-05,0.0001,0.0001,0.00021
-75101,1.0,1e-05,1e-05,1e-05,1e-05,0.0418,0.03546,1e-05,0.00389,1e-05,0.00248,0.0,2e-05,2e-05,4e-05,6e-05,2e-05,1e-05,5e-05,2e-05,0.00045,1e-05,0.0,0.00011,0.00023,0.0001,0.00041,6e-05,8e-05,0.0001,0.00018,0.00047,6e-05,0.0001,8e-05,0.00023
-75191,1.0,1e-05,1e-05,3e-05,1e-05,0.53436,0.40777,2e-05,0.09655,3e-05,0.0301,1e-05,2e-05,3e-05,5e-05,0.00011,7e-05,1e-05,0.00011,3e-05,0.00084,5e-05,7e-05,0.00017,0.00033,0.00023,0.00088,0.00012,0.00017,0.00032,0.00026,0.00068,9e-05,0.00012,0.00013,0.00035
-75187,1.0,1e-05,1e-05,2e-05,1e-05,0.00548,0.00473,1e-05,0.0005,2e-05,0.00029,1e-05,2e-05,2e-05,7e-05,9e-05,3e-05,1e-05,6e-05,1e-05,0.00062,1e-05,1e-05,0.00014,0.00033,0.00013,0.00038,7e-05,7e-05,8e-05,0.00016,0.00047,6e-05,9e-05,9e-05,0.00023
-266,1.0,1e-05,1e-05,1e-05,1e-05,0.0018,0.00146,1e-05,0.00023,2e-05,0.00015,2e-05,2e-05,2e-05,6e-05,8e-05,3e-05,1e-05,6e-05,1e-05,0.00059,1e-05,1e-05,0.00014,0.00031,0.00013,0.0004,5e-05,7e-05,8e-05,0.0002,0.00044,8e-05,8e-05,8e-05,0.0002
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-75217,1.0,1e-05,1e-05,2e-05,1e-05,0.00196,0.00158,1e-05,0.00026,2e-05,0.00016,1e-05,3e-05,3e-05,7e-05,0.0001,3e-05,1e-05,7e-05,2e-05,0.00089,1e-05,1e-05,0.00022,0.00053,0.00013,0.00074,9e-05,0.0001,0.00019,0.00036,0.00065,0.00013,0.0001,0.00011,0.00031
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-75103,1.0,1e-05,1e-05,1e-05,1e-05,0.01302,0.00819,3e-05,0.00234,3e-05,0.00255,0.0,2e-05,2e-05,6e-05,0.00012,5e-05,1e-05,0.00011,1e-05,0.0005,1e-05,0.0,9e-05,0.00029,0.00011,0.00054,8e-05,0.00011,0.00011,0.00024,0.00066,7e-05,0.00015,0.00011,0.00032
-75168,1.0,1e-05,0.0,1e-05,1e-05,0.02353,0.00601,0.00016,0.01754,0.00018,0.00031,0.0,0.00013,0.00013,0.00027,0.0003,0.00032,1e-05,0.00042,1e-05,0.0004,1e-05,0.0,8e-05,0.00021,0.0001,0.0011,0.00022,0.00025,0.00026,0.00037,0.00124,0.00022,0.00025,0.00028,0.00049
-75157,1.0,1e-05,1e-05,1e-05,1e-05,0.00125,0.00106,1e-05,0.00015,2e-05,7e-05,0.0,2e-05,2e-05,5e-05,6e-05,2e-05,1e-05,4e-05,1e-05,0.00048,2e-05,0.0,0.0001,0.00026,0.0001,0.00039,6e-05,7e-05,8e-05,0.00019,0.00043,6e-05,9e-05,8e-05,0.0002
-75092,1.0,1e-05,1e-05,1e-05,1e-05,0.00099,0.00072,1e-05,0.00018,1e-05,0.00011,0.0,2e-05,2e-05,4e-05,6e-05,2e-05,1e-05,5e-05,1e-05,0.00058,1e-05,0.0,0.00014,0.00034,9e-05,0.00041,6e-05,8e-05,9e-05,0.00017,0.00045,7e-05,0.0001,9e-05,0.00019
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-75119,1.0,1e-05,1e-05,2e-05,1e-05,0.04185,0.01398,0.00063,0.01614,0.0007,0.01306,0.0,0.00046,0.00045,0.00091,0.00097,0.0012,1e-05,0.0015,1e-05,0.00047,1e-05,0.0,8e-05,0.00026,0.00012,0.00421,0.00091,0.00097,0.00103,0.0013,0.00402,0.00082,0.0008,0.00097,0.00144
-75227,1.0,1e-05,1e-05,2e-05,1e-05,0.00416,0.00373,1e-05,0.00038,2e-05,9e-05,1e-05,2e-05,2e-05,6e-05,0.0001,3e-05,1e-05,7e-05,2e-05,0.00063,1e-05,1e-05,0.00015,0.00034,0.00013,0.00037,6e-05,8e-05,8e-05,0.00016,0.00043,6e-05,9e-05,9e-05,0.0002
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diff --git a/autosklearn/metalearning/files/average_precision_multiclass.classification_dense/feature_runstatus.arff b/autosklearn/metalearning/files/average_precision_multiclass.classification_dense/feature_runstatus.arff
index 0034ad2bd6..5fff2dfed3 100644
--- a/autosklearn/metalearning/files/average_precision_multiclass.classification_dense/feature_runstatus.arff
+++ b/autosklearn/metalearning/files/average_precision_multiclass.classification_dense/feature_runstatus.arff
@@ -4,6 +4,7 @@
 @ATTRIBUTE repetition NUMERIC
 @ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other}
@@ -21,6 +22,11 @@
 @ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other}
@@ -37,162 +43,138 @@
 @ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other}
 
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diff --git a/autosklearn/metalearning/files/average_precision_multiclass.classification_dense/feature_values.arff b/autosklearn/metalearning/files/average_precision_multiclass.classification_dense/feature_values.arff
index a60400e3ce..736dc64321 100644
--- a/autosklearn/metalearning/files/average_precision_multiclass.classification_dense/feature_values.arff
+++ b/autosklearn/metalearning/files/average_precision_multiclass.classification_dense/feature_values.arff
@@ -13,7 +13,6 @@
 @ATTRIBUTE KurtosisMean NUMERIC
 @ATTRIBUTE KurtosisMin NUMERIC
 @ATTRIBUTE KurtosisSTD NUMERIC
-@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC
 @ATTRIBUTE LogDatasetRatio NUMERIC
 @ATTRIBUTE LogInverseDatasetRatio NUMERIC
 @ATTRIBUTE LogNumberOfFeatures NUMERIC
@@ -42,160 +41,135 @@
 @ATTRIBUTE SymbolsSum NUMERIC
 
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diff --git a/autosklearn/metalearning/files/average_precision_multiclass.classification_sparse/algorithm_runs.arff b/autosklearn/metalearning/files/average_precision_multiclass.classification_sparse/algorithm_runs.arff
index 928accbecb..6e80d914a1 100644
--- a/autosklearn/metalearning/files/average_precision_multiclass.classification_sparse/algorithm_runs.arff
+++ b/autosklearn/metalearning/files/average_precision_multiclass.classification_sparse/algorithm_runs.arff
@@ -7,95 +7,133 @@
 @ATTRIBUTE runstatus {ok, timeout, memout, not_applicable, crash, other}
 
 @DATA
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-258,1.0,13,?,not_applicable
-75168,1.0,14,?,not_applicable
-75235,1.0,15,?,not_applicable
-244,1.0,16,?,not_applicable
-75221,1.0,17,?,not_applicable
-75219,1.0,18,0.006712691671677318,ok
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-275,1.0,23,?,not_applicable
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-236,1.0,30,?,not_applicable
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-242,1.0,38,?,not_applicable
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-75127,1.0,48,0.3152529081908554,ok
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-75207,1.0,54,?,not_applicable
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-75100,1.0,57,0.983500701962392,ok
-75169,1.0,58,?,not_applicable
-75121,1.0,59,0.0,ok
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-75116,1.0,62,0.0008296925826821733,ok
-75185,1.0,63,0.05923391940768985,ok
-2119,1.0,64,?,not_applicable
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-75113,1.0,66,0.01014718506549861,ok
-75203,1.0,67,?,not_applicable
-75182,1.0,68,0.10060431919463964,ok
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-75123,1.0,70,?,not_applicable
-75125,1.0,71,0.009583682216643852,ok
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-75103,1.0,73,0.031775122370021114,ok
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-75230,1.0,75,?,not_applicable
-75139,1.0,76,0.0019167649076011761,ok
-75227,1.0,77,0.0870759275296118,ok
-2120,1.0,78,?,not_applicable
-75124,1.0,79,0.4676991332383039,ok
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-75198,1.0,81,?,not_applicable
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-75177,1.0,85,0.08262533543110306,ok
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-75156,1.0,87,0.12925560311437256,ok
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-75172,1.0,89,?,not_applicable
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+258,1.0,60,?,not_applicable
+75166,1.0,227,0.02779561860055213,ok
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+75202,1.0,77,?,not_applicable
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+75174,1.0,88,0.10410861094426194,ok
+288,1.0,89,?,not_applicable
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+75179,1.0,363,0.22481062993849077,ok
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+75142,1.0,95,0.014340735646710567,ok
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+75243,1.0,99,?,not_applicable
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+75161,1.0,109,0.011200609583141508,ok
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+75236,1.0,181,?,not_applicable
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+75116,1.0,256,0.0006359144097769498,ok
+75223,1.0,189,?,not_applicable
+75109,1.0,196,?,not_applicable
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+248,1.0,203,?,not_applicable
+2119,1.0,206,?,not_applicable
+75127,1.0,207,0.3143148012874739,ok
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+266,1.0,258,?,not_applicable
+75134,1.0,274,?,not_applicable
+75096,1.0,277,?,not_applicable
+75203,1.0,280,?,not_applicable
+75123,1.0,288,?,not_applicable
+75237,1.0,291,1.0920271134029491e-06,ok
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+2120,1.0,297,?,not_applicable
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+75103,1.0,372,0.024583847751503685,ok
+242,1.0,307,?,not_applicable
+75230,1.0,312,?,not_applicable
+75240,1.0,321,0.018974692437447427,ok
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+75112,1.0,331,0.08596797767191378,ok
+75105,1.0,336,0.890912864676346,ok
+75154,1.0,338,?,not_applicable
+2117,1.0,344,0.029195020984121633,ok
+75156,1.0,348,0.11814063159869614,ok
+75097,1.0,349,0.014519472944802159,ok
+75101,1.0,352,0.18566222232178675,ok
+75172,1.0,353,?,not_applicable
+75106,1.0,359,0.8044503421707234,ok
+75120,1.0,365,0.002216917908264948,ok
+75193,1.0,369,?,not_applicable
diff --git a/autosklearn/metalearning/files/average_precision_multiclass.classification_sparse/configurations.csv b/autosklearn/metalearning/files/average_precision_multiclass.classification_sparse/configurations.csv
index ec2d231333..5748989e7b 100644
--- a/autosklearn/metalearning/files/average_precision_multiclass.classification_sparse/configurations.csv
+++ b/autosklearn/metalearning/files/average_precision_multiclass.classification_sparse/configurations.csv
@@ -1,93 +1,131 @@
 idx,balancing:strategy,classifier:__choice__,classifier:adaboost:algorithm,classifier:adaboost:learning_rate,classifier:adaboost:max_depth,classifier:adaboost:n_estimators,classifier:bernoulli_nb:alpha,classifier:bernoulli_nb:fit_prior,classifier:decision_tree:criterion,classifier:decision_tree:max_depth_factor,classifier:decision_tree:max_features,classifier:decision_tree:max_leaf_nodes,classifier:decision_tree:min_impurity_decrease,classifier:decision_tree:min_samples_leaf,classifier:decision_tree:min_samples_split,classifier:decision_tree:min_weight_fraction_leaf,classifier:extra_trees:bootstrap,classifier:extra_trees:criterion,classifier:extra_trees:max_depth,classifier:extra_trees:max_features,classifier:extra_trees:max_leaf_nodes,classifier:extra_trees:min_impurity_decrease,classifier:extra_trees:min_samples_leaf,classifier:extra_trees:min_samples_split,classifier:extra_trees:min_weight_fraction_leaf,classifier:gradient_boosting:early_stop,classifier:gradient_boosting:l2_regularization,classifier:gradient_boosting:learning_rate,classifier:gradient_boosting:loss,classifier:gradient_boosting:max_bins,classifier:gradient_boosting:max_depth,classifier:gradient_boosting:max_leaf_nodes,classifier:gradient_boosting:min_samples_leaf,classifier:gradient_boosting:n_iter_no_change,classifier:gradient_boosting:scoring,classifier:gradient_boosting:tol,classifier:gradient_boosting:validation_fraction,classifier:k_nearest_neighbors:n_neighbors,classifier:k_nearest_neighbors:p,classifier:k_nearest_neighbors:weights,classifier:lda:n_components,classifier:lda:shrinkage,classifier:lda:shrinkage_factor,classifier:lda:tol,classifier:liblinear_svc:C,classifier:liblinear_svc:dual,classifier:liblinear_svc:fit_intercept,classifier:liblinear_svc:intercept_scaling,classifier:liblinear_svc:loss,classifier:liblinear_svc:multi_class,classifier:liblinear_svc:penalty,classifier:liblinear_svc:tol,classifier:libsvm_svc:C,classifier:libsvm_svc:coef0,classifier:libsvm_svc:degree,classifier:libsvm_svc:gamma,classifier:libsvm_svc:kernel,classifier:libsvm_svc:max_iter,classifier:libsvm_svc:shrinking,classifier:libsvm_svc:tol,classifier:multinomial_nb:alpha,classifier:multinomial_nb:fit_prior,classifier:passive_aggressive:C,classifier:passive_aggressive:average,classifier:passive_aggressive:fit_intercept,classifier:passive_aggressive:loss,classifier:passive_aggressive:tol,classifier:qda:reg_param,classifier:random_forest:bootstrap,classifier:random_forest:criterion,classifier:random_forest:max_depth,classifier:random_forest:max_features,classifier:random_forest:max_leaf_nodes,classifier:random_forest:min_impurity_decrease,classifier:random_forest:min_samples_leaf,classifier:random_forest:min_samples_split,classifier:random_forest:min_weight_fraction_leaf,classifier:sgd:alpha,classifier:sgd:average,classifier:sgd:epsilon,classifier:sgd:eta0,classifier:sgd:fit_intercept,classifier:sgd:l1_ratio,classifier:sgd:learning_rate,classifier:sgd:loss,classifier:sgd:penalty,classifier:sgd:power_t,classifier:sgd:tol,data_preprocessing:categorical_transformer:categorical_encoding:__choice__,data_preprocessing:categorical_transformer:category_coalescence:__choice__,data_preprocessing:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction,data_preprocessing:numerical_transformer:imputation:strategy,data_preprocessing:numerical_transformer:rescaling:__choice__,data_preprocessing:numerical_transformer:rescaling:quantile_transformer:n_quantiles,data_preprocessing:numerical_transformer:rescaling:quantile_transformer:output_distribution,data_preprocessing:numerical_transformer:rescaling:robust_scaler:q_max,data_preprocessing:numerical_transformer:rescaling:robust_scaler:q_min,feature_preprocessor:__choice__,feature_preprocessor:extra_trees_preproc_for_classification:bootstrap,feature_preprocessor:extra_trees_preproc_for_classification:criterion,feature_preprocessor:extra_trees_preproc_for_classification:max_depth,feature_preprocessor:extra_trees_preproc_for_classification:max_features,feature_preprocessor:extra_trees_preproc_for_classification:max_leaf_nodes,feature_preprocessor:extra_trees_preproc_for_classification:min_impurity_decrease,feature_preprocessor:extra_trees_preproc_for_classification:min_samples_leaf,feature_preprocessor:extra_trees_preproc_for_classification:min_samples_split,feature_preprocessor:extra_trees_preproc_for_classification:min_weight_fraction_leaf,feature_preprocessor:extra_trees_preproc_for_classification:n_estimators,feature_preprocessor:kernel_pca:coef0,feature_preprocessor:kernel_pca:degree,feature_preprocessor:kernel_pca:gamma,feature_preprocessor:kernel_pca:kernel,feature_preprocessor:kernel_pca:n_components,feature_preprocessor:kitchen_sinks:gamma,feature_preprocessor:kitchen_sinks:n_components,feature_preprocessor:liblinear_svc_preprocessor:C,feature_preprocessor:liblinear_svc_preprocessor:dual,feature_preprocessor:liblinear_svc_preprocessor:fit_intercept,feature_preprocessor:liblinear_svc_preprocessor:intercept_scaling,feature_preprocessor:liblinear_svc_preprocessor:loss,feature_preprocessor:liblinear_svc_preprocessor:multi_class,feature_preprocessor:liblinear_svc_preprocessor:penalty,feature_preprocessor:liblinear_svc_preprocessor:tol,feature_preprocessor:nystroem_sampler:coef0,feature_preprocessor:nystroem_sampler:degree,feature_preprocessor:nystroem_sampler:gamma,feature_preprocessor:nystroem_sampler:kernel,feature_preprocessor:nystroem_sampler:n_components,feature_preprocessor:polynomial:degree,feature_preprocessor:polynomial:include_bias,feature_preprocessor:polynomial:interaction_only,feature_preprocessor:random_trees_embedding:bootstrap,feature_preprocessor:random_trees_embedding:max_depth,feature_preprocessor:random_trees_embedding:max_leaf_nodes,feature_preprocessor:random_trees_embedding:min_samples_leaf,feature_preprocessor:random_trees_embedding:min_samples_split,feature_preprocessor:random_trees_embedding:min_weight_fraction_leaf,feature_preprocessor:random_trees_embedding:n_estimators,feature_preprocessor:select_percentile_classification:percentile,feature_preprocessor:select_percentile_classification:score_func,feature_preprocessor:select_rates_classification:alpha,feature_preprocessor:select_rates_classification:mode,feature_preprocessor:select_rates_classification:score_func,feature_preprocessor:truncatedSVD:target_dim
-1,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.3163020577549344,None,0.0,15,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.01322981552715978,most_frequent,robust_scaler,,,0.9746667009556722,0.1974876581478311,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-2,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,23916.78290516781,False,True,1,squared_hinge,ovr,l2,0.0006655607726658531,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.008808715242823137,median,none,,,,,kernel_pca,,,,,,,,,,,0.2633153190620261,2,0.004770332714291669,poly,1483,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-3,weighting,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,88,2,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.254263872179305,median,normalize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-4,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.8295237320761981,None,0.0,14,13,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.4912899096849949,most_frequent,robust_scaler,,,0.8602962469260254,0.1681311280324052,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l1,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,
-5,none,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00011009762574324903,True,,0.031641726094190054,True,,invscaling,hinge,l2,0.2208821470267501,0.007895311091089785,one_hot_encoding,minority_coalescer,0.13807502937372682,mean,quantile_transformer,1340,normal,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,,
-6,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.5244313211416313,None,0.0,1,2,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.454300236339658,most_frequent,robust_scaler,,,0.7858830918128734,0.2705174720960696,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-7,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.541827838541001,True,True,squared_hinge,0.00015757576796329985,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,standardize,,,,,extra_trees_preproc_for_classification,False,entropy,None,0.5282111930398031,None,0.0,17,4,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-8,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.25657910619102237,None,0.0,6,6,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.021086303324492208,most_frequent,quantile_transformer,1247,uniform,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.16021572815319468,fpr,chi2,
-9,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.643761610990255,None,0.0,20,6,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.039859381630488695,mean,normalize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,,
-10,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.8253905225980761,None,0.0,18,20,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.003725255016700434,mean,robust_scaler,,,0.7701705977765594,0.2396520157991559,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.3579038776690903,False,True,1,squared_hinge,ovr,l1,4.994232534734023e-05,,,,,,,,,,,,,,,,,,,,,
-11,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.06263739023781334,None,0.0,18,8,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.02206379295100344,most_frequent,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,,
-12,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.07518858116896743,None,0.0,7,10,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0027133920607872518,mean,robust_scaler,,,0.7873452530915046,0.16215139072179952,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-13,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,199,None,,0.003239674765949011,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,robust_scaler,,,0.7228447795482008,0.25,kernel_pca,,,,,,,,,,,0.09440148939740256,3,0.0008114095405154861,poly,1996,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-14,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.6039096182414764,None,0.0,2,20,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.1939609823704447,most_frequent,robust_scaler,,,0.7331327008874781,0.29747488162877694,extra_trees_preproc_for_classification,True,entropy,None,0.33401603690569853,None,0.0,4,4,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-15,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7865652301288171,None,0.0,1,4,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,median,robust_scaler,,,0.8623635641193496,0.13298297278730625,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-16,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.905401147837377,-0.32832122529048147,3,1.001896309040499,poly,-1,False,5.1523484258030785e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-17,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.21794354428393548,None,0.0,2,16,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0025451910134387575,median,quantile_transformer,1477,uniform,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,,
-18,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5010952533165973,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.005838375247601407,mean,normalize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,,
-19,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5514127836113225,None,0.0,4,20,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.010750756228608195,most_frequent,robust_scaler,,,0.7471420685032172,0.2916152663822578,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.22557315057515023,fwe,chi2,
-20,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.9663114854040722,None,0.0,11,12,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.26932389682904634,median,quantile_transformer,23,uniform,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l1,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,
-21,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.506204385946851,None,0.0,2,8,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.03433007982751011,mean,none,,,,,extra_trees_preproc_for_classification,False,gini,None,0.32676415738837183,None,0.0,5,6,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-22,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9333237919291875,None,0.0,8,3,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,,
-23,none,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,184,manual,0.5022041786628263,2.3064966255468173e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,most_frequent,standardize,,,,,kernel_pca,,,,,,,,,,,-0.32023224593255817,2,0.09479590655633498,poly,1382,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-24,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,22194.242946902177,0.45210849669021247,,0.0005669713702171556,sigmoid,-1,False,0.03542142266081451,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,quantile_transformer,291,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-25,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.7772914874049878,None,0.0,10,15,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00015285482513309234,mean,robust_scaler,,,0.8373404000074934,0.14303151080827242,extra_trees_preproc_for_classification,True,entropy,None,0.5021713909357703,None,0.0,13,16,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-26,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7422641456052917,None,0.0,8,15,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.005561351266169067,most_frequent,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-27,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.887898394612674,None,0.0,10,19,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,robust_scaler,,,0.8350698288864961,0.2347163538066168,extra_trees_preproc_for_classification,False,entropy,None,0.49795108918064074,None,0.0,13,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-28,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7088164344533493,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,mean,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,,
-29,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,10,1,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0022606253047811435,median,quantile_transformer,1247,normal,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,25.327793585266683,chi2,,,,
-30,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,45.824472650016915,,,0.04733593018751384,rbf,-1,False,0.0001002563992422035,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.007882457896645694,most_frequent,robust_scaler,,,0.7360778699898849,0.27800774875566153,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-31,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,21.69482701422382,-0.9439355912923322,,0.012708037096662675,sigmoid,-1,True,1.3900680173654052e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-32,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.8095522998548572,None,0.0,17,10,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.031940595071539304,mean,quantile_transformer,1563,normal,,,extra_trees_preproc_for_classification,False,entropy,None,0.9330145598603196,None,0.0,18,10,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-33,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,26.502259985828186,,,9.475662388405154e-05,rbf,-1,False,0.00019690635968217866,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-34,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.339600708038189,False,True,1,squared_hinge,ovr,l2,9.572641158475304e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,quantile_transformer,1193,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-35,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5255991794296913,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-36,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.34697723213923637,None,0.0,15,18,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.1923594385988973,mean,normalize,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,25.77499302945827,chi2,,,,
-37,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.7610308078756552,None,0.0,1,2,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.010000000000000004,mean,robust_scaler,,,0.9205545465103644,0.20272761060743316,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-38,none,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4994182104906286,False,True,1,squared_hinge,ovr,l2,0.006324920556381277,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.06848370232187935,mean,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-39,none,bernoulli_nb,,,,,0.6524090653939782,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.005164169126808402,most_frequent,quantile_transformer,16,normal,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.033082145443631535,False,True,1,squared_hinge,ovr,l1,0.002615020898325393,,,,,,,,,,,,,,,,,,,,,
-40,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.6453555269772124,None,0.0,1,4,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,normalize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-41,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.20911875507660596,None,0.0,12,8,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,median,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,,
-42,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.49918755377415064,None,0.0,1,2,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.36992315774620843,mean,robust_scaler,,,0.7410494147895569,0.29895918805929733,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-43,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,29.55731828912983,False,True,1,squared_hinge,ovr,l2,0.0011332486951954968,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,,
-44,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.37801720673725603,True,True,squared_hinge,0.0001255132393635067,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.025993178344125793,most_frequent,standardize,,,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,1.4421737618429475,False,True,1,squared_hinge,ovr,l1,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,
-45,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.10581257327556097,None,0.0,7,12,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.004219392676189224,median,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,,
-46,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.4188764793561401,None,0.0,5,5,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.010589444903605501,mean,robust_scaler,,,0.9796997860990453,0.27262832749694405,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-47,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0023391956112474004,False,True,squared_hinge,0.011792446147470903,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.08295440990272292,most_frequent,quantile_transformer,962,normal,,,kernel_pca,,,,,,,,,,,,,,cosine,1778,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-48,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.41019363453549706,None,0.0,15,2,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.0003507389224306018,median,none,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.20614098085343638,fdr,chi2,
-49,none,adaboost,SAMME,0.1280458968526125,7,125,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,robust_scaler,,,0.7156460334356771,0.2981902862926971,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,65.16171885308417,chi2,,,,
-50,none,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9931779675565967,None,0.0,18,17,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,robust_scaler,,,0.75,0.2945917010472818,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,,
-51,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.08646118576686276,None,0.0,4,9,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,most_frequent,quantile_transformer,959,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-52,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.6271213423927893,None,0.0,1,4,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.009403509495453675,mean,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,,
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+344,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.9039555800017817e-05,False,True,squared_hinge,0.0011536981768375681,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,quantile_transformer,1122,uniform,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,,
+348,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7717211744793793,None,0.0,3,17,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,median,robust_scaler,,,0.7426192562776642,0.2500337305337367,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+349,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.4438263804770306,None,0.0,2,10,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,mean,robust_scaler,,,0.9824945141547571,0.24967364012300372,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+352,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5988524108452234,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,mean,robust_scaler,,,0.75,0.26961884796358077,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+353,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.6269076084367177,None,0.0,1,7,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.12943250859943486,most_frequent,standardize,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,62.84526554323969,chi2,,,,
+356,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,287.378894025996,False,True,1,squared_hinge,ovr,l2,3.191556254971646e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.13961362399652708,most_frequent,quantile_transformer,432,uniform,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+359,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.9282407375709691,None,0.0,16,3,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.015995805068365877,mean,standardize,,,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.10042232633120375,False,True,1,squared_hinge,ovr,l1,0.004451009526629745,,,,,,,,,,,,,,,,,,,,,
+363,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.1407739710068163,False,True,1,squared_hinge,ovr,l2,8.306336208544797e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,normalize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,,
+365,none,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.772617001926524e-05,False,,0.00531060153046999,True,,constant,log,l1,,0.00016225369247634786,one_hot_encoding,no_coalescense,,median,quantile_transformer,745,uniform,,,extra_trees_preproc_for_classification,False,entropy,None,0.6017654648980271,None,0.0,17,9,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+369,none,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.9796199105973851,None,0.0,2,13,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.00845950033601661,mean,robust_scaler,,,0.8275228560550734,0.23814615163106348,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+372,weighting,adaboost,SAMME.R,0.12302955318397957,1,332,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.006140009027349688,most_frequent,none,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,74.82103264977707,chi2,,,,
diff --git a/autosklearn/metalearning/files/average_precision_multiclass.classification_sparse/description.txt b/autosklearn/metalearning/files/average_precision_multiclass.classification_sparse/description.txt
index 0d89c72314..5a82016de7 100644
--- a/autosklearn/metalearning/files/average_precision_multiclass.classification_sparse/description.txt
+++ b/autosklearn/metalearning/files/average_precision_multiclass.classification_sparse/description.txt
@@ -1,8 +1,9 @@
 features_cutoff_time: 3600
 features_cutoff_memory: 3072
-number_of_feature_steps: 35
+number_of_feature_steps: 42
 feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances
 feature_step LogNumberOfInstances: LogNumberOfInstances
+feature_step NumberOfClasses: NumberOfClasses
 feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures
 feature_step LogNumberOfFeatures: LogNumberOfFeatures
 feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues
@@ -20,6 +21,11 @@ feature_step DatasetRatio: DatasetRatio, LogDatasetRatio
 feature_step LogDatasetRatio: LogDatasetRatio
 feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio
 feature_step LogInverseDatasetRatio: LogInverseDatasetRatio
+feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD
+feature_step ClassProbabilityMin: ClassProbabilityMin
+feature_step ClassProbabilityMax: ClassProbabilityMax
+feature_step ClassProbabilityMean: ClassProbabilityMean
+feature_step ClassProbabilitySTD: ClassProbabilitySTD
 feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum
 feature_step SymbolsMin: SymbolsMin
 feature_step SymbolsMax: SymbolsMax
@@ -36,11 +42,12 @@ feature_step SkewnessMin: SkewnessMin
 feature_step SkewnessMax: SkewnessMax
 feature_step SkewnessMean: SkewnessMean
 feature_step SkewnessSTD: SkewnessSTD
-features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
+feature_step ClassEntropy: ClassEntropy
+features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy
 features_stochastic: 
-default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
+default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy
 
-algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92
+algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235,236,237,238,239,240,241,242,243,244,245,246,247,248,249,250,251,252,253,254,255,256,257,258,259,260,261,262,263,264,265,266,267,268,269,270,271,272,273,274,275,276,277,278,279,280,281,282,283,284,285,286,287,288,289,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,312,313,314,315,316,317,318,319,320,321,322,323,324,325,326,327,328,329,330,331,332,333,334,335,336,337,338,339,340,341,342,343,344,345,346,347,348,349,350,351,352,353,354,355,356,357,358,359,360,361,362,363,364,365,366,367,368,369,370,371,372
 algorithms_stochastic: 
 performance_measures: average_precision
 performance_type: solution_quality
diff --git a/autosklearn/metalearning/files/average_precision_multiclass.classification_sparse/feature_costs.arff b/autosklearn/metalearning/files/average_precision_multiclass.classification_sparse/feature_costs.arff
index 0e17e20d22..8512fd9275 100644
--- a/autosklearn/metalearning/files/average_precision_multiclass.classification_sparse/feature_costs.arff
+++ b/autosklearn/metalearning/files/average_precision_multiclass.classification_sparse/feature_costs.arff
@@ -4,6 +4,7 @@
 @ATTRIBUTE repetition NUMERIC
 @ATTRIBUTE NumberOfInstances NUMERIC
 @ATTRIBUTE LogNumberOfInstances NUMERIC
+@ATTRIBUTE NumberOfClasses NUMERIC
 @ATTRIBUTE NumberOfFeatures NUMERIC
 @ATTRIBUTE LogNumberOfFeatures NUMERIC
 @ATTRIBUTE MissingValues NUMERIC
@@ -21,6 +22,11 @@
 @ATTRIBUTE LogDatasetRatio NUMERIC
 @ATTRIBUTE InverseDatasetRatio NUMERIC
 @ATTRIBUTE LogInverseDatasetRatio NUMERIC
+@ATTRIBUTE ClassOccurences NUMERIC
+@ATTRIBUTE ClassProbabilityMin NUMERIC
+@ATTRIBUTE ClassProbabilityMax NUMERIC
+@ATTRIBUTE ClassProbabilityMean NUMERIC
+@ATTRIBUTE ClassProbabilitySTD NUMERIC
 @ATTRIBUTE NumSymbols NUMERIC
 @ATTRIBUTE SymbolsMin NUMERIC
 @ATTRIBUTE SymbolsMax NUMERIC
@@ -37,162 +43,138 @@
 @ATTRIBUTE SkewnessMax NUMERIC
 @ATTRIBUTE SkewnessMean NUMERIC
 @ATTRIBUTE SkewnessSTD NUMERIC
+@ATTRIBUTE ClassEntropy NUMERIC
 
 @DATA
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diff --git a/autosklearn/metalearning/files/average_precision_multiclass.classification_sparse/feature_runstatus.arff b/autosklearn/metalearning/files/average_precision_multiclass.classification_sparse/feature_runstatus.arff
index 0034ad2bd6..5fff2dfed3 100644
--- a/autosklearn/metalearning/files/average_precision_multiclass.classification_sparse/feature_runstatus.arff
+++ b/autosklearn/metalearning/files/average_precision_multiclass.classification_sparse/feature_runstatus.arff
@@ -4,6 +4,7 @@
 @ATTRIBUTE repetition NUMERIC
 @ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other}
@@ -21,6 +22,11 @@
 @ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other}
@@ -37,162 +43,138 @@
 @ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other}
 
 @DATA
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diff --git a/autosklearn/metalearning/files/average_precision_multiclass.classification_sparse/feature_values.arff b/autosklearn/metalearning/files/average_precision_multiclass.classification_sparse/feature_values.arff
index a60400e3ce..736dc64321 100644
--- a/autosklearn/metalearning/files/average_precision_multiclass.classification_sparse/feature_values.arff
+++ b/autosklearn/metalearning/files/average_precision_multiclass.classification_sparse/feature_values.arff
@@ -13,7 +13,6 @@
 @ATTRIBUTE KurtosisMean NUMERIC
 @ATTRIBUTE KurtosisMin NUMERIC
 @ATTRIBUTE KurtosisSTD NUMERIC
-@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC
 @ATTRIBUTE LogDatasetRatio NUMERIC
 @ATTRIBUTE LogInverseDatasetRatio NUMERIC
 @ATTRIBUTE LogNumberOfFeatures NUMERIC
@@ -42,160 +41,135 @@
 @ATTRIBUTE SymbolsSum NUMERIC
 
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diff --git a/autosklearn/metalearning/files/balanced_accuracy_binary.classification_dense/algorithm_runs.arff b/autosklearn/metalearning/files/balanced_accuracy_binary.classification_dense/algorithm_runs.arff
index efdc813af5..e0edfe5d00 100644
--- a/autosklearn/metalearning/files/balanced_accuracy_binary.classification_dense/algorithm_runs.arff
+++ b/autosklearn/metalearning/files/balanced_accuracy_binary.classification_dense/algorithm_runs.arff
@@ -7,130 +7,135 @@
 @ATTRIBUTE runstatus {ok, timeout, memout, not_applicable, crash, other}
 
 @DATA
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diff --git a/autosklearn/metalearning/files/balanced_accuracy_binary.classification_dense/configurations.csv b/autosklearn/metalearning/files/balanced_accuracy_binary.classification_dense/configurations.csv
index d1a17e1911..ba6539ee41 100644
--- a/autosklearn/metalearning/files/balanced_accuracy_binary.classification_dense/configurations.csv
+++ b/autosklearn/metalearning/files/balanced_accuracy_binary.classification_dense/configurations.csv
@@ -1,128 +1,133 @@
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-37,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.7521969592385672,None,0.0,9,5,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.020402264943872365,median,robust_scaler,,,0.737227213630259,0.20752747663727789,fast_ica,,,,,,,,,,,parallel,exp,312,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-38,none,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.563056219822946,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.32793677336996485,most_frequent,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-39,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,1.6343482806592892e-09,0.19537524572679268,auto,255,None,32,29,13,loss,1e-07,0.05061738224302791,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-40,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7088164344533493,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,mean,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,
-41,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,1.943186107984505e-07,0.08852455702036988,auto,255,None,5,72,19,loss,1e-07,0.039837460762271175,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.01591265419367627,median,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,
-42,weighting,adaboost,SAMME,0.5210651042094924,10,401,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,standardize,,,,,feature_agglomeration,,,,,,,,,,,,,,,euclidean,complete,89,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-43,none,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0686374607394679,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,minmax,,,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.8490260132946401,False,,,,,,,,,,,,,,,
-44,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.6931271368243409,None,0.0,11,10,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.362940004438799,most_frequent,robust_scaler,,,0.9177587796305063,0.17084038458597867,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,30.491514144623952,mutual_info,,,
-45,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.2052035060269155,None,0.0,20,16,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,none,,,,,feature_agglomeration,,,,,,,,,,,,,,,cosine,complete,22,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-46,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,92.66144306787051,,,0.04654639302091653,rbf,-1,True,0.0022231544280610005,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.005194838694455963,mean,none,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,15,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-47,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2681.998807317996,-0.9558745731731211,,0.0011299948421773457,sigmoid,-1,False,1.5292507354478086e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-48,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.4467384469668449,None,0.0,19,19,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.00033708405018786203,median,quantile_transformer,711,normal,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,7.744829113696808,False,True,1,squared_hinge,ovr,l1,0.0001869446320464273,,,,,,,,,,,,,,,,,,,,,,
-49,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,26.502259985828186,,,9.475662388405154e-05,rbf,-1,False,0.00019690635968217866,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-50,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,2.5360196477156387e-07,0.011018756408558218,auto,255,None,40,122,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.00011749164080669445,most_frequent,quantile_transformer,757,uniform,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,5.097834697156853,False,True,1,squared_hinge,ovr,l1,0.053877834919477424,,,,,,,,,,,,,,,,,,,,,,
-51,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5255991794296913,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-52,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.34697723213923637,None,0.0,15,18,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.1923594385988973,mean,normalize,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,25.77499302945827,chi2,,,
-53,weighting,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.7776312540090575,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0005253715056715056,mean,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,False,,,,,,,,,,,,
-54,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,1.5661823810486637e-10,0.08996863554015262,auto,255,None,156,89,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.009331521286159971,median,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,
-55,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.6109468076475573,0.8748383232154038,4,0.41333070422336576,poly,-1,False,3.591268778393482e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.011804925819789203,median,minmax,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-56,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.7610308078756552,None,0.0,1,2,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.010000000000000004,mean,robust_scaler,,,0.9205545465103644,0.20272761060743316,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-57,weighting,adaboost,SAMME.R,0.03127273017917347,10,496,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,standardize,,,,,fast_ica,,,,,,,,,,,deflation,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-58,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,151,None,,0.0003283777607180646,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.2510527431514227,mean,normalize,,,,,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,-0.96093182232312,,0.05762510964607661,sigmoid,2632,,,,,,,,,,,,,,,,,
-59,none,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.4702538502800974e-07,True,,,True,0.14999999999999974,optimal,perceptron,elasticnet,,0.0011469929926494495,no_encoding,no_coalescense,,mean,quantile_transformer,1782,normal,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.9897679383028408,True,,,,,,,,,,,,,,,
-60,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.6453555269772124,None,0.0,1,4,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,normalize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-61,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,940.9437776449834,,,1.5839338084625942,rbf,-1,False,0.023072781592565438,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,none,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,77.25763685070707,mutual_info,,,
-62,weighting,extra_trees,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,13,18,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0001574994528707389,most_frequent,standardize,,,,,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,3,None,19,19,1.0,77,,,,,
-63,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.34534888916828577,0.02513634776687268,2,1.0733091880109409,poly,-1,True,0.0006974263047918307,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,quantile_transformer,1000,normal,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,77.99934218022861,mutual_info,,,
-64,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,29.55731828912983,False,True,1,squared_hinge,ovr,l2,0.0011332486951954968,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,
-65,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,1.1387795876755026e-06,0.12245605190343567,auto,255,None,58,7,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,robust_scaler,,,0.79305915648885,0.1841487406876042,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-66,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.8425560317492612,None,0.0,8,15,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.015276988502366128,median,none,,,,,fast_ica,,,,,,,,,,,parallel,cube,17,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-67,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,0.2410465573252999,0.022973469655856883,auto,255,None,272,8,3,loss,1e-07,0.0321932494461218,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.00807996498005836,most_frequent,none,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,65,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-68,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,925.3053743248586,False,True,1,squared_hinge,ovr,l2,0.02764481198395717,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.3850718018366796,most_frequent,quantile_transformer,786,uniform,,,feature_agglomeration,,,,,,,,,,,,,,,cosine,average,156,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-69,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7573078019937971,None,0.0,2,5,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,robust_scaler,,,0.7265312295801113,0.28332515882236425,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.09660564590146253,fpr,f_classif
-70,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5345540246011571,None,0.0,8,4,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.007441222223802043,most_frequent,normalize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-71,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.41019363453549706,None,0.0,15,2,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.0003507389224306018,median,none,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.20614098085343638,fdr,chi2
-72,none,adaboost,SAMME,0.1280458968526125,7,125,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,robust_scaler,,,0.7156460334356771,0.2981902862926971,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,65.16171885308417,chi2,,,
-73,none,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9931779675565967,None,0.0,18,17,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,robust_scaler,,,0.75,0.2945917010472818,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,
-74,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.08646118576686276,None,0.0,4,9,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,most_frequent,quantile_transformer,959,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-75,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,1.2430504242164082e-09,0.03359568307446533,auto,255,None,1609,29,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,standardize,,,,,feature_agglomeration,,,,,,,,,,,,,,,cosine,complete,40,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-76,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7.491039171224502,True,True,squared_hinge,2.8058138048327884e-05,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,minmax,,,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.7754372809893839,False,True,1,squared_hinge,ovr,l1,3.9643594596766314e-05,,,,,,,,,,,,,,,,,,,,,,
-77,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.34936025988397124,None,0.0,2,13,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00018601309647406987,median,robust_scaler,,,0.8634309348843472,0.26976989465367607,extra_trees_preproc_for_classification,False,entropy,None,0.48025175288975036,None,0.0,6,17,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-78,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,0.011547155223520492,0.13665554140837183,auto,255,None,13,5,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.1824584492028449,median,minmax,,,,,feature_agglomeration,,,,,,,,,,,,,,,euclidean,average,261,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-79,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.7308100742740112,None,0.0,18,19,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.014637629296743653,most_frequent,robust_scaler,,,0.7724670628916575,0.2233445340332948,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,29.07440444752882,False,True,1,squared_hinge,ovr,l1,0.00040479645225962257,,,,,,,,,,,,,,,,,,,,,,
-80,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1661.2274747409913,,,0.025821208852387936,rbf,-1,False,0.0017011435104921824,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,none,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,306,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-81,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0016941428709087083,True,,,True,,optimal,squared_hinge,l1,,5.124265599423001e-05,no_encoding,no_coalescense,,most_frequent,quantile_transformer,461,uniform,,,extra_trees_preproc_for_classification,True,gini,None,0.31371431180257103,None,0.0,5,17,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-82,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,21.85207494001697,0.18872999589786676,4,1.1863646021868761,poly,-1,False,0.0021338371901294443,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0034080852834770217,mean,normalize,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.11326005109886934,fdr,f_classif
-83,weighting,adaboost,SAMME,0.38341570193537416,9,250,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,minmax,,,,,feature_agglomeration,,,,,,,,,,,,,,,euclidean,average,13,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-84,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,0.037849720944694,0.1268815770019783,auto,255,None,11,99,11,loss,1e-07,0.10654523627313527,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,
-85,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.593728171200453,None,0.0,7,20,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.02830941310359363,mean,normalize,,,,,extra_trees_preproc_for_classification,False,entropy,None,0.7389446586281172,None,0.0,20,11,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-86,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,1,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.019797877010737826,mean,normalize,,,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.8239678741128165,False,,,,,,,,,,,,,,,
-87,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.46057831591617715,False,True,hinge,0.04557857428827514,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00027457445401600137,median,standardize,,,,,extra_trees_preproc_for_classification,True,gini,None,0.48190346970486964,None,0.0,17,18,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-88,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.46057831591617715,False,True,hinge,0.04557857428827514,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00027457445401600137,median,standardize,,,,,extra_trees_preproc_for_classification,True,gini,None,0.48190346970486964,None,0.0,17,18,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-89,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5722917944959481,None,0.0,1,14,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,
-90,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.26356754187780895,None,0.0,4,3,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010966178401758774,mean,robust_scaler,,,0.7817043861798886,0.24371720112407158,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
-91,weighting,adaboost,SAMME.R,0.13547552520095074,4,62,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.011851011202809002,most_frequent,standardize,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4888776875314595,fpr,chi2
-92,weighting,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.011656655127925412,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.48080598210703135,mean,none,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,205,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-93,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.6931271368243409,None,0.0,11,10,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.362940004438799,most_frequent,robust_scaler,,,0.9177587796305063,0.17084038458597867,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,30.491514144623952,mutual_info,,,
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-97,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,1.1522414705935405e-10,0.6132800183073315,auto,255,None,1607,67,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010096911249562407,mean,robust_scaler,,,0.7663493326967942,0.136094217337434,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-98,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2746221958279041,False,True,1,squared_hinge,ovr,l2,6.546077664669006e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,quantile_transformer,1054,normal,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,
-99,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.479405900046494,,,0.013340778632154543,rbf,-1,False,6.913586060890114e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,robust_scaler,,,0.9953232616818098,0.06450577886132274,fast_ica,,,,,,,,,,,deflation,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-100,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.9815182201168626,None,0.0,15,19,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,robust_scaler,,,0.8878662977383482,0.25666364929970126,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,44.15748219826322,mutual_info,,,
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-102,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.7426893291639965,None,0.0,4,19,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010127721455862085,median,quantile_transformer,1108,uniform,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,263,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-103,none,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,128,manual,0.8574560771111307,0.010886139381599514,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.24115007944216788,most_frequent,normalize,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,62.26514170991452,mutual_info,,,
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-105,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,1.430083734915855e-09,0.08779621406981941,auto,255,None,17,20,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.29749837779115257,mean,robust_scaler,,,0.7236445277814435,0.25,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
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-107,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,11.046854203258679,,,0.07809571976769347,rbf,-1,False,0.00036193159909053734,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.006445557635410205,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-108,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.6728827282646637,None,0.0,18,8,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,robust_scaler,,,0.8512985751386942,0.14092824896182807,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,
-109,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.18060139887649052,None,0.0,10,12,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.010000000000000004,most_frequent,robust_scaler,,,0.9298612094255864,0.1263597946722308,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
-110,none,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.563056219822946,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.32793677336996485,most_frequent,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-111,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6427543483503397,True,True,hinge,0.00045318775930916944,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.009338484332306904,median,quantile_transformer,174,normal,,,kernel_pca,,,,,,,,,,,,,,,,,,,,,,cosine,1848,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-112,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.04981080944617379,True,,0.07309386939525211,True,,constant,log,l2,,0.0006721243213777075,no_encoding,no_coalescense,,mean,quantile_transformer,574,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-113,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,9.029473608568458e-09,0.15779990576964315,auto,255,None,37,71,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.018093177195644097,mean,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,False,,,,,,,,,,,,
-114,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0025185447074997196,True,True,hinge,3.168531728129776e-05,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.05667297499772673,mean,quantile_transformer,796,uniform,,,feature_agglomeration,,,,,,,,,,,,,,,euclidean,average,315,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-115,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.129143822880219,True,True,hinge,0.00012612093569084375,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0016222546082911887,most_frequent,quantile_transformer,1309,uniform,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8.816417526015055,f_classif,,,
-116,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.4864284879550203,None,0.0,1,4,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.008665854799630503,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-117,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.8851946632713493,None,0.0,9,20,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.006095662608922393,most_frequent,quantile_transformer,1002,normal,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.10486328449423876,fpr,f_classif
-118,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.6922752267318475,True,True,squared_hinge,0.06299408576730353,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,quantile_transformer,1742,uniform,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,107,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-119,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.44727252219812963,None,0.0,2,10,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,standardize,,,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l1,8.17533189014765e-05,,,,,,,,,,,,,,,,,,,,,,
-120,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7408631116596028,None,0.0,19,2,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,mean,none,,,,,extra_trees_preproc_for_classification,True,entropy,None,0.8873056409960391,None,0.0,11,18,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-121,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.4210978663047845,None,0.0,5,6,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.006365988550833196,most_frequent,robust_scaler,,,0.8348891074186082,0.00974961635730845,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0695919708890599,fpr,f_classif
-122,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.3341112040803983,False,True,hinge,0.003045682798589668,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.005225537488332702,median,quantile_transformer,1225,uniform,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.10310163232668391,fpr,chi2
-123,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.20612848067724507,False,True,1,squared_hinge,ovr,l2,0.00017832980955957004,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0007502816173508168,median,quantile_transformer,1083,uniform,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.02820386332530596,fwe,f_classif
-124,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1561.1002600303584,,,0.0024048551704194507,rbf,-1,False,0.0001936214667496851,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,minmax,,,,,fast_ica,,,,,,,,,,,parallel,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-125,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.087313242780732,0.0636709914987994,3,0.004481917125024585,poly,-1,False,0.01848880618941367,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.002136015035740733,mean,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-126,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.03204653439894958,True,True,squared_hinge,0.00013006393401112688,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.013506575694382031,most_frequent,normalize,,,,,feature_agglomeration,,,,,,,,,,,,,,,euclidean,ward,356,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-127,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.6466927589470762,None,0.0,11,2,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.009672465311659264,most_frequent,standardize,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,369,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+1,none,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1514.059969351301,False,True,1,squared_hinge,ovr,l2,0.002448814309966599,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,standardize,,,,,extra_trees_preproc_for_classification,True,entropy,None,0.8221481751476923,None,0.0,2,3,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+4,none,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,quantile_transformer,111,uniform,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4748887602127273,fdr,f_classif
+6,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.7283683762613216,None,0.0,12,20,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,median,standardize,,,,,fast_ica,,,,,,,,,,,deflation,exp,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+8,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,114,manual,0.11778631707605758,0.0029112721339713465,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.015582195257240751,median,normalize,,,,,kernel_pca,,,,,,,,,,,,,,,,,,,0.42909623253327767,3,0.25407612000343777,poly,1689,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+11,weighting,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,2,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.3865962574104293,mean,normalize,,,,,fast_ica,,,,,,,,,,,deflation,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+15,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.21772092350860472,None,0.0,14,17,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.0020488212891693275,median,standardize,,,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,33.74257606530722,False,True,1,squared_hinge,ovr,l1,8.740452966205111e-05,,,,,,,,,,,,,,,,,,,,,,
+20,none,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.9866058571747695,None,0.0,16,9,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.1680676743605393,most_frequent,robust_scaler,,,0.8840072797399463,0.08535534311393947,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,False,,,,,,,,,,,,
+23,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,0.00022373024203887573,0.027149343206135476,auto,255,None,49,13,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.00777614320416325,most_frequent,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,
+26,weighting,adaboost,SAMME.R,0.01561270559347964,4,417,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,quantile_transformer,1617,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+28,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,1.0168466808008568e-09,0.21361657232950118,auto,255,None,463,4,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,robust_scaler,,,0.7965217268158734,0.2446550610908456,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,
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+399,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.5864398404787432,None,0.0,1,14,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0070663750850608895,most_frequent,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
+403,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1866.9172613016979,,,0.012606336762631641,rbf,-1,True,0.000504574634300575,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,
+405,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0001329242404968632,False,True,squared_hinge,0.000416219546803132,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.007568009633214092,median,quantile_transformer,1126,normal,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,29.06367291960815,chi2,,,
+408,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0005200565197711045,True,,0.010000000000000014,True,,constant,squared_hinge,l2,,0.00021764230789274295,one_hot_encoding,minority_coalescer,0.2691388203358419,mean,normalize,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,64.14926024499454,chi2,,,
+414,weighting,bernoulli_nb,,,,,0.030158213146175936,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,quantile_transformer,1820,uniform,,,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,9,None,11,2,1.0,96,,,,,
+418,weighting,adaboost,SAMME,0.1313487280585511,3,189,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,robust_scaler,,,0.8008090310131288,0.2935856792824304,extra_trees_preproc_for_classification,False,entropy,None,0.7722400064297721,None,0.0,5,4,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+419,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.985686293012224,,,0.007514476113632565,rbf,-1,True,0.0007610347922656268,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.006285936847328083,most_frequent,robust_scaler,,,0.7904917229582098,0.18967899969665603,feature_agglomeration,,,,,,,,,,,,,,,cosine,complete,354,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+422,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.36374112587320206,None,0.0,2,10,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010870084126959173,most_frequent,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,
+425,weighting,decision_tree,,,,,,,entropy,1.4732927621306418,1.0,None,0.0,10,3,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,standardize,,,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,4276.070150555713,False,True,1,squared_hinge,ovr,l1,0.00011385383984576719,,,,,,,,,,,,,,,,,,,,,,
+429,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.9039555800017817e-05,False,True,squared_hinge,0.0011536981768375681,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,quantile_transformer,1122,uniform,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,
+430,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5321814610661173,None,0.0,1,3,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,standardize,,,,,extra_trees_preproc_for_classification,True,gini,None,0.5,None,0.0,1,15,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+437,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.34649900620486696,None,0.0,10,20,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,mean,quantile_transformer,1135,normal,,,extra_trees_preproc_for_classification,True,gini,None,0.9038598364290225,None,0.0,16,18,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+438,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,6.090138036399552e-10,0.017305568433437588,auto,255,None,202,188,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,robust_scaler,,,0.7526196011313747,0.1563616005702636,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+445,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,68.60833575985336,,,0.03905068757955671,rbf,-1,False,0.02846559747114773,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.04775815990561371,mean,standardize,,,,,extra_trees_preproc_for_classification,False,entropy,None,0.45727987956076366,None,0.0,13,11,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+446,weighting,bernoulli_nb,,,,,2.493274808167669,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.16300567981899747,mean,standardize,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,17.02935019079249,mutual_info,,,
+447,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.32235597434544716,None,0.0,2,18,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.30368818748392196,median,robust_scaler,,,0.7176393529809699,0.19299987830067816,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.16283145936255025,fwe,chi2
+451,weighting,adaboost,SAMME,0.28074307158760897,3,166,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0030293384621179554,mean,quantile_transformer,965,uniform,,,feature_agglomeration,,,,,,,,,,,,,,,cosine,complete,66,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+453,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,162.34906836693145,,,2.470518094983172,rbf,-1,False,1.5366632541084908e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.33912551828528803,median,none,,,,,fast_ica,,,,,,,,,,,deflation,logcosh,412,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+459,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.745748188733041e-05,True,True,hinge,2.181659959258924e-05,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.13921206193066876,most_frequent,quantile_transformer,60,normal,,,extra_trees_preproc_for_classification,False,entropy,None,0.7619669647366906,None,0.0,3,4,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+461,weighting,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.9815588983620268,None,0.0,2,14,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010000000000000004,mean,robust_scaler,,,0.7617017260745782,0.2386998843971201,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
diff --git a/autosklearn/metalearning/files/balanced_accuracy_binary.classification_dense/description.txt b/autosklearn/metalearning/files/balanced_accuracy_binary.classification_dense/description.txt
index 4f35699bb4..0577152494 100644
--- a/autosklearn/metalearning/files/balanced_accuracy_binary.classification_dense/description.txt
+++ b/autosklearn/metalearning/files/balanced_accuracy_binary.classification_dense/description.txt
@@ -1,8 +1,9 @@
 features_cutoff_time: 3600
 features_cutoff_memory: 3072
-number_of_feature_steps: 35
+number_of_feature_steps: 42
 feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances
 feature_step LogNumberOfInstances: LogNumberOfInstances
+feature_step NumberOfClasses: NumberOfClasses
 feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures
 feature_step LogNumberOfFeatures: LogNumberOfFeatures
 feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues
@@ -20,6 +21,11 @@ feature_step DatasetRatio: DatasetRatio, LogDatasetRatio
 feature_step LogDatasetRatio: LogDatasetRatio
 feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio
 feature_step LogInverseDatasetRatio: LogInverseDatasetRatio
+feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD
+feature_step ClassProbabilityMin: ClassProbabilityMin
+feature_step ClassProbabilityMax: ClassProbabilityMax
+feature_step ClassProbabilityMean: ClassProbabilityMean
+feature_step ClassProbabilitySTD: ClassProbabilitySTD
 feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum
 feature_step SymbolsMin: SymbolsMin
 feature_step SymbolsMax: SymbolsMax
@@ -36,11 +42,12 @@ feature_step SkewnessMin: SkewnessMin
 feature_step SkewnessMax: SkewnessMax
 feature_step SkewnessMean: SkewnessMean
 feature_step SkewnessSTD: SkewnessSTD
-features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
+feature_step ClassEntropy: ClassEntropy
+features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy
 features_stochastic: 
-default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
+default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy
 
-algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127
+algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235,236,237,238,239,240,241,242,243,244,245,246,247,248,249,250,251,252,253,254,255,256,257,258,259,260,261,262,263,264,265,266,267,268,269,270,271,272,273,274,275,276,277,278,279,280,281,282,283,284,285,286,287,288,289,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,312,313,314,315,316,317,318,319,320,321,322,323,324,325,326,327,328,329,330,331,332,333,334,335,336,337,338,339,340,341,342,343,344,345,346,347,348,349,350,351,352,353,354,355,356,357,358,359,360,361,362,363,364,365,366,367,368,369,370,371,372,373,374,375,376,377,378,379,380,381,382,383,384,385,386,387,388,389,390,391,392,393,394,395,396,397,398,399,400,401,402,403,404,405,406,407,408,409,410,411,412,413,414,415,416,417,418,419,420,421,422,423,424,425,426,427,428,429,430,431,432,433,434,435,436,437,438,439,440,441,442,443,444,445,446,447,448,449,450,451,452,453,454,455,456,457,458,459,460,461,462,463,464
 algorithms_stochastic: 
 performance_measures: balanced_accuracy
 performance_type: solution_quality
diff --git a/autosklearn/metalearning/files/balanced_accuracy_binary.classification_dense/feature_costs.arff b/autosklearn/metalearning/files/balanced_accuracy_binary.classification_dense/feature_costs.arff
index 0e17e20d22..8512fd9275 100644
--- a/autosklearn/metalearning/files/balanced_accuracy_binary.classification_dense/feature_costs.arff
+++ b/autosklearn/metalearning/files/balanced_accuracy_binary.classification_dense/feature_costs.arff
@@ -4,6 +4,7 @@
 @ATTRIBUTE repetition NUMERIC
 @ATTRIBUTE NumberOfInstances NUMERIC
 @ATTRIBUTE LogNumberOfInstances NUMERIC
+@ATTRIBUTE NumberOfClasses NUMERIC
 @ATTRIBUTE NumberOfFeatures NUMERIC
 @ATTRIBUTE LogNumberOfFeatures NUMERIC
 @ATTRIBUTE MissingValues NUMERIC
@@ -21,6 +22,11 @@
 @ATTRIBUTE LogDatasetRatio NUMERIC
 @ATTRIBUTE InverseDatasetRatio NUMERIC
 @ATTRIBUTE LogInverseDatasetRatio NUMERIC
+@ATTRIBUTE ClassOccurences NUMERIC
+@ATTRIBUTE ClassProbabilityMin NUMERIC
+@ATTRIBUTE ClassProbabilityMax NUMERIC
+@ATTRIBUTE ClassProbabilityMean NUMERIC
+@ATTRIBUTE ClassProbabilitySTD NUMERIC
 @ATTRIBUTE NumSymbols NUMERIC
 @ATTRIBUTE SymbolsMin NUMERIC
 @ATTRIBUTE SymbolsMax NUMERIC
@@ -37,162 +43,138 @@
 @ATTRIBUTE SkewnessMax NUMERIC
 @ATTRIBUTE SkewnessMean NUMERIC
 @ATTRIBUTE SkewnessSTD NUMERIC
+@ATTRIBUTE ClassEntropy NUMERIC
 
 @DATA
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-75101,1.0,1e-05,1e-05,1e-05,1e-05,0.0418,0.03546,1e-05,0.00389,1e-05,0.00248,0.0,2e-05,2e-05,4e-05,6e-05,2e-05,1e-05,5e-05,2e-05,0.00045,1e-05,0.0,0.00011,0.00023,0.0001,0.00041,6e-05,8e-05,0.0001,0.00018,0.00047,6e-05,0.0001,8e-05,0.00023
-75191,1.0,1e-05,1e-05,3e-05,1e-05,0.53436,0.40777,2e-05,0.09655,3e-05,0.0301,1e-05,2e-05,3e-05,5e-05,0.00011,7e-05,1e-05,0.00011,3e-05,0.00084,5e-05,7e-05,0.00017,0.00033,0.00023,0.00088,0.00012,0.00017,0.00032,0.00026,0.00068,9e-05,0.00012,0.00013,0.00035
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+75240,1.0,2e-05,1e-05,0.00036,5e-05,4e-05,0.00845,0.00644,6e-05,0.00122,4e-05,0.00091,1e-05,4e-05,4e-05,9e-05,0.00013,5e-05,2e-05,9e-05,5e-05,0.00031,3e-05,1e-05,7e-05,0.0002,0.00073,3e-05,2e-05,0.00019,0.0003,0.00018,0.00055,9e-05,9e-05,0.00011,0.00025,0.0005,7e-05,8e-05,0.00015,0.0002,0.00365
+75243,1.0,1e-05,1e-05,0.00029,1e-05,1e-05,0.00461,0.00415,1e-05,0.00035,1e-05,0.00015,0.0,2e-05,2e-05,5e-05,7e-05,2e-05,1e-05,4e-05,1e-05,0.00018,2e-05,1e-05,4e-05,0.00011,0.00022,1e-05,1e-05,5e-05,0.00011,5e-05,0.0004,6e-05,6e-05,8e-05,0.0002,0.00039,6e-05,6e-05,8e-05,0.00019,0.00265
+75244,1.0,3e-05,2e-05,0.0004,3e-05,2e-05,0.0173,0.01298,4e-05,0.00267,4e-05,0.00174,1e-05,7e-05,9e-05,0.00024,0.00027,8e-05,2e-05,0.00013,3e-05,0.00045,7e-05,2e-05,0.00011,0.00025,0.00077,6e-05,2e-05,0.00021,0.00034,0.00013,0.00077,0.00014,0.00013,0.00026,0.00023,0.00044,7e-05,8e-05,0.0001,0.00019,0.00649
+75248,1.0,1e-05,1e-05,0.00017,1e-05,1e-05,0.00416,0.00305,1e-05,0.0006,2e-05,0.00054,1e-05,2e-05,2e-05,5e-05,7e-05,2e-05,1e-05,4e-05,1e-05,0.00017,2e-05,1e-05,4e-05,0.0001,0.00029,5e-05,1e-05,6e-05,0.00012,6e-05,0.00081,0.00011,0.00015,0.00016,0.00038,0.0005,7e-05,8e-05,0.00012,0.00024,0.00184
+75249,1.0,1e-05,1e-05,0.00011,1e-05,1e-05,0.00192,0.00153,1e-05,0.00025,1e-05,0.00017,1e-05,2e-05,2e-05,5e-05,7e-05,2e-05,1e-05,5e-05,3e-05,0.00014,2e-05,1e-05,3e-05,8e-05,0.00029,1e-05,3e-05,6e-05,0.00012,7e-05,0.00041,6e-05,8e-05,0.0001,0.00017,0.00043,6e-05,7e-05,9e-05,0.00019,0.00092
+75250,1.0,1e-05,1e-05,0.00375,1e-05,1e-05,0.04994,0.047,1e-05,0.00247,1e-05,0.00049,1e-05,2e-05,2e-05,6e-05,7e-05,2e-05,1e-05,3e-05,1e-05,0.00022,4e-05,1e-05,4e-05,0.00012,0.00041,0.0,1e-05,0.00011,0.00023,6e-05,0.00076,0.00012,0.00012,0.00019,0.00032,0.00086,0.00012,0.00013,0.00018,0.00043,0.04178
diff --git a/autosklearn/metalearning/files/balanced_accuracy_binary.classification_dense/feature_runstatus.arff b/autosklearn/metalearning/files/balanced_accuracy_binary.classification_dense/feature_runstatus.arff
index 0034ad2bd6..5fff2dfed3 100644
--- a/autosklearn/metalearning/files/balanced_accuracy_binary.classification_dense/feature_runstatus.arff
+++ b/autosklearn/metalearning/files/balanced_accuracy_binary.classification_dense/feature_runstatus.arff
@@ -4,6 +4,7 @@
 @ATTRIBUTE repetition NUMERIC
 @ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other}
@@ -21,6 +22,11 @@
 @ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other}
@@ -37,162 +43,138 @@
 @ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other}
 
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diff --git a/autosklearn/metalearning/files/balanced_accuracy_binary.classification_dense/feature_values.arff b/autosklearn/metalearning/files/balanced_accuracy_binary.classification_dense/feature_values.arff
index a60400e3ce..736dc64321 100644
--- a/autosklearn/metalearning/files/balanced_accuracy_binary.classification_dense/feature_values.arff
+++ b/autosklearn/metalearning/files/balanced_accuracy_binary.classification_dense/feature_values.arff
@@ -13,7 +13,6 @@
 @ATTRIBUTE KurtosisMean NUMERIC
 @ATTRIBUTE KurtosisMin NUMERIC
 @ATTRIBUTE KurtosisSTD NUMERIC
-@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC
 @ATTRIBUTE LogDatasetRatio NUMERIC
 @ATTRIBUTE LogInverseDatasetRatio NUMERIC
 @ATTRIBUTE LogNumberOfFeatures NUMERIC
@@ -42,160 +41,135 @@
 @ATTRIBUTE SymbolsSum NUMERIC
 
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diff --git a/autosklearn/metalearning/files/balanced_accuracy_binary.classification_sparse/algorithm_runs.arff b/autosklearn/metalearning/files/balanced_accuracy_binary.classification_sparse/algorithm_runs.arff
index 240cc6c874..506aab5615 100644
--- a/autosklearn/metalearning/files/balanced_accuracy_binary.classification_sparse/algorithm_runs.arff
+++ b/autosklearn/metalearning/files/balanced_accuracy_binary.classification_sparse/algorithm_runs.arff
@@ -7,95 +7,133 @@
 @ATTRIBUTE runstatus {ok, timeout, memout, not_applicable, crash, other}
 
 @DATA
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diff --git a/autosklearn/metalearning/files/balanced_accuracy_binary.classification_sparse/configurations.csv b/autosklearn/metalearning/files/balanced_accuracy_binary.classification_sparse/configurations.csv
index b81aa5389d..3fc1927040 100644
--- a/autosklearn/metalearning/files/balanced_accuracy_binary.classification_sparse/configurations.csv
+++ b/autosklearn/metalearning/files/balanced_accuracy_binary.classification_sparse/configurations.csv
@@ -1,93 +1,131 @@
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-91,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.087313242780732,0.0636709914987994,3,0.004481917125024585,poly,-1,False,0.01848880618941367,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.002136015035740733,mean,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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+273,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.7302408485410412,None,0.0,8,12,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.027592968439566132,median,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+276,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.10311576029720285,None,0.0,15,2,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,quantile_transformer,898,normal,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.06885692817737343,,mutual_info_classif,
+278,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5878222472475889,None,0.0,6,15,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,robust_scaler,,,0.7419183699029103,0.22028507583137247,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.21256588660504683,fdr,chi2,
+282,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.002017899394165588,False,,0.0005911937660372945,True,0.17801822098174286,constant,log,elasticnet,,0.00041682529601986016,no_encoding,no_coalescense,,median,quantile_transformer,1335,normal,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0642825062494559,fpr,chi2,
+286,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,33.440072270881174,-0.22811742088286835,3,0.05010299776905627,poly,-1,False,2.275627187537573e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,normalize,,,,,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,2,None,5,14,1.0,52,,,,,,
+287,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.3850609070926397,None,0.0,1,2,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.013992309764275707,median,normalize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,,
+289,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.818514683854827,None,0.0,15,20,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.4928285185491301,most_frequent,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,,
+292,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.0479982468066316,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,quantile_transformer,1097,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+296,weighting,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,1,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.10025594399125329,median,none,,,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.22421266641512966,False,True,1,squared_hinge,ovr,l1,0.0036601423446597503,,,,,,,,,,,,,,,,,,,,,
+303,none,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.15407723980990137,False,True,1,squared_hinge,ovr,l2,0.000145662551000115,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.047373081734019086,median,standardize,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,63.64551651621554,chi2,,,,
+306,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2677516008848513,True,True,hinge,0.05752557410901496,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0010152857217605206,median,quantile_transformer,1000,uniform,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,51.60974796845029,chi2,,,,
+307,none,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,249,None,,0.0005516162390755682,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010000000000000004,mean,quantile_transformer,1350,uniform,,,kernel_pca,,,,,,,,,,,0.162692683181769,3,1.0,poly,487,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+312,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,60.6761602925822,,,0.038224035513907226,rbf,-1,False,0.0018214343969776353,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.421025538999516,median,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+313,weighting,adaboost,SAMME,1.2423888874490505,5,284,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.01582563952987033,most_frequent,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+316,weighting,adaboost,SAMME,1.2615483379313783,8,190,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.00017789451703497617,mean,robust_scaler,,,0.7345811776810758,0.054715769043996304,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+318,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.843098530645432,None,0.0,1,4,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,mean,quantile_transformer,930,normal,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,,
+321,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.5864398404787432,None,0.0,1,14,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0070663750850608895,most_frequent,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,,
+326,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0001329242404968632,False,True,squared_hinge,0.000416219546803132,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.007568009633214092,median,quantile_transformer,1126,normal,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,29.06367291960815,chi2,,,,
+329,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0005200565197711045,True,,0.010000000000000014,True,,constant,squared_hinge,l2,,0.00021764230789274295,one_hot_encoding,minority_coalescer,0.2691388203358419,mean,normalize,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,64.14926024499454,chi2,,,,
+331,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.5,None,0.0,1,5,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.15244281596303316,mean,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+333,weighting,bernoulli_nb,,,,,0.030158213146175936,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,quantile_transformer,1820,uniform,,,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,9,None,11,2,1.0,96,,,,,,
+336,weighting,adaboost,SAMME,0.1313487280585511,3,189,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,robust_scaler,,,0.8008090310131288,0.2935856792824304,extra_trees_preproc_for_classification,False,entropy,None,0.7722400064297721,None,0.0,5,4,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+339,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.36374112587320206,None,0.0,2,10,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010870084126959173,most_frequent,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,,
+340,weighting,decision_tree,,,,,,,entropy,1.4732927621306418,1.0,None,0.0,10,3,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,standardize,,,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,4276.070150555713,False,True,1,squared_hinge,ovr,l1,0.00011385383984576719,,,,,,,,,,,,,,,,,,,,,
+344,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.9039555800017817e-05,False,True,squared_hinge,0.0011536981768375681,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,quantile_transformer,1122,uniform,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,,
+345,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5321814610661173,None,0.0,1,3,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,standardize,,,,,extra_trees_preproc_for_classification,True,gini,None,0.5,None,0.0,1,15,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+351,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.34649900620486696,None,0.0,10,20,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,mean,quantile_transformer,1135,normal,,,extra_trees_preproc_for_classification,True,gini,None,0.9038598364290225,None,0.0,16,18,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+352,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5988524108452234,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,mean,robust_scaler,,,0.75,0.26961884796358077,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+355,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,68.60833575985336,,,0.03905068757955671,rbf,-1,False,0.02846559747114773,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.04775815990561371,mean,standardize,,,,,extra_trees_preproc_for_classification,False,entropy,None,0.45727987956076366,None,0.0,13,11,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+356,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.01400651528258823,True,True,hinge,1.929818740575615e-05,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,normalize,,,,,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,5,None,3,19,1.0,78,,,,,,
+357,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.32235597434544716,None,0.0,2,18,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.30368818748392196,median,robust_scaler,,,0.7176393529809699,0.19299987830067816,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.16283145936255025,fwe,chi2,
+361,weighting,adaboost,SAMME,0.2786884011943926,3,263,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,quantile_transformer,1958,uniform,,,extra_trees_preproc_for_classification,False,entropy,None,0.2099259239256946,None,0.0,19,18,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+363,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.1393173890820956,False,True,1,squared_hinge,ovr,l2,8.283470008521272e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.015152352014390857,mean,normalize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,False,,,,,,,,,,,,,
+368,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.745748188733041e-05,True,True,hinge,2.181659959258924e-05,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.13921206193066876,most_frequent,quantile_transformer,60,normal,,,extra_trees_preproc_for_classification,False,entropy,None,0.7619669647366906,None,0.0,3,4,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+369,weighting,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.9815588983620268,None,0.0,2,14,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010000000000000004,mean,robust_scaler,,,0.7617017260745782,0.2386998843971201,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
diff --git a/autosklearn/metalearning/files/balanced_accuracy_binary.classification_sparse/description.txt b/autosklearn/metalearning/files/balanced_accuracy_binary.classification_sparse/description.txt
index 6be2ed2635..3ae9001ea3 100644
--- a/autosklearn/metalearning/files/balanced_accuracy_binary.classification_sparse/description.txt
+++ b/autosklearn/metalearning/files/balanced_accuracy_binary.classification_sparse/description.txt
@@ -1,8 +1,9 @@
 features_cutoff_time: 3600
 features_cutoff_memory: 3072
-number_of_feature_steps: 35
+number_of_feature_steps: 42
 feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances
 feature_step LogNumberOfInstances: LogNumberOfInstances
+feature_step NumberOfClasses: NumberOfClasses
 feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures
 feature_step LogNumberOfFeatures: LogNumberOfFeatures
 feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues
@@ -20,6 +21,11 @@ feature_step DatasetRatio: DatasetRatio, LogDatasetRatio
 feature_step LogDatasetRatio: LogDatasetRatio
 feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio
 feature_step LogInverseDatasetRatio: LogInverseDatasetRatio
+feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD
+feature_step ClassProbabilityMin: ClassProbabilityMin
+feature_step ClassProbabilityMax: ClassProbabilityMax
+feature_step ClassProbabilityMean: ClassProbabilityMean
+feature_step ClassProbabilitySTD: ClassProbabilitySTD
 feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum
 feature_step SymbolsMin: SymbolsMin
 feature_step SymbolsMax: SymbolsMax
@@ -36,11 +42,12 @@ feature_step SkewnessMin: SkewnessMin
 feature_step SkewnessMax: SkewnessMax
 feature_step SkewnessMean: SkewnessMean
 feature_step SkewnessSTD: SkewnessSTD
-features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
+feature_step ClassEntropy: ClassEntropy
+features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy
 features_stochastic: 
-default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
+default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy
 
-algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92
+algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235,236,237,238,239,240,241,242,243,244,245,246,247,248,249,250,251,252,253,254,255,256,257,258,259,260,261,262,263,264,265,266,267,268,269,270,271,272,273,274,275,276,277,278,279,280,281,282,283,284,285,286,287,288,289,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,312,313,314,315,316,317,318,319,320,321,322,323,324,325,326,327,328,329,330,331,332,333,334,335,336,337,338,339,340,341,342,343,344,345,346,347,348,349,350,351,352,353,354,355,356,357,358,359,360,361,362,363,364,365,366,367,368,369,370,371,372
 algorithms_stochastic: 
 performance_measures: balanced_accuracy
 performance_type: solution_quality
diff --git a/autosklearn/metalearning/files/balanced_accuracy_binary.classification_sparse/feature_costs.arff b/autosklearn/metalearning/files/balanced_accuracy_binary.classification_sparse/feature_costs.arff
index 0e17e20d22..8512fd9275 100644
--- a/autosklearn/metalearning/files/balanced_accuracy_binary.classification_sparse/feature_costs.arff
+++ b/autosklearn/metalearning/files/balanced_accuracy_binary.classification_sparse/feature_costs.arff
@@ -4,6 +4,7 @@
 @ATTRIBUTE repetition NUMERIC
 @ATTRIBUTE NumberOfInstances NUMERIC
 @ATTRIBUTE LogNumberOfInstances NUMERIC
+@ATTRIBUTE NumberOfClasses NUMERIC
 @ATTRIBUTE NumberOfFeatures NUMERIC
 @ATTRIBUTE LogNumberOfFeatures NUMERIC
 @ATTRIBUTE MissingValues NUMERIC
@@ -21,6 +22,11 @@
 @ATTRIBUTE LogDatasetRatio NUMERIC
 @ATTRIBUTE InverseDatasetRatio NUMERIC
 @ATTRIBUTE LogInverseDatasetRatio NUMERIC
+@ATTRIBUTE ClassOccurences NUMERIC
+@ATTRIBUTE ClassProbabilityMin NUMERIC
+@ATTRIBUTE ClassProbabilityMax NUMERIC
+@ATTRIBUTE ClassProbabilityMean NUMERIC
+@ATTRIBUTE ClassProbabilitySTD NUMERIC
 @ATTRIBUTE NumSymbols NUMERIC
 @ATTRIBUTE SymbolsMin NUMERIC
 @ATTRIBUTE SymbolsMax NUMERIC
@@ -37,162 +43,138 @@
 @ATTRIBUTE SkewnessMax NUMERIC
 @ATTRIBUTE SkewnessMean NUMERIC
 @ATTRIBUTE SkewnessSTD NUMERIC
+@ATTRIBUTE ClassEntropy NUMERIC
 
 @DATA
-75123,1.0,1e-05,1e-05,1e-05,1e-05,0.00253,0.00221,1e-05,0.00024,3e-05,0.00011,0.0,2e-05,2e-05,5e-05,8e-05,2e-05,1e-05,7e-05,1e-05,0.00072,1e-05,1e-05,0.00019,0.00041,0.00011,0.00061,0.00012,9e-05,0.00015,0.00025,0.00067,0.00014,0.00011,0.00016,0.00027
-75090,1.0,1e-05,1e-05,2e-05,2e-05,0.00815,0.00368,7e-05,0.00228,9e-05,0.00235,0.0,5e-05,5e-05,0.0001,0.00013,0.00013,1e-05,0.00021,2e-05,0.00064,0.0,0.0,0.00014,0.00038,0.00011,0.00066,0.00011,0.00014,0.00014,0.00026,0.00066,0.00011,0.00013,0.00015,0.00027
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-75217,1.0,1e-05,1e-05,2e-05,1e-05,0.00196,0.00158,1e-05,0.00026,2e-05,0.00016,1e-05,3e-05,3e-05,7e-05,0.0001,3e-05,1e-05,7e-05,2e-05,0.00089,1e-05,1e-05,0.00022,0.00053,0.00013,0.00074,9e-05,0.0001,0.00019,0.00036,0.00065,0.00013,0.0001,0.00011,0.00031
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-2117,1.0,1e-05,1e-05,1e-05,1e-05,0.01735,0.0154,1e-05,0.00141,1e-05,0.00057,0.0,1e-05,1e-05,3e-05,6e-05,2e-05,1e-05,5e-05,1e-05,0.0004,1e-05,0.0,8e-05,0.00021,0.0001,0.00065,0.00012,0.00011,0.00015,0.00026,0.00055,7e-05,0.00014,9e-05,0.00025
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-75157,1.0,1e-05,1e-05,1e-05,1e-05,0.00125,0.00106,1e-05,0.00015,2e-05,7e-05,0.0,2e-05,2e-05,5e-05,6e-05,2e-05,1e-05,4e-05,1e-05,0.00048,2e-05,0.0,0.0001,0.00026,0.0001,0.00039,6e-05,7e-05,8e-05,0.00019,0.00043,6e-05,9e-05,8e-05,0.0002
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-288,1.0,1e-05,1e-05,1e-05,1e-05,0.00248,0.00199,1e-05,0.00029,1e-05,0.00022,0.0,2e-05,2e-05,4e-05,6e-05,2e-05,1e-05,5e-05,1e-05,0.0006,1e-05,0.0,0.00014,0.00035,0.00011,0.00043,7e-05,0.00011,8e-05,0.00017,0.00044,6e-05,8e-05,8e-05,0.00021
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+75233,1.0,1e-05,1e-05,0.00012,1e-05,0.0,0.00257,0.00222,1e-05,0.00024,1e-05,0.00014,0.0,1e-05,1e-05,3e-05,5e-05,3e-05,1e-05,2e-05,1e-05,9e-05,1e-05,0.0,2e-05,5e-05,0.0003,0.0,0.0,8e-05,0.00017,4e-05,0.00076,0.00012,0.00013,0.00014,0.00037,0.00058,0.00012,8e-05,0.00013,0.00025,0.00119
+75234,1.0,1e-05,1e-05,0.00015,1e-05,1e-05,0.0058,0.00534,1e-05,0.00033,1e-05,0.00016,0.0,2e-05,2e-05,7e-05,0.00204,3e-05,1e-05,5e-05,1e-05,0.00024,3e-05,2e-05,0.0001,9e-05,0.00043,0.0,0.0,0.00012,0.00024,6e-05,0.00156,6e-05,7e-05,0.00121,0.00021,0.00054,6e-05,7e-05,9e-05,0.00032,0.00501
+75235,1.0,1e-05,1e-05,0.0003,2e-05,1e-05,0.00461,0.00385,2e-05,0.00055,2e-05,0.00026,1e-05,3e-05,3e-05,0.0001,0.00011,3e-05,1e-05,5e-05,2e-05,0.00026,3e-05,1e-05,8e-05,0.00014,0.00075,1e-05,1e-05,0.0002,0.00043,0.00011,0.00127,0.00017,0.0002,0.00029,0.00061,0.0008,0.00015,0.00012,0.00016,0.00038,0.00242
+75236,1.0,1e-05,1e-05,0.0001,1e-05,1e-05,0.00218,0.00118,2e-05,0.00065,3e-05,0.00041,1e-05,3e-05,3e-05,9e-05,9e-05,4e-05,1e-05,7e-05,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.0006,1e-05,1e-05,0.00021,0.00032,6e-05,0.00043,7e-05,7e-05,9e-05,0.00019,0.00062,7e-05,8e-05,0.0001,0.00037,0.00048
+75237,1.0,2e-05,1e-05,0.00785,2e-05,1e-05,0.31031,0.27854,2e-05,0.01161,3e-05,0.02022,1e-05,4e-05,3e-05,9e-05,0.00024,6e-05,2e-05,9e-05,2e-05,0.00062,7e-05,3e-05,0.00017,0.00035,0.0008,1e-05,1e-05,0.00028,0.00043,8e-05,0.00043,8e-05,7e-05,9e-05,0.0002,0.00044,6e-05,8e-05,9e-05,0.00021,0.14028
+75239,1.0,1e-05,1e-05,7e-05,1e-05,1e-05,0.00105,0.00084,1e-05,0.00015,1e-05,8e-05,1e-05,2e-05,5e-05,4e-05,8e-05,2e-05,1e-05,3e-05,1e-05,0.00016,2e-05,1e-05,5e-05,9e-05,0.00049,0.0,0.0,0.00012,0.00026,0.00011,0.00046,0.00013,8e-05,8e-05,0.00018,0.00044,6e-05,9e-05,0.00011,0.00018,0.00065
+75240,1.0,2e-05,1e-05,0.00036,5e-05,4e-05,0.00845,0.00644,6e-05,0.00122,4e-05,0.00091,1e-05,4e-05,4e-05,9e-05,0.00013,5e-05,2e-05,9e-05,5e-05,0.00031,3e-05,1e-05,7e-05,0.0002,0.00073,3e-05,2e-05,0.00019,0.0003,0.00018,0.00055,9e-05,9e-05,0.00011,0.00025,0.0005,7e-05,8e-05,0.00015,0.0002,0.00365
+75243,1.0,1e-05,1e-05,0.00029,1e-05,1e-05,0.00461,0.00415,1e-05,0.00035,1e-05,0.00015,0.0,2e-05,2e-05,5e-05,7e-05,2e-05,1e-05,4e-05,1e-05,0.00018,2e-05,1e-05,4e-05,0.00011,0.00022,1e-05,1e-05,5e-05,0.00011,5e-05,0.0004,6e-05,6e-05,8e-05,0.0002,0.00039,6e-05,6e-05,8e-05,0.00019,0.00265
+75244,1.0,3e-05,2e-05,0.0004,3e-05,2e-05,0.0173,0.01298,4e-05,0.00267,4e-05,0.00174,1e-05,7e-05,9e-05,0.00024,0.00027,8e-05,2e-05,0.00013,3e-05,0.00045,7e-05,2e-05,0.00011,0.00025,0.00077,6e-05,2e-05,0.00021,0.00034,0.00013,0.00077,0.00014,0.00013,0.00026,0.00023,0.00044,7e-05,8e-05,0.0001,0.00019,0.00649
+75248,1.0,1e-05,1e-05,0.00017,1e-05,1e-05,0.00416,0.00305,1e-05,0.0006,2e-05,0.00054,1e-05,2e-05,2e-05,5e-05,7e-05,2e-05,1e-05,4e-05,1e-05,0.00017,2e-05,1e-05,4e-05,0.0001,0.00029,5e-05,1e-05,6e-05,0.00012,6e-05,0.00081,0.00011,0.00015,0.00016,0.00038,0.0005,7e-05,8e-05,0.00012,0.00024,0.00184
+75249,1.0,1e-05,1e-05,0.00011,1e-05,1e-05,0.00192,0.00153,1e-05,0.00025,1e-05,0.00017,1e-05,2e-05,2e-05,5e-05,7e-05,2e-05,1e-05,5e-05,3e-05,0.00014,2e-05,1e-05,3e-05,8e-05,0.00029,1e-05,3e-05,6e-05,0.00012,7e-05,0.00041,6e-05,8e-05,0.0001,0.00017,0.00043,6e-05,7e-05,9e-05,0.00019,0.00092
+75250,1.0,1e-05,1e-05,0.00375,1e-05,1e-05,0.04994,0.047,1e-05,0.00247,1e-05,0.00049,1e-05,2e-05,2e-05,6e-05,7e-05,2e-05,1e-05,3e-05,1e-05,0.00022,4e-05,1e-05,4e-05,0.00012,0.00041,0.0,1e-05,0.00011,0.00023,6e-05,0.00076,0.00012,0.00012,0.00019,0.00032,0.00086,0.00012,0.00013,0.00018,0.00043,0.04178
diff --git a/autosklearn/metalearning/files/balanced_accuracy_binary.classification_sparse/feature_runstatus.arff b/autosklearn/metalearning/files/balanced_accuracy_binary.classification_sparse/feature_runstatus.arff
index 0034ad2bd6..5fff2dfed3 100644
--- a/autosklearn/metalearning/files/balanced_accuracy_binary.classification_sparse/feature_runstatus.arff
+++ b/autosklearn/metalearning/files/balanced_accuracy_binary.classification_sparse/feature_runstatus.arff
@@ -4,6 +4,7 @@
 @ATTRIBUTE repetition NUMERIC
 @ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other}
@@ -21,6 +22,11 @@
 @ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other}
@@ -37,162 +43,138 @@
 @ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other}
 
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diff --git a/autosklearn/metalearning/files/balanced_accuracy_binary.classification_sparse/feature_values.arff b/autosklearn/metalearning/files/balanced_accuracy_binary.classification_sparse/feature_values.arff
index a60400e3ce..736dc64321 100644
--- a/autosklearn/metalearning/files/balanced_accuracy_binary.classification_sparse/feature_values.arff
+++ b/autosklearn/metalearning/files/balanced_accuracy_binary.classification_sparse/feature_values.arff
@@ -13,7 +13,6 @@
 @ATTRIBUTE KurtosisMean NUMERIC
 @ATTRIBUTE KurtosisMin NUMERIC
 @ATTRIBUTE KurtosisSTD NUMERIC
-@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC
 @ATTRIBUTE LogDatasetRatio NUMERIC
 @ATTRIBUTE LogInverseDatasetRatio NUMERIC
 @ATTRIBUTE LogNumberOfFeatures NUMERIC
@@ -42,160 +41,135 @@
 @ATTRIBUTE SymbolsSum NUMERIC
 
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diff --git a/autosklearn/metalearning/files/balanced_accuracy_multiclass.classification_dense/algorithm_runs.arff b/autosklearn/metalearning/files/balanced_accuracy_multiclass.classification_dense/algorithm_runs.arff
index efdc813af5..e0edfe5d00 100644
--- a/autosklearn/metalearning/files/balanced_accuracy_multiclass.classification_dense/algorithm_runs.arff
+++ b/autosklearn/metalearning/files/balanced_accuracy_multiclass.classification_dense/algorithm_runs.arff
@@ -7,130 +7,135 @@
 @ATTRIBUTE runstatus {ok, timeout, memout, not_applicable, crash, other}
 
 @DATA
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diff --git a/autosklearn/metalearning/files/balanced_accuracy_multiclass.classification_dense/configurations.csv b/autosklearn/metalearning/files/balanced_accuracy_multiclass.classification_dense/configurations.csv
index d1a17e1911..ba6539ee41 100644
--- a/autosklearn/metalearning/files/balanced_accuracy_multiclass.classification_dense/configurations.csv
+++ b/autosklearn/metalearning/files/balanced_accuracy_multiclass.classification_dense/configurations.csv
@@ -1,128 +1,133 @@
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-20,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.6448542484717253,None,0.0,3,20,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.3306639470207569,most_frequent,robust_scaler,,,0.7248166700836332,0.2755431234957578,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-21,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.8054434875626825,None,0.0,1,20,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,median,standardize,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4241048505416849,fpr,f_classif
-22,weighting,adaboost,SAMME,0.2353770068926199,1,62,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.007159634821655197,most_frequent,quantile_transformer,1616,normal,,,feature_agglomeration,,,,,,,,,,,,,,,cosine,average,151,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-23,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7734.521233796252,,,2.6048661611376747,rbf,-1,True,4.68717607435371e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,none,,,,,feature_agglomeration,,,,,,,,,,,,,,,euclidean,average,25,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-24,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.21794354428393548,None,0.0,2,16,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0025451910134387575,median,quantile_transformer,1477,uniform,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
-25,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5010952533165973,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.005838375247601407,mean,normalize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,
-26,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5185406559495093,None,0.0,3,17,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.010000000000000004,median,normalize,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.13046020673147232,fpr,chi2
-27,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.9663114854040722,None,0.0,11,12,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.26932389682904634,median,quantile_transformer,23,uniform,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l1,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,
-28,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5607168729746175,None,0.0,2,3,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.009810589644392154,most_frequent,quantile_transformer,1132,normal,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.06211089008167909,fdr,chi2
-29,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.992156991246829,None,0.0,19,3,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.007016879673679419,median,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,
-30,none,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.9728532763078892,None,0.0,13,9,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,none,,,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6651071190864879,False,,,,,,,,,,,,,,,
-31,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9561075301148197,None,0.0,6,2,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,
-32,none,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,173,None,,1.3919208969870906e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,minmax,,,,,kernel_pca,,,,,,,,,,,,,,,,,,,0.1258530558153379,2,0.008499663955936211,poly,499,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-33,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,1.153931482107762e-10,0.011666190464009397,auto,255,None,29,190,18,loss,1e-07,0.147868307124396,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,
-34,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,22194.242946902177,0.45210849669021247,,0.0005669713702171556,sigmoid,-1,False,0.03542142266081451,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,quantile_transformer,291,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-35,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.8189454407806317,None,0.0,10,10,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,robust_scaler,,,0.7192027795318285,0.24912062393096632,fast_ica,,,,,,,,,,,parallel,logcosh,100,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-36,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,3.6797768538445263e-09,0.05511004217375807,auto,255,None,72,62,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,robust_scaler,,,0.7241481576763391,0.0501033899901458,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,36,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-37,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.7521969592385672,None,0.0,9,5,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.020402264943872365,median,robust_scaler,,,0.737227213630259,0.20752747663727789,fast_ica,,,,,,,,,,,parallel,exp,312,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-38,none,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.563056219822946,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.32793677336996485,most_frequent,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-39,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,1.6343482806592892e-09,0.19537524572679268,auto,255,None,32,29,13,loss,1e-07,0.05061738224302791,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-40,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7088164344533493,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,mean,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,
-41,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,1.943186107984505e-07,0.08852455702036988,auto,255,None,5,72,19,loss,1e-07,0.039837460762271175,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.01591265419367627,median,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,
-42,weighting,adaboost,SAMME,0.5210651042094924,10,401,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,standardize,,,,,feature_agglomeration,,,,,,,,,,,,,,,euclidean,complete,89,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-43,none,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0686374607394679,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,minmax,,,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.8490260132946401,False,,,,,,,,,,,,,,,
-44,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.6931271368243409,None,0.0,11,10,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.362940004438799,most_frequent,robust_scaler,,,0.9177587796305063,0.17084038458597867,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,30.491514144623952,mutual_info,,,
-45,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.2052035060269155,None,0.0,20,16,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,none,,,,,feature_agglomeration,,,,,,,,,,,,,,,cosine,complete,22,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-46,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,92.66144306787051,,,0.04654639302091653,rbf,-1,True,0.0022231544280610005,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.005194838694455963,mean,none,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,15,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-47,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2681.998807317996,-0.9558745731731211,,0.0011299948421773457,sigmoid,-1,False,1.5292507354478086e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-48,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.4467384469668449,None,0.0,19,19,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.00033708405018786203,median,quantile_transformer,711,normal,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,7.744829113696808,False,True,1,squared_hinge,ovr,l1,0.0001869446320464273,,,,,,,,,,,,,,,,,,,,,,
-49,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,26.502259985828186,,,9.475662388405154e-05,rbf,-1,False,0.00019690635968217866,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-50,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,2.5360196477156387e-07,0.011018756408558218,auto,255,None,40,122,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.00011749164080669445,most_frequent,quantile_transformer,757,uniform,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,5.097834697156853,False,True,1,squared_hinge,ovr,l1,0.053877834919477424,,,,,,,,,,,,,,,,,,,,,,
-51,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5255991794296913,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-52,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.34697723213923637,None,0.0,15,18,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.1923594385988973,mean,normalize,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,25.77499302945827,chi2,,,
-53,weighting,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.7776312540090575,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0005253715056715056,mean,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,False,,,,,,,,,,,,
-54,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,1.5661823810486637e-10,0.08996863554015262,auto,255,None,156,89,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.009331521286159971,median,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,
-55,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.6109468076475573,0.8748383232154038,4,0.41333070422336576,poly,-1,False,3.591268778393482e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.011804925819789203,median,minmax,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-56,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.7610308078756552,None,0.0,1,2,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.010000000000000004,mean,robust_scaler,,,0.9205545465103644,0.20272761060743316,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-57,weighting,adaboost,SAMME.R,0.03127273017917347,10,496,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,standardize,,,,,fast_ica,,,,,,,,,,,deflation,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-58,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,151,None,,0.0003283777607180646,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.2510527431514227,mean,normalize,,,,,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,-0.96093182232312,,0.05762510964607661,sigmoid,2632,,,,,,,,,,,,,,,,,
-59,none,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.4702538502800974e-07,True,,,True,0.14999999999999974,optimal,perceptron,elasticnet,,0.0011469929926494495,no_encoding,no_coalescense,,mean,quantile_transformer,1782,normal,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.9897679383028408,True,,,,,,,,,,,,,,,
-60,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.6453555269772124,None,0.0,1,4,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,normalize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-61,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,940.9437776449834,,,1.5839338084625942,rbf,-1,False,0.023072781592565438,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,none,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,77.25763685070707,mutual_info,,,
-62,weighting,extra_trees,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,13,18,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0001574994528707389,most_frequent,standardize,,,,,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,3,None,19,19,1.0,77,,,,,
-63,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.34534888916828577,0.02513634776687268,2,1.0733091880109409,poly,-1,True,0.0006974263047918307,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,quantile_transformer,1000,normal,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,77.99934218022861,mutual_info,,,
-64,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,29.55731828912983,False,True,1,squared_hinge,ovr,l2,0.0011332486951954968,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,
-65,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,1.1387795876755026e-06,0.12245605190343567,auto,255,None,58,7,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,robust_scaler,,,0.79305915648885,0.1841487406876042,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-66,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.8425560317492612,None,0.0,8,15,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.015276988502366128,median,none,,,,,fast_ica,,,,,,,,,,,parallel,cube,17,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-67,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,0.2410465573252999,0.022973469655856883,auto,255,None,272,8,3,loss,1e-07,0.0321932494461218,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.00807996498005836,most_frequent,none,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,65,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-68,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,925.3053743248586,False,True,1,squared_hinge,ovr,l2,0.02764481198395717,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.3850718018366796,most_frequent,quantile_transformer,786,uniform,,,feature_agglomeration,,,,,,,,,,,,,,,cosine,average,156,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-69,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7573078019937971,None,0.0,2,5,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,robust_scaler,,,0.7265312295801113,0.28332515882236425,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.09660564590146253,fpr,f_classif
-70,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5345540246011571,None,0.0,8,4,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.007441222223802043,most_frequent,normalize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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-72,none,adaboost,SAMME,0.1280458968526125,7,125,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,robust_scaler,,,0.7156460334356771,0.2981902862926971,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,65.16171885308417,chi2,,,
-73,none,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9931779675565967,None,0.0,18,17,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,robust_scaler,,,0.75,0.2945917010472818,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,
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-75,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,1.2430504242164082e-09,0.03359568307446533,auto,255,None,1609,29,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,standardize,,,,,feature_agglomeration,,,,,,,,,,,,,,,cosine,complete,40,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-76,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7.491039171224502,True,True,squared_hinge,2.8058138048327884e-05,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,minmax,,,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.7754372809893839,False,True,1,squared_hinge,ovr,l1,3.9643594596766314e-05,,,,,,,,,,,,,,,,,,,,,,
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-78,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,0.011547155223520492,0.13665554140837183,auto,255,None,13,5,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.1824584492028449,median,minmax,,,,,feature_agglomeration,,,,,,,,,,,,,,,euclidean,average,261,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-79,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.7308100742740112,None,0.0,18,19,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.014637629296743653,most_frequent,robust_scaler,,,0.7724670628916575,0.2233445340332948,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,29.07440444752882,False,True,1,squared_hinge,ovr,l1,0.00040479645225962257,,,,,,,,,,,,,,,,,,,,,,
-80,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1661.2274747409913,,,0.025821208852387936,rbf,-1,False,0.0017011435104921824,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,none,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,306,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-81,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0016941428709087083,True,,,True,,optimal,squared_hinge,l1,,5.124265599423001e-05,no_encoding,no_coalescense,,most_frequent,quantile_transformer,461,uniform,,,extra_trees_preproc_for_classification,True,gini,None,0.31371431180257103,None,0.0,5,17,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-82,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,21.85207494001697,0.18872999589786676,4,1.1863646021868761,poly,-1,False,0.0021338371901294443,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0034080852834770217,mean,normalize,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.11326005109886934,fdr,f_classif
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-85,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.593728171200453,None,0.0,7,20,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.02830941310359363,mean,normalize,,,,,extra_trees_preproc_for_classification,False,entropy,None,0.7389446586281172,None,0.0,20,11,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-86,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,1,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.019797877010737826,mean,normalize,,,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.8239678741128165,False,,,,,,,,,,,,,,,
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-88,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.46057831591617715,False,True,hinge,0.04557857428827514,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00027457445401600137,median,standardize,,,,,extra_trees_preproc_for_classification,True,gini,None,0.48190346970486964,None,0.0,17,18,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-89,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5722917944959481,None,0.0,1,14,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,
-90,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.26356754187780895,None,0.0,4,3,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010966178401758774,mean,robust_scaler,,,0.7817043861798886,0.24371720112407158,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
-91,weighting,adaboost,SAMME.R,0.13547552520095074,4,62,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.011851011202809002,most_frequent,standardize,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4888776875314595,fpr,chi2
-92,weighting,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.011656655127925412,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.48080598210703135,mean,none,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,205,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-93,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.6931271368243409,None,0.0,11,10,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.362940004438799,most_frequent,robust_scaler,,,0.9177587796305063,0.17084038458597867,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,30.491514144623952,mutual_info,,,
-94,weighting,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,2,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,none,,,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6342574285315175,False,,,,,,,,,,,,,,,
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-96,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.457928416564218,None,0.0,12,2,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,
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-98,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2746221958279041,False,True,1,squared_hinge,ovr,l2,6.546077664669006e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,quantile_transformer,1054,normal,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,
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-113,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,9.029473608568458e-09,0.15779990576964315,auto,255,None,37,71,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.018093177195644097,mean,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,False,,,,,,,,,,,,
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-118,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.6922752267318475,True,True,squared_hinge,0.06299408576730353,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,quantile_transformer,1742,uniform,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,107,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-119,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.44727252219812963,None,0.0,2,10,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,standardize,,,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l1,8.17533189014765e-05,,,,,,,,,,,,,,,,,,,,,,
-120,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7408631116596028,None,0.0,19,2,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,mean,none,,,,,extra_trees_preproc_for_classification,True,entropy,None,0.8873056409960391,None,0.0,11,18,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-121,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.4210978663047845,None,0.0,5,6,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.006365988550833196,most_frequent,robust_scaler,,,0.8348891074186082,0.00974961635730845,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0695919708890599,fpr,f_classif
-122,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.3341112040803983,False,True,hinge,0.003045682798589668,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.005225537488332702,median,quantile_transformer,1225,uniform,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.10310163232668391,fpr,chi2
-123,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.20612848067724507,False,True,1,squared_hinge,ovr,l2,0.00017832980955957004,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0007502816173508168,median,quantile_transformer,1083,uniform,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.02820386332530596,fwe,f_classif
-124,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1561.1002600303584,,,0.0024048551704194507,rbf,-1,False,0.0001936214667496851,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,minmax,,,,,fast_ica,,,,,,,,,,,parallel,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-125,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.087313242780732,0.0636709914987994,3,0.004481917125024585,poly,-1,False,0.01848880618941367,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.002136015035740733,mean,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-126,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.03204653439894958,True,True,squared_hinge,0.00013006393401112688,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.013506575694382031,most_frequent,normalize,,,,,feature_agglomeration,,,,,,,,,,,,,,,euclidean,ward,356,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-127,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.6466927589470762,None,0.0,11,2,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.009672465311659264,most_frequent,standardize,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,369,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+1,none,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1514.059969351301,False,True,1,squared_hinge,ovr,l2,0.002448814309966599,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,standardize,,,,,extra_trees_preproc_for_classification,True,entropy,None,0.8221481751476923,None,0.0,2,3,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+4,none,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,quantile_transformer,111,uniform,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4748887602127273,fdr,f_classif
+6,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.7283683762613216,None,0.0,12,20,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,median,standardize,,,,,fast_ica,,,,,,,,,,,deflation,exp,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+8,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,114,manual,0.11778631707605758,0.0029112721339713465,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.015582195257240751,median,normalize,,,,,kernel_pca,,,,,,,,,,,,,,,,,,,0.42909623253327767,3,0.25407612000343777,poly,1689,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+11,weighting,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,2,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.3865962574104293,mean,normalize,,,,,fast_ica,,,,,,,,,,,deflation,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+15,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.21772092350860472,None,0.0,14,17,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.0020488212891693275,median,standardize,,,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,33.74257606530722,False,True,1,squared_hinge,ovr,l1,8.740452966205111e-05,,,,,,,,,,,,,,,,,,,,,,
+20,none,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.9866058571747695,None,0.0,16,9,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.1680676743605393,most_frequent,robust_scaler,,,0.8840072797399463,0.08535534311393947,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,False,,,,,,,,,,,,
+23,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,0.00022373024203887573,0.027149343206135476,auto,255,None,49,13,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.00777614320416325,most_frequent,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,
+26,weighting,adaboost,SAMME.R,0.01561270559347964,4,417,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,quantile_transformer,1617,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+28,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,1.0168466808008568e-09,0.21361657232950118,auto,255,None,463,4,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,robust_scaler,,,0.7965217268158734,0.2446550610908456,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,
+32,none,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,174,manual,0.21501892897746921,2.0347110826262054e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.013754893527625522,most_frequent,normalize,,,,,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,-0.019221513778605592,3,0.10190948951339239,poly,2521,,,,,,,,,,,,,,,,,
+35,none,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9.608707084865884,False,True,1,squared_hinge,ovr,l2,0.008086910150491696,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.020901889243957545,median,minmax,,,,,kitchen_sinks,,,,,,,,,,,,,,,,,,,,,,,,0.5694399784333516,3230,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+38,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9025597189684735,None,0.0,18,8,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.007536467725572231,most_frequent,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
+40,none,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.563056219822946,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.32793677336996485,most_frequent,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+41,none,extra_trees,,,,,,,,,,,,,,,True,gini,None,0.5033866291997137,None,0.0,2,14,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,minmax,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+44,none,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l2,2.858526169098402e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0007827505640681123,median,standardize,,,,,extra_trees_preproc_for_classification,False,entropy,None,0.3443310459451119,None,0.0,3,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+48,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.36750008697181424,None,0.0,20,16,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,median,quantile_transformer,753,normal,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,
+51,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.3862436135511355e-05,False,,0.01139834246571156,True,,invscaling,log,l1,0.5,0.00025858980190005947,no_encoding,no_coalescense,,most_frequent,robust_scaler,,,0.7614693069989072,0.2515867504414451,extra_trees_preproc_for_classification,False,gini,None,0.6006906188943036,None,0.0,2,4,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+57,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.24407113189971e-05,True,,,True,,optimal,hinge,l2,,0.018411607920920666,one_hot_encoding,minority_coalescer,0.006594668208397739,mean,standardize,,,,,extra_trees_preproc_for_classification,True,entropy,None,0.20369732810864838,None,0.0,16,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+58,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5214197490559657,None,0.0,1,2,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.00826182798588248,mean,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,
+59,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.15115687152536414,True,True,hinge,0.009954631623104506,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0013410356664535208,most_frequent,quantile_transformer,1293,uniform,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+62,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.7848258054012829,None,0.0,18,15,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,standardize,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,44.11368738669913,chi2,,,
+65,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,1.2822815440468518e-10,0.12922700208645135,auto,255,None,31,44,13,loss,1e-07,0.08789566421608311,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.012230423571314038,mean,robust_scaler,,,0.71734690066056,0.20283418288016852,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,
+66,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,236.02119240292217,False,True,1,squared_hinge,ovr,l2,0.0017427914676337187,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.1020917547461355,mean,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,False,,,,,,,,,,,,
+68,weighting,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,minmax,,,,,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+75,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13.284077389890939,-0.2621334760621076,5,0.9060908941898536,poly,-1,False,0.06323853917206591,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0011743021592446861,most_frequent,normalize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,
+77,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.2309302197434343e-05,False,,0.006503403719704897,True,,constant,log,l1,,1.531583361000749e-05,one_hot_encoding,no_coalescense,,most_frequent,robust_scaler,,,0.7530789965606406,0.2430090971909385,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,cosine,1693,,,,,,,,,,,,,,,,,
+82,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,3.79982070767762e-10,0.10307173798074214,auto,255,None,30,25,6,loss,1e-07,0.09705336338563596,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.004889186641013006,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+87,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.07992258780660727,True,True,squared_hinge,0.022067056607853346,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,robust_scaler,,,0.7602075850543897,0.15799780977101488,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,95.09568612626826,chi2,,,
+94,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5079964624349133,None,0.0,7,5,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.37073794659062626,mean,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,
+97,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.4879444812369075,None,0.0,5,3,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.008704186680582812,mean,normalize,,,,,extra_trees_preproc_for_classification,False,entropy,None,0.6428064165338061,None,0.0,2,17,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+99,weighting,adaboost,SAMME,0.17983222024694556,4,357,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0008898781197407956,mean,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+105,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.268699692198443e-05,True,,4.1225573912733725e-06,True,,constant,log,l2,,4.729985534114764e-05,no_encoding,no_coalescense,,median,quantile_transformer,246,normal,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,20.73090458774975,chi2,,,
+109,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.05953492795583007,True,,1.2244794102245382e-07,True,,constant,perceptron,l2,,0.006468632691846376,no_encoding,no_coalescense,,most_frequent,quantile_transformer,424,uniform,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,False,,,,,,,,,,,,
+110,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0021814768111094564,False,True,hinge,8.726644999887521e-05,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,none,,,,,kitchen_sinks,,,,,,,,,,,,,,,,,,,,,,,,0.0010909910768059797,970,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+113,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9208911584010107,None,0.0,4,14,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,False,,,,,,,,,,,,
+115,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.463780563279276,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.011474025594258192,mean,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,
+116,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,236,None,,0.08905291537442941,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,normalize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,
+121,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.08836825866738698,None,0.0,5,19,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0003943043347109662,median,robust_scaler,,,0.7564808439539721,0.23469821956116474,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2206955933275169,fdr,f_classif
+125,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.6719618199439291,None,0.0,7,6,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0011674787451616529,most_frequent,none,,,,,fast_ica,,,,,,,,,,,parallel,logcosh,43,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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+322,none,adaboost,SAMME,0.6791611572325104,6,465,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,quantile_transformer,1464,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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+380,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2677516008848513,True,True,hinge,0.05752557410901496,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0010152857217605206,median,quantile_transformer,1000,uniform,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,51.60974796845029,chi2,,,
+385,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.8280243811834952,None,0.0,9,14,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.08913760502443781,median,none,,,,,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+388,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,53.67009896349757,,,0.03687753298495132,rbf,-1,False,0.028791286245157307,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.047859755696856916,mean,standardize,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,70,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+392,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,1.8250137234576287e-09,0.01865408018981487,auto,255,None,27,5,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0752156562901201,median,quantile_transformer,1281,normal,,,feature_agglomeration,,,,,,,,,,,,,,,cosine,complete,75,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+394,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0025736560290786094,False,True,squared_hinge,0.0006196872282311157,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.042446796272035237,most_frequent,quantile_transformer,773,normal,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,89.84127958910369,f_classif,,,
+399,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.5864398404787432,None,0.0,1,14,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0070663750850608895,most_frequent,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
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+408,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0005200565197711045,True,,0.010000000000000014,True,,constant,squared_hinge,l2,,0.00021764230789274295,one_hot_encoding,minority_coalescer,0.2691388203358419,mean,normalize,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,64.14926024499454,chi2,,,
+414,weighting,bernoulli_nb,,,,,0.030158213146175936,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,quantile_transformer,1820,uniform,,,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,9,None,11,2,1.0,96,,,,,
+418,weighting,adaboost,SAMME,0.1313487280585511,3,189,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,robust_scaler,,,0.8008090310131288,0.2935856792824304,extra_trees_preproc_for_classification,False,entropy,None,0.7722400064297721,None,0.0,5,4,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+419,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.985686293012224,,,0.007514476113632565,rbf,-1,True,0.0007610347922656268,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.006285936847328083,most_frequent,robust_scaler,,,0.7904917229582098,0.18967899969665603,feature_agglomeration,,,,,,,,,,,,,,,cosine,complete,354,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+422,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.36374112587320206,None,0.0,2,10,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010870084126959173,most_frequent,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,
+425,weighting,decision_tree,,,,,,,entropy,1.4732927621306418,1.0,None,0.0,10,3,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,standardize,,,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,4276.070150555713,False,True,1,squared_hinge,ovr,l1,0.00011385383984576719,,,,,,,,,,,,,,,,,,,,,,
+429,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.9039555800017817e-05,False,True,squared_hinge,0.0011536981768375681,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,quantile_transformer,1122,uniform,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,
+430,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5321814610661173,None,0.0,1,3,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,standardize,,,,,extra_trees_preproc_for_classification,True,gini,None,0.5,None,0.0,1,15,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+437,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.34649900620486696,None,0.0,10,20,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,mean,quantile_transformer,1135,normal,,,extra_trees_preproc_for_classification,True,gini,None,0.9038598364290225,None,0.0,16,18,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+438,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,6.090138036399552e-10,0.017305568433437588,auto,255,None,202,188,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,robust_scaler,,,0.7526196011313747,0.1563616005702636,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+445,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,68.60833575985336,,,0.03905068757955671,rbf,-1,False,0.02846559747114773,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.04775815990561371,mean,standardize,,,,,extra_trees_preproc_for_classification,False,entropy,None,0.45727987956076366,None,0.0,13,11,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+446,weighting,bernoulli_nb,,,,,2.493274808167669,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.16300567981899747,mean,standardize,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,17.02935019079249,mutual_info,,,
+447,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.32235597434544716,None,0.0,2,18,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.30368818748392196,median,robust_scaler,,,0.7176393529809699,0.19299987830067816,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.16283145936255025,fwe,chi2
+451,weighting,adaboost,SAMME,0.28074307158760897,3,166,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0030293384621179554,mean,quantile_transformer,965,uniform,,,feature_agglomeration,,,,,,,,,,,,,,,cosine,complete,66,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+453,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,162.34906836693145,,,2.470518094983172,rbf,-1,False,1.5366632541084908e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.33912551828528803,median,none,,,,,fast_ica,,,,,,,,,,,deflation,logcosh,412,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+459,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.745748188733041e-05,True,True,hinge,2.181659959258924e-05,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.13921206193066876,most_frequent,quantile_transformer,60,normal,,,extra_trees_preproc_for_classification,False,entropy,None,0.7619669647366906,None,0.0,3,4,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+461,weighting,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.9815588983620268,None,0.0,2,14,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010000000000000004,mean,robust_scaler,,,0.7617017260745782,0.2386998843971201,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
diff --git a/autosklearn/metalearning/files/balanced_accuracy_multiclass.classification_dense/description.txt b/autosklearn/metalearning/files/balanced_accuracy_multiclass.classification_dense/description.txt
index 4f35699bb4..0577152494 100644
--- a/autosklearn/metalearning/files/balanced_accuracy_multiclass.classification_dense/description.txt
+++ b/autosklearn/metalearning/files/balanced_accuracy_multiclass.classification_dense/description.txt
@@ -1,8 +1,9 @@
 features_cutoff_time: 3600
 features_cutoff_memory: 3072
-number_of_feature_steps: 35
+number_of_feature_steps: 42
 feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances
 feature_step LogNumberOfInstances: LogNumberOfInstances
+feature_step NumberOfClasses: NumberOfClasses
 feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures
 feature_step LogNumberOfFeatures: LogNumberOfFeatures
 feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues
@@ -20,6 +21,11 @@ feature_step DatasetRatio: DatasetRatio, LogDatasetRatio
 feature_step LogDatasetRatio: LogDatasetRatio
 feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio
 feature_step LogInverseDatasetRatio: LogInverseDatasetRatio
+feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD
+feature_step ClassProbabilityMin: ClassProbabilityMin
+feature_step ClassProbabilityMax: ClassProbabilityMax
+feature_step ClassProbabilityMean: ClassProbabilityMean
+feature_step ClassProbabilitySTD: ClassProbabilitySTD
 feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum
 feature_step SymbolsMin: SymbolsMin
 feature_step SymbolsMax: SymbolsMax
@@ -36,11 +42,12 @@ feature_step SkewnessMin: SkewnessMin
 feature_step SkewnessMax: SkewnessMax
 feature_step SkewnessMean: SkewnessMean
 feature_step SkewnessSTD: SkewnessSTD
-features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
+feature_step ClassEntropy: ClassEntropy
+features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy
 features_stochastic: 
-default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
+default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy
 
-algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127
+algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235,236,237,238,239,240,241,242,243,244,245,246,247,248,249,250,251,252,253,254,255,256,257,258,259,260,261,262,263,264,265,266,267,268,269,270,271,272,273,274,275,276,277,278,279,280,281,282,283,284,285,286,287,288,289,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,312,313,314,315,316,317,318,319,320,321,322,323,324,325,326,327,328,329,330,331,332,333,334,335,336,337,338,339,340,341,342,343,344,345,346,347,348,349,350,351,352,353,354,355,356,357,358,359,360,361,362,363,364,365,366,367,368,369,370,371,372,373,374,375,376,377,378,379,380,381,382,383,384,385,386,387,388,389,390,391,392,393,394,395,396,397,398,399,400,401,402,403,404,405,406,407,408,409,410,411,412,413,414,415,416,417,418,419,420,421,422,423,424,425,426,427,428,429,430,431,432,433,434,435,436,437,438,439,440,441,442,443,444,445,446,447,448,449,450,451,452,453,454,455,456,457,458,459,460,461,462,463,464
 algorithms_stochastic: 
 performance_measures: balanced_accuracy
 performance_type: solution_quality
diff --git a/autosklearn/metalearning/files/balanced_accuracy_multiclass.classification_dense/feature_costs.arff b/autosklearn/metalearning/files/balanced_accuracy_multiclass.classification_dense/feature_costs.arff
index 0e17e20d22..8512fd9275 100644
--- a/autosklearn/metalearning/files/balanced_accuracy_multiclass.classification_dense/feature_costs.arff
+++ b/autosklearn/metalearning/files/balanced_accuracy_multiclass.classification_dense/feature_costs.arff
@@ -4,6 +4,7 @@
 @ATTRIBUTE repetition NUMERIC
 @ATTRIBUTE NumberOfInstances NUMERIC
 @ATTRIBUTE LogNumberOfInstances NUMERIC
+@ATTRIBUTE NumberOfClasses NUMERIC
 @ATTRIBUTE NumberOfFeatures NUMERIC
 @ATTRIBUTE LogNumberOfFeatures NUMERIC
 @ATTRIBUTE MissingValues NUMERIC
@@ -21,6 +22,11 @@
 @ATTRIBUTE LogDatasetRatio NUMERIC
 @ATTRIBUTE InverseDatasetRatio NUMERIC
 @ATTRIBUTE LogInverseDatasetRatio NUMERIC
+@ATTRIBUTE ClassOccurences NUMERIC
+@ATTRIBUTE ClassProbabilityMin NUMERIC
+@ATTRIBUTE ClassProbabilityMax NUMERIC
+@ATTRIBUTE ClassProbabilityMean NUMERIC
+@ATTRIBUTE ClassProbabilitySTD NUMERIC
 @ATTRIBUTE NumSymbols NUMERIC
 @ATTRIBUTE SymbolsMin NUMERIC
 @ATTRIBUTE SymbolsMax NUMERIC
@@ -37,162 +43,138 @@
 @ATTRIBUTE SkewnessMax NUMERIC
 @ATTRIBUTE SkewnessMean NUMERIC
 @ATTRIBUTE SkewnessSTD NUMERIC
+@ATTRIBUTE ClassEntropy NUMERIC
 
 @DATA
-75123,1.0,1e-05,1e-05,1e-05,1e-05,0.00253,0.00221,1e-05,0.00024,3e-05,0.00011,0.0,2e-05,2e-05,5e-05,8e-05,2e-05,1e-05,7e-05,1e-05,0.00072,1e-05,1e-05,0.00019,0.00041,0.00011,0.00061,0.00012,9e-05,0.00015,0.00025,0.00067,0.00014,0.00011,0.00016,0.00027
-75090,1.0,1e-05,1e-05,2e-05,2e-05,0.00815,0.00368,7e-05,0.00228,9e-05,0.00235,0.0,5e-05,5e-05,0.0001,0.00013,0.00013,1e-05,0.00021,2e-05,0.00064,0.0,0.0,0.00014,0.00038,0.00011,0.00066,0.00011,0.00014,0.00014,0.00026,0.00066,0.00011,0.00013,0.00015,0.00027
-2123,1.0,1e-05,1e-05,1e-05,1e-05,0.00097,0.00079,1e-05,0.00013,1e-05,7e-05,0.0,2e-05,2e-05,5e-05,8e-05,2e-05,1e-05,5e-05,1e-05,0.0007,1e-05,0.0,0.00017,0.00042,0.0001,0.00052,6e-05,8e-05,0.00012,0.00026,0.00049,6e-05,0.00011,9e-05,0.00023
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-75101,1.0,1e-05,1e-05,1e-05,1e-05,0.0418,0.03546,1e-05,0.00389,1e-05,0.00248,0.0,2e-05,2e-05,4e-05,6e-05,2e-05,1e-05,5e-05,2e-05,0.00045,1e-05,0.0,0.00011,0.00023,0.0001,0.00041,6e-05,8e-05,0.0001,0.00018,0.00047,6e-05,0.0001,8e-05,0.00023
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-266,1.0,1e-05,1e-05,1e-05,1e-05,0.0018,0.00146,1e-05,0.00023,2e-05,0.00015,2e-05,2e-05,2e-05,6e-05,8e-05,3e-05,1e-05,6e-05,1e-05,0.00059,1e-05,1e-05,0.00014,0.00031,0.00013,0.0004,5e-05,7e-05,8e-05,0.0002,0.00044,8e-05,8e-05,8e-05,0.0002
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-75217,1.0,1e-05,1e-05,2e-05,1e-05,0.00196,0.00158,1e-05,0.00026,2e-05,0.00016,1e-05,3e-05,3e-05,7e-05,0.0001,3e-05,1e-05,7e-05,2e-05,0.00089,1e-05,1e-05,0.00022,0.00053,0.00013,0.00074,9e-05,0.0001,0.00019,0.00036,0.00065,0.00013,0.0001,0.00011,0.00031
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-2117,1.0,1e-05,1e-05,1e-05,1e-05,0.01735,0.0154,1e-05,0.00141,1e-05,0.00057,0.0,1e-05,1e-05,3e-05,6e-05,2e-05,1e-05,5e-05,1e-05,0.0004,1e-05,0.0,8e-05,0.00021,0.0001,0.00065,0.00012,0.00011,0.00015,0.00026,0.00055,7e-05,0.00014,9e-05,0.00025
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-75103,1.0,1e-05,1e-05,1e-05,1e-05,0.01302,0.00819,3e-05,0.00234,3e-05,0.00255,0.0,2e-05,2e-05,6e-05,0.00012,5e-05,1e-05,0.00011,1e-05,0.0005,1e-05,0.0,9e-05,0.00029,0.00011,0.00054,8e-05,0.00011,0.00011,0.00024,0.00066,7e-05,0.00015,0.00011,0.00032
-75168,1.0,1e-05,0.0,1e-05,1e-05,0.02353,0.00601,0.00016,0.01754,0.00018,0.00031,0.0,0.00013,0.00013,0.00027,0.0003,0.00032,1e-05,0.00042,1e-05,0.0004,1e-05,0.0,8e-05,0.00021,0.0001,0.0011,0.00022,0.00025,0.00026,0.00037,0.00124,0.00022,0.00025,0.00028,0.00049
-75157,1.0,1e-05,1e-05,1e-05,1e-05,0.00125,0.00106,1e-05,0.00015,2e-05,7e-05,0.0,2e-05,2e-05,5e-05,6e-05,2e-05,1e-05,4e-05,1e-05,0.00048,2e-05,0.0,0.0001,0.00026,0.0001,0.00039,6e-05,7e-05,8e-05,0.00019,0.00043,6e-05,9e-05,8e-05,0.0002
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-75132,1.0,1e-05,0.0,2e-05,1e-05,0.45642,0.41356,1e-05,0.03273,1e-05,0.01015,0.0,1e-05,1e-05,3e-05,6e-05,2e-05,1e-05,6e-05,2e-05,0.00049,1e-05,0.0,0.00012,0.00023,0.00013,0.00046,7e-05,8e-05,8e-05,0.00023,0.00053,6e-05,0.0001,9e-05,0.00028
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+75230,1.0,1e-05,0.0,9e-05,1e-05,1e-05,0.00087,0.0006,1e-05,0.00018,1e-05,0.00011,0.0,2e-05,2e-05,4e-05,6e-05,2e-05,1e-05,3e-05,1e-05,0.00015,2e-05,1e-05,4e-05,8e-05,0.00038,0.0,1e-05,0.0001,0.00021,5e-05,0.00037,5e-05,6e-05,8e-05,0.00018,0.00033,5e-05,6e-05,7e-05,0.00015,0.00048
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+75234,1.0,1e-05,1e-05,0.00015,1e-05,1e-05,0.0058,0.00534,1e-05,0.00033,1e-05,0.00016,0.0,2e-05,2e-05,7e-05,0.00204,3e-05,1e-05,5e-05,1e-05,0.00024,3e-05,2e-05,0.0001,9e-05,0.00043,0.0,0.0,0.00012,0.00024,6e-05,0.00156,6e-05,7e-05,0.00121,0.00021,0.00054,6e-05,7e-05,9e-05,0.00032,0.00501
+75235,1.0,1e-05,1e-05,0.0003,2e-05,1e-05,0.00461,0.00385,2e-05,0.00055,2e-05,0.00026,1e-05,3e-05,3e-05,0.0001,0.00011,3e-05,1e-05,5e-05,2e-05,0.00026,3e-05,1e-05,8e-05,0.00014,0.00075,1e-05,1e-05,0.0002,0.00043,0.00011,0.00127,0.00017,0.0002,0.00029,0.00061,0.0008,0.00015,0.00012,0.00016,0.00038,0.00242
+75236,1.0,1e-05,1e-05,0.0001,1e-05,1e-05,0.00218,0.00118,2e-05,0.00065,3e-05,0.00041,1e-05,3e-05,3e-05,9e-05,9e-05,4e-05,1e-05,7e-05,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.0006,1e-05,1e-05,0.00021,0.00032,6e-05,0.00043,7e-05,7e-05,9e-05,0.00019,0.00062,7e-05,8e-05,0.0001,0.00037,0.00048
+75237,1.0,2e-05,1e-05,0.00785,2e-05,1e-05,0.31031,0.27854,2e-05,0.01161,3e-05,0.02022,1e-05,4e-05,3e-05,9e-05,0.00024,6e-05,2e-05,9e-05,2e-05,0.00062,7e-05,3e-05,0.00017,0.00035,0.0008,1e-05,1e-05,0.00028,0.00043,8e-05,0.00043,8e-05,7e-05,9e-05,0.0002,0.00044,6e-05,8e-05,9e-05,0.00021,0.14028
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+75243,1.0,1e-05,1e-05,0.00029,1e-05,1e-05,0.00461,0.00415,1e-05,0.00035,1e-05,0.00015,0.0,2e-05,2e-05,5e-05,7e-05,2e-05,1e-05,4e-05,1e-05,0.00018,2e-05,1e-05,4e-05,0.00011,0.00022,1e-05,1e-05,5e-05,0.00011,5e-05,0.0004,6e-05,6e-05,8e-05,0.0002,0.00039,6e-05,6e-05,8e-05,0.00019,0.00265
+75244,1.0,3e-05,2e-05,0.0004,3e-05,2e-05,0.0173,0.01298,4e-05,0.00267,4e-05,0.00174,1e-05,7e-05,9e-05,0.00024,0.00027,8e-05,2e-05,0.00013,3e-05,0.00045,7e-05,2e-05,0.00011,0.00025,0.00077,6e-05,2e-05,0.00021,0.00034,0.00013,0.00077,0.00014,0.00013,0.00026,0.00023,0.00044,7e-05,8e-05,0.0001,0.00019,0.00649
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+75249,1.0,1e-05,1e-05,0.00011,1e-05,1e-05,0.00192,0.00153,1e-05,0.00025,1e-05,0.00017,1e-05,2e-05,2e-05,5e-05,7e-05,2e-05,1e-05,5e-05,3e-05,0.00014,2e-05,1e-05,3e-05,8e-05,0.00029,1e-05,3e-05,6e-05,0.00012,7e-05,0.00041,6e-05,8e-05,0.0001,0.00017,0.00043,6e-05,7e-05,9e-05,0.00019,0.00092
+75250,1.0,1e-05,1e-05,0.00375,1e-05,1e-05,0.04994,0.047,1e-05,0.00247,1e-05,0.00049,1e-05,2e-05,2e-05,6e-05,7e-05,2e-05,1e-05,3e-05,1e-05,0.00022,4e-05,1e-05,4e-05,0.00012,0.00041,0.0,1e-05,0.00011,0.00023,6e-05,0.00076,0.00012,0.00012,0.00019,0.00032,0.00086,0.00012,0.00013,0.00018,0.00043,0.04178
diff --git a/autosklearn/metalearning/files/balanced_accuracy_multiclass.classification_dense/feature_runstatus.arff b/autosklearn/metalearning/files/balanced_accuracy_multiclass.classification_dense/feature_runstatus.arff
index 0034ad2bd6..5fff2dfed3 100644
--- a/autosklearn/metalearning/files/balanced_accuracy_multiclass.classification_dense/feature_runstatus.arff
+++ b/autosklearn/metalearning/files/balanced_accuracy_multiclass.classification_dense/feature_runstatus.arff
@@ -4,6 +4,7 @@
 @ATTRIBUTE repetition NUMERIC
 @ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other}
@@ -21,6 +22,11 @@
 @ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other}
@@ -37,162 +43,138 @@
 @ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other}
 
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diff --git a/autosklearn/metalearning/files/balanced_accuracy_multiclass.classification_dense/feature_values.arff b/autosklearn/metalearning/files/balanced_accuracy_multiclass.classification_dense/feature_values.arff
index a60400e3ce..736dc64321 100644
--- a/autosklearn/metalearning/files/balanced_accuracy_multiclass.classification_dense/feature_values.arff
+++ b/autosklearn/metalearning/files/balanced_accuracy_multiclass.classification_dense/feature_values.arff
@@ -13,7 +13,6 @@
 @ATTRIBUTE KurtosisMean NUMERIC
 @ATTRIBUTE KurtosisMin NUMERIC
 @ATTRIBUTE KurtosisSTD NUMERIC
-@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC
 @ATTRIBUTE LogDatasetRatio NUMERIC
 @ATTRIBUTE LogInverseDatasetRatio NUMERIC
 @ATTRIBUTE LogNumberOfFeatures NUMERIC
@@ -42,160 +41,135 @@
 @ATTRIBUTE SymbolsSum NUMERIC
 
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diff --git a/autosklearn/metalearning/files/balanced_accuracy_multiclass.classification_sparse/algorithm_runs.arff b/autosklearn/metalearning/files/balanced_accuracy_multiclass.classification_sparse/algorithm_runs.arff
index 240cc6c874..506aab5615 100644
--- a/autosklearn/metalearning/files/balanced_accuracy_multiclass.classification_sparse/algorithm_runs.arff
+++ b/autosklearn/metalearning/files/balanced_accuracy_multiclass.classification_sparse/algorithm_runs.arff
@@ -7,95 +7,133 @@
 @ATTRIBUTE runstatus {ok, timeout, memout, not_applicable, crash, other}
 
 @DATA
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diff --git a/autosklearn/metalearning/files/balanced_accuracy_multiclass.classification_sparse/configurations.csv b/autosklearn/metalearning/files/balanced_accuracy_multiclass.classification_sparse/configurations.csv
index b81aa5389d..3fc1927040 100644
--- a/autosklearn/metalearning/files/balanced_accuracy_multiclass.classification_sparse/configurations.csv
+++ b/autosklearn/metalearning/files/balanced_accuracy_multiclass.classification_sparse/configurations.csv
@@ -1,93 +1,131 @@
 idx,balancing:strategy,classifier:__choice__,classifier:adaboost:algorithm,classifier:adaboost:learning_rate,classifier:adaboost:max_depth,classifier:adaboost:n_estimators,classifier:bernoulli_nb:alpha,classifier:bernoulli_nb:fit_prior,classifier:decision_tree:criterion,classifier:decision_tree:max_depth_factor,classifier:decision_tree:max_features,classifier:decision_tree:max_leaf_nodes,classifier:decision_tree:min_impurity_decrease,classifier:decision_tree:min_samples_leaf,classifier:decision_tree:min_samples_split,classifier:decision_tree:min_weight_fraction_leaf,classifier:extra_trees:bootstrap,classifier:extra_trees:criterion,classifier:extra_trees:max_depth,classifier:extra_trees:max_features,classifier:extra_trees:max_leaf_nodes,classifier:extra_trees:min_impurity_decrease,classifier:extra_trees:min_samples_leaf,classifier:extra_trees:min_samples_split,classifier:extra_trees:min_weight_fraction_leaf,classifier:gradient_boosting:early_stop,classifier:gradient_boosting:l2_regularization,classifier:gradient_boosting:learning_rate,classifier:gradient_boosting:loss,classifier:gradient_boosting:max_bins,classifier:gradient_boosting:max_depth,classifier:gradient_boosting:max_leaf_nodes,classifier:gradient_boosting:min_samples_leaf,classifier:gradient_boosting:n_iter_no_change,classifier:gradient_boosting:scoring,classifier:gradient_boosting:tol,classifier:gradient_boosting:validation_fraction,classifier:k_nearest_neighbors:n_neighbors,classifier:k_nearest_neighbors:p,classifier:k_nearest_neighbors:weights,classifier:lda:n_components,classifier:lda:shrinkage,classifier:lda:shrinkage_factor,classifier:lda:tol,classifier:liblinear_svc:C,classifier:liblinear_svc:dual,classifier:liblinear_svc:fit_intercept,classifier:liblinear_svc:intercept_scaling,classifier:liblinear_svc:loss,classifier:liblinear_svc:multi_class,classifier:liblinear_svc:penalty,classifier:liblinear_svc:tol,classifier:libsvm_svc:C,classifier:libsvm_svc:coef0,classifier:libsvm_svc:degree,classifier:libsvm_svc:gamma,classifier:libsvm_svc:kernel,classifier:libsvm_svc:max_iter,classifier:libsvm_svc:shrinking,classifier:libsvm_svc:tol,classifier:multinomial_nb:alpha,classifier:multinomial_nb:fit_prior,classifier:passive_aggressive:C,classifier:passive_aggressive:average,classifier:passive_aggressive:fit_intercept,classifier:passive_aggressive:loss,classifier:passive_aggressive:tol,classifier:qda:reg_param,classifier:random_forest:bootstrap,classifier:random_forest:criterion,classifier:random_forest:max_depth,classifier:random_forest:max_features,classifier:random_forest:max_leaf_nodes,classifier:random_forest:min_impurity_decrease,classifier:random_forest:min_samples_leaf,classifier:random_forest:min_samples_split,classifier:random_forest:min_weight_fraction_leaf,classifier:sgd:alpha,classifier:sgd:average,classifier:sgd:epsilon,classifier:sgd:eta0,classifier:sgd:fit_intercept,classifier:sgd:l1_ratio,classifier:sgd:learning_rate,classifier:sgd:loss,classifier:sgd:penalty,classifier:sgd:power_t,classifier:sgd:tol,data_preprocessing:categorical_transformer:categorical_encoding:__choice__,data_preprocessing:categorical_transformer:category_coalescence:__choice__,data_preprocessing:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction,data_preprocessing:numerical_transformer:imputation:strategy,data_preprocessing:numerical_transformer:rescaling:__choice__,data_preprocessing:numerical_transformer:rescaling:quantile_transformer:n_quantiles,data_preprocessing:numerical_transformer:rescaling:quantile_transformer:output_distribution,data_preprocessing:numerical_transformer:rescaling:robust_scaler:q_max,data_preprocessing:numerical_transformer:rescaling:robust_scaler:q_min,feature_preprocessor:__choice__,feature_preprocessor:extra_trees_preproc_for_classification:bootstrap,feature_preprocessor:extra_trees_preproc_for_classification:criterion,feature_preprocessor:extra_trees_preproc_for_classification:max_depth,feature_preprocessor:extra_trees_preproc_for_classification:max_features,feature_preprocessor:extra_trees_preproc_for_classification:max_leaf_nodes,feature_preprocessor:extra_trees_preproc_for_classification:min_impurity_decrease,feature_preprocessor:extra_trees_preproc_for_classification:min_samples_leaf,feature_preprocessor:extra_trees_preproc_for_classification:min_samples_split,feature_preprocessor:extra_trees_preproc_for_classification:min_weight_fraction_leaf,feature_preprocessor:extra_trees_preproc_for_classification:n_estimators,feature_preprocessor:kernel_pca:coef0,feature_preprocessor:kernel_pca:degree,feature_preprocessor:kernel_pca:gamma,feature_preprocessor:kernel_pca:kernel,feature_preprocessor:kernel_pca:n_components,feature_preprocessor:kitchen_sinks:gamma,feature_preprocessor:kitchen_sinks:n_components,feature_preprocessor:liblinear_svc_preprocessor:C,feature_preprocessor:liblinear_svc_preprocessor:dual,feature_preprocessor:liblinear_svc_preprocessor:fit_intercept,feature_preprocessor:liblinear_svc_preprocess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-1,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.3163020577549344,None,0.0,15,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.01322981552715978,most_frequent,robust_scaler,,,0.9746667009556722,0.1974876581478311,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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-11,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.06263739023781334,None,0.0,18,8,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.02206379295100344,most_frequent,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,,
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-13,none,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,200,None,,0.0017457216340699586,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,none,,,,,kernel_pca,,,,,,,,,,,0.0,4,0.0007494648939847078,poly,1996,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-14,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.6448542484717253,None,0.0,3,20,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.3306639470207569,most_frequent,robust_scaler,,,0.7248166700836332,0.2755431234957578,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-15,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7865652301288171,None,0.0,1,4,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,median,robust_scaler,,,0.8623635641193496,0.13298297278730625,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-16,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.905401147837377,-0.32832122529048147,3,1.001896309040499,poly,-1,False,5.1523484258030785e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-17,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.21794354428393548,None,0.0,2,16,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0025451910134387575,median,quantile_transformer,1477,uniform,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,,
-18,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5010952533165973,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.005838375247601407,mean,normalize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,,
-19,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5185406559495093,None,0.0,3,17,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.010000000000000004,median,normalize,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.13046020673147232,fpr,chi2,
-20,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.9663114854040722,None,0.0,11,12,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.26932389682904634,median,quantile_transformer,23,uniform,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l1,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,
-21,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5607168729746175,None,0.0,2,3,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.009810589644392154,most_frequent,quantile_transformer,1132,normal,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.06211089008167909,fdr,chi2,
-22,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9333237919291875,None,0.0,8,3,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,,
-23,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,173,None,,3.188383865493971e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,quantile_transformer,880,normal,,,kernel_pca,,,,,,,,,,,,,0.008499663955936211,rbf,450,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-24,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,22194.242946902177,0.45210849669021247,,0.0005669713702171556,sigmoid,-1,False,0.03542142266081451,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,quantile_transformer,291,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-25,weighting,extra_trees,,,,,,,,,,,,,,,True,gini,None,0.8424814442101096,None,0.0,17,16,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0012963479651174736,most_frequent,robust_scaler,,,0.7721224171367294,0.019641775400724187,extra_trees_preproc_for_classification,True,entropy,None,0.5260601497769445,None,0.0,2,12,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-26,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7422641456052917,None,0.0,8,15,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.005561351266169067,most_frequent,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-27,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.756167892711991e-05,False,True,squared_hinge,5.235791933868702e-05,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0001632172987320906,most_frequent,standardize,,,,,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,5,None,19,12,1.0,70,,,,,,
-28,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7088164344533493,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,mean,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,,
-29,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,10,1,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0022606253047811435,median,quantile_transformer,1247,normal,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,25.327793585266683,chi2,,,,
-30,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,24.786439230569382,,,0.03411197494635044,rbf,-1,True,0.003003351499585181,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0007994838629325874,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-31,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2681.998807317996,-0.9558745731731211,,0.0011299948421773457,sigmoid,-1,False,1.5292507354478086e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-32,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.4467384469668449,None,0.0,19,19,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.00033708405018786203,median,quantile_transformer,711,normal,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,7.744829113696808,False,True,1,squared_hinge,ovr,l1,0.0001869446320464273,,,,,,,,,,,,,,,,,,,,,
-33,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,26.502259985828186,,,9.475662388405154e-05,rbf,-1,False,0.00019690635968217866,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-34,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.339600708038189,False,True,1,squared_hinge,ovr,l2,9.572641158475304e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,quantile_transformer,1193,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-35,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5255991794296913,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-36,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.34697723213923637,None,0.0,15,18,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.1923594385988973,mean,normalize,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,25.77499302945827,chi2,,,,
-37,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.7610308078756552,None,0.0,1,2,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.010000000000000004,mean,robust_scaler,,,0.9205545465103644,0.20272761060743316,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-38,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,249.12499213127998,,,0.0005283374995918348,rbf,-1,False,0.0016932707039261864,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.48910068367909115,most_frequent,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-39,none,bernoulli_nb,,,,,0.6524090653939782,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.005164169126808402,most_frequent,quantile_transformer,16,normal,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.033082145443631535,False,True,1,squared_hinge,ovr,l1,0.002615020898325393,,,,,,,,,,,,,,,,,,,,,
-40,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.6453555269772124,None,0.0,1,4,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,normalize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-41,weighting,extra_trees,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,13,18,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0001574994528707389,most_frequent,standardize,,,,,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,3,None,19,19,1.0,77,,,,,,
-42,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5138705053438934,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.14411802159506462,most_frequent,quantile_transformer,857,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-43,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,29.55731828912983,False,True,1,squared_hinge,ovr,l2,0.0011332486951954968,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,,
-44,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.37801720673725603,True,True,squared_hinge,0.0001255132393635067,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.025993178344125793,most_frequent,standardize,,,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,1.4421737618429475,False,True,1,squared_hinge,ovr,l1,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,
-45,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.10581257327556097,None,0.0,7,12,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.004219392676189224,median,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,,
-46,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.4188764793561401,None,0.0,5,5,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.010589444903605501,mean,robust_scaler,,,0.9796997860990453,0.27262832749694405,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-47,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5345540246011571,None,0.0,8,4,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.007441222223802043,most_frequent,normalize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-48,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.41019363453549706,None,0.0,15,2,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.0003507389224306018,median,none,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.20614098085343638,fdr,chi2,
-49,none,adaboost,SAMME,0.1280458968526125,7,125,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,robust_scaler,,,0.7156460334356771,0.2981902862926971,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,65.16171885308417,chi2,,,,
-50,none,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9931779675565967,None,0.0,18,17,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,robust_scaler,,,0.75,0.2945917010472818,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,,
-51,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.08646118576686276,None,0.0,4,9,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,most_frequent,quantile_transformer,959,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-52,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.6271213423927893,None,0.0,1,4,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.009403509495453675,mean,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,,
-53,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8.85547546146731,True,True,squared_hinge,4.0111304608748804e-05,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-54,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.34936025988397124,None,0.0,2,13,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00018601309647406987,median,robust_scaler,,,0.8634309348843472,0.26976989465367607,extra_trees_preproc_for_classification,False,entropy,None,0.48025175288975036,None,0.0,6,17,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-55,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.7308100742740112,None,0.0,18,19,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.014637629296743653,most_frequent,robust_scaler,,,0.7724670628916575,0.2233445340332948,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,29.07440444752882,False,True,1,squared_hinge,ovr,l1,0.00040479645225962257,,,,,,,,,,,,,,,,,,,,,
-56,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.014911374426433241,None,0.0,6,2,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,,
-57,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0016941428709087083,True,,,True,,optimal,squared_hinge,l1,,5.124265599423001e-05,no_encoding,no_coalescense,,most_frequent,quantile_transformer,461,uniform,,,extra_trees_preproc_for_classification,True,gini,None,0.31371431180257103,None,0.0,5,17,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-58,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.7591396576940418,-0.02447104417007273,2,0.7325180366342191,poly,-1,True,0.00045922794962860523,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,standardize,,,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,2.057731506094293,False,True,1,squared_hinge,ovr,l1,0.03575998247131604,,,,,,,,,,,,,,,,,,,,,
-59,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.593728171200453,None,0.0,7,20,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.02830941310359363,mean,normalize,,,,,extra_trees_preproc_for_classification,False,entropy,None,0.7389446586281172,None,0.0,20,11,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-60,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5542793596955801,None,0.0,1,3,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,most_frequent,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-61,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.46057831591617715,False,True,hinge,0.04557857428827514,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00027457445401600137,median,standardize,,,,,extra_trees_preproc_for_classification,True,gini,None,0.48190346970486964,None,0.0,17,18,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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+356,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.01400651528258823,True,True,hinge,1.929818740575615e-05,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,normalize,,,,,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,5,None,3,19,1.0,78,,,,,,
+357,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.32235597434544716,None,0.0,2,18,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.30368818748392196,median,robust_scaler,,,0.7176393529809699,0.19299987830067816,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.16283145936255025,fwe,chi2,
+361,weighting,adaboost,SAMME,0.2786884011943926,3,263,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,quantile_transformer,1958,uniform,,,extra_trees_preproc_for_classification,False,entropy,None,0.2099259239256946,None,0.0,19,18,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+363,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.1393173890820956,False,True,1,squared_hinge,ovr,l2,8.283470008521272e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.015152352014390857,mean,normalize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,False,,,,,,,,,,,,,
+368,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.745748188733041e-05,True,True,hinge,2.181659959258924e-05,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.13921206193066876,most_frequent,quantile_transformer,60,normal,,,extra_trees_preproc_for_classification,False,entropy,None,0.7619669647366906,None,0.0,3,4,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+369,weighting,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.9815588983620268,None,0.0,2,14,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010000000000000004,mean,robust_scaler,,,0.7617017260745782,0.2386998843971201,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
diff --git a/autosklearn/metalearning/files/balanced_accuracy_multiclass.classification_sparse/description.txt b/autosklearn/metalearning/files/balanced_accuracy_multiclass.classification_sparse/description.txt
index 6be2ed2635..3ae9001ea3 100644
--- a/autosklearn/metalearning/files/balanced_accuracy_multiclass.classification_sparse/description.txt
+++ b/autosklearn/metalearning/files/balanced_accuracy_multiclass.classification_sparse/description.txt
@@ -1,8 +1,9 @@
 features_cutoff_time: 3600
 features_cutoff_memory: 3072
-number_of_feature_steps: 35
+number_of_feature_steps: 42
 feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances
 feature_step LogNumberOfInstances: LogNumberOfInstances
+feature_step NumberOfClasses: NumberOfClasses
 feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures
 feature_step LogNumberOfFeatures: LogNumberOfFeatures
 feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues
@@ -20,6 +21,11 @@ feature_step DatasetRatio: DatasetRatio, LogDatasetRatio
 feature_step LogDatasetRatio: LogDatasetRatio
 feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio
 feature_step LogInverseDatasetRatio: LogInverseDatasetRatio
+feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD
+feature_step ClassProbabilityMin: ClassProbabilityMin
+feature_step ClassProbabilityMax: ClassProbabilityMax
+feature_step ClassProbabilityMean: ClassProbabilityMean
+feature_step ClassProbabilitySTD: ClassProbabilitySTD
 feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum
 feature_step SymbolsMin: SymbolsMin
 feature_step SymbolsMax: SymbolsMax
@@ -36,11 +42,12 @@ feature_step SkewnessMin: SkewnessMin
 feature_step SkewnessMax: SkewnessMax
 feature_step SkewnessMean: SkewnessMean
 feature_step SkewnessSTD: SkewnessSTD
-features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
+feature_step ClassEntropy: ClassEntropy
+features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy
 features_stochastic: 
-default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
+default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy
 
-algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92
+algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235,236,237,238,239,240,241,242,243,244,245,246,247,248,249,250,251,252,253,254,255,256,257,258,259,260,261,262,263,264,265,266,267,268,269,270,271,272,273,274,275,276,277,278,279,280,281,282,283,284,285,286,287,288,289,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,312,313,314,315,316,317,318,319,320,321,322,323,324,325,326,327,328,329,330,331,332,333,334,335,336,337,338,339,340,341,342,343,344,345,346,347,348,349,350,351,352,353,354,355,356,357,358,359,360,361,362,363,364,365,366,367,368,369,370,371,372
 algorithms_stochastic: 
 performance_measures: balanced_accuracy
 performance_type: solution_quality
diff --git a/autosklearn/metalearning/files/balanced_accuracy_multiclass.classification_sparse/feature_costs.arff b/autosklearn/metalearning/files/balanced_accuracy_multiclass.classification_sparse/feature_costs.arff
index 0e17e20d22..8512fd9275 100644
--- a/autosklearn/metalearning/files/balanced_accuracy_multiclass.classification_sparse/feature_costs.arff
+++ b/autosklearn/metalearning/files/balanced_accuracy_multiclass.classification_sparse/feature_costs.arff
@@ -4,6 +4,7 @@
 @ATTRIBUTE repetition NUMERIC
 @ATTRIBUTE NumberOfInstances NUMERIC
 @ATTRIBUTE LogNumberOfInstances NUMERIC
+@ATTRIBUTE NumberOfClasses NUMERIC
 @ATTRIBUTE NumberOfFeatures NUMERIC
 @ATTRIBUTE LogNumberOfFeatures NUMERIC
 @ATTRIBUTE MissingValues NUMERIC
@@ -21,6 +22,11 @@
 @ATTRIBUTE LogDatasetRatio NUMERIC
 @ATTRIBUTE InverseDatasetRatio NUMERIC
 @ATTRIBUTE LogInverseDatasetRatio NUMERIC
+@ATTRIBUTE ClassOccurences NUMERIC
+@ATTRIBUTE ClassProbabilityMin NUMERIC
+@ATTRIBUTE ClassProbabilityMax NUMERIC
+@ATTRIBUTE ClassProbabilityMean NUMERIC
+@ATTRIBUTE ClassProbabilitySTD NUMERIC
 @ATTRIBUTE NumSymbols NUMERIC
 @ATTRIBUTE SymbolsMin NUMERIC
 @ATTRIBUTE SymbolsMax NUMERIC
@@ -37,162 +43,138 @@
 @ATTRIBUTE SkewnessMax NUMERIC
 @ATTRIBUTE SkewnessMean NUMERIC
 @ATTRIBUTE SkewnessSTD NUMERIC
+@ATTRIBUTE ClassEntropy NUMERIC
 
 @DATA
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diff --git a/autosklearn/metalearning/files/balanced_accuracy_multiclass.classification_sparse/feature_runstatus.arff b/autosklearn/metalearning/files/balanced_accuracy_multiclass.classification_sparse/feature_runstatus.arff
index 0034ad2bd6..5fff2dfed3 100644
--- a/autosklearn/metalearning/files/balanced_accuracy_multiclass.classification_sparse/feature_runstatus.arff
+++ b/autosklearn/metalearning/files/balanced_accuracy_multiclass.classification_sparse/feature_runstatus.arff
@@ -4,6 +4,7 @@
 @ATTRIBUTE repetition NUMERIC
 @ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other}
@@ -21,6 +22,11 @@
 @ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other}
@@ -37,162 +43,138 @@
 @ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other}
 
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diff --git a/autosklearn/metalearning/files/balanced_accuracy_multiclass.classification_sparse/feature_values.arff b/autosklearn/metalearning/files/balanced_accuracy_multiclass.classification_sparse/feature_values.arff
index a60400e3ce..736dc64321 100644
--- a/autosklearn/metalearning/files/balanced_accuracy_multiclass.classification_sparse/feature_values.arff
+++ b/autosklearn/metalearning/files/balanced_accuracy_multiclass.classification_sparse/feature_values.arff
@@ -13,7 +13,6 @@
 @ATTRIBUTE KurtosisMean NUMERIC
 @ATTRIBUTE KurtosisMin NUMERIC
 @ATTRIBUTE KurtosisSTD NUMERIC
-@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC
 @ATTRIBUTE LogDatasetRatio NUMERIC
 @ATTRIBUTE LogInverseDatasetRatio NUMERIC
 @ATTRIBUTE LogNumberOfFeatures NUMERIC
@@ -42,160 +41,135 @@
 @ATTRIBUTE SymbolsSum NUMERIC
 
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+75250,1.0,3.992354626842552,0.146342438507591,0.04545454545454545,0.005796927628356971,0.03949259311161838,3.997881123004808e-05,25013.25,2.1476065760596077,1.4719570787187288,0.3045189380645752,0.6973345081454226,-10.127160963449944,10.127160963449944,1.3862943611198906,11.513455324569835,0.0,22.0,4.0,0.0,100053.0,0.0,0.0,4.0,0.0,0.0,0.0,0.0,0.0,1.2135857343673706,0.3996680751442909,-0.3896799087524414,0.7616029272290122,0.0,0.0,0.0,0.0,0.0
diff --git a/autosklearn/metalearning/files/f1_binary.classification_dense/algorithm_runs.arff b/autosklearn/metalearning/files/f1_binary.classification_dense/algorithm_runs.arff
index da4c911485..e5cb6fc118 100644
--- a/autosklearn/metalearning/files/f1_binary.classification_dense/algorithm_runs.arff
+++ b/autosklearn/metalearning/files/f1_binary.classification_dense/algorithm_runs.arff
@@ -7,130 +7,135 @@
 @ATTRIBUTE runstatus {ok, timeout, memout, not_applicable, crash, other}
 
 @DATA
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-75233,1.0,8,0.04661574927574397,ok
-248,1.0,9,?,not_applicable
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-75196,1.0,12,0.009615384615384692,ok
-75188,1.0,13,?,not_applicable
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-75159,1.0,22,0.6068376068376068,ok
-244,1.0,23,?,not_applicable
-75221,1.0,24,?,not_applicable
-75219,1.0,25,0.04292527821939596,ok
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-288,1.0,30,?,not_applicable
-75250,1.0,31,?,not_applicable
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-75142,1.0,33,0.07069521853916105,ok
-75213,1.0,34,0.14754098360655743,ok
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-75243,1.0,36,?,not_applicable
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-75107,1.0,48,0.44127597873368773,ok
-262,1.0,49,?,not_applicable
-75146,1.0,50,0.09389760060457208,ok
-75189,1.0,51,0.01519371757012311,ok
-2350,1.0,52,0.5124081136351075,ok
-253,1.0,53,?,not_applicable
-2122,1.0,54,?,not_applicable
-75110,1.0,55,?,not_applicable
-75249,1.0,56,0.015228426395939132,ok
-75108,1.0,57,0.0,ok
-242,1.0,58,?,not_applicable
-75117,1.0,59,0.04281009879253561,ok
-75191,1.0,60,0.13383895433274173,ok
-75226,1.0,61,0.00035984166966529507,ok
-261,1.0,62,0.39495798319327735,ok
-75236,1.0,63,?,not_applicable
-75095,1.0,64,0.15508021390374327,ok
-75148,1.0,65,0.12068965517241381,ok
-75093,1.0,66,0.542528735632184,ok
-75223,1.0,67,?,not_applicable
-75244,1.0,68,0.6586306653809064,ok
-75109,1.0,69,?,not_applicable
-75197,1.0,70,?,not_applicable
-75127,1.0,71,0.37609277769688987,ok
-75143,1.0,72,0.0084283589489339,ok
-75153,1.0,73,0.08062750820868303,ok
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-75215,1.0,75,0.0229191797346201,ok
-75195,1.0,76,0.00012514078338132784,ok
-75207,1.0,77,?,not_applicable
-266,1.0,78,?,not_applicable
-75225,1.0,79,0.5495495495495496,ok
-75166,1.0,80,0.09247472856608019,ok
-75100,1.0,81,0.7777777777777778,ok
-75169,1.0,82,?,not_applicable
-75132,1.0,83,0.855792318434351,ok
-273,1.0,84,0.051914893617021396,ok
-75121,1.0,85,0.0,ok
-75098,1.0,86,?,not_applicable
-75115,1.0,87,0.013844515441959526,ok
-75116,1.0,88,0.007025761124121788,ok
-75185,1.0,89,0.12967459932005843,ok
-2119,1.0,90,?,not_applicable
-75157,1.0,91,0.5551894563426689,ok
-75113,1.0,92,0.04885993485342022,ok
-75134,1.0,93,?,not_applicable
-75096,1.0,94,?,not_applicable
-75203,1.0,95,?,not_applicable
-75182,1.0,96,0.1910112359550561,ok
-251,1.0,97,?,not_applicable
-75123,1.0,98,?,not_applicable
-75237,1.0,99,0.00020259319286874966,ok
-75125,1.0,100,0.020310633213859064,ok
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-75192,1.0,103,0.5028157683024941,ok
-75230,1.0,104,?,not_applicable
-75139,1.0,105,0.014989293361884481,ok
-75227,1.0,106,0.16682738669238184,ok
-2120,1.0,107,?,not_applicable
-75124,1.0,108,0.5010752688172044,ok
-75240,1.0,109,0.028349082823791005,ok
-75129,1.0,110,0.5874999999999999,ok
-75198,1.0,111,?,not_applicable
-75201,1.0,112,?,not_applicable
-75112,1.0,113,0.1668278529980658,ok
-75133,1.0,114,0.5483870967741935,ok
-75105,1.0,115,0.9094173982442139,ok
-75154,1.0,116,?,not_applicable
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-75172,1.0,122,?,not_applicable
-75106,1.0,123,0.7961218836565097,ok
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-75187,1.0,125,0.016820857863751093,ok
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-75193,1.0,127,?,not_applicable
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+75177,1.0,13,0.06249999999999989,ok
+75092,1.0,38,0.3885350318471338,ok
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+253,1.0,27,?,not_applicable
+75157,1.0,87,0.4296875000000001,ok
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+75124,1.0,31,0.45247148288973393,ok
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+75231,1.0,34,?,not_applicable
+75185,1.0,325,0.12857836001940803,ok
+2123,1.0,39,?,not_applicable
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+75143,1.0,43,0.006413418845584551,ok
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+75188,1.0,49,?,not_applicable
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+75113,1.0,335,0.031825795644891186,ok
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+288,1.0,64,?,not_applicable
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+75184,1.0,156,0.1615120274914088,ok
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+258,1.0,73,?,not_applicable
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+3043,1.0,99,0.06249999999999989,ok
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+254,1.0,166,0.0,ok
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+75181,1.0,175,?,not_applicable
+75189,1.0,187,0.014132546793966805,ok
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+2122,1.0,195,?,not_applicable
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+75213,1.0,424,0.11904761904761907,ok
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+75236,1.0,223,?,not_applicable
+75169,1.0,227,?,not_applicable
+75116,1.0,320,0.003516998827667206,ok
+75223,1.0,235,?,not_applicable
+75109,1.0,242,?,not_applicable
+75197,1.0,245,?,not_applicable
+75237,1.0,365,0.00021815348656006872,ok
+248,1.0,250,?,not_applicable
+2119,1.0,254,?,not_applicable
+75127,1.0,255,0.3767906126181041,ok
+75153,1.0,263,0.08159531650201235,ok
+75195,1.0,274,0.00018772292096869148,ok
+266,1.0,277,?,not_applicable
+75225,1.0,280,0.569620253164557,ok
+75100,1.0,286,0.7777777777777778,ok
+75132,1.0,296,0.8615541098125012,ok
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+75133,1.0,413,0.4054054054054055,ok
+75121,1.0,304,0.0,ok
+75098,1.0,310,?,not_applicable
+75115,1.0,317,0.007423117709437932,ok
+75217,1.0,337,?,not_applicable
+75134,1.0,341,?,not_applicable
+75096,1.0,346,?,not_applicable
+75203,1.0,350,?,not_applicable
+75123,1.0,360,?,not_applicable
+75125,1.0,370,0.017878426698450633,ok
+2120,1.0,371,?,not_applicable
+75232,1.0,374,0.1806167400881057,ok
+75103,1.0,380,0.043887147335423204,ok
+75230,1.0,386,?,not_applicable
+75240,1.0,399,0.027808676307007896,ok
+75198,1.0,404,?,not_applicable
+75201,1.0,407,?,not_applicable
+75105,1.0,418,0.8969978032706859,ok
+75154,1.0,420,?,not_applicable
+2117,1.0,428,0.08940568475452193,ok
+75156,1.0,430,0.18486171761280923,ok
+75097,1.0,435,0.025833815307499397,ok
+75101,1.0,438,0.2539724190209627,ok
+75172,1.0,443,?,not_applicable
+75106,1.0,451,0.7721443483447659,ok
+75120,1.0,458,0.016227180527383478,ok
+75193,1.0,461,?,not_applicable
diff --git a/autosklearn/metalearning/files/f1_binary.classification_dense/configurations.csv b/autosklearn/metalearning/files/f1_binary.classification_dense/configurations.csv
index cc0d063874..512362e6eb 100644
--- a/autosklearn/metalearning/files/f1_binary.classification_dense/configurations.csv
+++ b/autosklearn/metalearning/files/f1_binary.classification_dense/configurations.csv
@@ -1,128 +1,133 @@
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-39,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,1.6343482806592892e-09,0.19537524572679268,auto,255,None,32,29,13,loss,1e-07,0.05061738224302791,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-40,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7088164344533493,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,mean,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,
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-43,none,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0686374607394679,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,minmax,,,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.8490260132946401,False,,,,,,,,,,,,,,,
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-46,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,92.66144306787051,,,0.04654639302091653,rbf,-1,True,0.0022231544280610005,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.005194838694455963,mean,none,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,15,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-47,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2681.998807317996,-0.9558745731731211,,0.0011299948421773457,sigmoid,-1,False,1.5292507354478086e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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-49,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,26.502259985828186,,,9.475662388405154e-05,rbf,-1,False,0.00019690635968217866,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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-52,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,12949.73191522963,0.722395694371404,2,0.00017896201985332012,poly,-1,True,0.0004183743529226739,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0003763319541577531,mean,normalize,,,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,505.9404894398784,False,True,1,squared_hinge,ovr,l1,0.0001920230051228414,,,,,,,,,,,,,,,,,,,,,,
-53,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1252.7997374154156,0.20811431041897355,5,0.0018140593074227453,poly,-1,False,0.03149782358621565,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.001930822730474197,most_frequent,quantile_transformer,233,normal,,,feature_agglomeration,,,,,,,,,,,,,,,euclidean,ward,29,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-54,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,1.5661823810486637e-10,0.08996863554015262,auto,255,None,156,89,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.009331521286159971,median,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,
-55,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.6109468076475573,0.8748383232154038,4,0.41333070422336576,poly,-1,False,3.591268778393482e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.011804925819789203,median,minmax,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-56,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.7610308078756552,None,0.0,1,2,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.010000000000000004,mean,robust_scaler,,,0.9205545465103644,0.20272761060743316,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-57,weighting,adaboost,SAMME.R,0.03127273017917347,10,496,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,standardize,,,,,fast_ica,,,,,,,,,,,deflation,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-58,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,151,None,,0.0003283777607180646,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.2510527431514227,mean,normalize,,,,,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,-0.96093182232312,,0.05762510964607661,sigmoid,2632,,,,,,,,,,,,,,,,,
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-61,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,940.9437776449834,,,1.5839338084625942,rbf,-1,False,0.023072781592565438,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,none,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,77.25763685070707,mutual_info,,,
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-63,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.34534888916828577,0.02513634776687268,2,1.0733091880109409,poly,-1,True,0.0006974263047918307,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,quantile_transformer,1000,normal,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,77.99934218022861,mutual_info,,,
-64,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,29.55731828912983,False,True,1,squared_hinge,ovr,l2,0.0011332486951954968,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,
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-68,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,925.3053743248586,False,True,1,squared_hinge,ovr,l2,0.02764481198395717,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.3850718018366796,most_frequent,quantile_transformer,786,uniform,,,feature_agglomeration,,,,,,,,,,,,,,,cosine,average,156,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-69,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.8278163313806293,None,0.0,3,5,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,robust_scaler,,,0.725835793765774,0.2571431961341018,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.07784020380291123,fdr,f_classif
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-71,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.41019363453549706,None,0.0,15,2,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.0003507389224306018,median,none,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.20614098085343638,fdr,chi2
-72,none,adaboost,SAMME,0.1280458968526125,7,125,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,robust_scaler,,,0.7156460334356771,0.2981902862926971,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,65.16171885308417,chi2,,,
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-76,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7.491039171224502,True,True,squared_hinge,2.8058138048327884e-05,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,minmax,,,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.7754372809893839,False,True,1,squared_hinge,ovr,l1,3.9643594596766314e-05,,,,,,,,,,,,,,,,,,,,,,
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-78,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,2.925599188129995e-05,0.12560285030651847,auto,255,None,300,193,17,loss,1e-07,0.039442997915546514,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00299400754997757,mean,robust_scaler,,,0.7093292916146515,0.009366983030047793,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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-80,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1661.2274747409913,,,0.025821208852387936,rbf,-1,False,0.0017011435104921824,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,none,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,306,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-81,none,adaboost,SAMME,0.011233995624432622,9,477,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.018370622484682127,mean,standardize,,,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6039710338898471,False,,,,,,,,,,,,,,,
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-84,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,0.037849720944694,0.1268815770019783,auto,255,None,11,99,11,loss,1e-07,0.10654523627313527,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,
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-86,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,1,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.019797877010737826,mean,normalize,,,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.8239678741128165,False,,,,,,,,,,,,,,,
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+360,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.5720469164289109,None,0.0,1,17,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.03310727058091163,median,robust_scaler,,,0.8680123006808074,0.024825227678742904,fast_ica,,,,,,,,,,,parallel,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+365,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.0479982468066316,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,quantile_transformer,1097,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+370,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,161,manual,0.06874267877159168,0.0024806196360048064,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.02346562053758342,median,quantile_transformer,1087,normal,,,extra_trees_preproc_for_classification,True,gini,None,0.6515955878602243,None,0.0,17,7,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+371,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,4.198700105243736e-09,0.011070860038925674,auto,255,None,9,116,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010247554402833578,median,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,
+372,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.5431102417332107,None,0.0,1,16,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010731370069741165,median,none,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.35426195730988774,fdr,chi2
+374,none,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.7304811343030777,None,0.0,2,3,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,robust_scaler,,,0.7202654714984674,0.21555973677200435,feature_agglomeration,,,,,,,,,,,,,,,euclidean,average,149,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+380,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2677516008848513,True,True,hinge,0.05752557410901496,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0010152857217605206,median,quantile_transformer,1000,uniform,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,51.60974796845029,chi2,,,
+384,weighting,adaboost,SAMME,0.0924407457907304,8,61,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.3090968001340013,median,robust_scaler,,,0.75,0.26158375682461943,fast_ica,,,,,,,,,,,deflation,exp,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+386,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9358939105347388,None,0.0,1,5,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,none,,,,,fast_ica,,,,,,,,,,,parallel,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+399,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.5864398404787432,None,0.0,1,14,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0070663750850608895,most_frequent,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
+404,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8.380057363340162,False,True,hinge,0.0020653831067503525,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.05020677662128942,median,quantile_transformer,560,normal,,,kernel_pca,,,,,,,,,,,,,,,,,,,,,,cosine,891,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+407,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.7292011996224884,True,True,hinge,8.162763086383311e-05,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010337761588362885,median,quantile_transformer,877,uniform,,,kernel_pca,,,,,,,,,,,,,,,,,,,,,,cosine,1317,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+413,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,7.194851113809193e-10,0.013882834218943657,auto,255,None,18,2,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0005124238915083231,mean,quantile_transformer,948,normal,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,60.45885911036194,f_classif,,,
+418,weighting,adaboost,SAMME,0.1313487280585511,3,189,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,robust_scaler,,,0.8008090310131288,0.2935856792824304,extra_trees_preproc_for_classification,False,entropy,None,0.7722400064297721,None,0.0,5,4,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+420,weighting,adaboost,SAMME.R,0.05920795019389743,8,313,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,standardize,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,104,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+424,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,2.344883102482138e-07,0.0328548789988159,auto,255,None,42,24,16,loss,1e-07,0.13015466346727866,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,most_frequent,robust_scaler,,,0.7010898774332888,0.2534280821273264,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,
+428,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,3.738096432152635e-10,0.04468500715913894,auto,255,None,31,156,20,loss,1e-07,0.10279783687118692,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010000000000000004,most_frequent,quantile_transformer,1132,normal,,,feature_agglomeration,,,,,,,,,,,,,,,euclidean,ward,43,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+430,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5321814610661173,None,0.0,1,3,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,standardize,,,,,extra_trees_preproc_for_classification,True,gini,None,0.5,None,0.0,1,15,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+435,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.44903177399085925,None,0.0,2,17,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,mean,robust_scaler,,,0.7812408516672696,0.12791233577400205,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,
+438,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,6.090138036399552e-10,0.017305568433437588,auto,255,None,202,188,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,robust_scaler,,,0.7526196011313747,0.1563616005702636,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+443,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.6269076084367177,None,0.0,1,7,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.12943250859943486,most_frequent,standardize,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,62.84526554323969,chi2,,,
+451,weighting,adaboost,SAMME,0.2814550343434438,5,87,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.00018428537998177612,mean,standardize,,,,,feature_agglomeration,,,,,,,,,,,,,,,cosine,average,307,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+453,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,162.34906836693145,,,2.470518094983172,rbf,-1,False,1.5366632541084908e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.33912551828528803,median,none,,,,,fast_ica,,,,,,,,,,,deflation,logcosh,412,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+458,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,5.581658271288433e-10,0.06171988234311011,auto,255,None,25,6,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.014859405637537319,most_frequent,robust_scaler,,,0.7983900845251328,0.2545046978458648,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,1.380085213170737,False,True,1,squared_hinge,ovr,l1,0.0061450732015354784,,,,,,,,,,,,,,,,,,,,,,
+461,none,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.9796199105973851,None,0.0,2,13,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.00845950033601661,mean,robust_scaler,,,0.8275228560550734,0.23814615163106348,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
diff --git a/autosklearn/metalearning/files/f1_binary.classification_dense/description.txt b/autosklearn/metalearning/files/f1_binary.classification_dense/description.txt
index f7031dc919..d68f4e7291 100644
--- a/autosklearn/metalearning/files/f1_binary.classification_dense/description.txt
+++ b/autosklearn/metalearning/files/f1_binary.classification_dense/description.txt
@@ -1,8 +1,9 @@
 features_cutoff_time: 3600
 features_cutoff_memory: 3072
-number_of_feature_steps: 35
+number_of_feature_steps: 42
 feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances
 feature_step LogNumberOfInstances: LogNumberOfInstances
+feature_step NumberOfClasses: NumberOfClasses
 feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures
 feature_step LogNumberOfFeatures: LogNumberOfFeatures
 feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues
@@ -20,6 +21,11 @@ feature_step DatasetRatio: DatasetRatio, LogDatasetRatio
 feature_step LogDatasetRatio: LogDatasetRatio
 feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio
 feature_step LogInverseDatasetRatio: LogInverseDatasetRatio
+feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD
+feature_step ClassProbabilityMin: ClassProbabilityMin
+feature_step ClassProbabilityMax: ClassProbabilityMax
+feature_step ClassProbabilityMean: ClassProbabilityMean
+feature_step ClassProbabilitySTD: ClassProbabilitySTD
 feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum
 feature_step SymbolsMin: SymbolsMin
 feature_step SymbolsMax: SymbolsMax
@@ -36,11 +42,12 @@ feature_step SkewnessMin: SkewnessMin
 feature_step SkewnessMax: SkewnessMax
 feature_step SkewnessMean: SkewnessMean
 feature_step SkewnessSTD: SkewnessSTD
-features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
+feature_step ClassEntropy: ClassEntropy
+features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy
 features_stochastic: 
-default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
+default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy
 
-algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127
+algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235,236,237,238,239,240,241,242,243,244,245,246,247,248,249,250,251,252,253,254,255,256,257,258,259,260,261,262,263,264,265,266,267,268,269,270,271,272,273,274,275,276,277,278,279,280,281,282,283,284,285,286,287,288,289,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,312,313,314,315,316,317,318,319,320,321,322,323,324,325,326,327,328,329,330,331,332,333,334,335,336,337,338,339,340,341,342,343,344,345,346,347,348,349,350,351,352,353,354,355,356,357,358,359,360,361,362,363,364,365,366,367,368,369,370,371,372,373,374,375,376,377,378,379,380,381,382,383,384,385,386,387,388,389,390,391,392,393,394,395,396,397,398,399,400,401,402,403,404,405,406,407,408,409,410,411,412,413,414,415,416,417,418,419,420,421,422,423,424,425,426,427,428,429,430,431,432,433,434,435,436,437,438,439,440,441,442,443,444,445,446,447,448,449,450,451,452,453,454,455,456,457,458,459,460,461,462,463,464
 algorithms_stochastic: 
 performance_measures: f1
 performance_type: solution_quality
diff --git a/autosklearn/metalearning/files/f1_binary.classification_dense/feature_costs.arff b/autosklearn/metalearning/files/f1_binary.classification_dense/feature_costs.arff
index 0e17e20d22..8512fd9275 100644
--- a/autosklearn/metalearning/files/f1_binary.classification_dense/feature_costs.arff
+++ b/autosklearn/metalearning/files/f1_binary.classification_dense/feature_costs.arff
@@ -4,6 +4,7 @@
 @ATTRIBUTE repetition NUMERIC
 @ATTRIBUTE NumberOfInstances NUMERIC
 @ATTRIBUTE LogNumberOfInstances NUMERIC
+@ATTRIBUTE NumberOfClasses NUMERIC
 @ATTRIBUTE NumberOfFeatures NUMERIC
 @ATTRIBUTE LogNumberOfFeatures NUMERIC
 @ATTRIBUTE MissingValues NUMERIC
@@ -21,6 +22,11 @@
 @ATTRIBUTE LogDatasetRatio NUMERIC
 @ATTRIBUTE InverseDatasetRatio NUMERIC
 @ATTRIBUTE LogInverseDatasetRatio NUMERIC
+@ATTRIBUTE ClassOccurences NUMERIC
+@ATTRIBUTE ClassProbabilityMin NUMERIC
+@ATTRIBUTE ClassProbabilityMax NUMERIC
+@ATTRIBUTE ClassProbabilityMean NUMERIC
+@ATTRIBUTE ClassProbabilitySTD NUMERIC
 @ATTRIBUTE NumSymbols NUMERIC
 @ATTRIBUTE SymbolsMin NUMERIC
 @ATTRIBUTE SymbolsMax NUMERIC
@@ -37,162 +43,138 @@
 @ATTRIBUTE SkewnessMax NUMERIC
 @ATTRIBUTE SkewnessMean NUMERIC
 @ATTRIBUTE SkewnessSTD NUMERIC
+@ATTRIBUTE ClassEntropy NUMERIC
 
 @DATA
-75123,1.0,1e-05,1e-05,1e-05,1e-05,0.00253,0.00221,1e-05,0.00024,3e-05,0.00011,0.0,2e-05,2e-05,5e-05,8e-05,2e-05,1e-05,7e-05,1e-05,0.00072,1e-05,1e-05,0.00019,0.00041,0.00011,0.00061,0.00012,9e-05,0.00015,0.00025,0.00067,0.00014,0.00011,0.00016,0.00027
-75090,1.0,1e-05,1e-05,2e-05,2e-05,0.00815,0.00368,7e-05,0.00228,9e-05,0.00235,0.0,5e-05,5e-05,0.0001,0.00013,0.00013,1e-05,0.00021,2e-05,0.00064,0.0,0.0,0.00014,0.00038,0.00011,0.00066,0.00011,0.00014,0.00014,0.00026,0.00066,0.00011,0.00013,0.00015,0.00027
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+75236,1.0,1e-05,1e-05,0.0001,1e-05,1e-05,0.00218,0.00118,2e-05,0.00065,3e-05,0.00041,1e-05,3e-05,3e-05,9e-05,9e-05,4e-05,1e-05,7e-05,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.0006,1e-05,1e-05,0.00021,0.00032,6e-05,0.00043,7e-05,7e-05,9e-05,0.00019,0.00062,7e-05,8e-05,0.0001,0.00037,0.00048
+75237,1.0,2e-05,1e-05,0.00785,2e-05,1e-05,0.31031,0.27854,2e-05,0.01161,3e-05,0.02022,1e-05,4e-05,3e-05,9e-05,0.00024,6e-05,2e-05,9e-05,2e-05,0.00062,7e-05,3e-05,0.00017,0.00035,0.0008,1e-05,1e-05,0.00028,0.00043,8e-05,0.00043,8e-05,7e-05,9e-05,0.0002,0.00044,6e-05,8e-05,9e-05,0.00021,0.14028
+75239,1.0,1e-05,1e-05,7e-05,1e-05,1e-05,0.00105,0.00084,1e-05,0.00015,1e-05,8e-05,1e-05,2e-05,5e-05,4e-05,8e-05,2e-05,1e-05,3e-05,1e-05,0.00016,2e-05,1e-05,5e-05,9e-05,0.00049,0.0,0.0,0.00012,0.00026,0.00011,0.00046,0.00013,8e-05,8e-05,0.00018,0.00044,6e-05,9e-05,0.00011,0.00018,0.00065
+75240,1.0,2e-05,1e-05,0.00036,5e-05,4e-05,0.00845,0.00644,6e-05,0.00122,4e-05,0.00091,1e-05,4e-05,4e-05,9e-05,0.00013,5e-05,2e-05,9e-05,5e-05,0.00031,3e-05,1e-05,7e-05,0.0002,0.00073,3e-05,2e-05,0.00019,0.0003,0.00018,0.00055,9e-05,9e-05,0.00011,0.00025,0.0005,7e-05,8e-05,0.00015,0.0002,0.00365
+75243,1.0,1e-05,1e-05,0.00029,1e-05,1e-05,0.00461,0.00415,1e-05,0.00035,1e-05,0.00015,0.0,2e-05,2e-05,5e-05,7e-05,2e-05,1e-05,4e-05,1e-05,0.00018,2e-05,1e-05,4e-05,0.00011,0.00022,1e-05,1e-05,5e-05,0.00011,5e-05,0.0004,6e-05,6e-05,8e-05,0.0002,0.00039,6e-05,6e-05,8e-05,0.00019,0.00265
+75244,1.0,3e-05,2e-05,0.0004,3e-05,2e-05,0.0173,0.01298,4e-05,0.00267,4e-05,0.00174,1e-05,7e-05,9e-05,0.00024,0.00027,8e-05,2e-05,0.00013,3e-05,0.00045,7e-05,2e-05,0.00011,0.00025,0.00077,6e-05,2e-05,0.00021,0.00034,0.00013,0.00077,0.00014,0.00013,0.00026,0.00023,0.00044,7e-05,8e-05,0.0001,0.00019,0.00649
+75248,1.0,1e-05,1e-05,0.00017,1e-05,1e-05,0.00416,0.00305,1e-05,0.0006,2e-05,0.00054,1e-05,2e-05,2e-05,5e-05,7e-05,2e-05,1e-05,4e-05,1e-05,0.00017,2e-05,1e-05,4e-05,0.0001,0.00029,5e-05,1e-05,6e-05,0.00012,6e-05,0.00081,0.00011,0.00015,0.00016,0.00038,0.0005,7e-05,8e-05,0.00012,0.00024,0.00184
+75249,1.0,1e-05,1e-05,0.00011,1e-05,1e-05,0.00192,0.00153,1e-05,0.00025,1e-05,0.00017,1e-05,2e-05,2e-05,5e-05,7e-05,2e-05,1e-05,5e-05,3e-05,0.00014,2e-05,1e-05,3e-05,8e-05,0.00029,1e-05,3e-05,6e-05,0.00012,7e-05,0.00041,6e-05,8e-05,0.0001,0.00017,0.00043,6e-05,7e-05,9e-05,0.00019,0.00092
+75250,1.0,1e-05,1e-05,0.00375,1e-05,1e-05,0.04994,0.047,1e-05,0.00247,1e-05,0.00049,1e-05,2e-05,2e-05,6e-05,7e-05,2e-05,1e-05,3e-05,1e-05,0.00022,4e-05,1e-05,4e-05,0.00012,0.00041,0.0,1e-05,0.00011,0.00023,6e-05,0.00076,0.00012,0.00012,0.00019,0.00032,0.00086,0.00012,0.00013,0.00018,0.00043,0.04178
diff --git a/autosklearn/metalearning/files/f1_binary.classification_dense/feature_runstatus.arff b/autosklearn/metalearning/files/f1_binary.classification_dense/feature_runstatus.arff
index 0034ad2bd6..5fff2dfed3 100644
--- a/autosklearn/metalearning/files/f1_binary.classification_dense/feature_runstatus.arff
+++ b/autosklearn/metalearning/files/f1_binary.classification_dense/feature_runstatus.arff
@@ -4,6 +4,7 @@
 @ATTRIBUTE repetition NUMERIC
 @ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other}
@@ -21,6 +22,11 @@
 @ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other}
@@ -37,162 +43,138 @@
 @ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other}
 
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diff --git a/autosklearn/metalearning/files/f1_binary.classification_dense/feature_values.arff b/autosklearn/metalearning/files/f1_binary.classification_dense/feature_values.arff
index a60400e3ce..736dc64321 100644
--- a/autosklearn/metalearning/files/f1_binary.classification_dense/feature_values.arff
+++ b/autosklearn/metalearning/files/f1_binary.classification_dense/feature_values.arff
@@ -13,7 +13,6 @@
 @ATTRIBUTE KurtosisMean NUMERIC
 @ATTRIBUTE KurtosisMin NUMERIC
 @ATTRIBUTE KurtosisSTD NUMERIC
-@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC
 @ATTRIBUTE LogDatasetRatio NUMERIC
 @ATTRIBUTE LogInverseDatasetRatio NUMERIC
 @ATTRIBUTE LogNumberOfFeatures NUMERIC
@@ -42,160 +41,135 @@
 @ATTRIBUTE SymbolsSum NUMERIC
 
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+75232,1.0,0.9201702027239149,0.6647807637906648,0.5,0.3352192362093352,0.16478076379066478,0.05799151343705799,17.24390243902439,585.4510498046875,41.48030315044416,-0.2529125213623047,95.17568446650067,-2.847458599192265,2.8474585991922647,3.713572066704308,6.561030665896573,0.0,2.0,41.0,0.0,707.0,0.0,0.0,41.0,0.0,0.0,0.0,0.0,0.0,23.23834228515625,3.832647103316537,-1.5003634691238403,4.508387764393873,0.0,0.0,0.0,0.0,0.0
+75233,1.0,0.8824680010722367,0.6990344325013663,0.5,0.3009655674986336,0.19903443250136635,0.0038258334851521223,261.3809523809524,619.9618265719739,76.9042121753925,1.1050496101379395,139.01256776590665,-5.565978930826466,5.565978930826466,3.044522437723423,8.610501368549889,0.0,2.0,21.0,0.0,5489.0,0.0,0.0,21.0,0.0,0.0,0.0,0.0,0.0,21.77604866027832,5.3457508484522505,-0.7721167802810669,4.832918145056104,0.0,0.0,0.0,0.0,0.0
+75234,1.0,0.9998393017810485,0.5074626865671642,0.5,0.4925373134328358,0.007462686567164201,0.004033884630899556,247.9,1.9309582710266113,1.5332300662994385,1.3253521919250488,0.13647900129618118,-5.513025439041145,5.513025439041145,2.995732273553991,8.508757712595136,0.0,2.0,20.0,0.0,4958.0,0.0,0.0,20.0,0.0,0.0,0.0,0.0,0.0,-0.1076231524348259,-0.23315090499818325,-0.33683285117149353,0.052567253463639066,0.0,0.0,0.0,0.0,0.0
+75235,1.0,1.7067919449664923,0.4105579868708972,0.25,0.05579868708971553,0.1492903085996621,0.006564551422319475,152.33333333333334,13.76956558227539,2.346758103856697,-1.6145207422400454,4.524616106763882,-5.026071102226095,5.026071102226095,3.1780538303479458,8.204124932574041,0.0,4.0,24.0,0.0,3656.0,0.0,0.0,24.0,0.0,0.0,0.0,0.0,0.0,3.779214382171631,1.515954251983203,0.004355928860604763,1.0657977665170062,0.0,0.0,0.0,0.0,0.0
+75236,1.0,3.3208751934243548,0.10486891385767791,0.1,0.09363295880149813,0.003810297743328803,0.2397003745318352,4.171875,22.422517776489258,-0.9225467168815645,-1.9997756481170654,1.9352856443240884,-1.4283655750405777,1.4283655750405777,5.545177444479562,6.97354301952014,0.0,10.0,256.0,0.0,1068.0,0.0,0.0,256.0,0.0,0.0,0.0,0.0,0.0,4.94191312789917,0.8186543448100565,-0.6194983720779419,0.6381679017395948,0.0,0.0,0.0,0.0,0.0
+75237,1.0,0.7378736147990802,0.7918800894091566,0.5,0.20811991059084348,0.29188008940915655,1.8271625991997027e-05,54729.666666666664,-0.5975548261535093,-0.7926032911766772,-0.9140236377716064,0.13929334047064063,-10.910161193614513,10.910161193614513,1.0986122886681098,12.008773482282624,0.0,2.0,3.0,0.0,164189.0,0.0,0.0,3.0,0.0,0.0,0.0,0.0,0.0,-0.039879728108644485,-0.3348021370669206,-0.6223916411399841,0.2378660138627058,0.0,0.0,0.0,0.0,0.0
+75239,1.0,0.9443547030267275,0.6379707916986933,0.5,0.3620292083013067,0.1379707916986933,0.02536510376633359,39.42424242424242,30.96304702758789,5.229958094222913,-1.8830041885375977,9.159290860687747,-3.674380917046025,3.674380917046025,3.4965075614664802,7.170888478512505,0.0,2.0,33.0,0.0,1301.0,0.0,0.0,33.0,0.0,0.0,0.0,0.0,0.0,5.591684341430664,1.5151810998266393,-0.8763390183448792,1.68213778251707,0.0,0.0,0.0,0.0,0.0
+75240,1.0,0.9473321982231081,0.6342759940463534,0.5,0.3657240059536466,0.13427599404635338,0.01275781416117372,78.38333333333334,1911.103482572295,38.512800597597035,-3.0,145.76440092342054,-4.361611319722948,4.361611319722948,4.0943445622221,8.455955881945048,27.0,2.0,60.0,17.0,4703.0,4703.0,29447.0,33.0,0.2833333333333333,1.0,0.10435537600113402,0.8181818181818182,1.2222222222222223,43.144344696960424,3.913912678196276,-22.79380338813253,4.943976022970944,173.0,22.59259259259259,1.0,43.77997111758117,610.0
+75243,1.0,1.7118844784475225,0.33529140750979036,0.25,0.024648698456576825,0.13045500689907696,0.0009214466712739,1085.25,0.37898291200086787,-0.9656219973073414,-1.9999312246360998,0.7132002663612025,-6.989565654179703,6.989565654179703,2.0794415416798357,9.06900719585954,8.0,4.0,8.0,0.0,8682.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.5423951867147632,0.9342552225820131,-0.008293091335592482,0.40192683634075954,5.0,3.375,2.0,0.8569568250501305,27.0
+75244,1.0,0.33901826511567296,0.9370570367870401,0.5,0.06294296321295983,0.43705703678704017,0.009449881876476544,105.82142857142857,1180.200844451951,40.51648623113333,-3.0,132.43484378998235,-4.661753037398361,4.661753037398361,4.02535169073515,8.68710472813351,41.0,2.0,56.0,14.0,5926.0,5926.0,23694.0,15.0,0.25,1.0,0.0713984378766694,2.7333333333333334,0.36585365853658536,34.38314768097812,3.7769009011075547,-8.659484722748674,5.307279668054912,16.0,5.975609756097561,1.0,3.692363938923924,245.0
+75248,1.0,0.4971128374263667,0.8909272183449651,0.5,0.1090727816550349,0.39092721834496513,0.010967098703888335,91.18181818181819,5010.000199441565,104.23108191482348,-3.0,468.735807382562,-4.512855515163565,4.512855515163565,4.007333185232471,8.520188700396035,39.0,2.0,55.0,12.0,5015.0,4938.0,21515.0,16.0,0.21818181818181817,0.9846460618145564,0.07800235656666364,2.4375,0.41025641025641024,70.79548149028699,4.06998328231248,-50.04498375424548,9.467860234489164,14.0,5.769230769230769,1.0,3.407845687351005,225.0
+75249,1.0,0.3891675492147647,0.9236550632911392,0.5,0.07634493670886076,0.4236550632911392,0.011471518987341773,87.17241379310344,416.3357123975682,46.57141260912883,-3.0,84.69777944086748,-4.467887925280274,4.467887925280274,3.367295829986474,7.8351837552667485,22.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,7.0,0.2413793103448276,1.0,0.056048123090353556,3.142857142857143,0.3181818181818182,20.453256767506936,0.8884855226401125,-20.453256767506936,6.7466910388456185,5.0,2.0454545454545454,1.0,0.7056443043754556,45.0
+75250,1.0,3.992354626842552,0.146342438507591,0.04545454545454545,0.005796927628356971,0.03949259311161838,3.997881123004808e-05,25013.25,2.1476065760596077,1.4719570787187288,0.3045189380645752,0.6973345081454226,-10.127160963449944,10.127160963449944,1.3862943611198906,11.513455324569835,0.0,22.0,4.0,0.0,100053.0,0.0,0.0,4.0,0.0,0.0,0.0,0.0,0.0,1.2135857343673706,0.3996680751442909,-0.3896799087524414,0.7616029272290122,0.0,0.0,0.0,0.0,0.0
diff --git a/autosklearn/metalearning/files/f1_binary.classification_sparse/algorithm_runs.arff b/autosklearn/metalearning/files/f1_binary.classification_sparse/algorithm_runs.arff
index 45809e172b..cefe06bb72 100644
--- a/autosklearn/metalearning/files/f1_binary.classification_sparse/algorithm_runs.arff
+++ b/autosklearn/metalearning/files/f1_binary.classification_sparse/algorithm_runs.arff
@@ -7,95 +7,133 @@
 @ATTRIBUTE runstatus {ok, timeout, memout, not_applicable, crash, other}
 
 @DATA
-75212,1.0,1,0.27577937649880113,ok
-246,1.0,2,?,not_applicable
-75178,1.0,3,?,not_applicable
-75171,1.0,4,0.16716417910447756,ok
-248,1.0,5,?,not_applicable
-75231,1.0,6,?,not_applicable
-75196,1.0,7,0.009615384615384692,ok
-75188,1.0,8,?,not_applicable
-75248,1.0,9,0.6215177713736791,ok
-75126,1.0,10,0.0305775764439411,ok
-75234,1.0,11,0.04135649296939625,ok
-75150,1.0,12,0.2549019607843137,ok
-258,1.0,13,?,not_applicable
-75168,1.0,14,?,not_applicable
-75235,1.0,15,?,not_applicable
-244,1.0,16,?,not_applicable
-75221,1.0,17,?,not_applicable
-75219,1.0,18,0.04292527821939596,ok
-75202,1.0,19,?,not_applicable
-3043,1.0,20,0.14942528735632177,ok
-75205,1.0,21,?,not_applicable
-75174,1.0,22,0.1995599559955995,ok
-275,1.0,23,?,not_applicable
-75213,1.0,24,0.14754098360655743,ok
-75099,1.0,25,0.5384615384615385,ok
-75184,1.0,26,0.2218203737191078,ok
-75222,1.0,27,0.3982300884955753,ok
-233,1.0,28,0.003058103975535076,ok
-75114,1.0,29,0.02244389027431415,ok
-236,1.0,30,?,not_applicable
-75141,1.0,31,0.06528590724898709,ok
-75107,1.0,32,0.44127597873368773,ok
-262,1.0,33,?,not_applicable
-75146,1.0,34,0.10646460771149047,ok
-75189,1.0,35,0.01519371757012311,ok
-2350,1.0,36,0.5124081136351075,ok
-75249,1.0,37,0.015228426395939132,ok
-242,1.0,38,?,not_applicable
-75117,1.0,39,0.04281009879253561,ok
-75191,1.0,40,0.13383895433274173,ok
-261,1.0,41,0.39495798319327735,ok
-75236,1.0,42,?,not_applicable
-75095,1.0,43,0.15508021390374327,ok
-75093,1.0,44,0.5832426550598477,ok
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-75109,1.0,46,?,not_applicable
-75197,1.0,47,?,not_applicable
-75127,1.0,48,0.37609277769688987,ok
-75143,1.0,49,0.0084283589489339,ok
-75153,1.0,50,0.08062750820868303,ok
-75173,1.0,51,0.1164987405541561,ok
-75215,1.0,52,0.023660067600193124,ok
-75195,1.0,53,0.0007510326699211589,ok
-75207,1.0,54,?,not_applicable
-75225,1.0,55,0.608695652173913,ok
-75166,1.0,56,0.15290519877675834,ok
-75100,1.0,57,0.9621212121212122,ok
-75169,1.0,58,?,not_applicable
-75121,1.0,59,0.0,ok
-75098,1.0,60,?,not_applicable
-75115,1.0,61,0.013844515441959526,ok
-75116,1.0,62,0.011820330969267157,ok
-75185,1.0,63,0.14093959731543626,ok
-2119,1.0,64,?,not_applicable
-75157,1.0,65,0.6242171189979123,ok
-75113,1.0,66,0.04885993485342022,ok
-75203,1.0,67,?,not_applicable
-75182,1.0,68,0.1910112359550561,ok
-251,1.0,69,?,not_applicable
-75123,1.0,70,?,not_applicable
-75125,1.0,71,0.020310633213859064,ok
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-75103,1.0,73,0.07858243451463787,ok
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-75139,1.0,76,0.019908116385911057,ok
-75227,1.0,77,0.16682738669238184,ok
-2120,1.0,78,?,not_applicable
-75124,1.0,79,0.5010752688172044,ok
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-75177,1.0,85,0.16167664670658688,ok
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-75172,1.0,89,?,not_applicable
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+75177,1.0,11,0.06249999999999989,ok
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+75179,1.0,363,0.2897696839850028,ok
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+75142,1.0,94,0.06979143636229634,ok
+75099,1.0,98,0.5319148936170213,ok
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+75175,1.0,211,0.1333333333333333,ok
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+75161,1.0,109,0.06113118501585424,ok
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+262,1.0,113,?,not_applicable
+75129,1.0,114,0.5614973262032086,ok
+261,1.0,115,0.38596491228070173,ok
+75090,1.0,116,?,not_applicable
+75114,1.0,120,0.012499999999999956,ok
+75093,1.0,187,0.551433389544688,ok
+260,1.0,124,?,not_applicable
+236,1.0,126,?,not_applicable
+254,1.0,132,0.0,ok
+75107,1.0,133,0.4384577360355908,ok
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+75213,1.0,162,0.12994350282485867,ok
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+75116,1.0,185,0.005847953216374324,ok
+75223,1.0,189,?,not_applicable
+75109,1.0,196,?,not_applicable
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+248,1.0,203,?,not_applicable
+2119,1.0,206,?,not_applicable
+75127,1.0,207,0.3767906126181041,ok
+75153,1.0,215,0.08159531650201235,ok
+75173,1.0,216,0.11768407803650094,ok
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+75100,1.0,232,0.9166666666666666,ok
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+75210,1.0,239,0.0,ok
+273,1.0,241,0.055698371893744714,ok
+75133,1.0,332,0.48571428571428577,ok
+75121,1.0,244,0.0010256410256410664,ok
+75098,1.0,248,?,not_applicable
+75115,1.0,254,0.007423117709437932,ok
+266,1.0,258,?,not_applicable
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+75154,1.0,338,?,not_applicable
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+75097,1.0,350,0.025833815307499397,ok
+75101,1.0,352,0.26037383722625607,ok
+75172,1.0,353,?,not_applicable
+75106,1.0,358,0.7728826433725788,ok
+75120,1.0,367,0.018218623481781382,ok
+75193,1.0,369,?,not_applicable
diff --git a/autosklearn/metalearning/files/f1_binary.classification_sparse/configurations.csv b/autosklearn/metalearning/files/f1_binary.classification_sparse/configurations.csv
index ce7ec55100..08474b4111 100644
--- a/autosklearn/metalearning/files/f1_binary.classification_sparse/configurations.csv
+++ b/autosklearn/metalearning/files/f1_binary.classification_sparse/configurations.csv
@@ -1,93 +1,131 @@
 idx,balancing:strategy,classifier:__choice__,classifier:adaboost:algorithm,classifier:adaboost:learning_rate,classifier:adaboost:max_depth,classifier:adaboost:n_estimators,classifier:bernoulli_nb:alpha,classifier:bernoulli_nb:fit_prior,classifier:decision_tree:criterion,classifier:decision_tree:max_depth_factor,classifier:decision_tree:max_features,classifier:decision_tree:max_leaf_nodes,classifier:decision_tree:min_impurity_decrease,classifier:decision_tree:min_samples_leaf,classifier:decision_tree:min_samples_split,classifier:decision_tree:min_weight_fraction_leaf,classifier:extra_trees:bootstrap,classifier:extra_trees:criterion,classifier:extra_trees:max_depth,classifier:extra_trees:max_features,classifier:extra_trees:max_leaf_nodes,classifier:extra_trees:min_impurity_decrease,classifier:extra_trees:min_samples_leaf,classifier:extra_trees:min_samples_split,classifier:extra_trees:min_weight_fraction_leaf,classifier:gradient_boosting:early_stop,classifier:gradient_boosting:l2_regularization,classifier:gradient_boosting:learning_rate,classifier:gradient_boosting:loss,classifier:gradient_boosting:max_bins,classifier:gradient_boosting:max_depth,classifier:gradient_boosting:max_leaf_nodes,classifier:gradient_boosting:min_samples_leaf,classifier:gradient_boosting:n_iter_no_change,classifier:gradient_boosting:scoring,classifier:gradient_boosting:tol,classifier:gradient_boosting:validation_fraction,classifier:k_nearest_neighbors:n_neighbors,classifier:k_nearest_neighbors:p,classifier:k_nearest_neighbors:weights,classifier:lda:n_components,classifier:lda:shrinkage,classifier:lda:shrinkage_factor,classifier:lda:tol,classifier:liblinear_svc:C,classifier:liblinear_svc:dual,classifier:liblinear_svc:fit_intercept,classifier:liblinear_svc:intercept_scaling,classifier:liblinear_svc:loss,classifier:liblinear_svc:multi_class,classifier:liblinear_svc:penalty,classifier:liblinear_svc:tol,classifier:libsvm_svc:C,classifier:libsvm_svc:coef0,classifier:libsvm_svc:degree,classifier:libsvm_svc:gamma,classifier:libsvm_svc:kernel,classifier:libsvm_svc:max_iter,classifier:libsvm_svc:shrinking,classifier:libsvm_svc:tol,classifier:multinomial_nb:alpha,classifier:multinomial_nb:fit_prior,classifier:passive_aggressive:C,classifier:passive_aggressive:average,classifier:passive_aggressive:fit_intercept,classifier:passive_aggressive:loss,classifier:passive_aggressive:tol,classifier:qda:reg_param,classifier:random_forest:bootstrap,classifier:random_forest:criterion,classifier:random_forest:max_depth,classifier:random_forest:max_features,classifier:random_forest:max_leaf_nodes,classifier:random_forest:min_impurity_decrease,classifier:random_forest:min_samples_leaf,classifier:random_forest:min_samples_split,classifier:random_forest:min_weight_fraction_leaf,classifier:sgd:alpha,classifier:sgd:average,classifier:sgd:epsilon,classifier:sgd:eta0,classifier:sgd:fit_intercept,classifier:sgd:l1_ratio,classifier:sgd:learning_rate,classifier:sgd:loss,classifier:sgd:penalty,classifier:sgd:power_t,classifier:sgd:tol,data_preprocessing:categorical_transformer:categorical_encoding:__choice__,data_preprocessing:categorical_transformer:category_coalescence:__choice__,data_preprocessing:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction,data_preprocessing:numerical_transformer:imputation:strategy,data_preprocessing:numerical_transformer:rescaling:__choice__,data_preprocessing:numerical_transformer:rescaling:quantile_transformer:n_quantiles,data_preprocessing:numerical_transformer:rescaling:quantile_transformer:output_distribution,data_preprocessing:numerical_transformer:rescaling:robust_scaler:q_max,data_preprocessing:numerical_transformer:rescaling:robust_scaler:q_min,feature_preprocessor:__choice__,feature_preprocessor:extra_trees_preproc_for_classification:bootstrap,feature_preprocessor:extra_trees_preproc_for_classification:criterion,feature_preprocessor:extra_trees_preproc_for_classification:max_depth,feature_preprocessor:extra_trees_preproc_for_classification:max_features,feature_preprocessor:extra_trees_preproc_for_classification:max_leaf_nodes,feature_preprocessor:extra_trees_preproc_for_classification:min_impurity_decrease,feature_preprocessor:extra_trees_preproc_for_classification:min_samples_leaf,feature_preprocessor:extra_trees_preproc_for_classification:min_samples_split,feature_preprocessor:extra_trees_preproc_for_classification:min_weight_fraction_leaf,feature_preprocessor:extra_trees_preproc_for_classification:n_estimators,feature_preprocessor:kernel_pca:coef0,feature_preprocessor:kernel_pca:degree,feature_preprocessor:kernel_pca:gamma,feature_preprocessor:kernel_pca:kernel,feature_preprocessor:kernel_pca:n_components,feature_preprocessor:kitchen_sinks:gamma,feature_preprocessor:kitchen_sinks:n_components,feature_preprocessor:liblinear_svc_preprocessor:C,feature_preprocessor:liblinear_svc_preprocessor:dual,feature_preprocessor:liblinear_svc_preprocessor:fit_intercept,feature_preprocessor:liblinear_svc_preprocess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+258,none,adaboost,SAMME,0.6791611572325104,6,465,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,quantile_transformer,1464,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+259,none,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.9142389417553813,None,0.0,12,10,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00043131916878244537,median,quantile_transformer,1494,uniform,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,,
+264,weighting,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.3526996137836971,most_frequent,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,,
+266,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.8361188336674341,None,0.0,16,2,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,none,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.12061921346314308,fpr,chi2,
+274,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.513560345508016,None,0.0,3,2,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,median,quantile_transformer,1044,uniform,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.07550342883878675,,mutual_info_classif,
+277,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,57,2,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+280,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.2318705830210126,True,True,hinge,0.0002994421772278671,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.13310408167721913,most_frequent,quantile_transformer,1036,uniform,,,kernel_pca,,,,,,,,,,,,,,cosine,703,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+287,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.3850609070926397,None,0.0,1,2,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.013992309764275707,median,normalize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,,
+288,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.5719833791553314,None,0.0,16,3,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.13804007452389966,mean,normalize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+292,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.0479982468066316,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,quantile_transformer,1097,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+293,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7040496889211508,None,0.0,17,19,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.016450784709133564,mean,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+297,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.0014200038703931547,most_frequent,robust_scaler,,,0.7373450748248416,0.26419938540085264,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+298,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.5431102417332107,None,0.0,1,16,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010731370069741165,median,none,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.35426195730988774,fdr,chi2,
+301,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.5,None,0.0,2,4,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.008039249201522535,most_frequent,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,,
+306,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2677516008848513,True,True,hinge,0.05752557410901496,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0010152857217605206,median,quantile_transformer,1000,uniform,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,51.60974796845029,chi2,,,,
+307,none,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,249,None,,0.0005516162390755682,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010000000000000004,mean,quantile_transformer,1350,uniform,,,kernel_pca,,,,,,,,,,,0.162692683181769,3,1.0,poly,487,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+308,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.06575254148205681,True,,3.538145104482663e-07,True,,constant,log,l2,,0.002481534944163305,one_hot_encoding,no_coalescense,,most_frequent,normalize,,,,,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,4,None,9,8,1.0,49,,,,,,
+312,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,60.6761602925822,,,0.038224035513907226,rbf,-1,False,0.0018214343969776353,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.421025538999516,median,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+313,weighting,adaboost,SAMME,1.2423888874490505,5,284,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.01582563952987033,most_frequent,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+319,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5663133321395308,None,0.0,5,19,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0244551766287634,median,robust_scaler,,,0.75,0.21219212178051497,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,,
+321,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.5864398404787432,None,0.0,1,14,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0070663750850608895,most_frequent,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,,
+325,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8.380057363340162,False,True,hinge,0.0020653831067503525,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.05020677662128942,median,quantile_transformer,560,normal,,,kernel_pca,,,,,,,,,,,,,,cosine,891,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+328,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.7292011996224884,True,True,hinge,8.162763086383311e-05,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010337761588362885,median,quantile_transformer,877,uniform,,,kernel_pca,,,,,,,,,,,,,,cosine,1317,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+331,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.5,None,0.0,1,5,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.15244281596303316,mean,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+332,none,adaboost,SAMME,0.22805500898886877,8,172,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.019649593891454008,mean,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+336,weighting,adaboost,SAMME,0.1313487280585511,3,189,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,robust_scaler,,,0.8008090310131288,0.2935856792824304,extra_trees_preproc_for_classification,False,entropy,None,0.7722400064297721,None,0.0,5,4,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+338,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.37035966336010345,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.007136588202285817,mean,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+343,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.7139498722492832,None,0.0,14,5,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0006591123720866668,mean,normalize,,,,,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,6,None,2,4,1.0,87,,,,,,
+345,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5321814610661173,None,0.0,1,3,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,standardize,,,,,extra_trees_preproc_for_classification,True,gini,None,0.5,None,0.0,1,15,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+350,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.44903177399085925,None,0.0,2,17,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,mean,robust_scaler,,,0.7812408516672696,0.12791233577400205,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,,
+352,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5988524108452234,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,mean,robust_scaler,,,0.75,0.26961884796358077,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+353,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.6269076084367177,None,0.0,1,7,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.12943250859943486,most_frequent,standardize,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,62.84526554323969,chi2,,,,
+358,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.48489062404612904,None,0.0,16,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.10901814259355092,median,standardize,,,,,extra_trees_preproc_for_classification,True,entropy,None,0.6579971061972761,None,0.0,18,15,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+363,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.1393173890820956,False,True,1,squared_hinge,ovr,l2,8.283470008521272e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.015152352014390857,mean,normalize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,False,,,,,,,,,,,,,
+367,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.938372431886818e-05,True,True,hinge,0.0016143408148969647,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00024949978971758427,median,quantile_transformer,754,uniform,,,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,-0.22339839702350717,2,0.5718982387915436,poly,5260,,,,,,,,,,,,,,,,
+369,none,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.9796199105973851,None,0.0,2,13,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.00845950033601661,mean,robust_scaler,,,0.8275228560550734,0.23814615163106348,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
diff --git a/autosklearn/metalearning/files/f1_binary.classification_sparse/description.txt b/autosklearn/metalearning/files/f1_binary.classification_sparse/description.txt
index c826b0b627..6faf309c30 100644
--- a/autosklearn/metalearning/files/f1_binary.classification_sparse/description.txt
+++ b/autosklearn/metalearning/files/f1_binary.classification_sparse/description.txt
@@ -1,8 +1,9 @@
 features_cutoff_time: 3600
 features_cutoff_memory: 3072
-number_of_feature_steps: 35
+number_of_feature_steps: 42
 feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances
 feature_step LogNumberOfInstances: LogNumberOfInstances
+feature_step NumberOfClasses: NumberOfClasses
 feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures
 feature_step LogNumberOfFeatures: LogNumberOfFeatures
 feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues
@@ -20,6 +21,11 @@ feature_step DatasetRatio: DatasetRatio, LogDatasetRatio
 feature_step LogDatasetRatio: LogDatasetRatio
 feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio
 feature_step LogInverseDatasetRatio: LogInverseDatasetRatio
+feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD
+feature_step ClassProbabilityMin: ClassProbabilityMin
+feature_step ClassProbabilityMax: ClassProbabilityMax
+feature_step ClassProbabilityMean: ClassProbabilityMean
+feature_step ClassProbabilitySTD: ClassProbabilitySTD
 feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum
 feature_step SymbolsMin: SymbolsMin
 feature_step SymbolsMax: SymbolsMax
@@ -36,11 +42,12 @@ feature_step SkewnessMin: SkewnessMin
 feature_step SkewnessMax: SkewnessMax
 feature_step SkewnessMean: SkewnessMean
 feature_step SkewnessSTD: SkewnessSTD
-features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
+feature_step ClassEntropy: ClassEntropy
+features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy
 features_stochastic: 
-default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
+default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy
 
-algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92
+algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235,236,237,238,239,240,241,242,243,244,245,246,247,248,249,250,251,252,253,254,255,256,257,258,259,260,261,262,263,264,265,266,267,268,269,270,271,272,273,274,275,276,277,278,279,280,281,282,283,284,285,286,287,288,289,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,312,313,314,315,316,317,318,319,320,321,322,323,324,325,326,327,328,329,330,331,332,333,334,335,336,337,338,339,340,341,342,343,344,345,346,347,348,349,350,351,352,353,354,355,356,357,358,359,360,361,362,363,364,365,366,367,368,369,370,371,372
 algorithms_stochastic: 
 performance_measures: f1
 performance_type: solution_quality
diff --git a/autosklearn/metalearning/files/f1_binary.classification_sparse/feature_costs.arff b/autosklearn/metalearning/files/f1_binary.classification_sparse/feature_costs.arff
index 0e17e20d22..8512fd9275 100644
--- a/autosklearn/metalearning/files/f1_binary.classification_sparse/feature_costs.arff
+++ b/autosklearn/metalearning/files/f1_binary.classification_sparse/feature_costs.arff
@@ -4,6 +4,7 @@
 @ATTRIBUTE repetition NUMERIC
 @ATTRIBUTE NumberOfInstances NUMERIC
 @ATTRIBUTE LogNumberOfInstances NUMERIC
+@ATTRIBUTE NumberOfClasses NUMERIC
 @ATTRIBUTE NumberOfFeatures NUMERIC
 @ATTRIBUTE LogNumberOfFeatures NUMERIC
 @ATTRIBUTE MissingValues NUMERIC
@@ -21,6 +22,11 @@
 @ATTRIBUTE LogDatasetRatio NUMERIC
 @ATTRIBUTE InverseDatasetRatio NUMERIC
 @ATTRIBUTE LogInverseDatasetRatio NUMERIC
+@ATTRIBUTE ClassOccurences NUMERIC
+@ATTRIBUTE ClassProbabilityMin NUMERIC
+@ATTRIBUTE ClassProbabilityMax NUMERIC
+@ATTRIBUTE ClassProbabilityMean NUMERIC
+@ATTRIBUTE ClassProbabilitySTD NUMERIC
 @ATTRIBUTE NumSymbols NUMERIC
 @ATTRIBUTE SymbolsMin NUMERIC
 @ATTRIBUTE SymbolsMax NUMERIC
@@ -37,162 +43,138 @@
 @ATTRIBUTE SkewnessMax NUMERIC
 @ATTRIBUTE SkewnessMean NUMERIC
 @ATTRIBUTE SkewnessSTD NUMERIC
+@ATTRIBUTE ClassEntropy NUMERIC
 
 @DATA
-75123,1.0,1e-05,1e-05,1e-05,1e-05,0.00253,0.00221,1e-05,0.00024,3e-05,0.00011,0.0,2e-05,2e-05,5e-05,8e-05,2e-05,1e-05,7e-05,1e-05,0.00072,1e-05,1e-05,0.00019,0.00041,0.00011,0.00061,0.00012,9e-05,0.00015,0.00025,0.00067,0.00014,0.00011,0.00016,0.00027
-75090,1.0,1e-05,1e-05,2e-05,2e-05,0.00815,0.00368,7e-05,0.00228,9e-05,0.00235,0.0,5e-05,5e-05,0.0001,0.00013,0.00013,1e-05,0.00021,2e-05,0.00064,0.0,0.0,0.00014,0.00038,0.00011,0.00066,0.00011,0.00014,0.00014,0.00026,0.00066,0.00011,0.00013,0.00015,0.00027
-2123,1.0,1e-05,1e-05,1e-05,1e-05,0.00097,0.00079,1e-05,0.00013,1e-05,7e-05,0.0,2e-05,2e-05,5e-05,8e-05,2e-05,1e-05,5e-05,1e-05,0.0007,1e-05,0.0,0.00017,0.00042,0.0001,0.00052,6e-05,8e-05,0.00012,0.00026,0.00049,6e-05,0.00011,9e-05,0.00023
-248,1.0,1e-05,1e-05,1e-05,1e-05,0.00088,0.00077,1e-05,0.0001,1e-05,4e-05,0.0,1e-05,1e-05,4e-05,5e-05,2e-05,1e-05,4e-05,1e-05,0.00041,1e-05,0.0,9e-05,0.00022,9e-05,0.00046,6e-05,8e-05,9e-05,0.00023,0.00049,7e-05,0.0001,0.0001,0.00021
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-75191,1.0,1e-05,1e-05,3e-05,1e-05,0.53436,0.40777,2e-05,0.09655,3e-05,0.0301,1e-05,2e-05,3e-05,5e-05,0.00011,7e-05,1e-05,0.00011,3e-05,0.00084,5e-05,7e-05,0.00017,0.00033,0.00023,0.00088,0.00012,0.00017,0.00032,0.00026,0.00068,9e-05,0.00012,0.00013,0.00035
-75187,1.0,1e-05,1e-05,2e-05,1e-05,0.00548,0.00473,1e-05,0.0005,2e-05,0.00029,1e-05,2e-05,2e-05,7e-05,9e-05,3e-05,1e-05,6e-05,1e-05,0.00062,1e-05,1e-05,0.00014,0.00033,0.00013,0.00038,7e-05,7e-05,8e-05,0.00016,0.00047,6e-05,9e-05,9e-05,0.00023
-266,1.0,1e-05,1e-05,1e-05,1e-05,0.0018,0.00146,1e-05,0.00023,2e-05,0.00015,2e-05,2e-05,2e-05,6e-05,8e-05,3e-05,1e-05,6e-05,1e-05,0.00059,1e-05,1e-05,0.00014,0.00031,0.00013,0.0004,5e-05,7e-05,8e-05,0.0002,0.00044,8e-05,8e-05,8e-05,0.0002
-253,1.0,1e-05,1e-05,1e-05,1e-05,0.00082,0.00063,1e-05,0.00016,2e-05,5e-05,0.0,1e-05,1e-05,4e-05,5e-05,2e-05,1e-05,5e-05,2e-05,0.00046,1e-05,0.0,9e-05,0.00024,0.00012,0.00044,6e-05,7e-05,8e-05,0.00023,0.00043,6e-05,0.0001,8e-05,0.00019
-75217,1.0,1e-05,1e-05,2e-05,1e-05,0.00196,0.00158,1e-05,0.00026,2e-05,0.00016,1e-05,3e-05,3e-05,7e-05,0.0001,3e-05,1e-05,7e-05,2e-05,0.00089,1e-05,1e-05,0.00022,0.00053,0.00013,0.00074,9e-05,0.0001,0.00019,0.00036,0.00065,0.00013,0.0001,0.00011,0.00031
-75176,1.0,1e-05,1e-05,2e-05,1e-05,0.01406,0.01285,1e-05,0.00094,2e-05,0.00031,1e-05,3e-05,3e-05,6e-05,9e-05,3e-05,1e-05,7e-05,2e-05,0.00069,1e-05,1e-05,0.00017,0.00035,0.00015,0.00085,0.00018,0.00011,0.0002,0.00036,0.0011,0.00022,0.00023,0.00023,0.00042
-2117,1.0,1e-05,1e-05,1e-05,1e-05,0.01735,0.0154,1e-05,0.00141,1e-05,0.00057,0.0,1e-05,1e-05,3e-05,6e-05,2e-05,1e-05,5e-05,1e-05,0.0004,1e-05,0.0,8e-05,0.00021,0.0001,0.00065,0.00012,0.00011,0.00015,0.00026,0.00055,7e-05,0.00014,9e-05,0.00025
-75148,1.0,1e-05,1e-05,2e-05,2e-05,0.00129,0.00115,1e-05,0.00012,1e-05,4e-05,0.0,2e-05,2e-05,4e-05,5e-05,2e-05,1e-05,4e-05,1e-05,0.0004,1e-05,0.0,9e-05,0.00021,9e-05,0.00036,7e-05,7e-05,8e-05,0.00015,0.00041,6e-05,8e-05,8e-05,0.00019
-75103,1.0,1e-05,1e-05,1e-05,1e-05,0.01302,0.00819,3e-05,0.00234,3e-05,0.00255,0.0,2e-05,2e-05,6e-05,0.00012,5e-05,1e-05,0.00011,1e-05,0.0005,1e-05,0.0,9e-05,0.00029,0.00011,0.00054,8e-05,0.00011,0.00011,0.00024,0.00066,7e-05,0.00015,0.00011,0.00032
-75168,1.0,1e-05,0.0,1e-05,1e-05,0.02353,0.00601,0.00016,0.01754,0.00018,0.00031,0.0,0.00013,0.00013,0.00027,0.0003,0.00032,1e-05,0.00042,1e-05,0.0004,1e-05,0.0,8e-05,0.00021,0.0001,0.0011,0.00022,0.00025,0.00026,0.00037,0.00124,0.00022,0.00025,0.00028,0.00049
-75157,1.0,1e-05,1e-05,1e-05,1e-05,0.00125,0.00106,1e-05,0.00015,2e-05,7e-05,0.0,2e-05,2e-05,5e-05,6e-05,2e-05,1e-05,4e-05,1e-05,0.00048,2e-05,0.0,0.0001,0.00026,0.0001,0.00039,6e-05,7e-05,8e-05,0.00019,0.00043,6e-05,9e-05,8e-05,0.0002
-75092,1.0,1e-05,1e-05,1e-05,1e-05,0.00099,0.00072,1e-05,0.00018,1e-05,0.00011,0.0,2e-05,2e-05,4e-05,6e-05,2e-05,1e-05,5e-05,1e-05,0.00058,1e-05,0.0,0.00014,0.00034,9e-05,0.00041,6e-05,8e-05,9e-05,0.00017,0.00045,7e-05,0.0001,9e-05,0.00019
-75213,1.0,1e-05,1e-05,1e-05,1e-05,0.00096,0.00079,1e-05,0.00013,1e-05,7e-05,1e-05,2e-05,2e-05,5e-05,7e-05,2e-05,1e-05,6e-05,1e-05,0.00056,1e-05,1e-05,0.00013,0.00029,0.00012,0.00056,6e-05,8e-05,9e-05,0.00033,0.00063,8e-05,0.00014,0.00013,0.00029
-75236,1.0,1e-05,1e-05,1e-05,1e-05,0.00208,0.00115,2e-05,0.00054,3e-05,0.00044,0.0,3e-05,3e-05,7e-05,8e-05,5e-05,1e-05,8e-05,1e-05,0.00062,0.0,0.0,0.00016,0.00036,0.0001,0.00068,0.0001,0.00014,0.00016,0.00029,0.00072,0.0001,0.00015,0.00015,0.00032
-75119,1.0,1e-05,1e-05,2e-05,1e-05,0.04185,0.01398,0.00063,0.01614,0.0007,0.01306,0.0,0.00046,0.00045,0.00091,0.00097,0.0012,1e-05,0.0015,1e-05,0.00047,1e-05,0.0,8e-05,0.00026,0.00012,0.00421,0.00091,0.00097,0.00103,0.0013,0.00402,0.00082,0.0008,0.00097,0.00144
-75227,1.0,1e-05,1e-05,2e-05,1e-05,0.00416,0.00373,1e-05,0.00038,2e-05,9e-05,1e-05,2e-05,2e-05,6e-05,0.0001,3e-05,1e-05,7e-05,2e-05,0.00063,1e-05,1e-05,0.00015,0.00034,0.00013,0.00037,6e-05,8e-05,8e-05,0.00016,0.00043,6e-05,9e-05,9e-05,0.0002
-75141,1.0,1e-05,1e-05,2e-05,1e-05,0.00631,0.00572,1e-05,0.00047,2e-05,0.00016,1e-05,3e-05,3e-05,8e-05,9e-05,3e-05,1e-05,7e-05,2e-05,0.00068,1e-05,1e-05,0.00015,0.00036,0.00014,0.00039,6e-05,8e-05,9e-05,0.00017,0.00047,8e-05,9e-05,0.0001,0.00021
-75188,1.0,1e-05,1e-05,2e-05,1e-05,0.05522,0.00659,0.00042,0.04887,0.00048,0.00066,0.0,0.00042,0.00037,0.00065,0.00077,0.0009,1e-05,0.00098,2e-05,0.00073,4e-05,7e-05,0.00018,0.00027,0.00017,0.00228,0.0005,0.00055,0.00052,0.00071,0.00253,0.00049,0.00055,0.00058,0.00091
-75159,1.0,2e-05,2e-05,2e-05,2e-05,0.00136,0.00099,7e-05,0.00033,8e-05,0.00021,2e-05,0.0001,0.0001,0.00014,0.00016,0.00016,7e-05,0.00019,8e-05,0.00092,1e-05,9e-05,0.0002,0.00039,0.00023,0.00049,7e-05,8e-05,0.00012,0.00023,0.00043,6e-05,8e-05,8e-05,0.0002
-75192,1.0,1e-05,1e-05,2e-05,1e-05,0.00188,0.00165,1e-05,0.00016,1e-05,9e-05,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,1e-05,4e-05,1e-05,0.00039,1e-05,0.0,9e-05,0.00021,9e-05,0.00048,5e-05,8e-05,0.0001,0.00025,0.00045,6e-05,0.0001,0.0001,0.0002
-262,1.0,1e-05,1e-05,1e-05,1e-05,0.00488,0.00434,1e-05,0.00037,1e-05,0.00019,0.0,2e-05,1e-05,4e-05,6e-05,2e-05,1e-05,4e-05,1e-05,0.0004,1e-05,0.0,9e-05,0.00021,9e-05,0.00037,6e-05,7e-05,8e-05,0.00017,0.00045,6e-05,0.00012,9e-05,0.00018
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-75177,1.0,1e-05,1e-05,1e-05,1e-05,0.0017,0.0014,1e-05,0.0002,1e-05,0.00013,0.0,2e-05,1e-05,3e-05,5e-05,2e-05,1e-05,4e-05,1e-05,0.00038,1e-05,0.0,8e-05,0.0002,0.0001,0.00038,6e-05,7e-05,9e-05,0.00017,0.00045,6e-05,9e-05,8e-05,0.00021
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-2120,1.0,1e-05,1e-05,1e-05,1e-05,0.00459,0.00383,1e-05,0.00045,2e-05,0.00033,1e-05,3e-05,2e-05,7e-05,8e-05,3e-05,1e-05,6e-05,1e-05,0.00058,1e-05,0.0,0.00013,0.00032,0.00012,0.00051,6e-05,9e-05,8e-05,0.00029,0.0005,6e-05,0.0001,0.00011,0.00023
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-75212,1.0,1e-05,1e-05,1e-05,1e-05,0.0008,0.00059,1e-05,0.00015,1e-05,9e-05,0.0,2e-05,1e-05,3e-05,5e-05,2e-05,1e-05,4e-05,1e-05,0.00037,0.0,0.0,8e-05,0.00019,9e-05,0.00055,8e-05,0.00012,0.00011,0.00024,0.00047,6e-05,0.0001,9e-05,0.00022
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-260,1.0,2e-05,1e-05,2e-05,2e-05,0.00413,0.00362,7e-05,0.0005,8e-05,0.00017,1e-05,9e-05,0.0001,0.00014,0.00016,0.00016,7e-05,0.00014,2e-05,0.00094,8e-05,1e-05,0.00027,0.00042,0.00016,0.00036,6e-05,7e-05,8e-05,0.00016,0.00044,6e-05,8e-05,9e-05,0.0002
-75153,1.0,1e-05,1e-05,2e-05,1e-05,0.00606,0.00513,1e-05,0.00054,2e-05,0.00042,1e-05,3e-05,4e-05,6e-05,8e-05,4e-05,1e-05,6e-05,2e-05,0.00061,1e-05,1e-05,0.00015,0.00032,0.00013,0.00038,6e-05,7e-05,8e-05,0.00016,0.00048,6e-05,9e-05,0.0001,0.00023
-75127,1.0,1e-05,1e-05,2e-05,1e-05,0.16654,0.15337,1e-05,0.01043,1e-05,0.00277,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,1e-05,4e-05,2e-05,0.00038,0.0,0.0,8e-05,0.00019,0.0001,0.00045,6e-05,9e-05,8e-05,0.00021,0.00054,7e-05,0.00012,0.0001,0.00026
-75171,1.0,1e-05,0.0,1e-05,1e-05,0.00304,0.00277,1e-05,0.00021,1e-05,8e-05,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,1e-05,4e-05,1e-05,0.00036,1e-05,0.0,7e-05,0.00019,8e-05,0.00048,8e-05,0.0001,0.00011,0.0002,0.00055,8e-05,0.00012,0.00012,0.00023
-75232,1.0,1e-05,1e-05,1e-05,1e-05,0.00108,0.00076,1e-05,0.00021,2e-05,0.00015,1e-05,2e-05,2e-05,5e-05,8e-05,3e-05,1e-05,6e-05,1e-05,0.00055,1e-05,1e-05,0.00013,0.00028,0.00012,0.00039,7e-05,7e-05,8e-05,0.00016,0.00046,6e-05,8e-05,0.0001,0.00022
-75235,1.0,1e-05,1e-05,2e-05,1e-05,0.00408,0.0035,1e-05,0.00039,1e-05,0.00022,1e-05,2e-05,2e-05,5e-05,0.0001,3e-05,1e-05,7e-05,2e-05,0.00067,1e-05,1e-05,0.00013,0.00034,0.00018,0.00039,6e-05,9e-05,8e-05,0.00017,0.00051,6e-05,9e-05,0.00011,0.00024
-75097,1.0,1e-05,1e-05,2e-05,1e-05,0.01132,0.01021,1e-05,0.00084,1e-05,0.0003,0.0,1e-05,1e-05,3e-05,6e-05,2e-05,1e-05,5e-05,2e-05,0.0005,1e-05,0.0,9e-05,0.00027,0.00012,0.00074,0.00014,0.0001,0.00017,0.00033,0.00065,9e-05,0.00018,0.00013,0.00024
-288,1.0,1e-05,1e-05,1e-05,1e-05,0.00248,0.00199,1e-05,0.00029,1e-05,0.00022,0.0,2e-05,2e-05,4e-05,6e-05,2e-05,1e-05,5e-05,1e-05,0.0006,1e-05,0.0,0.00014,0.00035,0.00011,0.00043,7e-05,0.00011,8e-05,0.00017,0.00044,6e-05,8e-05,8e-05,0.00021
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+75232,1.0,1e-05,1e-05,5e-05,1e-05,1e-05,0.00061,0.00044,1e-05,0.00013,1e-05,7e-05,0.0,2e-05,2e-05,4e-05,7e-05,2e-05,1e-05,3e-05,1e-05,0.00014,2e-05,1e-05,4e-05,8e-05,0.0004,0.0,0.0,0.00011,0.00022,5e-05,0.0004,5e-05,6e-05,8e-05,0.00021,0.00036,5e-05,6e-05,8e-05,0.00016,0.00029
+75233,1.0,1e-05,1e-05,0.00012,1e-05,0.0,0.00257,0.00222,1e-05,0.00024,1e-05,0.00014,0.0,1e-05,1e-05,3e-05,5e-05,3e-05,1e-05,2e-05,1e-05,9e-05,1e-05,0.0,2e-05,5e-05,0.0003,0.0,0.0,8e-05,0.00017,4e-05,0.00076,0.00012,0.00013,0.00014,0.00037,0.00058,0.00012,8e-05,0.00013,0.00025,0.00119
+75234,1.0,1e-05,1e-05,0.00015,1e-05,1e-05,0.0058,0.00534,1e-05,0.00033,1e-05,0.00016,0.0,2e-05,2e-05,7e-05,0.00204,3e-05,1e-05,5e-05,1e-05,0.00024,3e-05,2e-05,0.0001,9e-05,0.00043,0.0,0.0,0.00012,0.00024,6e-05,0.00156,6e-05,7e-05,0.00121,0.00021,0.00054,6e-05,7e-05,9e-05,0.00032,0.00501
+75235,1.0,1e-05,1e-05,0.0003,2e-05,1e-05,0.00461,0.00385,2e-05,0.00055,2e-05,0.00026,1e-05,3e-05,3e-05,0.0001,0.00011,3e-05,1e-05,5e-05,2e-05,0.00026,3e-05,1e-05,8e-05,0.00014,0.00075,1e-05,1e-05,0.0002,0.00043,0.00011,0.00127,0.00017,0.0002,0.00029,0.00061,0.0008,0.00015,0.00012,0.00016,0.00038,0.00242
+75236,1.0,1e-05,1e-05,0.0001,1e-05,1e-05,0.00218,0.00118,2e-05,0.00065,3e-05,0.00041,1e-05,3e-05,3e-05,9e-05,9e-05,4e-05,1e-05,7e-05,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.0006,1e-05,1e-05,0.00021,0.00032,6e-05,0.00043,7e-05,7e-05,9e-05,0.00019,0.00062,7e-05,8e-05,0.0001,0.00037,0.00048
+75237,1.0,2e-05,1e-05,0.00785,2e-05,1e-05,0.31031,0.27854,2e-05,0.01161,3e-05,0.02022,1e-05,4e-05,3e-05,9e-05,0.00024,6e-05,2e-05,9e-05,2e-05,0.00062,7e-05,3e-05,0.00017,0.00035,0.0008,1e-05,1e-05,0.00028,0.00043,8e-05,0.00043,8e-05,7e-05,9e-05,0.0002,0.00044,6e-05,8e-05,9e-05,0.00021,0.14028
+75239,1.0,1e-05,1e-05,7e-05,1e-05,1e-05,0.00105,0.00084,1e-05,0.00015,1e-05,8e-05,1e-05,2e-05,5e-05,4e-05,8e-05,2e-05,1e-05,3e-05,1e-05,0.00016,2e-05,1e-05,5e-05,9e-05,0.00049,0.0,0.0,0.00012,0.00026,0.00011,0.00046,0.00013,8e-05,8e-05,0.00018,0.00044,6e-05,9e-05,0.00011,0.00018,0.00065
+75240,1.0,2e-05,1e-05,0.00036,5e-05,4e-05,0.00845,0.00644,6e-05,0.00122,4e-05,0.00091,1e-05,4e-05,4e-05,9e-05,0.00013,5e-05,2e-05,9e-05,5e-05,0.00031,3e-05,1e-05,7e-05,0.0002,0.00073,3e-05,2e-05,0.00019,0.0003,0.00018,0.00055,9e-05,9e-05,0.00011,0.00025,0.0005,7e-05,8e-05,0.00015,0.0002,0.00365
+75243,1.0,1e-05,1e-05,0.00029,1e-05,1e-05,0.00461,0.00415,1e-05,0.00035,1e-05,0.00015,0.0,2e-05,2e-05,5e-05,7e-05,2e-05,1e-05,4e-05,1e-05,0.00018,2e-05,1e-05,4e-05,0.00011,0.00022,1e-05,1e-05,5e-05,0.00011,5e-05,0.0004,6e-05,6e-05,8e-05,0.0002,0.00039,6e-05,6e-05,8e-05,0.00019,0.00265
+75244,1.0,3e-05,2e-05,0.0004,3e-05,2e-05,0.0173,0.01298,4e-05,0.00267,4e-05,0.00174,1e-05,7e-05,9e-05,0.00024,0.00027,8e-05,2e-05,0.00013,3e-05,0.00045,7e-05,2e-05,0.00011,0.00025,0.00077,6e-05,2e-05,0.00021,0.00034,0.00013,0.00077,0.00014,0.00013,0.00026,0.00023,0.00044,7e-05,8e-05,0.0001,0.00019,0.00649
+75248,1.0,1e-05,1e-05,0.00017,1e-05,1e-05,0.00416,0.00305,1e-05,0.0006,2e-05,0.00054,1e-05,2e-05,2e-05,5e-05,7e-05,2e-05,1e-05,4e-05,1e-05,0.00017,2e-05,1e-05,4e-05,0.0001,0.00029,5e-05,1e-05,6e-05,0.00012,6e-05,0.00081,0.00011,0.00015,0.00016,0.00038,0.0005,7e-05,8e-05,0.00012,0.00024,0.00184
+75249,1.0,1e-05,1e-05,0.00011,1e-05,1e-05,0.00192,0.00153,1e-05,0.00025,1e-05,0.00017,1e-05,2e-05,2e-05,5e-05,7e-05,2e-05,1e-05,5e-05,3e-05,0.00014,2e-05,1e-05,3e-05,8e-05,0.00029,1e-05,3e-05,6e-05,0.00012,7e-05,0.00041,6e-05,8e-05,0.0001,0.00017,0.00043,6e-05,7e-05,9e-05,0.00019,0.00092
+75250,1.0,1e-05,1e-05,0.00375,1e-05,1e-05,0.04994,0.047,1e-05,0.00247,1e-05,0.00049,1e-05,2e-05,2e-05,6e-05,7e-05,2e-05,1e-05,3e-05,1e-05,0.00022,4e-05,1e-05,4e-05,0.00012,0.00041,0.0,1e-05,0.00011,0.00023,6e-05,0.00076,0.00012,0.00012,0.00019,0.00032,0.00086,0.00012,0.00013,0.00018,0.00043,0.04178
diff --git a/autosklearn/metalearning/files/f1_binary.classification_sparse/feature_runstatus.arff b/autosklearn/metalearning/files/f1_binary.classification_sparse/feature_runstatus.arff
index 0034ad2bd6..5fff2dfed3 100644
--- a/autosklearn/metalearning/files/f1_binary.classification_sparse/feature_runstatus.arff
+++ b/autosklearn/metalearning/files/f1_binary.classification_sparse/feature_runstatus.arff
@@ -4,6 +4,7 @@
 @ATTRIBUTE repetition NUMERIC
 @ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other}
@@ -21,6 +22,11 @@
 @ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other}
@@ -37,162 +43,138 @@
 @ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other}
 
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diff --git a/autosklearn/metalearning/files/f1_binary.classification_sparse/feature_values.arff b/autosklearn/metalearning/files/f1_binary.classification_sparse/feature_values.arff
index a60400e3ce..736dc64321 100644
--- a/autosklearn/metalearning/files/f1_binary.classification_sparse/feature_values.arff
+++ b/autosklearn/metalearning/files/f1_binary.classification_sparse/feature_values.arff
@@ -13,7 +13,6 @@
 @ATTRIBUTE KurtosisMean NUMERIC
 @ATTRIBUTE KurtosisMin NUMERIC
 @ATTRIBUTE KurtosisSTD NUMERIC
-@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC
 @ATTRIBUTE LogDatasetRatio NUMERIC
 @ATTRIBUTE LogInverseDatasetRatio NUMERIC
 @ATTRIBUTE LogNumberOfFeatures NUMERIC
@@ -42,160 +41,135 @@
 @ATTRIBUTE SymbolsSum NUMERIC
 
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diff --git a/autosklearn/metalearning/files/f1_macro_binary.classification_dense/algorithm_runs.arff b/autosklearn/metalearning/files/f1_macro_binary.classification_dense/algorithm_runs.arff
index bf9119c4ed..b9f355794a 100644
--- a/autosklearn/metalearning/files/f1_macro_binary.classification_dense/algorithm_runs.arff
+++ b/autosklearn/metalearning/files/f1_macro_binary.classification_dense/algorithm_runs.arff
@@ -7,130 +7,135 @@
 @ATTRIBUTE runstatus {ok, timeout, memout, not_applicable, crash, other}
 
 @DATA
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diff --git a/autosklearn/metalearning/files/f1_macro_binary.classification_dense/configurations.csv b/autosklearn/metalearning/files/f1_macro_binary.classification_dense/configurations.csv
index 4e7fdb802c..f488576cc9 100644
--- a/autosklearn/metalearning/files/f1_macro_binary.classification_dense/configurations.csv
+++ b/autosklearn/metalearning/files/f1_macro_binary.classification_dense/configurations.csv
@@ -1,128 +1,133 @@
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-20,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.6039096182414764,None,0.0,2,20,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.1939609823704447,most_frequent,robust_scaler,,,0.7331327008874781,0.29747488162877694,extra_trees_preproc_for_classification,True,entropy,None,0.33401603690569853,None,0.0,4,4,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-21,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.8054434875626825,None,0.0,1,20,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,median,standardize,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4241048505416849,fpr,f_classif
-22,weighting,adaboost,SAMME,0.2353770068926199,1,62,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.007159634821655197,most_frequent,quantile_transformer,1616,normal,,,feature_agglomeration,,,,,,,,,,,,,,,cosine,average,151,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-23,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7734.521233796252,,,2.6048661611376747,rbf,-1,True,4.68717607435371e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,none,,,,,feature_agglomeration,,,,,,,,,,,,,,,euclidean,average,25,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-24,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.8367655423352977,None,0.0,1,5,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.01866043949164283,median,standardize,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.3824115704370156,fpr,f_classif
-25,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5010952533165973,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.005838375247601407,mean,normalize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,
-26,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.7297122444166401,False,True,1,squared_hinge,ovr,l2,0.00023416745444541063,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.03941815152197094,most_frequent,normalize,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0973649546833947,fpr,chi2
-27,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.4426097579931897,None,0.0,16,4,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,mean,robust_scaler,,,0.7407323456920554,0.0804756921037867,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,
-28,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5607168729746175,None,0.0,2,3,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.009810589644392154,most_frequent,quantile_transformer,1132,normal,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.06211089008167909,fdr,chi2
-29,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.9555038836458501,None,0.0,7,4,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
-30,none,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.9728532763078892,None,0.0,13,9,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,none,,,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6651071190864879,False,,,,,,,,,,,,,,,
-31,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9561075301148197,None,0.0,6,2,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,
-32,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.557321265077371,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-33,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,1.153931482107762e-10,0.011666190464009397,auto,255,None,29,190,18,loss,1e-07,0.147868307124396,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,
-34,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,22194.242946902177,0.45210849669021247,,0.0005669713702171556,sigmoid,-1,False,0.03542142266081451,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,quantile_transformer,291,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-35,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.47147939823444396,None,0.0,18,13,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,robust_scaler,,,0.9414880591550729,0.19219631546028484,fast_ica,,,,,,,,,,,parallel,logcosh,1978,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-36,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,3.6797768538445263e-09,0.05511004217375807,auto,255,None,72,62,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,robust_scaler,,,0.7241481576763391,0.0501033899901458,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,36,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-37,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.7521969592385672,None,0.0,9,5,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.020402264943872365,median,robust_scaler,,,0.737227213630259,0.20752747663727789,fast_ica,,,,,,,,,,,parallel,exp,312,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-38,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.887898394612674,None,0.0,10,19,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,robust_scaler,,,0.8350698288864961,0.2347163538066168,extra_trees_preproc_for_classification,False,entropy,None,0.49795108918064074,None,0.0,13,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-39,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,1.6343482806592892e-09,0.19537524572679268,auto,255,None,32,29,13,loss,1e-07,0.05061738224302791,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-40,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7088164344533493,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,mean,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,
-41,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,1.943186107984505e-07,0.08852455702036988,auto,255,None,5,72,19,loss,1e-07,0.039837460762271175,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.01591265419367627,median,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,
-42,weighting,adaboost,SAMME,0.5210651042094924,10,401,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,standardize,,,,,feature_agglomeration,,,,,,,,,,,,,,,euclidean,complete,89,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-43,none,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0686374607394679,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,minmax,,,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.8490260132946401,False,,,,,,,,,,,,,,,
-44,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.6931271368243409,None,0.0,11,10,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.362940004438799,most_frequent,robust_scaler,,,0.9177587796305063,0.17084038458597867,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,30.491514144623952,mutual_info,,,
-45,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.9398869869697849,None,0.0,20,15,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,normalize,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,269,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-46,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,92.66144306787051,,,0.04654639302091653,rbf,-1,True,0.0022231544280610005,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.005194838694455963,mean,none,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,15,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-47,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2681.998807317996,-0.9558745731731211,,0.0011299948421773457,sigmoid,-1,False,1.5292507354478086e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-48,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.8095522998548572,None,0.0,17,10,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.031940595071539304,mean,quantile_transformer,1563,normal,,,extra_trees_preproc_for_classification,False,entropy,None,0.9330145598603196,None,0.0,18,10,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-49,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,26.502259985828186,,,9.475662388405154e-05,rbf,-1,False,0.00019690635968217866,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-50,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,2.5360196477156387e-07,0.011018756408558218,auto,255,None,40,122,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.00011749164080669445,most_frequent,quantile_transformer,757,uniform,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,5.097834697156853,False,True,1,squared_hinge,ovr,l1,0.053877834919477424,,,,,,,,,,,,,,,,,,,,,,
-51,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5255991794296913,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-52,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.34697723213923637,None,0.0,15,18,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.1923594385988973,mean,normalize,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,25.77499302945827,chi2,,,
-53,weighting,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.7776312540090575,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0005253715056715056,mean,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,False,,,,,,,,,,,,
-54,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,1.5661823810486637e-10,0.08996863554015262,auto,255,None,156,89,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.009331521286159971,median,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,
-55,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.6109468076475573,0.8748383232154038,4,0.41333070422336576,poly,-1,False,3.591268778393482e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.011804925819789203,median,minmax,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-56,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.7610308078756552,None,0.0,1,2,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.010000000000000004,mean,robust_scaler,,,0.9205545465103644,0.20272761060743316,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-57,weighting,adaboost,SAMME.R,0.03127273017917347,10,496,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,standardize,,,,,fast_ica,,,,,,,,,,,deflation,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-58,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,151,None,,0.0003283777607180646,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.2510527431514227,mean,normalize,,,,,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,-0.96093182232312,,0.05762510964607661,sigmoid,2632,,,,,,,,,,,,,,,,,
-59,none,bernoulli_nb,,,,,0.6524090653939782,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.005164169126808402,most_frequent,quantile_transformer,16,normal,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.033082145443631535,False,True,1,squared_hinge,ovr,l1,0.002615020898325393,,,,,,,,,,,,,,,,,,,,,,
-60,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.6453555269772124,None,0.0,1,4,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,normalize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-61,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,940.9437776449834,,,1.5839338084625942,rbf,-1,False,0.023072781592565438,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,none,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,77.25763685070707,mutual_info,,,
-62,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.20911875507660596,None,0.0,12,8,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,median,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,
-63,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.7326858450533382,0.02513634776687268,2,0.8635510319471909,poly,-1,True,0.0014598272110141993,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.06447266871225255,most_frequent,quantile_transformer,1000,normal,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,79.01961823033078,f_classif,,,
-64,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,29.55731828912983,False,True,1,squared_hinge,ovr,l2,0.0011332486951954968,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,
-65,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,1.1387795876755026e-06,0.12245605190343567,auto,255,None,58,7,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,robust_scaler,,,0.79305915648885,0.1841487406876042,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-66,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.8425560317492612,None,0.0,8,15,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.015276988502366128,median,none,,,,,fast_ica,,,,,,,,,,,parallel,cube,17,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-67,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,0.2638627266631576,0.022360820165005443,auto,255,None,171,6,19,loss,1e-07,0.016209060012924756,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.008763072395677399,mean,standardize,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,78,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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-69,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7573078019937971,None,0.0,2,5,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,robust_scaler,,,0.7265312295801113,0.28332515882236425,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.09660564590146253,fpr,f_classif
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-72,none,adaboost,SAMME,0.1280458968526125,7,125,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,robust_scaler,,,0.7156460334356771,0.2981902862926971,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,65.16171885308417,chi2,,,
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-76,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7.491039171224502,True,True,squared_hinge,2.8058138048327884e-05,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,minmax,,,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.7754372809893839,False,True,1,squared_hinge,ovr,l1,3.9643594596766314e-05,,,,,,,,,,,,,,,,,,,,,,
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-78,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,0.011547155223520492,0.13665554140837183,auto,255,None,13,5,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.1824584492028449,median,minmax,,,,,feature_agglomeration,,,,,,,,,,,,,,,euclidean,average,261,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-79,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.933900673392165,None,0.0,7,4,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,minmax,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-80,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1661.2274747409913,,,0.025821208852387936,rbf,-1,False,0.0017011435104921824,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,none,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,306,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-81,none,adaboost,SAMME,0.011233995624432622,9,477,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.018370622484682127,mean,standardize,,,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6039710338898471,False,,,,,,,,,,,,,,,
-82,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,21.85207494001697,0.18872999589786676,4,1.1863646021868761,poly,-1,False,0.0021338371901294443,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0034080852834770217,mean,normalize,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.11326005109886934,fdr,f_classif
-83,none,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.563056219822946,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.32793677336996485,most_frequent,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-84,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,0.037849720944694,0.1268815770019783,auto,255,None,11,99,11,loss,1e-07,0.10654523627313527,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,
-85,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.593728171200453,None,0.0,7,20,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.02830941310359363,mean,normalize,,,,,extra_trees_preproc_for_classification,False,entropy,None,0.7389446586281172,None,0.0,20,11,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-86,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,1,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.019797877010737826,mean,normalize,,,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.8239678741128165,False,,,,,,,,,,,,,,,
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-88,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,38,auto,,0.00019398431171606024,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010000000000000004,median,quantile_transformer,1440,normal,,,extra_trees_preproc_for_classification,True,gini,None,0.5126639956845847,None,0.0,16,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-89,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5722917944959481,None,0.0,1,14,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,
-90,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.26356754187780895,None,0.0,4,3,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010966178401758774,mean,robust_scaler,,,0.7817043861798886,0.24371720112407158,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
-91,none,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.563056219822946,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.32793677336996485,most_frequent,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-92,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,80.0028902468169,False,True,1,squared_hinge,ovr,l2,0.0024481999829574722,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.15047970487774687,mean,quantile_transformer,1130,normal,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,1.1912646635544712,False,True,1,squared_hinge,ovr,l1,0.00014314728486450857,,,,,,,,,,,,,,,,,,,,,,
-93,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.22879311080499845,None,0.0,8,4,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.014320022547615783,median,minmax,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.238630703440461,mutual_info,,,
-94,weighting,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,1,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,robust_scaler,,,0.7136729468599508,0.2308995232542769,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.636620620188117,False,,,,,,,,,,,,,,,
-95,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.107967527639527e-05,True,True,hinge,0.00448294745544775,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,quantile_transformer,953,normal,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,35.00114345405331,chi2,,,
-96,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.14148638702297855,None,0.0,2,6,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.0038499650338378643,most_frequent,quantile_transformer,1759,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-97,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,1.1522414705935405e-10,0.6132800183073315,auto,255,None,1607,67,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010096911249562407,mean,robust_scaler,,,0.7663493326967942,0.136094217337434,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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-108,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.7922837297504705,False,True,1,squared_hinge,ovr,l2,0.0005476597326342352,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.012674631437254894,most_frequent,none,,,,,extra_trees_preproc_for_classification,False,entropy,None,0.23145146971319638,None,0.0,7,20,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-109,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.18060139887649052,None,0.0,10,12,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.010000000000000004,most_frequent,robust_scaler,,,0.9298612094255864,0.1263597946722308,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
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-111,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6427543483503397,True,True,hinge,0.00045318775930916944,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.009338484332306904,median,quantile_transformer,174,normal,,,kernel_pca,,,,,,,,,,,,,,,,,,,,,,cosine,1848,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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-113,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,9.029473608568458e-09,0.15779990576964315,auto,255,None,37,71,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.018093177195644097,mean,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,False,,,,,,,,,,,,
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-115,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l2,2.9468189962573666e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.014870368357163507,most_frequent,quantile_transformer,1000,uniform,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,83.43414935094258,f_classif,,,
-116,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.4864284879550203,None,0.0,1,4,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.008665854799630503,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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-118,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.6922752267318475,True,True,squared_hinge,0.06299408576730353,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,quantile_transformer,1742,uniform,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,107,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-119,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.44727252219812963,None,0.0,2,10,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,standardize,,,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l1,8.17533189014765e-05,,,,,,,,,,,,,,,,,,,,,,
-120,weighting,extra_trees,,,,,,,,,,,,,,,True,gini,None,0.9999521514529074,None,0.0,3,10,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,minmax,,,,,extra_trees_preproc_for_classification,False,gini,None,0.24745225001074223,None,0.0,14,18,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-121,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.4210978663047845,None,0.0,5,6,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.006365988550833196,most_frequent,robust_scaler,,,0.8348891074186082,0.00974961635730845,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0695919708890599,fpr,f_classif
-122,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.3341112040803983,False,True,hinge,0.003045682798589668,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.005225537488332702,median,quantile_transformer,1225,uniform,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.10310163232668391,fpr,chi2
-123,weighting,decision_tree,,,,,,,entropy,1.0435267397863155,1.0,None,0.0,15,3,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.36913659113454333,most_frequent,normalize,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4947786362821451,fpr,f_classif
-124,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1561.1002600303584,,,0.0024048551704194507,rbf,-1,False,0.0001936214667496851,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,minmax,,,,,fast_ica,,,,,,,,,,,parallel,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-125,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.087313242780732,0.0636709914987994,3,0.004481917125024585,poly,-1,False,0.01848880618941367,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.002136015035740733,mean,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-126,weighting,adaboost,SAMME,0.029932847627447023,8,483,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,quantile_transformer,1290,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-127,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.6466927589470762,None,0.0,11,2,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.009672465311659264,most_frequent,standardize,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,369,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+1,weighting,bernoulli_nb,,,,,3.80819075993021,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,quantile_transformer,1649,normal,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.06432376826473184,,mutual_info_classif
+3,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5707458022222313,None,0.0,3,12,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,minmax,,,,,extra_trees_preproc_for_classification,False,gini,None,0.5651056128131684,None,0.0,9,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+6,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.7283683762613216,None,0.0,12,20,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,median,standardize,,,,,fast_ica,,,,,,,,,,,deflation,exp,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+8,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,114,manual,0.11778631707605758,0.0029112721339713465,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.015582195257240751,median,normalize,,,,,kernel_pca,,,,,,,,,,,,,,,,,,,0.42909623253327767,3,0.25407612000343777,poly,1689,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+11,weighting,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,2,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.3865962574104293,mean,normalize,,,,,fast_ica,,,,,,,,,,,deflation,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+13,weighting,adaboost,SAMME,0.17983222024694556,4,357,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0008898781197407956,mean,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+15,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.21772092350860472,None,0.0,14,17,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.0020488212891693275,median,standardize,,,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,33.74257606530722,False,True,1,squared_hinge,ovr,l1,8.740452966205111e-05,,,,,,,,,,,,,,,,,,,,,,
+18,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,2.4243922131268786e-05,0.07570328162448307,auto,255,None,71,42,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,robust_scaler,,,0.8898729094898769,0.12138152991327475,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,94.96086286583851,chi2,,,
+20,none,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.9866058571747695,None,0.0,16,9,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.1680676743605393,most_frequent,robust_scaler,,,0.8840072797399463,0.08535534311393947,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,False,,,,,,,,,,,,
+23,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,0.00022373024203887573,0.027149343206135476,auto,255,None,49,13,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.00777614320416325,most_frequent,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,
+27,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.41025844265119926,None,0.0,18,15,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,median,quantile_transformer,1911,normal,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,False,,,,,,,,,,,,
+28,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,1.0168466808008568e-09,0.21361657232950118,auto,255,None,463,4,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,robust_scaler,,,0.7965217268158734,0.2446550610908456,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,
+31,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,8.757412830636845e-06,0.10095376320667454,auto,255,None,3,2,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.015401766535365336,median,quantile_transformer,647,normal,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,
+32,none,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,174,manual,0.21501892897746921,2.0347110826262054e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.013754893527625522,most_frequent,normalize,,,,,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,-0.019221513778605592,3,0.10190948951339239,poly,2521,,,,,,,,,,,,,,,,,
+35,none,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9.608707084865884,False,True,1,squared_hinge,ovr,l2,0.008086910150491696,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.020901889243957545,median,minmax,,,,,kitchen_sinks,,,,,,,,,,,,,,,,,,,,,,,,0.5694399784333516,3230,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+39,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.6799215694343422,None,0.0,1,5,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.004724310659953132,most_frequent,quantile_transformer,1037,normal,,,fast_ica,,,,,,,,,,,parallel,exp,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+41,none,extra_trees,,,,,,,,,,,,,,,True,gini,None,0.5033866291997137,None,0.0,2,14,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,minmax,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+44,none,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l2,2.858526169098402e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0007827505640681123,median,standardize,,,,,extra_trees_preproc_for_classification,False,entropy,None,0.3443310459451119,None,0.0,3,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+49,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.049504283446131e-05,False,,0.0004438773852753879,True,2.4110816673432424e-07,constant,log,elasticnet,,0.01321814853186568,one_hot_encoding,minority_coalescer,0.01510416593631535,most_frequent,none,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,66.65285268958851,chi2,,,
+52,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,1.3755106326683952e-06,0.06364972357697396,auto,255,None,48,32,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.009206641394891614,median,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,
+54,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.4844769436921236,None,0.0,2,9,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.029482404970147116,mean,quantile_transformer,1034,uniform,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+58,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5214197490559657,None,0.0,1,2,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.00826182798588248,mean,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,
+63,none,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,130,manual,0.5446276346297756,0.00846307015171434,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.005736348762561667,median,quantile_transformer,973,uniform,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.08483615095863663,fdr,chi2
+65,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,1.2822815440468518e-10,0.12922700208645135,auto,255,None,31,44,13,loss,1e-07,0.08789566421608311,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.012230423571314038,mean,robust_scaler,,,0.71734690066056,0.20283418288016852,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,
+68,weighting,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,minmax,,,,,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+75,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13.284077389890939,-0.2621334760621076,5,0.9060908941898536,poly,-1,False,0.06323853917206591,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0011743021592446861,most_frequent,normalize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,
+78,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.21560977635919207,False,True,1,squared_hinge,ovr,l2,0.0002467518763531023,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0006970228909766146,median,normalize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+82,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,3.79982070767762e-10,0.10307173798074214,auto,255,None,30,25,6,loss,1e-07,0.09705336338563596,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.004889186641013006,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+84,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.5507560981489436,None,0.0,7,4,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,
+91,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,0.0006322356877253852,0.02155479872417127,auto,255,None,6,15,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
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+97,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.4879444812369075,None,0.0,5,3,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.008704186680582812,mean,normalize,,,,,extra_trees_preproc_for_classification,False,entropy,None,0.6428064165338061,None,0.0,2,17,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+99,weighting,adaboost,SAMME,0.17983222024694556,4,357,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0008898781197407956,mean,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+105,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.268699692198443e-05,True,,4.1225573912733725e-06,True,,constant,log,l2,,4.729985534114764e-05,no_encoding,no_coalescense,,median,quantile_transformer,246,normal,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,20.73090458774975,chi2,,,
+108,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,0.007711054520469362,0.13923064504073807,auto,255,None,118,39,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0025215931379977794,most_frequent,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,
+110,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0021814768111094564,False,True,hinge,8.726644999887521e-05,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,none,,,,,kitchen_sinks,,,,,,,,,,,,,,,,,,,,,,,,0.0010909910768059797,970,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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+114,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,83,auto,,0.024875973599406742,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,normalize,,,,,kitchen_sinks,,,,,,,,,,,,,,,,,,,,,,,,0.041059688084044955,7269,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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+116,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,236,None,,0.08905291537442941,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,normalize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,
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+369,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.9347805195624181,None,0.0,2,19,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.1594813217854149,mean,normalize,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,52.65009549586377,f_classif,,,
+372,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.5431102417332107,None,0.0,1,16,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010731370069741165,median,none,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.35426195730988774,fdr,chi2
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+380,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2677516008848513,True,True,hinge,0.05752557410901496,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0010152857217605206,median,quantile_transformer,1000,uniform,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,51.60974796845029,chi2,,,
+385,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.8280243811834952,None,0.0,9,14,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.08913760502443781,median,none,,,,,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+388,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,53.67009896349757,,,0.03687753298495132,rbf,-1,False,0.028791286245157307,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.047859755696856916,mean,standardize,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,70,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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+399,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.5864398404787432,None,0.0,1,14,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0070663750850608895,most_frequent,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
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+408,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.426289271149965,False,True,1,squared_hinge,ovr,l2,0.00012788535194515696,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,normalize,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,51.45463995444033,chi2,,,
+413,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,7.194851113809193e-10,0.013882834218943657,auto,255,None,18,2,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0005124238915083231,mean,quantile_transformer,948,normal,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,60.45885911036194,f_classif,,,
+415,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,5.215923689311285e-05,0.01709129738847392,auto,255,None,10,19,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0004854193510398933,most_frequent,quantile_transformer,709,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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+428,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,3.738096432152635e-10,0.04468500715913894,auto,255,None,31,156,20,loss,1e-07,0.10279783687118692,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010000000000000004,most_frequent,quantile_transformer,1132,normal,,,feature_agglomeration,,,,,,,,,,,,,,,euclidean,ward,43,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+430,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5321814610661173,None,0.0,1,3,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,standardize,,,,,extra_trees_preproc_for_classification,True,gini,None,0.5,None,0.0,1,15,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+434,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.47966534402952865,None,0.0,1,8,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,mean,normalize,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,238,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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+447,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.32235597434544716,None,0.0,2,18,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.30368818748392196,median,robust_scaler,,,0.7176393529809699,0.19299987830067816,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.16283145936255025,fwe,chi2
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+462,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6,1,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.017471096998391875,median,none,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,67.7801145492284,f_classif,,,
diff --git a/autosklearn/metalearning/files/f1_macro_binary.classification_dense/description.txt b/autosklearn/metalearning/files/f1_macro_binary.classification_dense/description.txt
index 7303dae983..4b90245e02 100644
--- a/autosklearn/metalearning/files/f1_macro_binary.classification_dense/description.txt
+++ b/autosklearn/metalearning/files/f1_macro_binary.classification_dense/description.txt
@@ -1,8 +1,9 @@
 features_cutoff_time: 3600
 features_cutoff_memory: 3072
-number_of_feature_steps: 35
+number_of_feature_steps: 42
 feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances
 feature_step LogNumberOfInstances: LogNumberOfInstances
+feature_step NumberOfClasses: NumberOfClasses
 feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures
 feature_step LogNumberOfFeatures: LogNumberOfFeatures
 feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues
@@ -20,6 +21,11 @@ feature_step DatasetRatio: DatasetRatio, LogDatasetRatio
 feature_step LogDatasetRatio: LogDatasetRatio
 feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio
 feature_step LogInverseDatasetRatio: LogInverseDatasetRatio
+feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD
+feature_step ClassProbabilityMin: ClassProbabilityMin
+feature_step ClassProbabilityMax: ClassProbabilityMax
+feature_step ClassProbabilityMean: ClassProbabilityMean
+feature_step ClassProbabilitySTD: ClassProbabilitySTD
 feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum
 feature_step SymbolsMin: SymbolsMin
 feature_step SymbolsMax: SymbolsMax
@@ -36,11 +42,12 @@ feature_step SkewnessMin: SkewnessMin
 feature_step SkewnessMax: SkewnessMax
 feature_step SkewnessMean: SkewnessMean
 feature_step SkewnessSTD: SkewnessSTD
-features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
+feature_step ClassEntropy: ClassEntropy
+features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy
 features_stochastic: 
-default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
+default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy
 
-algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127
+algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235,236,237,238,239,240,241,242,243,244,245,246,247,248,249,250,251,252,253,254,255,256,257,258,259,260,261,262,263,264,265,266,267,268,269,270,271,272,273,274,275,276,277,278,279,280,281,282,283,284,285,286,287,288,289,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,312,313,314,315,316,317,318,319,320,321,322,323,324,325,326,327,328,329,330,331,332,333,334,335,336,337,338,339,340,341,342,343,344,345,346,347,348,349,350,351,352,353,354,355,356,357,358,359,360,361,362,363,364,365,366,367,368,369,370,371,372,373,374,375,376,377,378,379,380,381,382,383,384,385,386,387,388,389,390,391,392,393,394,395,396,397,398,399,400,401,402,403,404,405,406,407,408,409,410,411,412,413,414,415,416,417,418,419,420,421,422,423,424,425,426,427,428,429,430,431,432,433,434,435,436,437,438,439,440,441,442,443,444,445,446,447,448,449,450,451,452,453,454,455,456,457,458,459,460,461,462,463,464
 algorithms_stochastic: 
 performance_measures: f1_macro
 performance_type: solution_quality
diff --git a/autosklearn/metalearning/files/f1_macro_binary.classification_dense/feature_costs.arff b/autosklearn/metalearning/files/f1_macro_binary.classification_dense/feature_costs.arff
index 0e17e20d22..8512fd9275 100644
--- a/autosklearn/metalearning/files/f1_macro_binary.classification_dense/feature_costs.arff
+++ b/autosklearn/metalearning/files/f1_macro_binary.classification_dense/feature_costs.arff
@@ -4,6 +4,7 @@
 @ATTRIBUTE repetition NUMERIC
 @ATTRIBUTE NumberOfInstances NUMERIC
 @ATTRIBUTE LogNumberOfInstances NUMERIC
+@ATTRIBUTE NumberOfClasses NUMERIC
 @ATTRIBUTE NumberOfFeatures NUMERIC
 @ATTRIBUTE LogNumberOfFeatures NUMERIC
 @ATTRIBUTE MissingValues NUMERIC
@@ -21,6 +22,11 @@
 @ATTRIBUTE LogDatasetRatio NUMERIC
 @ATTRIBUTE InverseDatasetRatio NUMERIC
 @ATTRIBUTE LogInverseDatasetRatio NUMERIC
+@ATTRIBUTE ClassOccurences NUMERIC
+@ATTRIBUTE ClassProbabilityMin NUMERIC
+@ATTRIBUTE ClassProbabilityMax NUMERIC
+@ATTRIBUTE ClassProbabilityMean NUMERIC
+@ATTRIBUTE ClassProbabilitySTD NUMERIC
 @ATTRIBUTE NumSymbols NUMERIC
 @ATTRIBUTE SymbolsMin NUMERIC
 @ATTRIBUTE SymbolsMax NUMERIC
@@ -37,162 +43,138 @@
 @ATTRIBUTE SkewnessMax NUMERIC
 @ATTRIBUTE SkewnessMean NUMERIC
 @ATTRIBUTE SkewnessSTD NUMERIC
+@ATTRIBUTE ClassEntropy NUMERIC
 
 @DATA
-75123,1.0,1e-05,1e-05,1e-05,1e-05,0.00253,0.00221,1e-05,0.00024,3e-05,0.00011,0.0,2e-05,2e-05,5e-05,8e-05,2e-05,1e-05,7e-05,1e-05,0.00072,1e-05,1e-05,0.00019,0.00041,0.00011,0.00061,0.00012,9e-05,0.00015,0.00025,0.00067,0.00014,0.00011,0.00016,0.00027
-75090,1.0,1e-05,1e-05,2e-05,2e-05,0.00815,0.00368,7e-05,0.00228,9e-05,0.00235,0.0,5e-05,5e-05,0.0001,0.00013,0.00013,1e-05,0.00021,2e-05,0.00064,0.0,0.0,0.00014,0.00038,0.00011,0.00066,0.00011,0.00014,0.00014,0.00026,0.00066,0.00011,0.00013,0.00015,0.00027
-2123,1.0,1e-05,1e-05,1e-05,1e-05,0.00097,0.00079,1e-05,0.00013,1e-05,7e-05,0.0,2e-05,2e-05,5e-05,8e-05,2e-05,1e-05,5e-05,1e-05,0.0007,1e-05,0.0,0.00017,0.00042,0.0001,0.00052,6e-05,8e-05,0.00012,0.00026,0.00049,6e-05,0.00011,9e-05,0.00023
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-266,1.0,1e-05,1e-05,1e-05,1e-05,0.0018,0.00146,1e-05,0.00023,2e-05,0.00015,2e-05,2e-05,2e-05,6e-05,8e-05,3e-05,1e-05,6e-05,1e-05,0.00059,1e-05,1e-05,0.00014,0.00031,0.00013,0.0004,5e-05,7e-05,8e-05,0.0002,0.00044,8e-05,8e-05,8e-05,0.0002
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-75217,1.0,1e-05,1e-05,2e-05,1e-05,0.00196,0.00158,1e-05,0.00026,2e-05,0.00016,1e-05,3e-05,3e-05,7e-05,0.0001,3e-05,1e-05,7e-05,2e-05,0.00089,1e-05,1e-05,0.00022,0.00053,0.00013,0.00074,9e-05,0.0001,0.00019,0.00036,0.00065,0.00013,0.0001,0.00011,0.00031
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-2117,1.0,1e-05,1e-05,1e-05,1e-05,0.01735,0.0154,1e-05,0.00141,1e-05,0.00057,0.0,1e-05,1e-05,3e-05,6e-05,2e-05,1e-05,5e-05,1e-05,0.0004,1e-05,0.0,8e-05,0.00021,0.0001,0.00065,0.00012,0.00011,0.00015,0.00026,0.00055,7e-05,0.00014,9e-05,0.00025
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-75157,1.0,1e-05,1e-05,1e-05,1e-05,0.00125,0.00106,1e-05,0.00015,2e-05,7e-05,0.0,2e-05,2e-05,5e-05,6e-05,2e-05,1e-05,4e-05,1e-05,0.00048,2e-05,0.0,0.0001,0.00026,0.0001,0.00039,6e-05,7e-05,8e-05,0.00019,0.00043,6e-05,9e-05,8e-05,0.0002
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+75235,1.0,1e-05,1e-05,0.0003,2e-05,1e-05,0.00461,0.00385,2e-05,0.00055,2e-05,0.00026,1e-05,3e-05,3e-05,0.0001,0.00011,3e-05,1e-05,5e-05,2e-05,0.00026,3e-05,1e-05,8e-05,0.00014,0.00075,1e-05,1e-05,0.0002,0.00043,0.00011,0.00127,0.00017,0.0002,0.00029,0.00061,0.0008,0.00015,0.00012,0.00016,0.00038,0.00242
+75236,1.0,1e-05,1e-05,0.0001,1e-05,1e-05,0.00218,0.00118,2e-05,0.00065,3e-05,0.00041,1e-05,3e-05,3e-05,9e-05,9e-05,4e-05,1e-05,7e-05,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.0006,1e-05,1e-05,0.00021,0.00032,6e-05,0.00043,7e-05,7e-05,9e-05,0.00019,0.00062,7e-05,8e-05,0.0001,0.00037,0.00048
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+75249,1.0,1e-05,1e-05,0.00011,1e-05,1e-05,0.00192,0.00153,1e-05,0.00025,1e-05,0.00017,1e-05,2e-05,2e-05,5e-05,7e-05,2e-05,1e-05,5e-05,3e-05,0.00014,2e-05,1e-05,3e-05,8e-05,0.00029,1e-05,3e-05,6e-05,0.00012,7e-05,0.00041,6e-05,8e-05,0.0001,0.00017,0.00043,6e-05,7e-05,9e-05,0.00019,0.00092
+75250,1.0,1e-05,1e-05,0.00375,1e-05,1e-05,0.04994,0.047,1e-05,0.00247,1e-05,0.00049,1e-05,2e-05,2e-05,6e-05,7e-05,2e-05,1e-05,3e-05,1e-05,0.00022,4e-05,1e-05,4e-05,0.00012,0.00041,0.0,1e-05,0.00011,0.00023,6e-05,0.00076,0.00012,0.00012,0.00019,0.00032,0.00086,0.00012,0.00013,0.00018,0.00043,0.04178
diff --git a/autosklearn/metalearning/files/f1_macro_binary.classification_dense/feature_runstatus.arff b/autosklearn/metalearning/files/f1_macro_binary.classification_dense/feature_runstatus.arff
index 0034ad2bd6..5fff2dfed3 100644
--- a/autosklearn/metalearning/files/f1_macro_binary.classification_dense/feature_runstatus.arff
+++ b/autosklearn/metalearning/files/f1_macro_binary.classification_dense/feature_runstatus.arff
@@ -4,6 +4,7 @@
 @ATTRIBUTE repetition NUMERIC
 @ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other}
@@ -21,6 +22,11 @@
 @ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other}
@@ -37,162 +43,138 @@
 @ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other}
 
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diff --git a/autosklearn/metalearning/files/f1_macro_binary.classification_dense/feature_values.arff b/autosklearn/metalearning/files/f1_macro_binary.classification_dense/feature_values.arff
index a60400e3ce..736dc64321 100644
--- a/autosklearn/metalearning/files/f1_macro_binary.classification_dense/feature_values.arff
+++ b/autosklearn/metalearning/files/f1_macro_binary.classification_dense/feature_values.arff
@@ -13,7 +13,6 @@
 @ATTRIBUTE KurtosisMean NUMERIC
 @ATTRIBUTE KurtosisMin NUMERIC
 @ATTRIBUTE KurtosisSTD NUMERIC
-@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC
 @ATTRIBUTE LogDatasetRatio NUMERIC
 @ATTRIBUTE LogInverseDatasetRatio NUMERIC
 @ATTRIBUTE LogNumberOfFeatures NUMERIC
@@ -42,160 +41,135 @@
 @ATTRIBUTE SymbolsSum NUMERIC
 
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diff --git a/autosklearn/metalearning/files/f1_macro_binary.classification_sparse/algorithm_runs.arff b/autosklearn/metalearning/files/f1_macro_binary.classification_sparse/algorithm_runs.arff
index d22f4d197f..151516032c 100644
--- a/autosklearn/metalearning/files/f1_macro_binary.classification_sparse/algorithm_runs.arff
+++ b/autosklearn/metalearning/files/f1_macro_binary.classification_sparse/algorithm_runs.arff
@@ -7,95 +7,133 @@
 @ATTRIBUTE runstatus {ok, timeout, memout, not_applicable, crash, other}
 
 @DATA
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diff --git a/autosklearn/metalearning/files/f1_macro_binary.classification_sparse/configurations.csv b/autosklearn/metalearning/files/f1_macro_binary.classification_sparse/configurations.csv
index 46002988d0..77343b8c69 100644
--- a/autosklearn/metalearning/files/f1_macro_binary.classification_sparse/configurations.csv
+++ b/autosklearn/metalearning/files/f1_macro_binary.classification_sparse/configurations.csv
@@ -1,93 +1,131 @@
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-13,none,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,200,None,,0.0017457216340699586,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,none,,,,,kernel_pca,,,,,,,,,,,0.0,4,0.0007494648939847078,poly,1996,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-14,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.6039096182414764,None,0.0,2,20,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.1939609823704447,most_frequent,robust_scaler,,,0.7331327008874781,0.29747488162877694,extra_trees_preproc_for_classification,True,entropy,None,0.33401603690569853,None,0.0,4,4,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-15,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7865652301288171,None,0.0,1,4,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,median,robust_scaler,,,0.8623635641193496,0.13298297278730625,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-16,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.905401147837377,-0.32832122529048147,3,1.001896309040499,poly,-1,False,5.1523484258030785e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-17,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.21794354428393548,None,0.0,2,16,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0025451910134387575,median,quantile_transformer,1477,uniform,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,,
-18,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5010952533165973,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.005838375247601407,mean,normalize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,,
-19,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.7297122444166401,False,True,1,squared_hinge,ovr,l2,0.00023416745444541063,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.03941815152197094,most_frequent,normalize,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0973649546833947,fpr,chi2,
-20,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.4426097579931897,None,0.0,16,4,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,mean,robust_scaler,,,0.7407323456920554,0.0804756921037867,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,,
-21,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5607168729746175,None,0.0,2,3,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.009810589644392154,most_frequent,quantile_transformer,1132,normal,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.06211089008167909,fdr,chi2,
-22,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9333237919291875,None,0.0,8,3,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,,
-23,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.557321265077371,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-24,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,22194.242946902177,0.45210849669021247,,0.0005669713702171556,sigmoid,-1,False,0.03542142266081451,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,quantile_transformer,291,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-25,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.7772914874049878,None,0.0,10,15,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00015285482513309234,mean,robust_scaler,,,0.8373404000074934,0.14303151080827242,extra_trees_preproc_for_classification,True,entropy,None,0.5021713909357703,None,0.0,13,16,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-26,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7422641456052917,None,0.0,8,15,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.005561351266169067,most_frequent,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-27,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.887898394612674,None,0.0,10,19,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,robust_scaler,,,0.8350698288864961,0.2347163538066168,extra_trees_preproc_for_classification,False,entropy,None,0.49795108918064074,None,0.0,13,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-28,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7088164344533493,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,mean,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,,
-29,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,10,1,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0022606253047811435,median,quantile_transformer,1247,normal,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,25.327793585266683,chi2,,,,
-30,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,24.786439230569382,,,0.03411197494635044,rbf,-1,True,0.003003351499585181,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0007994838629325874,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-31,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2681.998807317996,-0.9558745731731211,,0.0011299948421773457,sigmoid,-1,False,1.5292507354478086e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-32,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.8095522998548572,None,0.0,17,10,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.031940595071539304,mean,quantile_transformer,1563,normal,,,extra_trees_preproc_for_classification,False,entropy,None,0.9330145598603196,None,0.0,18,10,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-33,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,26.502259985828186,,,9.475662388405154e-05,rbf,-1,False,0.00019690635968217866,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-34,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.339600708038189,False,True,1,squared_hinge,ovr,l2,9.572641158475304e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,quantile_transformer,1193,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-35,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5255991794296913,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-36,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.34697723213923637,None,0.0,15,18,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.1923594385988973,mean,normalize,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,25.77499302945827,chi2,,,,
-37,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.7610308078756552,None,0.0,1,2,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.010000000000000004,mean,robust_scaler,,,0.9205545465103644,0.20272761060743316,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-38,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,249.12499213127998,,,0.0005283374995918348,rbf,-1,False,0.0016932707039261864,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.48910068367909115,most_frequent,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-39,none,bernoulli_nb,,,,,0.6524090653939782,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.005164169126808402,most_frequent,quantile_transformer,16,normal,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.033082145443631535,False,True,1,squared_hinge,ovr,l1,0.002615020898325393,,,,,,,,,,,,,,,,,,,,,
-40,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.6453555269772124,None,0.0,1,4,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,normalize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-41,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.20911875507660596,None,0.0,12,8,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,median,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,,
-42,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5138705053438934,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.14411802159506462,most_frequent,quantile_transformer,857,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-43,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,29.55731828912983,False,True,1,squared_hinge,ovr,l2,0.0011332486951954968,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,,
-44,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.37801720673725603,True,True,squared_hinge,0.0001255132393635067,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.025993178344125793,most_frequent,standardize,,,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,1.4421737618429475,False,True,1,squared_hinge,ovr,l1,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,
-45,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.10581257327556097,None,0.0,7,12,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.004219392676189224,median,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,,
-46,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.4188764793561401,None,0.0,5,5,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.010589444903605501,mean,robust_scaler,,,0.9796997860990453,0.27262832749694405,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-47,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0057657098053164255,False,True,squared_hinge,0.013468519636061941,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.484357187898904,mean,quantile_transformer,675,normal,,,kernel_pca,,,,,,,,,,,,,,cosine,1876,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-48,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.41019363453549706,None,0.0,15,2,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.0003507389224306018,median,none,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.20614098085343638,fdr,chi2,
-49,none,adaboost,SAMME,0.1280458968526125,7,125,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,robust_scaler,,,0.7156460334356771,0.2981902862926971,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,65.16171885308417,chi2,,,,
-50,none,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9931779675565967,None,0.0,18,17,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,robust_scaler,,,0.75,0.2945917010472818,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,,
-51,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.08646118576686276,None,0.0,4,9,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,most_frequent,quantile_transformer,959,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-52,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.6271213423927893,None,0.0,1,4,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.009403509495453675,mean,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,,
-53,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8.85547546146731,True,True,squared_hinge,4.0111304608748804e-05,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-54,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.34936025988397124,None,0.0,2,13,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00018601309647406987,median,robust_scaler,,,0.8634309348843472,0.26976989465367607,extra_trees_preproc_for_classification,False,entropy,None,0.48025175288975036,None,0.0,6,17,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-55,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.8793175987664956,None,0.0,10,5,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,robust_scaler,,,0.7645748130904642,0.2677156877155214,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,130.79089036625916,False,True,1,squared_hinge,ovr,l1,0.005538878421721295,,,,,,,,,,,,,,,,,,,,,
-56,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.014911374426433241,None,0.0,6,2,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,,
-57,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0016941428709087083,True,,,True,,optimal,squared_hinge,l1,,5.124265599423001e-05,no_encoding,no_coalescense,,most_frequent,quantile_transformer,461,uniform,,,extra_trees_preproc_for_classification,True,gini,None,0.31371431180257103,None,0.0,5,17,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-58,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.7591396576940418,-0.02447104417007273,2,0.7325180366342191,poly,-1,True,0.00045922794962860523,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,standardize,,,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,2.057731506094293,False,True,1,squared_hinge,ovr,l1,0.03575998247131604,,,,,,,,,,,,,,,,,,,,,
-59,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.593728171200453,None,0.0,7,20,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.02830941310359363,mean,normalize,,,,,extra_trees_preproc_for_classification,False,entropy,None,0.7389446586281172,None,0.0,20,11,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-60,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5542793596955801,None,0.0,1,3,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,most_frequent,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-61,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.46057831591617715,False,True,hinge,0.04557857428827514,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00027457445401600137,median,standardize,,,,,extra_trees_preproc_for_classification,True,gini,None,0.48190346970486964,None,0.0,17,18,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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-63,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.8494314157539654,None,0.0,3,19,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,mean,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,,
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-78,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,39.22980757485479,,,0.13597389869623427,rbf,-1,False,0.00011639060052687874,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.006288306814260224,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-79,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.7922837297504705,False,True,1,squared_hinge,ovr,l2,0.0005476597326342352,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.012674631437254894,most_frequent,none,,,,,extra_trees_preproc_for_classification,False,entropy,None,0.23145146971319638,None,0.0,7,20,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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-82,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.044449043853721795,True,,,True,,optimal,log,l2,,0.0005323728333290294,no_encoding,no_coalescense,,most_frequent,quantile_transformer,588,normal,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,47.34146395379382,chi2,,,,
-83,weighting,adaboost,SAMME.R,0.23766684501868082,10,97,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.004737885672001703,median,none,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50.0,chi2,,,,
-84,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.4864284879550203,None,0.0,1,4,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.008665854799630503,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-85,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.43699642289829416,None,0.0,6,20,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,quantile_transformer,1604,uniform,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-86,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.9289590349108043,None,0.0,8,16,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,quantile_transformer,1207,uniform,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.20885612826860853,fwe,chi2,
-87,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.44727252219812963,None,0.0,2,10,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,standardize,,,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l1,8.17533189014765e-05,,,,,,,,,,,,,,,,,,,,,
-88,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.6047465690000334,None,0.0,7,19,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,quantile_transformer,924,uniform,,,extra_trees_preproc_for_classification,True,gini,None,0.47688457431800646,None,0.0,9,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-89,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.3341112040803983,False,True,hinge,0.003045682798589668,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.005225537488332702,median,quantile_transformer,1225,uniform,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.10310163232668391,fpr,chi2,
-90,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4834106677905965,False,True,1,squared_hinge,ovr,l2,0.00017625027586147415,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0009157492873444966,most_frequent,quantile_transformer,1150,normal,,,extra_trees_preproc_for_classification,False,entropy,None,0.4981700453792278,None,0.0,9,15,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-91,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.087313242780732,0.0636709914987994,3,0.004481917125024585,poly,-1,False,0.01848880618941367,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.002136015035740733,mean,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-92,weighting,adaboost,SAMME,0.029932847627447023,8,483,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,quantile_transformer,1290,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+1,weighting,bernoulli_nb,,,,,3.80819075993021,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,quantile_transformer,1649,normal,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.06432376826473184,,mutual_info_classif,
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+6,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,114,manual,0.11778631707605758,0.0029112721339713465,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.015582195257240751,median,normalize,,,,,kernel_pca,,,,,,,,,,,0.42909623253327767,3,0.25407612000343777,poly,1689,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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+35,none,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l2,2.858526169098402e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0007827505640681123,median,standardize,,,,,extra_trees_preproc_for_classification,False,entropy,None,0.3443310459451119,None,0.0,3,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+40,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.049504283446131e-05,False,,0.0004438773852753879,True,2.4110816673432424e-07,constant,log,elasticnet,,0.01321814853186568,one_hot_encoding,minority_coalescer,0.01510416593631535,most_frequent,none,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,66.65285268958851,chi2,,,,
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+50,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.3346304039997583,True,True,hinge,0.009979612080672754,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,quantile_transformer,1304,uniform,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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+358,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.48489062404612904,None,0.0,16,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.10901814259355092,median,standardize,,,,,extra_trees_preproc_for_classification,True,entropy,None,0.6579971061972761,None,0.0,18,15,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+362,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.8488355956640288,None,0.0,8,8,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.007464773332656255,most_frequent,normalize,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.47842687276127616,fdr,chi2,
+367,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.938372431886818e-05,True,True,hinge,0.0016143408148969647,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00024949978971758427,median,quantile_transformer,754,uniform,,,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,-0.22339839702350717,2,0.5718982387915436,poly,5260,,,,,,,,,,,,,,,,
+371,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4,1,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.01020427226149318,median,none,,,,,extra_trees_preproc_for_classification,True,entropy,None,0.3663460889081009,None,0.0,2,7,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
diff --git a/autosklearn/metalearning/files/f1_macro_binary.classification_sparse/description.txt b/autosklearn/metalearning/files/f1_macro_binary.classification_sparse/description.txt
index 8258a44058..c050617dff 100644
--- a/autosklearn/metalearning/files/f1_macro_binary.classification_sparse/description.txt
+++ b/autosklearn/metalearning/files/f1_macro_binary.classification_sparse/description.txt
@@ -1,8 +1,9 @@
 features_cutoff_time: 3600
 features_cutoff_memory: 3072
-number_of_feature_steps: 35
+number_of_feature_steps: 42
 feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances
 feature_step LogNumberOfInstances: LogNumberOfInstances
+feature_step NumberOfClasses: NumberOfClasses
 feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures
 feature_step LogNumberOfFeatures: LogNumberOfFeatures
 feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues
@@ -20,6 +21,11 @@ feature_step DatasetRatio: DatasetRatio, LogDatasetRatio
 feature_step LogDatasetRatio: LogDatasetRatio
 feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio
 feature_step LogInverseDatasetRatio: LogInverseDatasetRatio
+feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD
+feature_step ClassProbabilityMin: ClassProbabilityMin
+feature_step ClassProbabilityMax: ClassProbabilityMax
+feature_step ClassProbabilityMean: ClassProbabilityMean
+feature_step ClassProbabilitySTD: ClassProbabilitySTD
 feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum
 feature_step SymbolsMin: SymbolsMin
 feature_step SymbolsMax: SymbolsMax
@@ -36,11 +42,12 @@ feature_step SkewnessMin: SkewnessMin
 feature_step SkewnessMax: SkewnessMax
 feature_step SkewnessMean: SkewnessMean
 feature_step SkewnessSTD: SkewnessSTD
-features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
+feature_step ClassEntropy: ClassEntropy
+features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy
 features_stochastic: 
-default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
+default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy
 
-algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92
+algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235,236,237,238,239,240,241,242,243,244,245,246,247,248,249,250,251,252,253,254,255,256,257,258,259,260,261,262,263,264,265,266,267,268,269,270,271,272,273,274,275,276,277,278,279,280,281,282,283,284,285,286,287,288,289,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,312,313,314,315,316,317,318,319,320,321,322,323,324,325,326,327,328,329,330,331,332,333,334,335,336,337,338,339,340,341,342,343,344,345,346,347,348,349,350,351,352,353,354,355,356,357,358,359,360,361,362,363,364,365,366,367,368,369,370,371,372
 algorithms_stochastic: 
 performance_measures: f1_macro
 performance_type: solution_quality
diff --git a/autosklearn/metalearning/files/f1_macro_binary.classification_sparse/feature_costs.arff b/autosklearn/metalearning/files/f1_macro_binary.classification_sparse/feature_costs.arff
index 0e17e20d22..8512fd9275 100644
--- a/autosklearn/metalearning/files/f1_macro_binary.classification_sparse/feature_costs.arff
+++ b/autosklearn/metalearning/files/f1_macro_binary.classification_sparse/feature_costs.arff
@@ -4,6 +4,7 @@
 @ATTRIBUTE repetition NUMERIC
 @ATTRIBUTE NumberOfInstances NUMERIC
 @ATTRIBUTE LogNumberOfInstances NUMERIC
+@ATTRIBUTE NumberOfClasses NUMERIC
 @ATTRIBUTE NumberOfFeatures NUMERIC
 @ATTRIBUTE LogNumberOfFeatures NUMERIC
 @ATTRIBUTE MissingValues NUMERIC
@@ -21,6 +22,11 @@
 @ATTRIBUTE LogDatasetRatio NUMERIC
 @ATTRIBUTE InverseDatasetRatio NUMERIC
 @ATTRIBUTE LogInverseDatasetRatio NUMERIC
+@ATTRIBUTE ClassOccurences NUMERIC
+@ATTRIBUTE ClassProbabilityMin NUMERIC
+@ATTRIBUTE ClassProbabilityMax NUMERIC
+@ATTRIBUTE ClassProbabilityMean NUMERIC
+@ATTRIBUTE ClassProbabilitySTD NUMERIC
 @ATTRIBUTE NumSymbols NUMERIC
 @ATTRIBUTE SymbolsMin NUMERIC
 @ATTRIBUTE SymbolsMax NUMERIC
@@ -37,162 +43,138 @@
 @ATTRIBUTE SkewnessMax NUMERIC
 @ATTRIBUTE SkewnessMean NUMERIC
 @ATTRIBUTE SkewnessSTD NUMERIC
+@ATTRIBUTE ClassEntropy NUMERIC
 
 @DATA
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diff --git a/autosklearn/metalearning/files/f1_macro_binary.classification_sparse/feature_runstatus.arff b/autosklearn/metalearning/files/f1_macro_binary.classification_sparse/feature_runstatus.arff
index 0034ad2bd6..5fff2dfed3 100644
--- a/autosklearn/metalearning/files/f1_macro_binary.classification_sparse/feature_runstatus.arff
+++ b/autosklearn/metalearning/files/f1_macro_binary.classification_sparse/feature_runstatus.arff
@@ -4,6 +4,7 @@
 @ATTRIBUTE repetition NUMERIC
 @ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other}
@@ -21,6 +22,11 @@
 @ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other}
@@ -37,162 +43,138 @@
 @ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other}
 
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diff --git a/autosklearn/metalearning/files/f1_macro_binary.classification_sparse/feature_values.arff b/autosklearn/metalearning/files/f1_macro_binary.classification_sparse/feature_values.arff
index a60400e3ce..736dc64321 100644
--- a/autosklearn/metalearning/files/f1_macro_binary.classification_sparse/feature_values.arff
+++ b/autosklearn/metalearning/files/f1_macro_binary.classification_sparse/feature_values.arff
@@ -13,7 +13,6 @@
 @ATTRIBUTE KurtosisMean NUMERIC
 @ATTRIBUTE KurtosisMin NUMERIC
 @ATTRIBUTE KurtosisSTD NUMERIC
-@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC
 @ATTRIBUTE LogDatasetRatio NUMERIC
 @ATTRIBUTE LogInverseDatasetRatio NUMERIC
 @ATTRIBUTE LogNumberOfFeatures NUMERIC
@@ -42,160 +41,135 @@
 @ATTRIBUTE SymbolsSum NUMERIC
 
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diff --git a/autosklearn/metalearning/files/f1_macro_multiclass.classification_dense/algorithm_runs.arff b/autosklearn/metalearning/files/f1_macro_multiclass.classification_dense/algorithm_runs.arff
index bf9119c4ed..b9f355794a 100644
--- a/autosklearn/metalearning/files/f1_macro_multiclass.classification_dense/algorithm_runs.arff
+++ b/autosklearn/metalearning/files/f1_macro_multiclass.classification_dense/algorithm_runs.arff
@@ -7,130 +7,135 @@
 @ATTRIBUTE runstatus {ok, timeout, memout, not_applicable, crash, other}
 
 @DATA
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diff --git a/autosklearn/metalearning/files/f1_macro_multiclass.classification_dense/configurations.csv b/autosklearn/metalearning/files/f1_macro_multiclass.classification_dense/configurations.csv
index 4e7fdb802c..f488576cc9 100644
--- a/autosklearn/metalearning/files/f1_macro_multiclass.classification_dense/configurations.csv
+++ b/autosklearn/metalearning/files/f1_macro_multiclass.classification_dense/configurations.csv
@@ -1,128 +1,133 @@
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-90,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.26356754187780895,None,0.0,4,3,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010966178401758774,mean,robust_scaler,,,0.7817043861798886,0.24371720112407158,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
-91,none,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.563056219822946,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.32793677336996485,most_frequent,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-92,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,80.0028902468169,False,True,1,squared_hinge,ovr,l2,0.0024481999829574722,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.15047970487774687,mean,quantile_transformer,1130,normal,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,1.1912646635544712,False,True,1,squared_hinge,ovr,l1,0.00014314728486450857,,,,,,,,,,,,,,,,,,,,,,
-93,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.22879311080499845,None,0.0,8,4,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.014320022547615783,median,minmax,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.238630703440461,mutual_info,,,
-94,weighting,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,1,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,robust_scaler,,,0.7136729468599508,0.2308995232542769,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.636620620188117,False,,,,,,,,,,,,,,,
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-96,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.14148638702297855,None,0.0,2,6,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.0038499650338378643,most_frequent,quantile_transformer,1759,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-97,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,1.1522414705935405e-10,0.6132800183073315,auto,255,None,1607,67,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010096911249562407,mean,robust_scaler,,,0.7663493326967942,0.136094217337434,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-98,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.35863733679484333,None,0.0,1,11,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.0009777923655842618,median,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-99,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.479405900046494,,,0.013340778632154543,rbf,-1,False,6.913586060890114e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,robust_scaler,,,0.9953232616818098,0.06450577886132274,fast_ica,,,,,,,,,,,deflation,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-100,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.825844582229201,None,0.0,12,9,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0008422508222149647,median,robust_scaler,,,0.8658803880857064,0.2528423686293341,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-101,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2574971638664341,True,True,hinge,1.8027411099296446e-05,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.00536396328125103,median,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,
-102,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.7033210835501171,None,0.0,3,19,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,robust_scaler,,,0.7699195245692886,0.29258311638476237,feature_agglomeration,,,,,,,,,,,,,,,euclidean,ward,143,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-103,none,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,128,manual,0.8574560771111307,0.010886139381599514,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.24115007944216788,most_frequent,normalize,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,62.26514170991452,mutual_info,,,
-104,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.6282663389679584,None,0.0,1,3,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,median,minmax,,,,,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-105,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,1.430083734915855e-09,0.08779621406981941,auto,255,None,17,20,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.29749837779115257,mean,robust_scaler,,,0.7236445277814435,0.25,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
-106,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.3294588572341214,None,0.0,1,3,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
-107,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,39.22980757485479,,,0.13597389869623427,rbf,-1,False,0.00011639060052687874,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.006288306814260224,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-108,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.7922837297504705,False,True,1,squared_hinge,ovr,l2,0.0005476597326342352,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.012674631437254894,most_frequent,none,,,,,extra_trees_preproc_for_classification,False,entropy,None,0.23145146971319638,None,0.0,7,20,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-109,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.18060139887649052,None,0.0,10,12,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.010000000000000004,most_frequent,robust_scaler,,,0.9298612094255864,0.1263597946722308,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
-110,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.4111348710613201,None,0.0,16,8,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.01224348527546641,most_frequent,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,
-111,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6427543483503397,True,True,hinge,0.00045318775930916944,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.009338484332306904,median,quantile_transformer,174,normal,,,kernel_pca,,,,,,,,,,,,,,,,,,,,,,cosine,1848,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-112,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.044449043853721795,True,,,True,,optimal,log,l2,,0.0005323728333290294,no_encoding,no_coalescense,,most_frequent,quantile_transformer,588,normal,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,47.34146395379382,chi2,,,
-113,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,9.029473608568458e-09,0.15779990576964315,auto,255,None,37,71,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.018093177195644097,mean,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,False,,,,,,,,,,,,
-114,weighting,adaboost,SAMME.R,0.10000000000000002,3,50,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,robust_scaler,,,0.7639429738274788,0.25,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,67.72501979095493,f_classif,,,
-115,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l2,2.9468189962573666e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.014870368357163507,most_frequent,quantile_transformer,1000,uniform,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,83.43414935094258,f_classif,,,
-116,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.4864284879550203,None,0.0,1,4,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.008665854799630503,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-117,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.8851946632713493,None,0.0,2,20,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.006095662608922393,median,quantile_transformer,1861,normal,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.10486328449423876,fdr,f_classif
-118,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.6922752267318475,True,True,squared_hinge,0.06299408576730353,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,quantile_transformer,1742,uniform,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,107,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-119,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.44727252219812963,None,0.0,2,10,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,standardize,,,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l1,8.17533189014765e-05,,,,,,,,,,,,,,,,,,,,,,
-120,weighting,extra_trees,,,,,,,,,,,,,,,True,gini,None,0.9999521514529074,None,0.0,3,10,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,minmax,,,,,extra_trees_preproc_for_classification,False,gini,None,0.24745225001074223,None,0.0,14,18,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-121,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.4210978663047845,None,0.0,5,6,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.006365988550833196,most_frequent,robust_scaler,,,0.8348891074186082,0.00974961635730845,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0695919708890599,fpr,f_classif
-122,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.3341112040803983,False,True,hinge,0.003045682798589668,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.005225537488332702,median,quantile_transformer,1225,uniform,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.10310163232668391,fpr,chi2
-123,weighting,decision_tree,,,,,,,entropy,1.0435267397863155,1.0,None,0.0,15,3,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.36913659113454333,most_frequent,normalize,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4947786362821451,fpr,f_classif
-124,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1561.1002600303584,,,0.0024048551704194507,rbf,-1,False,0.0001936214667496851,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,minmax,,,,,fast_ica,,,,,,,,,,,parallel,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-125,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.087313242780732,0.0636709914987994,3,0.004481917125024585,poly,-1,False,0.01848880618941367,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.002136015035740733,mean,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-126,weighting,adaboost,SAMME,0.029932847627447023,8,483,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,quantile_transformer,1290,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-127,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.6466927589470762,None,0.0,11,2,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.009672465311659264,most_frequent,standardize,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,369,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+1,weighting,bernoulli_nb,,,,,3.80819075993021,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,quantile_transformer,1649,normal,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.06432376826473184,,mutual_info_classif
+3,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5707458022222313,None,0.0,3,12,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,minmax,,,,,extra_trees_preproc_for_classification,False,gini,None,0.5651056128131684,None,0.0,9,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+6,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.7283683762613216,None,0.0,12,20,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,median,standardize,,,,,fast_ica,,,,,,,,,,,deflation,exp,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+8,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,114,manual,0.11778631707605758,0.0029112721339713465,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.015582195257240751,median,normalize,,,,,kernel_pca,,,,,,,,,,,,,,,,,,,0.42909623253327767,3,0.25407612000343777,poly,1689,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+11,weighting,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,2,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.3865962574104293,mean,normalize,,,,,fast_ica,,,,,,,,,,,deflation,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+13,weighting,adaboost,SAMME,0.17983222024694556,4,357,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0008898781197407956,mean,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+15,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.21772092350860472,None,0.0,14,17,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.0020488212891693275,median,standardize,,,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,33.74257606530722,False,True,1,squared_hinge,ovr,l1,8.740452966205111e-05,,,,,,,,,,,,,,,,,,,,,,
+18,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,2.4243922131268786e-05,0.07570328162448307,auto,255,None,71,42,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,robust_scaler,,,0.8898729094898769,0.12138152991327475,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,94.96086286583851,chi2,,,
+20,none,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.9866058571747695,None,0.0,16,9,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.1680676743605393,most_frequent,robust_scaler,,,0.8840072797399463,0.08535534311393947,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,False,,,,,,,,,,,,
+23,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,0.00022373024203887573,0.027149343206135476,auto,255,None,49,13,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.00777614320416325,most_frequent,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,
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+385,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.8280243811834952,None,0.0,9,14,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.08913760502443781,median,none,,,,,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+388,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,53.67009896349757,,,0.03687753298495132,rbf,-1,False,0.028791286245157307,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.047859755696856916,mean,standardize,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,70,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+392,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,1.8250137234576287e-09,0.01865408018981487,auto,255,None,27,5,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0752156562901201,median,quantile_transformer,1281,normal,,,feature_agglomeration,,,,,,,,,,,,,,,cosine,complete,75,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+399,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.5864398404787432,None,0.0,1,14,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0070663750850608895,most_frequent,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
+403,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1866.9172613016979,,,0.012606336762631641,rbf,-1,True,0.000504574634300575,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,
+405,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0001329242404968632,False,True,squared_hinge,0.000416219546803132,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.007568009633214092,median,quantile_transformer,1126,normal,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,29.06367291960815,chi2,,,
+408,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.426289271149965,False,True,1,squared_hinge,ovr,l2,0.00012788535194515696,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,normalize,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,51.45463995444033,chi2,,,
+413,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,7.194851113809193e-10,0.013882834218943657,auto,255,None,18,2,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0005124238915083231,mean,quantile_transformer,948,normal,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,60.45885911036194,f_classif,,,
+415,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,5.215923689311285e-05,0.01709129738847392,auto,255,None,10,19,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0004854193510398933,most_frequent,quantile_transformer,709,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+422,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.36374112587320206,None,0.0,2,10,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010870084126959173,most_frequent,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,
+424,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,2.344883102482138e-07,0.0328548789988159,auto,255,None,42,24,16,loss,1e-07,0.13015466346727866,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,most_frequent,robust_scaler,,,0.7010898774332888,0.2534280821273264,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,
+428,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,3.738096432152635e-10,0.04468500715913894,auto,255,None,31,156,20,loss,1e-07,0.10279783687118692,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010000000000000004,most_frequent,quantile_transformer,1132,normal,,,feature_agglomeration,,,,,,,,,,,,,,,euclidean,ward,43,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+430,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5321814610661173,None,0.0,1,3,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,standardize,,,,,extra_trees_preproc_for_classification,True,gini,None,0.5,None,0.0,1,15,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+434,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.47966534402952865,None,0.0,1,8,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,mean,normalize,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,238,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+438,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,6.090138036399552e-10,0.017305568433437588,auto,255,None,202,188,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,robust_scaler,,,0.7526196011313747,0.1563616005702636,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+445,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,68.60833575985336,,,0.03905068757955671,rbf,-1,False,0.02846559747114773,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.04775815990561371,mean,standardize,,,,,extra_trees_preproc_for_classification,False,entropy,None,0.45727987956076366,None,0.0,13,11,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+447,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.32235597434544716,None,0.0,2,18,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.30368818748392196,median,robust_scaler,,,0.7176393529809699,0.19299987830067816,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.16283145936255025,fwe,chi2
+448,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.48489062404612904,None,0.0,16,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.10901814259355092,median,standardize,,,,,extra_trees_preproc_for_classification,True,entropy,None,0.6579971061972761,None,0.0,18,15,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+458,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,5.581658271288433e-10,0.06171988234311011,auto,255,None,25,6,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.014859405637537319,most_frequent,robust_scaler,,,0.7983900845251328,0.2545046978458648,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,1.380085213170737,False,True,1,squared_hinge,ovr,l1,0.0061450732015354784,,,,,,,,,,,,,,,,,,,,,,
+462,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6,1,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.017471096998391875,median,none,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,67.7801145492284,f_classif,,,
diff --git a/autosklearn/metalearning/files/f1_macro_multiclass.classification_dense/description.txt b/autosklearn/metalearning/files/f1_macro_multiclass.classification_dense/description.txt
index 7303dae983..4b90245e02 100644
--- a/autosklearn/metalearning/files/f1_macro_multiclass.classification_dense/description.txt
+++ b/autosklearn/metalearning/files/f1_macro_multiclass.classification_dense/description.txt
@@ -1,8 +1,9 @@
 features_cutoff_time: 3600
 features_cutoff_memory: 3072
-number_of_feature_steps: 35
+number_of_feature_steps: 42
 feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances
 feature_step LogNumberOfInstances: LogNumberOfInstances
+feature_step NumberOfClasses: NumberOfClasses
 feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures
 feature_step LogNumberOfFeatures: LogNumberOfFeatures
 feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues
@@ -20,6 +21,11 @@ feature_step DatasetRatio: DatasetRatio, LogDatasetRatio
 feature_step LogDatasetRatio: LogDatasetRatio
 feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio
 feature_step LogInverseDatasetRatio: LogInverseDatasetRatio
+feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD
+feature_step ClassProbabilityMin: ClassProbabilityMin
+feature_step ClassProbabilityMax: ClassProbabilityMax
+feature_step ClassProbabilityMean: ClassProbabilityMean
+feature_step ClassProbabilitySTD: ClassProbabilitySTD
 feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum
 feature_step SymbolsMin: SymbolsMin
 feature_step SymbolsMax: SymbolsMax
@@ -36,11 +42,12 @@ feature_step SkewnessMin: SkewnessMin
 feature_step SkewnessMax: SkewnessMax
 feature_step SkewnessMean: SkewnessMean
 feature_step SkewnessSTD: SkewnessSTD
-features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
+feature_step ClassEntropy: ClassEntropy
+features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy
 features_stochastic: 
-default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
+default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy
 
-algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127
+algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235,236,237,238,239,240,241,242,243,244,245,246,247,248,249,250,251,252,253,254,255,256,257,258,259,260,261,262,263,264,265,266,267,268,269,270,271,272,273,274,275,276,277,278,279,280,281,282,283,284,285,286,287,288,289,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,312,313,314,315,316,317,318,319,320,321,322,323,324,325,326,327,328,329,330,331,332,333,334,335,336,337,338,339,340,341,342,343,344,345,346,347,348,349,350,351,352,353,354,355,356,357,358,359,360,361,362,363,364,365,366,367,368,369,370,371,372,373,374,375,376,377,378,379,380,381,382,383,384,385,386,387,388,389,390,391,392,393,394,395,396,397,398,399,400,401,402,403,404,405,406,407,408,409,410,411,412,413,414,415,416,417,418,419,420,421,422,423,424,425,426,427,428,429,430,431,432,433,434,435,436,437,438,439,440,441,442,443,444,445,446,447,448,449,450,451,452,453,454,455,456,457,458,459,460,461,462,463,464
 algorithms_stochastic: 
 performance_measures: f1_macro
 performance_type: solution_quality
diff --git a/autosklearn/metalearning/files/f1_macro_multiclass.classification_dense/feature_costs.arff b/autosklearn/metalearning/files/f1_macro_multiclass.classification_dense/feature_costs.arff
index 0e17e20d22..8512fd9275 100644
--- a/autosklearn/metalearning/files/f1_macro_multiclass.classification_dense/feature_costs.arff
+++ b/autosklearn/metalearning/files/f1_macro_multiclass.classification_dense/feature_costs.arff
@@ -4,6 +4,7 @@
 @ATTRIBUTE repetition NUMERIC
 @ATTRIBUTE NumberOfInstances NUMERIC
 @ATTRIBUTE LogNumberOfInstances NUMERIC
+@ATTRIBUTE NumberOfClasses NUMERIC
 @ATTRIBUTE NumberOfFeatures NUMERIC
 @ATTRIBUTE LogNumberOfFeatures NUMERIC
 @ATTRIBUTE MissingValues NUMERIC
@@ -21,6 +22,11 @@
 @ATTRIBUTE LogDatasetRatio NUMERIC
 @ATTRIBUTE InverseDatasetRatio NUMERIC
 @ATTRIBUTE LogInverseDatasetRatio NUMERIC
+@ATTRIBUTE ClassOccurences NUMERIC
+@ATTRIBUTE ClassProbabilityMin NUMERIC
+@ATTRIBUTE ClassProbabilityMax NUMERIC
+@ATTRIBUTE ClassProbabilityMean NUMERIC
+@ATTRIBUTE ClassProbabilitySTD NUMERIC
 @ATTRIBUTE NumSymbols NUMERIC
 @ATTRIBUTE SymbolsMin NUMERIC
 @ATTRIBUTE SymbolsMax NUMERIC
@@ -37,162 +43,138 @@
 @ATTRIBUTE SkewnessMax NUMERIC
 @ATTRIBUTE SkewnessMean NUMERIC
 @ATTRIBUTE SkewnessSTD NUMERIC
+@ATTRIBUTE ClassEntropy NUMERIC
 
 @DATA
-75123,1.0,1e-05,1e-05,1e-05,1e-05,0.00253,0.00221,1e-05,0.00024,3e-05,0.00011,0.0,2e-05,2e-05,5e-05,8e-05,2e-05,1e-05,7e-05,1e-05,0.00072,1e-05,1e-05,0.00019,0.00041,0.00011,0.00061,0.00012,9e-05,0.00015,0.00025,0.00067,0.00014,0.00011,0.00016,0.00027
-75090,1.0,1e-05,1e-05,2e-05,2e-05,0.00815,0.00368,7e-05,0.00228,9e-05,0.00235,0.0,5e-05,5e-05,0.0001,0.00013,0.00013,1e-05,0.00021,2e-05,0.00064,0.0,0.0,0.00014,0.00038,0.00011,0.00066,0.00011,0.00014,0.00014,0.00026,0.00066,0.00011,0.00013,0.00015,0.00027
-2123,1.0,1e-05,1e-05,1e-05,1e-05,0.00097,0.00079,1e-05,0.00013,1e-05,7e-05,0.0,2e-05,2e-05,5e-05,8e-05,2e-05,1e-05,5e-05,1e-05,0.0007,1e-05,0.0,0.00017,0.00042,0.0001,0.00052,6e-05,8e-05,0.00012,0.00026,0.00049,6e-05,0.00011,9e-05,0.00023
-248,1.0,1e-05,1e-05,1e-05,1e-05,0.00088,0.00077,1e-05,0.0001,1e-05,4e-05,0.0,1e-05,1e-05,4e-05,5e-05,2e-05,1e-05,4e-05,1e-05,0.00041,1e-05,0.0,9e-05,0.00022,9e-05,0.00046,6e-05,8e-05,9e-05,0.00023,0.00049,7e-05,0.0001,0.0001,0.00021
-75101,1.0,1e-05,1e-05,1e-05,1e-05,0.0418,0.03546,1e-05,0.00389,1e-05,0.00248,0.0,2e-05,2e-05,4e-05,6e-05,2e-05,1e-05,5e-05,2e-05,0.00045,1e-05,0.0,0.00011,0.00023,0.0001,0.00041,6e-05,8e-05,0.0001,0.00018,0.00047,6e-05,0.0001,8e-05,0.00023
-75191,1.0,1e-05,1e-05,3e-05,1e-05,0.53436,0.40777,2e-05,0.09655,3e-05,0.0301,1e-05,2e-05,3e-05,5e-05,0.00011,7e-05,1e-05,0.00011,3e-05,0.00084,5e-05,7e-05,0.00017,0.00033,0.00023,0.00088,0.00012,0.00017,0.00032,0.00026,0.00068,9e-05,0.00012,0.00013,0.00035
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+75240,1.0,2e-05,1e-05,0.00036,5e-05,4e-05,0.00845,0.00644,6e-05,0.00122,4e-05,0.00091,1e-05,4e-05,4e-05,9e-05,0.00013,5e-05,2e-05,9e-05,5e-05,0.00031,3e-05,1e-05,7e-05,0.0002,0.00073,3e-05,2e-05,0.00019,0.0003,0.00018,0.00055,9e-05,9e-05,0.00011,0.00025,0.0005,7e-05,8e-05,0.00015,0.0002,0.00365
+75243,1.0,1e-05,1e-05,0.00029,1e-05,1e-05,0.00461,0.00415,1e-05,0.00035,1e-05,0.00015,0.0,2e-05,2e-05,5e-05,7e-05,2e-05,1e-05,4e-05,1e-05,0.00018,2e-05,1e-05,4e-05,0.00011,0.00022,1e-05,1e-05,5e-05,0.00011,5e-05,0.0004,6e-05,6e-05,8e-05,0.0002,0.00039,6e-05,6e-05,8e-05,0.00019,0.00265
+75244,1.0,3e-05,2e-05,0.0004,3e-05,2e-05,0.0173,0.01298,4e-05,0.00267,4e-05,0.00174,1e-05,7e-05,9e-05,0.00024,0.00027,8e-05,2e-05,0.00013,3e-05,0.00045,7e-05,2e-05,0.00011,0.00025,0.00077,6e-05,2e-05,0.00021,0.00034,0.00013,0.00077,0.00014,0.00013,0.00026,0.00023,0.00044,7e-05,8e-05,0.0001,0.00019,0.00649
+75248,1.0,1e-05,1e-05,0.00017,1e-05,1e-05,0.00416,0.00305,1e-05,0.0006,2e-05,0.00054,1e-05,2e-05,2e-05,5e-05,7e-05,2e-05,1e-05,4e-05,1e-05,0.00017,2e-05,1e-05,4e-05,0.0001,0.00029,5e-05,1e-05,6e-05,0.00012,6e-05,0.00081,0.00011,0.00015,0.00016,0.00038,0.0005,7e-05,8e-05,0.00012,0.00024,0.00184
+75249,1.0,1e-05,1e-05,0.00011,1e-05,1e-05,0.00192,0.00153,1e-05,0.00025,1e-05,0.00017,1e-05,2e-05,2e-05,5e-05,7e-05,2e-05,1e-05,5e-05,3e-05,0.00014,2e-05,1e-05,3e-05,8e-05,0.00029,1e-05,3e-05,6e-05,0.00012,7e-05,0.00041,6e-05,8e-05,0.0001,0.00017,0.00043,6e-05,7e-05,9e-05,0.00019,0.00092
+75250,1.0,1e-05,1e-05,0.00375,1e-05,1e-05,0.04994,0.047,1e-05,0.00247,1e-05,0.00049,1e-05,2e-05,2e-05,6e-05,7e-05,2e-05,1e-05,3e-05,1e-05,0.00022,4e-05,1e-05,4e-05,0.00012,0.00041,0.0,1e-05,0.00011,0.00023,6e-05,0.00076,0.00012,0.00012,0.00019,0.00032,0.00086,0.00012,0.00013,0.00018,0.00043,0.04178
diff --git a/autosklearn/metalearning/files/f1_macro_multiclass.classification_dense/feature_runstatus.arff b/autosklearn/metalearning/files/f1_macro_multiclass.classification_dense/feature_runstatus.arff
index 0034ad2bd6..5fff2dfed3 100644
--- a/autosklearn/metalearning/files/f1_macro_multiclass.classification_dense/feature_runstatus.arff
+++ b/autosklearn/metalearning/files/f1_macro_multiclass.classification_dense/feature_runstatus.arff
@@ -4,6 +4,7 @@
 @ATTRIBUTE repetition NUMERIC
 @ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other}
@@ -21,6 +22,11 @@
 @ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other}
@@ -37,162 +43,138 @@
 @ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other}
 
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diff --git a/autosklearn/metalearning/files/f1_macro_multiclass.classification_dense/feature_values.arff b/autosklearn/metalearning/files/f1_macro_multiclass.classification_dense/feature_values.arff
index a60400e3ce..736dc64321 100644
--- a/autosklearn/metalearning/files/f1_macro_multiclass.classification_dense/feature_values.arff
+++ b/autosklearn/metalearning/files/f1_macro_multiclass.classification_dense/feature_values.arff
@@ -13,7 +13,6 @@
 @ATTRIBUTE KurtosisMean NUMERIC
 @ATTRIBUTE KurtosisMin NUMERIC
 @ATTRIBUTE KurtosisSTD NUMERIC
-@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC
 @ATTRIBUTE LogDatasetRatio NUMERIC
 @ATTRIBUTE LogInverseDatasetRatio NUMERIC
 @ATTRIBUTE LogNumberOfFeatures NUMERIC
@@ -42,160 +41,135 @@
 @ATTRIBUTE SymbolsSum NUMERIC
 
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diff --git a/autosklearn/metalearning/files/f1_macro_multiclass.classification_sparse/algorithm_runs.arff b/autosklearn/metalearning/files/f1_macro_multiclass.classification_sparse/algorithm_runs.arff
index d22f4d197f..151516032c 100644
--- a/autosklearn/metalearning/files/f1_macro_multiclass.classification_sparse/algorithm_runs.arff
+++ b/autosklearn/metalearning/files/f1_macro_multiclass.classification_sparse/algorithm_runs.arff
@@ -7,95 +7,133 @@
 @ATTRIBUTE runstatus {ok, timeout, memout, not_applicable, crash, other}
 
 @DATA
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diff --git a/autosklearn/metalearning/files/f1_macro_multiclass.classification_sparse/configurations.csv b/autosklearn/metalearning/files/f1_macro_multiclass.classification_sparse/configurations.csv
index 46002988d0..77343b8c69 100644
--- a/autosklearn/metalearning/files/f1_macro_multiclass.classification_sparse/configurations.csv
+++ b/autosklearn/metalearning/files/f1_macro_multiclass.classification_sparse/configurations.csv
@@ -1,93 +1,131 @@
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-87,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.44727252219812963,None,0.0,2,10,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,standardize,,,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l1,8.17533189014765e-05,,,,,,,,,,,,,,,,,,,,,
-88,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.6047465690000334,None,0.0,7,19,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,quantile_transformer,924,uniform,,,extra_trees_preproc_for_classification,True,gini,None,0.47688457431800646,None,0.0,9,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-89,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.3341112040803983,False,True,hinge,0.003045682798589668,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.005225537488332702,median,quantile_transformer,1225,uniform,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.10310163232668391,fpr,chi2,
-90,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4834106677905965,False,True,1,squared_hinge,ovr,l2,0.00017625027586147415,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0009157492873444966,most_frequent,quantile_transformer,1150,normal,,,extra_trees_preproc_for_classification,False,entropy,None,0.4981700453792278,None,0.0,9,15,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-91,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.087313242780732,0.0636709914987994,3,0.004481917125024585,poly,-1,False,0.01848880618941367,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.002136015035740733,mean,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-92,weighting,adaboost,SAMME,0.029932847627447023,8,483,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,quantile_transformer,1290,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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+278,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5878222472475889,None,0.0,6,15,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,robust_scaler,,,0.7419183699029103,0.22028507583137247,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.21256588660504683,fdr,chi2,
+282,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.002017899394165588,False,,0.0005911937660372945,True,0.17801822098174286,constant,log,elasticnet,,0.00041682529601986016,no_encoding,no_coalescense,,median,quantile_transformer,1335,normal,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0642825062494559,fpr,chi2,
+284,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5609113236355627,None,0.0,2,2,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.47917908217902894,mean,quantile_transformer,673,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+286,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,33.440072270881174,-0.22811742088286835,3,0.05010299776905627,poly,-1,False,2.275627187537573e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,normalize,,,,,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,2,None,5,14,1.0,52,,,,,,
+287,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.3850609070926397,None,0.0,1,2,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.013992309764275707,median,normalize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,,
+288,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.5719833791553314,None,0.0,16,3,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.13804007452389966,mean,normalize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+292,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.0479982468066316,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,quantile_transformer,1097,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+293,weighting,adaboost,SAMME,0.2540331297425094,2,187,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,robust_scaler,,,0.9867887266047534,0.041631156927011324,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50.70828969428249,chi2,,,,
+298,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.5431102417332107,None,0.0,1,16,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010731370069741165,median,none,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.35426195730988774,fdr,chi2,
+301,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.5,None,0.0,2,4,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.008039249201522535,most_frequent,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,,
+306,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2677516008848513,True,True,hinge,0.05752557410901496,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0010152857217605206,median,quantile_transformer,1000,uniform,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,51.60974796845029,chi2,,,,
+307,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,33,None,,0.006687546229372618,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.06231343712708962,mean,none,,,,,kernel_pca,,,,,,,,,,,-0.7000777299811654,3,0.008780219231661734,poly,498,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+312,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,60.6761602925822,,,0.038224035513907226,rbf,-1,False,0.0018214343969776353,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.421025538999516,median,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+313,weighting,adaboost,SAMME,1.2423888874490505,5,284,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.01582563952987033,most_frequent,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+316,weighting,adaboost,SAMME,1.2615483379313783,8,190,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.00017789451703497617,mean,robust_scaler,,,0.7345811776810758,0.054715769043996304,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+318,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.843098530645432,None,0.0,1,4,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,mean,quantile_transformer,930,normal,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,,
+321,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.5864398404787432,None,0.0,1,14,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0070663750850608895,most_frequent,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,,
+326,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0001329242404968632,False,True,squared_hinge,0.000416219546803132,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.007568009633214092,median,quantile_transformer,1126,normal,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,29.06367291960815,chi2,,,,
+329,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.426289271149965,False,True,1,squared_hinge,ovr,l2,0.00012788535194515696,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,normalize,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,51.45463995444033,chi2,,,,
+331,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.5,None,0.0,1,5,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.15244281596303316,mean,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+332,none,adaboost,SAMME,0.22805500898886877,8,172,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.019649593891454008,mean,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+334,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7516795829719571,None,0.0,14,7,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0010013681780844724,mean,none,,,,,extra_trees_preproc_for_classification,True,entropy,None,0.6123323873969666,None,0.0,17,7,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+339,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.36374112587320206,None,0.0,2,10,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010870084126959173,most_frequent,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,,
+342,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.6733296869726862,None,0.0,3,16,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,median,normalize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+345,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5321814610661173,None,0.0,1,3,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,standardize,,,,,extra_trees_preproc_for_classification,True,gini,None,0.5,None,0.0,1,15,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+351,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.5009519468316077,None,0.0,5,19,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,mean,standardize,,,,,extra_trees_preproc_for_classification,False,entropy,None,0.9633670899220576,None,0.0,18,20,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+352,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5988524108452234,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,mean,robust_scaler,,,0.75,0.26961884796358077,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+355,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,68.60833575985336,,,0.03905068757955671,rbf,-1,False,0.02846559747114773,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.04775815990561371,mean,standardize,,,,,extra_trees_preproc_for_classification,False,entropy,None,0.45727987956076366,None,0.0,13,11,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+357,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.32235597434544716,None,0.0,2,18,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.30368818748392196,median,robust_scaler,,,0.7176393529809699,0.19299987830067816,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.16283145936255025,fwe,chi2,
+358,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.48489062404612904,None,0.0,16,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.10901814259355092,median,standardize,,,,,extra_trees_preproc_for_classification,True,entropy,None,0.6579971061972761,None,0.0,18,15,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+362,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.8488355956640288,None,0.0,8,8,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.007464773332656255,most_frequent,normalize,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.47842687276127616,fdr,chi2,
+367,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.938372431886818e-05,True,True,hinge,0.0016143408148969647,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00024949978971758427,median,quantile_transformer,754,uniform,,,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,-0.22339839702350717,2,0.5718982387915436,poly,5260,,,,,,,,,,,,,,,,
+371,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4,1,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.01020427226149318,median,none,,,,,extra_trees_preproc_for_classification,True,entropy,None,0.3663460889081009,None,0.0,2,7,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
diff --git a/autosklearn/metalearning/files/f1_macro_multiclass.classification_sparse/description.txt b/autosklearn/metalearning/files/f1_macro_multiclass.classification_sparse/description.txt
index 8258a44058..c050617dff 100644
--- a/autosklearn/metalearning/files/f1_macro_multiclass.classification_sparse/description.txt
+++ b/autosklearn/metalearning/files/f1_macro_multiclass.classification_sparse/description.txt
@@ -1,8 +1,9 @@
 features_cutoff_time: 3600
 features_cutoff_memory: 3072
-number_of_feature_steps: 35
+number_of_feature_steps: 42
 feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances
 feature_step LogNumberOfInstances: LogNumberOfInstances
+feature_step NumberOfClasses: NumberOfClasses
 feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures
 feature_step LogNumberOfFeatures: LogNumberOfFeatures
 feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues
@@ -20,6 +21,11 @@ feature_step DatasetRatio: DatasetRatio, LogDatasetRatio
 feature_step LogDatasetRatio: LogDatasetRatio
 feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio
 feature_step LogInverseDatasetRatio: LogInverseDatasetRatio
+feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD
+feature_step ClassProbabilityMin: ClassProbabilityMin
+feature_step ClassProbabilityMax: ClassProbabilityMax
+feature_step ClassProbabilityMean: ClassProbabilityMean
+feature_step ClassProbabilitySTD: ClassProbabilitySTD
 feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum
 feature_step SymbolsMin: SymbolsMin
 feature_step SymbolsMax: SymbolsMax
@@ -36,11 +42,12 @@ feature_step SkewnessMin: SkewnessMin
 feature_step SkewnessMax: SkewnessMax
 feature_step SkewnessMean: SkewnessMean
 feature_step SkewnessSTD: SkewnessSTD
-features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
+feature_step ClassEntropy: ClassEntropy
+features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy
 features_stochastic: 
-default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
+default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy
 
-algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92
+algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235,236,237,238,239,240,241,242,243,244,245,246,247,248,249,250,251,252,253,254,255,256,257,258,259,260,261,262,263,264,265,266,267,268,269,270,271,272,273,274,275,276,277,278,279,280,281,282,283,284,285,286,287,288,289,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,312,313,314,315,316,317,318,319,320,321,322,323,324,325,326,327,328,329,330,331,332,333,334,335,336,337,338,339,340,341,342,343,344,345,346,347,348,349,350,351,352,353,354,355,356,357,358,359,360,361,362,363,364,365,366,367,368,369,370,371,372
 algorithms_stochastic: 
 performance_measures: f1_macro
 performance_type: solution_quality
diff --git a/autosklearn/metalearning/files/f1_macro_multiclass.classification_sparse/feature_costs.arff b/autosklearn/metalearning/files/f1_macro_multiclass.classification_sparse/feature_costs.arff
index 0e17e20d22..8512fd9275 100644
--- a/autosklearn/metalearning/files/f1_macro_multiclass.classification_sparse/feature_costs.arff
+++ b/autosklearn/metalearning/files/f1_macro_multiclass.classification_sparse/feature_costs.arff
@@ -4,6 +4,7 @@
 @ATTRIBUTE repetition NUMERIC
 @ATTRIBUTE NumberOfInstances NUMERIC
 @ATTRIBUTE LogNumberOfInstances NUMERIC
+@ATTRIBUTE NumberOfClasses NUMERIC
 @ATTRIBUTE NumberOfFeatures NUMERIC
 @ATTRIBUTE LogNumberOfFeatures NUMERIC
 @ATTRIBUTE MissingValues NUMERIC
@@ -21,6 +22,11 @@
 @ATTRIBUTE LogDatasetRatio NUMERIC
 @ATTRIBUTE InverseDatasetRatio NUMERIC
 @ATTRIBUTE LogInverseDatasetRatio NUMERIC
+@ATTRIBUTE ClassOccurences NUMERIC
+@ATTRIBUTE ClassProbabilityMin NUMERIC
+@ATTRIBUTE ClassProbabilityMax NUMERIC
+@ATTRIBUTE ClassProbabilityMean NUMERIC
+@ATTRIBUTE ClassProbabilitySTD NUMERIC
 @ATTRIBUTE NumSymbols NUMERIC
 @ATTRIBUTE SymbolsMin NUMERIC
 @ATTRIBUTE SymbolsMax NUMERIC
@@ -37,162 +43,138 @@
 @ATTRIBUTE SkewnessMax NUMERIC
 @ATTRIBUTE SkewnessMean NUMERIC
 @ATTRIBUTE SkewnessSTD NUMERIC
+@ATTRIBUTE ClassEntropy NUMERIC
 
 @DATA
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-75090,1.0,1e-05,1e-05,2e-05,2e-05,0.00815,0.00368,7e-05,0.00228,9e-05,0.00235,0.0,5e-05,5e-05,0.0001,0.00013,0.00013,1e-05,0.00021,2e-05,0.00064,0.0,0.0,0.00014,0.00038,0.00011,0.00066,0.00011,0.00014,0.00014,0.00026,0.00066,0.00011,0.00013,0.00015,0.00027
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-75217,1.0,1e-05,1e-05,2e-05,1e-05,0.00196,0.00158,1e-05,0.00026,2e-05,0.00016,1e-05,3e-05,3e-05,7e-05,0.0001,3e-05,1e-05,7e-05,2e-05,0.00089,1e-05,1e-05,0.00022,0.00053,0.00013,0.00074,9e-05,0.0001,0.00019,0.00036,0.00065,0.00013,0.0001,0.00011,0.00031
-75176,1.0,1e-05,1e-05,2e-05,1e-05,0.01406,0.01285,1e-05,0.00094,2e-05,0.00031,1e-05,3e-05,3e-05,6e-05,9e-05,3e-05,1e-05,7e-05,2e-05,0.00069,1e-05,1e-05,0.00017,0.00035,0.00015,0.00085,0.00018,0.00011,0.0002,0.00036,0.0011,0.00022,0.00023,0.00023,0.00042
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-75157,1.0,1e-05,1e-05,1e-05,1e-05,0.00125,0.00106,1e-05,0.00015,2e-05,7e-05,0.0,2e-05,2e-05,5e-05,6e-05,2e-05,1e-05,4e-05,1e-05,0.00048,2e-05,0.0,0.0001,0.00026,0.0001,0.00039,6e-05,7e-05,8e-05,0.00019,0.00043,6e-05,9e-05,8e-05,0.0002
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+75234,1.0,1e-05,1e-05,0.00015,1e-05,1e-05,0.0058,0.00534,1e-05,0.00033,1e-05,0.00016,0.0,2e-05,2e-05,7e-05,0.00204,3e-05,1e-05,5e-05,1e-05,0.00024,3e-05,2e-05,0.0001,9e-05,0.00043,0.0,0.0,0.00012,0.00024,6e-05,0.00156,6e-05,7e-05,0.00121,0.00021,0.00054,6e-05,7e-05,9e-05,0.00032,0.00501
+75235,1.0,1e-05,1e-05,0.0003,2e-05,1e-05,0.00461,0.00385,2e-05,0.00055,2e-05,0.00026,1e-05,3e-05,3e-05,0.0001,0.00011,3e-05,1e-05,5e-05,2e-05,0.00026,3e-05,1e-05,8e-05,0.00014,0.00075,1e-05,1e-05,0.0002,0.00043,0.00011,0.00127,0.00017,0.0002,0.00029,0.00061,0.0008,0.00015,0.00012,0.00016,0.00038,0.00242
+75236,1.0,1e-05,1e-05,0.0001,1e-05,1e-05,0.00218,0.00118,2e-05,0.00065,3e-05,0.00041,1e-05,3e-05,3e-05,9e-05,9e-05,4e-05,1e-05,7e-05,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.0006,1e-05,1e-05,0.00021,0.00032,6e-05,0.00043,7e-05,7e-05,9e-05,0.00019,0.00062,7e-05,8e-05,0.0001,0.00037,0.00048
+75237,1.0,2e-05,1e-05,0.00785,2e-05,1e-05,0.31031,0.27854,2e-05,0.01161,3e-05,0.02022,1e-05,4e-05,3e-05,9e-05,0.00024,6e-05,2e-05,9e-05,2e-05,0.00062,7e-05,3e-05,0.00017,0.00035,0.0008,1e-05,1e-05,0.00028,0.00043,8e-05,0.00043,8e-05,7e-05,9e-05,0.0002,0.00044,6e-05,8e-05,9e-05,0.00021,0.14028
+75239,1.0,1e-05,1e-05,7e-05,1e-05,1e-05,0.00105,0.00084,1e-05,0.00015,1e-05,8e-05,1e-05,2e-05,5e-05,4e-05,8e-05,2e-05,1e-05,3e-05,1e-05,0.00016,2e-05,1e-05,5e-05,9e-05,0.00049,0.0,0.0,0.00012,0.00026,0.00011,0.00046,0.00013,8e-05,8e-05,0.00018,0.00044,6e-05,9e-05,0.00011,0.00018,0.00065
+75240,1.0,2e-05,1e-05,0.00036,5e-05,4e-05,0.00845,0.00644,6e-05,0.00122,4e-05,0.00091,1e-05,4e-05,4e-05,9e-05,0.00013,5e-05,2e-05,9e-05,5e-05,0.00031,3e-05,1e-05,7e-05,0.0002,0.00073,3e-05,2e-05,0.00019,0.0003,0.00018,0.00055,9e-05,9e-05,0.00011,0.00025,0.0005,7e-05,8e-05,0.00015,0.0002,0.00365
+75243,1.0,1e-05,1e-05,0.00029,1e-05,1e-05,0.00461,0.00415,1e-05,0.00035,1e-05,0.00015,0.0,2e-05,2e-05,5e-05,7e-05,2e-05,1e-05,4e-05,1e-05,0.00018,2e-05,1e-05,4e-05,0.00011,0.00022,1e-05,1e-05,5e-05,0.00011,5e-05,0.0004,6e-05,6e-05,8e-05,0.0002,0.00039,6e-05,6e-05,8e-05,0.00019,0.00265
+75244,1.0,3e-05,2e-05,0.0004,3e-05,2e-05,0.0173,0.01298,4e-05,0.00267,4e-05,0.00174,1e-05,7e-05,9e-05,0.00024,0.00027,8e-05,2e-05,0.00013,3e-05,0.00045,7e-05,2e-05,0.00011,0.00025,0.00077,6e-05,2e-05,0.00021,0.00034,0.00013,0.00077,0.00014,0.00013,0.00026,0.00023,0.00044,7e-05,8e-05,0.0001,0.00019,0.00649
+75248,1.0,1e-05,1e-05,0.00017,1e-05,1e-05,0.00416,0.00305,1e-05,0.0006,2e-05,0.00054,1e-05,2e-05,2e-05,5e-05,7e-05,2e-05,1e-05,4e-05,1e-05,0.00017,2e-05,1e-05,4e-05,0.0001,0.00029,5e-05,1e-05,6e-05,0.00012,6e-05,0.00081,0.00011,0.00015,0.00016,0.00038,0.0005,7e-05,8e-05,0.00012,0.00024,0.00184
+75249,1.0,1e-05,1e-05,0.00011,1e-05,1e-05,0.00192,0.00153,1e-05,0.00025,1e-05,0.00017,1e-05,2e-05,2e-05,5e-05,7e-05,2e-05,1e-05,5e-05,3e-05,0.00014,2e-05,1e-05,3e-05,8e-05,0.00029,1e-05,3e-05,6e-05,0.00012,7e-05,0.00041,6e-05,8e-05,0.0001,0.00017,0.00043,6e-05,7e-05,9e-05,0.00019,0.00092
+75250,1.0,1e-05,1e-05,0.00375,1e-05,1e-05,0.04994,0.047,1e-05,0.00247,1e-05,0.00049,1e-05,2e-05,2e-05,6e-05,7e-05,2e-05,1e-05,3e-05,1e-05,0.00022,4e-05,1e-05,4e-05,0.00012,0.00041,0.0,1e-05,0.00011,0.00023,6e-05,0.00076,0.00012,0.00012,0.00019,0.00032,0.00086,0.00012,0.00013,0.00018,0.00043,0.04178
diff --git a/autosklearn/metalearning/files/f1_macro_multiclass.classification_sparse/feature_runstatus.arff b/autosklearn/metalearning/files/f1_macro_multiclass.classification_sparse/feature_runstatus.arff
index 0034ad2bd6..5fff2dfed3 100644
--- a/autosklearn/metalearning/files/f1_macro_multiclass.classification_sparse/feature_runstatus.arff
+++ b/autosklearn/metalearning/files/f1_macro_multiclass.classification_sparse/feature_runstatus.arff
@@ -4,6 +4,7 @@
 @ATTRIBUTE repetition NUMERIC
 @ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other}
@@ -21,6 +22,11 @@
 @ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other}
@@ -37,162 +43,138 @@
 @ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other}
 
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diff --git a/autosklearn/metalearning/files/f1_macro_multiclass.classification_sparse/feature_values.arff b/autosklearn/metalearning/files/f1_macro_multiclass.classification_sparse/feature_values.arff
index a60400e3ce..736dc64321 100644
--- a/autosklearn/metalearning/files/f1_macro_multiclass.classification_sparse/feature_values.arff
+++ b/autosklearn/metalearning/files/f1_macro_multiclass.classification_sparse/feature_values.arff
@@ -13,7 +13,6 @@
 @ATTRIBUTE KurtosisMean NUMERIC
 @ATTRIBUTE KurtosisMin NUMERIC
 @ATTRIBUTE KurtosisSTD NUMERIC
-@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC
 @ATTRIBUTE LogDatasetRatio NUMERIC
 @ATTRIBUTE LogInverseDatasetRatio NUMERIC
 @ATTRIBUTE LogNumberOfFeatures NUMERIC
@@ -42,160 +41,135 @@
 @ATTRIBUTE SymbolsSum NUMERIC
 
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diff --git a/autosklearn/metalearning/files/f1_micro_binary.classification_dense/algorithm_runs.arff b/autosklearn/metalearning/files/f1_micro_binary.classification_dense/algorithm_runs.arff
index 87f19d069e..1046840bcb 100644
--- a/autosklearn/metalearning/files/f1_micro_binary.classification_dense/algorithm_runs.arff
+++ b/autosklearn/metalearning/files/f1_micro_binary.classification_dense/algorithm_runs.arff
@@ -7,130 +7,135 @@
 @ATTRIBUTE runstatus {ok, timeout, memout, not_applicable, crash, other}
 
 @DATA
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diff --git a/autosklearn/metalearning/files/f1_micro_binary.classification_dense/configurations.csv b/autosklearn/metalearning/files/f1_micro_binary.classification_dense/configurations.csv
index 14ccf74d94..85e767fd63 100644
--- a/autosklearn/metalearning/files/f1_micro_binary.classification_dense/configurations.csv
+++ b/autosklearn/metalearning/files/f1_micro_binary.classification_dense/configurations.csv
@@ -1,128 +1,133 @@
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-22,none,adaboost,SAMME,0.011233995624432622,9,477,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.018370622484682127,mean,standardize,,,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6039710338898471,False,,,,,,,,,,,,,,,
-23,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7734.521233796252,,,2.6048661611376747,rbf,-1,True,4.68717607435371e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,none,,,,,feature_agglomeration,,,,,,,,,,,,,,,euclidean,average,25,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-24,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.8367655423352977,None,0.0,1,5,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.01866043949164283,median,standardize,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.3824115704370156,fpr,f_classif
-25,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5010952533165973,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.005838375247601407,mean,normalize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,
-26,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.7297122444166401,False,True,1,squared_hinge,ovr,l2,0.00023416745444541063,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.03941815152197094,most_frequent,normalize,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0973649546833947,fpr,chi2
-27,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.4426097579931897,None,0.0,16,4,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,mean,robust_scaler,,,0.7407323456920554,0.0804756921037867,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,
-28,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5607168729746175,None,0.0,2,3,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.009810589644392154,most_frequent,quantile_transformer,1132,normal,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.06211089008167909,fdr,chi2
-29,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9333237919291875,None,0.0,8,3,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,
-30,none,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.9728532763078892,None,0.0,13,9,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,none,,,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6651071190864879,False,,,,,,,,,,,,,,,
-31,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9561075301148197,None,0.0,6,2,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,
-32,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.557321265077371,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-33,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,1.153931482107762e-10,0.011666190464009397,auto,255,None,29,190,18,loss,1e-07,0.147868307124396,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,
-34,none,adaboost,SAMME,0.6255555220658239,6,446,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.008833097306364968,most_frequent,none,,,,,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,10,None,4,17,1.0,76,,,,,
-35,none,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.563056219822946,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.32793677336996485,most_frequent,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-36,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,3.6797768538445263e-09,0.05511004217375807,auto,255,None,72,62,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,robust_scaler,,,0.7241481576763391,0.0501033899901458,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,36,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-37,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.7521969592385672,None,0.0,9,5,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.020402264943872365,median,robust_scaler,,,0.737227213630259,0.20752747663727789,fast_ica,,,,,,,,,,,parallel,exp,312,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-38,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.887898394612674,None,0.0,10,19,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,robust_scaler,,,0.8350698288864961,0.2347163538066168,extra_trees_preproc_for_classification,False,entropy,None,0.49795108918064074,None,0.0,13,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-39,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,1.6343482806592892e-09,0.19537524572679268,auto,255,None,32,29,13,loss,1e-07,0.05061738224302791,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-40,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7088164344533493,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,mean,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,
-41,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,1.943186107984505e-07,0.08852455702036988,auto,255,None,5,72,19,loss,1e-07,0.039837460762271175,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.01591265419367627,median,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,
-42,weighting,adaboost,SAMME,0.5210651042094924,10,401,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,standardize,,,,,feature_agglomeration,,,,,,,,,,,,,,,euclidean,complete,89,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-43,none,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0686374607394679,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,minmax,,,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.8490260132946401,False,,,,,,,,,,,,,,,
-44,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.6931271368243409,None,0.0,11,10,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.362940004438799,most_frequent,robust_scaler,,,0.9177587796305063,0.17084038458597867,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,30.491514144623952,mutual_info,,,
-45,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.9398869869697849,None,0.0,20,15,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,normalize,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,269,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-46,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,92.66144306787051,,,0.04654639302091653,rbf,-1,True,0.0022231544280610005,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.005194838694455963,mean,none,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,15,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-47,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2681.998807317996,-0.9558745731731211,,0.0011299948421773457,sigmoid,-1,False,1.5292507354478086e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-48,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.8095522998548572,None,0.0,17,10,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.031940595071539304,mean,quantile_transformer,1563,normal,,,extra_trees_preproc_for_classification,False,entropy,None,0.9330145598603196,None,0.0,18,10,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-49,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,26.502259985828186,,,9.475662388405154e-05,rbf,-1,False,0.00019690635968217866,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-50,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,2.5360196477156387e-07,0.011018756408558218,auto,255,None,40,122,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.00011749164080669445,most_frequent,quantile_transformer,757,uniform,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,5.097834697156853,False,True,1,squared_hinge,ovr,l1,0.053877834919477424,,,,,,,,,,,,,,,,,,,,,,
-51,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5255991794296913,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-52,weighting,decision_tree,,,,,,,gini,1.7853447328017091,1.0,None,0.0,13,7,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0001265964225445088,median,normalize,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.26399878867042303,,mutual_info_classif
-53,weighting,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.7776312540090575,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0005253715056715056,mean,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,False,,,,,,,,,,,,
-54,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,1.488333818193668e-10,0.09526676471267805,auto,255,None,258,87,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,normalize,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.3561953264350478,fpr,f_classif
-55,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.9523435078770708,0.8748383232154038,4,0.2195992292488049,poly,-1,True,4.849685143365339e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,minmax,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-56,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.7610308078756552,None,0.0,1,2,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.010000000000000004,mean,robust_scaler,,,0.9205545465103644,0.20272761060743316,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-57,weighting,adaboost,SAMME.R,0.03127273017917347,10,496,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,standardize,,,,,fast_ica,,,,,,,,,,,deflation,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-58,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,151,None,,0.0003283777607180646,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.2510527431514227,mean,normalize,,,,,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,-0.96093182232312,,0.05762510964607661,sigmoid,2632,,,,,,,,,,,,,,,,,
-59,none,bernoulli_nb,,,,,0.6524090653939782,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.005164169126808402,most_frequent,quantile_transformer,16,normal,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.033082145443631535,False,True,1,squared_hinge,ovr,l1,0.002615020898325393,,,,,,,,,,,,,,,,,,,,,,
-60,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.6453555269772124,None,0.0,1,4,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,normalize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-61,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,940.9437776449834,,,1.5839338084625942,rbf,-1,False,0.023072781592565438,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,none,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,77.25763685070707,mutual_info,,,
-62,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,220,auto,,0.0013824567992319003,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00021328932075284388,mean,minmax,,,,,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.039972474835314076,,0.01017810882899714,sigmoid,295,,,,,,,,,,,,,,,,,
-63,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.7326858450533382,0.02513634776687268,2,0.8635510319471909,poly,-1,True,0.0014598272110141993,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.06447266871225255,most_frequent,quantile_transformer,1000,normal,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,79.01961823033078,f_classif,,,
-64,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,29.55731828912983,False,True,1,squared_hinge,ovr,l2,0.0011332486951954968,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,
-65,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,1.1387795876755026e-06,0.12245605190343567,auto,255,None,58,7,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,robust_scaler,,,0.79305915648885,0.1841487406876042,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-66,none,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.563056219822946,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.32793677336996485,most_frequent,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-67,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,0.2638627266631576,0.022360820165005443,auto,255,None,171,6,19,loss,1e-07,0.016209060012924756,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.008763072395677399,mean,standardize,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,78,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-68,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,925.3053743248586,False,True,1,squared_hinge,ovr,l2,0.02764481198395717,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.3850718018366796,most_frequent,quantile_transformer,786,uniform,,,feature_agglomeration,,,,,,,,,,,,,,,cosine,average,156,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-69,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7573078019937971,None,0.0,2,5,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,robust_scaler,,,0.7265312295801113,0.28332515882236425,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.09660564590146253,fpr,f_classif
-70,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.005753626505113041,False,True,squared_hinge,0.00924401505172238,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.3192271696445429,median,quantile_transformer,913,normal,,,kernel_pca,,,,,,,,,,,,,,,,,,,,,,cosine,1963,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-71,none,adaboost,SAMME.R,0.22802213935525836,6,284,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,robust_scaler,,,0.7468645381288924,0.22838821662367073,feature_agglomeration,,,,,,,,,,,,,,,cosine,average,128,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-72,none,adaboost,SAMME,0.1280458968526125,7,125,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,robust_scaler,,,0.7156460334356771,0.2981902862926971,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,65.16171885308417,chi2,,,
-73,none,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9931779675565967,None,0.0,18,17,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,robust_scaler,,,0.75,0.2945917010472818,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,
-74,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.08646118576686276,None,0.0,4,9,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,most_frequent,quantile_transformer,959,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-75,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,1.2430504242164082e-09,0.03359568307446533,auto,255,None,1609,29,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,standardize,,,,,feature_agglomeration,,,,,,,,,,,,,,,cosine,complete,40,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-76,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7.491039171224502,True,True,squared_hinge,2.8058138048327884e-05,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,minmax,,,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.7754372809893839,False,True,1,squared_hinge,ovr,l1,3.9643594596766314e-05,,,,,,,,,,,,,,,,,,,,,,
-77,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.34936025988397124,None,0.0,2,13,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00018601309647406987,median,robust_scaler,,,0.8634309348843472,0.26976989465367607,extra_trees_preproc_for_classification,False,entropy,None,0.48025175288975036,None,0.0,6,17,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-78,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,0.011547155223520492,0.13665554140837183,auto,255,None,13,5,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.1824584492028449,median,minmax,,,,,feature_agglomeration,,,,,,,,,,,,,,,euclidean,average,261,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-79,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.933900673392165,None,0.0,7,4,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,minmax,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-80,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1661.2274747409913,,,0.025821208852387936,rbf,-1,False,0.0017011435104921824,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,none,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,306,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-81,none,adaboost,SAMME,0.011233995624432622,9,477,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.018370622484682127,mean,standardize,,,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6039710338898471,False,,,,,,,,,,,,,,,
-82,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,21.85207494001697,0.18872999589786676,4,1.1863646021868761,poly,-1,False,0.0021338371901294443,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0034080852834770217,mean,normalize,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.11326005109886934,fdr,f_classif
-83,none,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.563056219822946,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.32793677336996485,most_frequent,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-84,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,0.037849720944694,0.1268815770019783,auto,255,None,11,99,11,loss,1e-07,0.10654523627313527,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,
-85,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.593728171200453,None,0.0,7,20,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.02830941310359363,mean,normalize,,,,,extra_trees_preproc_for_classification,False,entropy,None,0.7389446586281172,None,0.0,20,11,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-86,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,1,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.019797877010737826,mean,normalize,,,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.8239678741128165,False,,,,,,,,,,,,,,,
-87,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.46057831591617715,False,True,hinge,0.04557857428827514,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00027457445401600137,median,standardize,,,,,extra_trees_preproc_for_classification,True,gini,None,0.48190346970486964,None,0.0,17,18,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-88,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,38,auto,,0.00019398431171606024,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010000000000000004,median,quantile_transformer,1440,normal,,,extra_trees_preproc_for_classification,True,gini,None,0.5126639956845847,None,0.0,16,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-89,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5722917944959481,None,0.0,1,14,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,
-90,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.26356754187780895,None,0.0,4,3,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010966178401758774,mean,robust_scaler,,,0.7817043861798886,0.24371720112407158,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
-91,weighting,adaboost,SAMME.R,0.13547552520095074,4,62,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.011851011202809002,most_frequent,standardize,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4888776875314595,fpr,chi2
-92,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,80.0028902468169,False,True,1,squared_hinge,ovr,l2,0.0024481999829574722,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.15047970487774687,mean,quantile_transformer,1130,normal,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,1.1912646635544712,False,True,1,squared_hinge,ovr,l1,0.00014314728486450857,,,,,,,,,,,,,,,,,,,,,,
-93,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.22879311080499845,None,0.0,8,4,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.014320022547615783,median,minmax,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.238630703440461,mutual_info,,,
-94,weighting,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,2,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,none,,,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6342574285315175,False,,,,,,,,,,,,,,,
-95,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.107967527639527e-05,True,True,hinge,0.00448294745544775,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,quantile_transformer,953,normal,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,35.00114345405331,chi2,,,
-96,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.14148638702297855,None,0.0,2,6,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.0038499650338378643,most_frequent,quantile_transformer,1759,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-97,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,1.1522414705935405e-10,0.6132800183073315,auto,255,None,1607,67,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010096911249562407,mean,robust_scaler,,,0.7663493326967942,0.136094217337434,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-98,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2746221958279041,False,True,1,squared_hinge,ovr,l2,6.546077664669006e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,quantile_transformer,1054,normal,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,
-99,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.479405900046494,,,0.013340778632154543,rbf,-1,False,6.913586060890114e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,robust_scaler,,,0.9953232616818098,0.06450577886132274,fast_ica,,,,,,,,,,,deflation,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-100,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.825844582229201,None,0.0,12,9,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0008422508222149647,median,robust_scaler,,,0.8658803880857064,0.2528423686293341,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-101,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2574971638664341,True,True,hinge,1.8027411099296446e-05,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.00536396328125103,median,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,
-102,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.7033210835501171,None,0.0,3,19,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,robust_scaler,,,0.7699195245692886,0.29258311638476237,feature_agglomeration,,,,,,,,,,,,,,,euclidean,ward,143,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-103,none,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,128,manual,0.8574560771111307,0.010886139381599514,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.24115007944216788,most_frequent,normalize,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,62.26514170991452,mutual_info,,,
-104,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.6282663389679584,None,0.0,1,3,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,median,minmax,,,,,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-105,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,1.430083734915855e-09,0.08779621406981941,auto,255,None,17,20,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.29749837779115257,mean,robust_scaler,,,0.7236445277814435,0.25,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
-106,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.3294588572341214,None,0.0,1,3,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
-107,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,39.22980757485479,,,0.13597389869623427,rbf,-1,False,0.00011639060052687874,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.006288306814260224,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-108,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.7922837297504705,False,True,1,squared_hinge,ovr,l2,0.0005476597326342352,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.012674631437254894,most_frequent,none,,,,,extra_trees_preproc_for_classification,False,entropy,None,0.23145146971319638,None,0.0,7,20,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-109,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.18060139887649052,None,0.0,10,12,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.010000000000000004,most_frequent,robust_scaler,,,0.9298612094255864,0.1263597946722308,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
-110,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.4111348710613201,None,0.0,16,8,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.01224348527546641,most_frequent,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,
-111,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6427543483503397,True,True,hinge,0.00045318775930916944,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.009338484332306904,median,quantile_transformer,174,normal,,,kernel_pca,,,,,,,,,,,,,,,,,,,,,,cosine,1848,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-112,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.04981080944617379,True,,0.07309386939525211,True,,constant,log,l2,,0.0006721243213777075,no_encoding,no_coalescense,,mean,quantile_transformer,574,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-113,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,9.029473608568458e-09,0.15779990576964315,auto,255,None,37,71,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.018093177195644097,mean,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,False,,,,,,,,,,,,
-114,none,adaboost,SAMME,0.011233995624432622,9,477,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.018370622484682127,mean,standardize,,,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6039710338898471,False,,,,,,,,,,,,,,,
-115,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l2,2.9468189962573666e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.014870368357163507,most_frequent,quantile_transformer,1000,uniform,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,83.43414935094258,f_classif,,,
-116,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.4864284879550203,None,0.0,1,4,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.008665854799630503,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-117,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.43699642289829416,None,0.0,6,20,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,quantile_transformer,1604,uniform,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-118,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.6922752267318475,True,True,squared_hinge,0.06299408576730353,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,quantile_transformer,1742,uniform,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,107,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-119,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.44727252219812963,None,0.0,2,10,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,standardize,,,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l1,8.17533189014765e-05,,,,,,,,,,,,,,,,,,,,,,
-120,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.46057831591617715,False,True,hinge,0.04557857428827514,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00027457445401600137,median,standardize,,,,,extra_trees_preproc_for_classification,True,gini,None,0.48190346970486964,None,0.0,17,18,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-121,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.4210978663047845,None,0.0,5,6,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.006365988550833196,most_frequent,robust_scaler,,,0.8348891074186082,0.00974961635730845,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0695919708890599,fpr,f_classif
-122,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4424195769042466,False,True,hinge,0.02490432909709204,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,mean,quantile_transformer,1000,uniform,,,kernel_pca,,,,,,,,,,,,,,,,,,,,,,cosine,769,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-123,weighting,decision_tree,,,,,,,entropy,1.0435267397863155,1.0,None,0.0,15,3,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.36913659113454333,most_frequent,normalize,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4947786362821451,fpr,f_classif
-124,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1561.1002600303584,,,0.0024048551704194507,rbf,-1,False,0.0001936214667496851,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,minmax,,,,,fast_ica,,,,,,,,,,,parallel,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-125,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.087313242780732,0.0636709914987994,3,0.004481917125024585,poly,-1,False,0.01848880618941367,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.002136015035740733,mean,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-126,weighting,adaboost,SAMME.R,1.696588612003168,9,455,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,minmax,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,82.18726095926662,chi2,,,
-127,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.6466927589470762,None,0.0,11,2,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.009672465311659264,most_frequent,standardize,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,369,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+1,none,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1514.059969351301,False,True,1,squared_hinge,ovr,l2,0.002448814309966599,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,standardize,,,,,extra_trees_preproc_for_classification,True,entropy,None,0.8221481751476923,None,0.0,2,3,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+3,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5707458022222313,None,0.0,3,12,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,minmax,,,,,extra_trees_preproc_for_classification,False,gini,None,0.5651056128131684,None,0.0,9,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+6,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.7283683762613216,None,0.0,12,20,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,median,standardize,,,,,fast_ica,,,,,,,,,,,deflation,exp,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+8,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,114,manual,0.11778631707605758,0.0029112721339713465,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.015582195257240751,median,normalize,,,,,kernel_pca,,,,,,,,,,,,,,,,,,,0.42909623253327767,3,0.25407612000343777,poly,1689,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+11,weighting,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,2,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.3865962574104293,mean,normalize,,,,,fast_ica,,,,,,,,,,,deflation,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+13,weighting,adaboost,SAMME,0.17983222024694556,4,357,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0008898781197407956,mean,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+15,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.21772092350860472,None,0.0,14,17,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.0020488212891693275,median,standardize,,,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,33.74257606530722,False,True,1,squared_hinge,ovr,l1,8.740452966205111e-05,,,,,,,,,,,,,,,,,,,,,,
+18,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,2.4243922131268786e-05,0.07570328162448307,auto,255,None,71,42,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,robust_scaler,,,0.8898729094898769,0.12138152991327475,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,94.96086286583851,chi2,,,
+20,none,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.9866058571747695,None,0.0,16,9,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.1680676743605393,most_frequent,robust_scaler,,,0.8840072797399463,0.08535534311393947,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,False,,,,,,,,,,,,
+23,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,0.00022373024203887573,0.027149343206135476,auto,255,None,49,13,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.00777614320416325,most_frequent,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,
+27,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.41025844265119926,None,0.0,18,15,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,median,quantile_transformer,1911,normal,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,False,,,,,,,,,,,,
+29,none,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.563056219822946,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.32793677336996485,most_frequent,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+31,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,8.757412830636845e-06,0.10095376320667454,auto,255,None,3,2,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.015401766535365336,median,quantile_transformer,647,normal,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,
+32,none,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,174,manual,0.21501892897746921,2.0347110826262054e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.013754893527625522,most_frequent,normalize,,,,,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,-0.019221513778605592,3,0.10190948951339239,poly,2521,,,,,,,,,,,,,,,,,
+35,none,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9.608707084865884,False,True,1,squared_hinge,ovr,l2,0.008086910150491696,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.020901889243957545,median,minmax,,,,,kitchen_sinks,,,,,,,,,,,,,,,,,,,,,,,,0.5694399784333516,3230,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+39,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.6799215694343422,None,0.0,1,5,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.004724310659953132,most_frequent,quantile_transformer,1037,normal,,,fast_ica,,,,,,,,,,,parallel,exp,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+41,none,extra_trees,,,,,,,,,,,,,,,True,gini,None,0.5033866291997137,None,0.0,2,14,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,minmax,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+43,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.9018061166635376,None,0.0,7,9,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,quantile_transformer,1802,normal,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,84.54647012968938,chi2,,,
+44,none,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l2,2.858526169098402e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0007827505640681123,median,standardize,,,,,extra_trees_preproc_for_classification,False,entropy,None,0.3443310459451119,None,0.0,3,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+49,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.049504283446131e-05,False,,0.0004438773852753879,True,2.4110816673432424e-07,constant,log,elasticnet,,0.01321814853186568,one_hot_encoding,minority_coalescer,0.01510416593631535,most_frequent,none,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,66.65285268958851,chi2,,,
+52,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,6.421059381158897e-08,0.11261253191288709,auto,255,None,45,28,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,
+55,none,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.9822179211645446,None,0.0,14,8,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.013966871874533052,most_frequent,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,
+58,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5214197490559657,None,0.0,1,2,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.00826182798588248,mean,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,
+63,none,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,130,manual,0.5446276346297756,0.00846307015171434,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.005736348762561667,median,quantile_transformer,973,uniform,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.08483615095863663,fdr,chi2
+65,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,1.2822815440468518e-10,0.12922700208645135,auto,255,None,31,44,13,loss,1e-07,0.08789566421608311,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.012230423571314038,mean,robust_scaler,,,0.71734690066056,0.20283418288016852,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,
+68,weighting,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,minmax,,,,,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+75,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13.284077389890939,-0.2621334760621076,5,0.9060908941898536,poly,-1,False,0.06323853917206591,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0011743021592446861,most_frequent,normalize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,
+82,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,3.79982070767762e-10,0.10307173798074214,auto,255,None,30,25,6,loss,1e-07,0.09705336338563596,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.004889186641013006,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+84,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.5507560981489436,None,0.0,7,4,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,
+91,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,0.0006322356877253852,0.02155479872417127,auto,255,None,6,15,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
+93,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5012519528536651,None,0.0,1,8,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,mean,standardize,,,,,feature_agglomeration,,,,,,,,,,,,,,,cosine,complete,323,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+96,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5602498483292023,None,0.0,1,3,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.3195602228752535,mean,standardize,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.09782235573464702,fdr,chi2
+99,weighting,adaboost,SAMME,0.17983222024694556,4,357,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0008898781197407956,mean,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+105,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.268699692198443e-05,True,,4.1225573912733725e-06,True,,constant,log,l2,,4.729985534114764e-05,no_encoding,no_coalescense,,median,quantile_transformer,246,normal,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,20.73090458774975,chi2,,,
+106,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,2.047681354714616e-06,0.016073071519249763,auto,255,None,127,60,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.005262156195293648,median,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,
+110,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0021814768111094564,False,True,hinge,8.726644999887521e-05,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,none,,,,,kitchen_sinks,,,,,,,,,,,,,,,,,,,,,,,,0.0010909910768059797,970,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+111,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.4990576176544739,None,0.0,6,6,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
+114,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,83,auto,,0.024875973599406742,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,normalize,,,,,kitchen_sinks,,,,,,,,,,,,,,,,,,,,,,,,0.041059688084044955,7269,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+115,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.463780563279276,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.011474025594258192,mean,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,
+116,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,236,None,,0.08905291537442941,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,normalize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,
+121,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.08836825866738698,None,0.0,5,19,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0003943043347109662,median,robust_scaler,,,0.7564808439539721,0.23469821956116474,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2206955933275169,fdr,f_classif
+124,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.3955965159441456,None,0.0,3,7,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,median,minmax,,,,,extra_trees_preproc_for_classification,False,entropy,None,0.029027785145223306,None,0.0,17,7,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+126,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,1.4547955251450584e-08,0.03465981099276111,auto,255,None,10,4,7,loss,1e-07,0.12579700249284784,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.001559260617305279,most_frequent,normalize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,False,,,,,,,,,,,,
+130,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,9.471664460969401e-09,0.05921990503832894,auto,255,None,1357,22,15,loss,1e-07,0.10690431328779236,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.08690713722954983,most_frequent,robust_scaler,,,0.9305626247167209,0.22989015341001506,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.33284956824843637,fwe,f_classif
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diff --git a/autosklearn/metalearning/files/f1_micro_binary.classification_dense/description.txt b/autosklearn/metalearning/files/f1_micro_binary.classification_dense/description.txt
index 0ae8a491a5..e175cf7325 100644
--- a/autosklearn/metalearning/files/f1_micro_binary.classification_dense/description.txt
+++ b/autosklearn/metalearning/files/f1_micro_binary.classification_dense/description.txt
@@ -1,8 +1,9 @@
 features_cutoff_time: 3600
 features_cutoff_memory: 3072
-number_of_feature_steps: 35
+number_of_feature_steps: 42
 feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances
 feature_step LogNumberOfInstances: LogNumberOfInstances
+feature_step NumberOfClasses: NumberOfClasses
 feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures
 feature_step LogNumberOfFeatures: LogNumberOfFeatures
 feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues
@@ -20,6 +21,11 @@ feature_step DatasetRatio: DatasetRatio, LogDatasetRatio
 feature_step LogDatasetRatio: LogDatasetRatio
 feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio
 feature_step LogInverseDatasetRatio: LogInverseDatasetRatio
+feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD
+feature_step ClassProbabilityMin: ClassProbabilityMin
+feature_step ClassProbabilityMax: ClassProbabilityMax
+feature_step ClassProbabilityMean: ClassProbabilityMean
+feature_step ClassProbabilitySTD: ClassProbabilitySTD
 feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum
 feature_step SymbolsMin: SymbolsMin
 feature_step SymbolsMax: SymbolsMax
@@ -36,11 +42,12 @@ feature_step SkewnessMin: SkewnessMin
 feature_step SkewnessMax: SkewnessMax
 feature_step SkewnessMean: SkewnessMean
 feature_step SkewnessSTD: SkewnessSTD
-features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
+feature_step ClassEntropy: ClassEntropy
+features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy
 features_stochastic: 
-default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
+default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy
 
-algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127
+algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235,236,237,238,239,240,241,242,243,244,245,246,247,248,249,250,251,252,253,254,255,256,257,258,259,260,261,262,263,264,265,266,267,268,269,270,271,272,273,274,275,276,277,278,279,280,281,282,283,284,285,286,287,288,289,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,312,313,314,315,316,317,318,319,320,321,322,323,324,325,326,327,328,329,330,331,332,333,334,335,336,337,338,339,340,341,342,343,344,345,346,347,348,349,350,351,352,353,354,355,356,357,358,359,360,361,362,363,364,365,366,367,368,369,370,371,372,373,374,375,376,377,378,379,380,381,382,383,384,385,386,387,388,389,390,391,392,393,394,395,396,397,398,399,400,401,402,403,404,405,406,407,408,409,410,411,412,413,414,415,416,417,418,419,420,421,422,423,424,425,426,427,428,429,430,431,432,433,434,435,436,437,438,439,440,441,442,443,444,445,446,447,448,449,450,451,452,453,454,455,456,457,458,459,460,461,462,463,464
 algorithms_stochastic: 
 performance_measures: f1_micro
 performance_type: solution_quality
diff --git a/autosklearn/metalearning/files/f1_micro_binary.classification_dense/feature_costs.arff b/autosklearn/metalearning/files/f1_micro_binary.classification_dense/feature_costs.arff
index 0e17e20d22..8512fd9275 100644
--- a/autosklearn/metalearning/files/f1_micro_binary.classification_dense/feature_costs.arff
+++ b/autosklearn/metalearning/files/f1_micro_binary.classification_dense/feature_costs.arff
@@ -4,6 +4,7 @@
 @ATTRIBUTE repetition NUMERIC
 @ATTRIBUTE NumberOfInstances NUMERIC
 @ATTRIBUTE LogNumberOfInstances NUMERIC
+@ATTRIBUTE NumberOfClasses NUMERIC
 @ATTRIBUTE NumberOfFeatures NUMERIC
 @ATTRIBUTE LogNumberOfFeatures NUMERIC
 @ATTRIBUTE MissingValues NUMERIC
@@ -21,6 +22,11 @@
 @ATTRIBUTE LogDatasetRatio NUMERIC
 @ATTRIBUTE InverseDatasetRatio NUMERIC
 @ATTRIBUTE LogInverseDatasetRatio NUMERIC
+@ATTRIBUTE ClassOccurences NUMERIC
+@ATTRIBUTE ClassProbabilityMin NUMERIC
+@ATTRIBUTE ClassProbabilityMax NUMERIC
+@ATTRIBUTE ClassProbabilityMean NUMERIC
+@ATTRIBUTE ClassProbabilitySTD NUMERIC
 @ATTRIBUTE NumSymbols NUMERIC
 @ATTRIBUTE SymbolsMin NUMERIC
 @ATTRIBUTE SymbolsMax NUMERIC
@@ -37,162 +43,138 @@
 @ATTRIBUTE SkewnessMax NUMERIC
 @ATTRIBUTE SkewnessMean NUMERIC
 @ATTRIBUTE SkewnessSTD NUMERIC
+@ATTRIBUTE ClassEntropy NUMERIC
 
 @DATA
-75123,1.0,1e-05,1e-05,1e-05,1e-05,0.00253,0.00221,1e-05,0.00024,3e-05,0.00011,0.0,2e-05,2e-05,5e-05,8e-05,2e-05,1e-05,7e-05,1e-05,0.00072,1e-05,1e-05,0.00019,0.00041,0.00011,0.00061,0.00012,9e-05,0.00015,0.00025,0.00067,0.00014,0.00011,0.00016,0.00027
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-2123,1.0,1e-05,1e-05,1e-05,1e-05,0.00097,0.00079,1e-05,0.00013,1e-05,7e-05,0.0,2e-05,2e-05,5e-05,8e-05,2e-05,1e-05,5e-05,1e-05,0.0007,1e-05,0.0,0.00017,0.00042,0.0001,0.00052,6e-05,8e-05,0.00012,0.00026,0.00049,6e-05,0.00011,9e-05,0.00023
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-75217,1.0,1e-05,1e-05,2e-05,1e-05,0.00196,0.00158,1e-05,0.00026,2e-05,0.00016,1e-05,3e-05,3e-05,7e-05,0.0001,3e-05,1e-05,7e-05,2e-05,0.00089,1e-05,1e-05,0.00022,0.00053,0.00013,0.00074,9e-05,0.0001,0.00019,0.00036,0.00065,0.00013,0.0001,0.00011,0.00031
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+75235,1.0,1e-05,1e-05,0.0003,2e-05,1e-05,0.00461,0.00385,2e-05,0.00055,2e-05,0.00026,1e-05,3e-05,3e-05,0.0001,0.00011,3e-05,1e-05,5e-05,2e-05,0.00026,3e-05,1e-05,8e-05,0.00014,0.00075,1e-05,1e-05,0.0002,0.00043,0.00011,0.00127,0.00017,0.0002,0.00029,0.00061,0.0008,0.00015,0.00012,0.00016,0.00038,0.00242
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+75249,1.0,1e-05,1e-05,0.00011,1e-05,1e-05,0.00192,0.00153,1e-05,0.00025,1e-05,0.00017,1e-05,2e-05,2e-05,5e-05,7e-05,2e-05,1e-05,5e-05,3e-05,0.00014,2e-05,1e-05,3e-05,8e-05,0.00029,1e-05,3e-05,6e-05,0.00012,7e-05,0.00041,6e-05,8e-05,0.0001,0.00017,0.00043,6e-05,7e-05,9e-05,0.00019,0.00092
+75250,1.0,1e-05,1e-05,0.00375,1e-05,1e-05,0.04994,0.047,1e-05,0.00247,1e-05,0.00049,1e-05,2e-05,2e-05,6e-05,7e-05,2e-05,1e-05,3e-05,1e-05,0.00022,4e-05,1e-05,4e-05,0.00012,0.00041,0.0,1e-05,0.00011,0.00023,6e-05,0.00076,0.00012,0.00012,0.00019,0.00032,0.00086,0.00012,0.00013,0.00018,0.00043,0.04178
diff --git a/autosklearn/metalearning/files/f1_micro_binary.classification_dense/feature_runstatus.arff b/autosklearn/metalearning/files/f1_micro_binary.classification_dense/feature_runstatus.arff
index 0034ad2bd6..5fff2dfed3 100644
--- a/autosklearn/metalearning/files/f1_micro_binary.classification_dense/feature_runstatus.arff
+++ b/autosklearn/metalearning/files/f1_micro_binary.classification_dense/feature_runstatus.arff
@@ -4,6 +4,7 @@
 @ATTRIBUTE repetition NUMERIC
 @ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other}
@@ -21,6 +22,11 @@
 @ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other}
@@ -37,162 +43,138 @@
 @ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other}
 
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diff --git a/autosklearn/metalearning/files/f1_micro_binary.classification_dense/feature_values.arff b/autosklearn/metalearning/files/f1_micro_binary.classification_dense/feature_values.arff
index a60400e3ce..736dc64321 100644
--- a/autosklearn/metalearning/files/f1_micro_binary.classification_dense/feature_values.arff
+++ b/autosklearn/metalearning/files/f1_micro_binary.classification_dense/feature_values.arff
@@ -13,7 +13,6 @@
 @ATTRIBUTE KurtosisMean NUMERIC
 @ATTRIBUTE KurtosisMin NUMERIC
 @ATTRIBUTE KurtosisSTD NUMERIC
-@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC
 @ATTRIBUTE LogDatasetRatio NUMERIC
 @ATTRIBUTE LogInverseDatasetRatio NUMERIC
 @ATTRIBUTE LogNumberOfFeatures NUMERIC
@@ -42,160 +41,135 @@
 @ATTRIBUTE SymbolsSum NUMERIC
 
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diff --git a/autosklearn/metalearning/files/f1_micro_binary.classification_sparse/algorithm_runs.arff b/autosklearn/metalearning/files/f1_micro_binary.classification_sparse/algorithm_runs.arff
index ecc90a2d27..d9f6fad13f 100644
--- a/autosklearn/metalearning/files/f1_micro_binary.classification_sparse/algorithm_runs.arff
+++ b/autosklearn/metalearning/files/f1_micro_binary.classification_sparse/algorithm_runs.arff
@@ -7,95 +7,133 @@
 @ATTRIBUTE runstatus {ok, timeout, memout, not_applicable, crash, other}
 
 @DATA
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diff --git a/autosklearn/metalearning/files/f1_micro_binary.classification_sparse/configurations.csv b/autosklearn/metalearning/files/f1_micro_binary.classification_sparse/configurations.csv
index 75662496d2..36fd991e13 100644
--- a/autosklearn/metalearning/files/f1_micro_binary.classification_sparse/configurations.csv
+++ b/autosklearn/metalearning/files/f1_micro_binary.classification_sparse/configurations.csv
@@ -1,93 +1,131 @@
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-14,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.6039096182414764,None,0.0,2,20,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.1939609823704447,most_frequent,robust_scaler,,,0.7331327008874781,0.29747488162877694,extra_trees_preproc_for_classification,True,entropy,None,0.33401603690569853,None,0.0,4,4,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-15,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7865652301288171,None,0.0,1,4,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,median,robust_scaler,,,0.8623635641193496,0.13298297278730625,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-16,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.905401147837377,-0.32832122529048147,3,1.001896309040499,poly,-1,False,5.1523484258030785e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-17,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.3304182089730454,None,0.0,1,11,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.015634546384927752,median,quantile_transformer,332,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-18,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5010952533165973,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.005838375247601407,mean,normalize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,,
-19,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.7297122444166401,False,True,1,squared_hinge,ovr,l2,0.00023416745444541063,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.03941815152197094,most_frequent,normalize,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0973649546833947,fpr,chi2,
-20,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.4426097579931897,None,0.0,16,4,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,mean,robust_scaler,,,0.7407323456920554,0.0804756921037867,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,,
-21,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5607168729746175,None,0.0,2,3,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.009810589644392154,most_frequent,quantile_transformer,1132,normal,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.06211089008167909,fdr,chi2,
-22,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9333237919291875,None,0.0,8,3,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,,
-23,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.557321265077371,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-24,none,adaboost,SAMME,0.6255555220658239,6,446,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.008833097306364968,most_frequent,none,,,,,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,10,None,4,17,1.0,76,,,,,,
-25,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.7772914874049878,None,0.0,10,15,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00015285482513309234,mean,robust_scaler,,,0.8373404000074934,0.14303151080827242,extra_trees_preproc_for_classification,True,entropy,None,0.5021713909357703,None,0.0,13,16,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-26,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7422641456052917,None,0.0,8,15,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.005561351266169067,most_frequent,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-27,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.887898394612674,None,0.0,10,19,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,robust_scaler,,,0.8350698288864961,0.2347163538066168,extra_trees_preproc_for_classification,False,entropy,None,0.49795108918064074,None,0.0,13,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-28,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7088164344533493,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,mean,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,,
-29,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,10,1,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0022606253047811435,median,quantile_transformer,1247,normal,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,25.327793585266683,chi2,,,,
-30,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,24.786439230569382,,,0.03411197494635044,rbf,-1,True,0.003003351499585181,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0007994838629325874,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-31,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2681.998807317996,-0.9558745731731211,,0.0011299948421773457,sigmoid,-1,False,1.5292507354478086e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-32,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.8095522998548572,None,0.0,17,10,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.031940595071539304,mean,quantile_transformer,1563,normal,,,extra_trees_preproc_for_classification,False,entropy,None,0.9330145598603196,None,0.0,18,10,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-33,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,26.502259985828186,,,9.475662388405154e-05,rbf,-1,False,0.00019690635968217866,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-34,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.339600708038189,False,True,1,squared_hinge,ovr,l2,9.572641158475304e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,quantile_transformer,1193,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-35,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5255991794296913,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-36,weighting,decision_tree,,,,,,,gini,1.7853447328017091,1.0,None,0.0,13,7,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0001265964225445088,median,normalize,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.26399878867042303,,mutual_info_classif,
-37,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.7610308078756552,None,0.0,1,2,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.010000000000000004,mean,robust_scaler,,,0.9205545465103644,0.20272761060743316,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-38,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,249.12499213127998,,,0.0005283374995918348,rbf,-1,False,0.0016932707039261864,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.48910068367909115,most_frequent,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-39,none,bernoulli_nb,,,,,0.6524090653939782,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.005164169126808402,most_frequent,quantile_transformer,16,normal,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.033082145443631535,False,True,1,squared_hinge,ovr,l1,0.002615020898325393,,,,,,,,,,,,,,,,,,,,,
-40,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.6453555269772124,None,0.0,1,4,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,normalize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-41,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.20911875507660596,None,0.0,12,8,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,median,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,,
-42,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5138705053438934,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.14411802159506462,most_frequent,quantile_transformer,857,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-43,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,29.55731828912983,False,True,1,squared_hinge,ovr,l2,0.0011332486951954968,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,,
-44,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.37801720673725603,True,True,squared_hinge,0.0001255132393635067,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.025993178344125793,most_frequent,standardize,,,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,1.4421737618429475,False,True,1,squared_hinge,ovr,l1,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,
-45,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.10581257327556097,None,0.0,7,12,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.004219392676189224,median,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,,
-46,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.4188764793561401,None,0.0,5,5,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.010589444903605501,mean,robust_scaler,,,0.9796997860990453,0.27262832749694405,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-47,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.005753626505113041,False,True,squared_hinge,0.00924401505172238,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.3192271696445429,median,quantile_transformer,913,normal,,,kernel_pca,,,,,,,,,,,,,,cosine,1963,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-48,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.41019363453549706,None,0.0,15,2,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.0003507389224306018,median,none,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.20614098085343638,fdr,chi2,
-49,none,adaboost,SAMME,0.1280458968526125,7,125,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,robust_scaler,,,0.7156460334356771,0.2981902862926971,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,65.16171885308417,chi2,,,,
-50,none,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9931779675565967,None,0.0,18,17,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,robust_scaler,,,0.75,0.2945917010472818,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,,
-51,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.08646118576686276,None,0.0,4,9,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,most_frequent,quantile_transformer,959,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-52,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.6271213423927893,None,0.0,1,4,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.009403509495453675,mean,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,,
-53,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8.85547546146731,True,True,squared_hinge,4.0111304608748804e-05,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-54,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.34936025988397124,None,0.0,2,13,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00018601309647406987,median,robust_scaler,,,0.8634309348843472,0.26976989465367607,extra_trees_preproc_for_classification,False,entropy,None,0.48025175288975036,None,0.0,6,17,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-55,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.8793175987664956,None,0.0,10,5,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,robust_scaler,,,0.7645748130904642,0.2677156877155214,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,130.79089036625916,False,True,1,squared_hinge,ovr,l1,0.005538878421721295,,,,,,,,,,,,,,,,,,,,,
-56,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.014911374426433241,None,0.0,6,2,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,,
-57,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0016941428709087083,True,,,True,,optimal,squared_hinge,l1,,5.124265599423001e-05,no_encoding,no_coalescense,,most_frequent,quantile_transformer,461,uniform,,,extra_trees_preproc_for_classification,True,gini,None,0.31371431180257103,None,0.0,5,17,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-58,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.7591396576940418,-0.02447104417007273,2,0.7325180366342191,poly,-1,True,0.00045922794962860523,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,standardize,,,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,2.057731506094293,False,True,1,squared_hinge,ovr,l1,0.03575998247131604,,,,,,,,,,,,,,,,,,,,,
-59,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.593728171200453,None,0.0,7,20,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.02830941310359363,mean,normalize,,,,,extra_trees_preproc_for_classification,False,entropy,None,0.7389446586281172,None,0.0,20,11,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-60,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5542793596955801,None,0.0,1,3,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,most_frequent,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-61,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.46057831591617715,False,True,hinge,0.04557857428827514,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00027457445401600137,median,standardize,,,,,extra_trees_preproc_for_classification,True,gini,None,0.48190346970486964,None,0.0,17,18,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-62,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.46057831591617715,False,True,hinge,0.04557857428827514,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00027457445401600137,median,standardize,,,,,extra_trees_preproc_for_classification,True,gini,None,0.48190346970486964,None,0.0,17,18,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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+367,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.938372431886818e-05,True,True,hinge,0.0016143408148969647,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00024949978971758427,median,quantile_transformer,754,uniform,,,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,-0.22339839702350717,2,0.5718982387915436,poly,5260,,,,,,,,,,,,,,,,
+371,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4,1,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.01020427226149318,median,none,,,,,extra_trees_preproc_for_classification,True,entropy,None,0.3663460889081009,None,0.0,2,7,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
diff --git a/autosklearn/metalearning/files/f1_micro_binary.classification_sparse/description.txt b/autosklearn/metalearning/files/f1_micro_binary.classification_sparse/description.txt
index 4646f7cc6f..87b98e89d3 100644
--- a/autosklearn/metalearning/files/f1_micro_binary.classification_sparse/description.txt
+++ b/autosklearn/metalearning/files/f1_micro_binary.classification_sparse/description.txt
@@ -1,8 +1,9 @@
 features_cutoff_time: 3600
 features_cutoff_memory: 3072
-number_of_feature_steps: 35
+number_of_feature_steps: 42
 feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances
 feature_step LogNumberOfInstances: LogNumberOfInstances
+feature_step NumberOfClasses: NumberOfClasses
 feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures
 feature_step LogNumberOfFeatures: LogNumberOfFeatures
 feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues
@@ -20,6 +21,11 @@ feature_step DatasetRatio: DatasetRatio, LogDatasetRatio
 feature_step LogDatasetRatio: LogDatasetRatio
 feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio
 feature_step LogInverseDatasetRatio: LogInverseDatasetRatio
+feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD
+feature_step ClassProbabilityMin: ClassProbabilityMin
+feature_step ClassProbabilityMax: ClassProbabilityMax
+feature_step ClassProbabilityMean: ClassProbabilityMean
+feature_step ClassProbabilitySTD: ClassProbabilitySTD
 feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum
 feature_step SymbolsMin: SymbolsMin
 feature_step SymbolsMax: SymbolsMax
@@ -36,11 +42,12 @@ feature_step SkewnessMin: SkewnessMin
 feature_step SkewnessMax: SkewnessMax
 feature_step SkewnessMean: SkewnessMean
 feature_step SkewnessSTD: SkewnessSTD
-features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
+feature_step ClassEntropy: ClassEntropy
+features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy
 features_stochastic: 
-default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
+default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy
 
-algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92
+algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235,236,237,238,239,240,241,242,243,244,245,246,247,248,249,250,251,252,253,254,255,256,257,258,259,260,261,262,263,264,265,266,267,268,269,270,271,272,273,274,275,276,277,278,279,280,281,282,283,284,285,286,287,288,289,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,312,313,314,315,316,317,318,319,320,321,322,323,324,325,326,327,328,329,330,331,332,333,334,335,336,337,338,339,340,341,342,343,344,345,346,347,348,349,350,351,352,353,354,355,356,357,358,359,360,361,362,363,364,365,366,367,368,369,370,371,372
 algorithms_stochastic: 
 performance_measures: f1_micro
 performance_type: solution_quality
diff --git a/autosklearn/metalearning/files/f1_micro_binary.classification_sparse/feature_costs.arff b/autosklearn/metalearning/files/f1_micro_binary.classification_sparse/feature_costs.arff
index 0e17e20d22..8512fd9275 100644
--- a/autosklearn/metalearning/files/f1_micro_binary.classification_sparse/feature_costs.arff
+++ b/autosklearn/metalearning/files/f1_micro_binary.classification_sparse/feature_costs.arff
@@ -4,6 +4,7 @@
 @ATTRIBUTE repetition NUMERIC
 @ATTRIBUTE NumberOfInstances NUMERIC
 @ATTRIBUTE LogNumberOfInstances NUMERIC
+@ATTRIBUTE NumberOfClasses NUMERIC
 @ATTRIBUTE NumberOfFeatures NUMERIC
 @ATTRIBUTE LogNumberOfFeatures NUMERIC
 @ATTRIBUTE MissingValues NUMERIC
@@ -21,6 +22,11 @@
 @ATTRIBUTE LogDatasetRatio NUMERIC
 @ATTRIBUTE InverseDatasetRatio NUMERIC
 @ATTRIBUTE LogInverseDatasetRatio NUMERIC
+@ATTRIBUTE ClassOccurences NUMERIC
+@ATTRIBUTE ClassProbabilityMin NUMERIC
+@ATTRIBUTE ClassProbabilityMax NUMERIC
+@ATTRIBUTE ClassProbabilityMean NUMERIC
+@ATTRIBUTE ClassProbabilitySTD NUMERIC
 @ATTRIBUTE NumSymbols NUMERIC
 @ATTRIBUTE SymbolsMin NUMERIC
 @ATTRIBUTE SymbolsMax NUMERIC
@@ -37,162 +43,138 @@
 @ATTRIBUTE SkewnessMax NUMERIC
 @ATTRIBUTE SkewnessMean NUMERIC
 @ATTRIBUTE SkewnessSTD NUMERIC
+@ATTRIBUTE ClassEntropy NUMERIC
 
 @DATA
-75123,1.0,1e-05,1e-05,1e-05,1e-05,0.00253,0.00221,1e-05,0.00024,3e-05,0.00011,0.0,2e-05,2e-05,5e-05,8e-05,2e-05,1e-05,7e-05,1e-05,0.00072,1e-05,1e-05,0.00019,0.00041,0.00011,0.00061,0.00012,9e-05,0.00015,0.00025,0.00067,0.00014,0.00011,0.00016,0.00027
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+75235,1.0,1e-05,1e-05,0.0003,2e-05,1e-05,0.00461,0.00385,2e-05,0.00055,2e-05,0.00026,1e-05,3e-05,3e-05,0.0001,0.00011,3e-05,1e-05,5e-05,2e-05,0.00026,3e-05,1e-05,8e-05,0.00014,0.00075,1e-05,1e-05,0.0002,0.00043,0.00011,0.00127,0.00017,0.0002,0.00029,0.00061,0.0008,0.00015,0.00012,0.00016,0.00038,0.00242
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+75243,1.0,1e-05,1e-05,0.00029,1e-05,1e-05,0.00461,0.00415,1e-05,0.00035,1e-05,0.00015,0.0,2e-05,2e-05,5e-05,7e-05,2e-05,1e-05,4e-05,1e-05,0.00018,2e-05,1e-05,4e-05,0.00011,0.00022,1e-05,1e-05,5e-05,0.00011,5e-05,0.0004,6e-05,6e-05,8e-05,0.0002,0.00039,6e-05,6e-05,8e-05,0.00019,0.00265
+75244,1.0,3e-05,2e-05,0.0004,3e-05,2e-05,0.0173,0.01298,4e-05,0.00267,4e-05,0.00174,1e-05,7e-05,9e-05,0.00024,0.00027,8e-05,2e-05,0.00013,3e-05,0.00045,7e-05,2e-05,0.00011,0.00025,0.00077,6e-05,2e-05,0.00021,0.00034,0.00013,0.00077,0.00014,0.00013,0.00026,0.00023,0.00044,7e-05,8e-05,0.0001,0.00019,0.00649
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+75249,1.0,1e-05,1e-05,0.00011,1e-05,1e-05,0.00192,0.00153,1e-05,0.00025,1e-05,0.00017,1e-05,2e-05,2e-05,5e-05,7e-05,2e-05,1e-05,5e-05,3e-05,0.00014,2e-05,1e-05,3e-05,8e-05,0.00029,1e-05,3e-05,6e-05,0.00012,7e-05,0.00041,6e-05,8e-05,0.0001,0.00017,0.00043,6e-05,7e-05,9e-05,0.00019,0.00092
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diff --git a/autosklearn/metalearning/files/f1_micro_binary.classification_sparse/feature_runstatus.arff b/autosklearn/metalearning/files/f1_micro_binary.classification_sparse/feature_runstatus.arff
index 0034ad2bd6..5fff2dfed3 100644
--- a/autosklearn/metalearning/files/f1_micro_binary.classification_sparse/feature_runstatus.arff
+++ b/autosklearn/metalearning/files/f1_micro_binary.classification_sparse/feature_runstatus.arff
@@ -4,6 +4,7 @@
 @ATTRIBUTE repetition NUMERIC
 @ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other}
@@ -21,6 +22,11 @@
 @ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other}
@@ -37,162 +43,138 @@
 @ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other}
 
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+75249,1.0,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,other,ok,ok,ok,ok,ok
+75250,1.0,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,other,ok,ok,ok,ok,ok
diff --git a/autosklearn/metalearning/files/f1_micro_binary.classification_sparse/feature_values.arff b/autosklearn/metalearning/files/f1_micro_binary.classification_sparse/feature_values.arff
index a60400e3ce..736dc64321 100644
--- a/autosklearn/metalearning/files/f1_micro_binary.classification_sparse/feature_values.arff
+++ b/autosklearn/metalearning/files/f1_micro_binary.classification_sparse/feature_values.arff
@@ -13,7 +13,6 @@
 @ATTRIBUTE KurtosisMean NUMERIC
 @ATTRIBUTE KurtosisMin NUMERIC
 @ATTRIBUTE KurtosisSTD NUMERIC
-@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC
 @ATTRIBUTE LogDatasetRatio NUMERIC
 @ATTRIBUTE LogInverseDatasetRatio NUMERIC
 @ATTRIBUTE LogNumberOfFeatures NUMERIC
@@ -42,160 +41,135 @@
 @ATTRIBUTE SymbolsSum NUMERIC
 
 @DATA
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diff --git a/autosklearn/metalearning/files/f1_micro_multiclass.classification_dense/algorithm_runs.arff b/autosklearn/metalearning/files/f1_micro_multiclass.classification_dense/algorithm_runs.arff
index 87f19d069e..1046840bcb 100644
--- a/autosklearn/metalearning/files/f1_micro_multiclass.classification_dense/algorithm_runs.arff
+++ b/autosklearn/metalearning/files/f1_micro_multiclass.classification_dense/algorithm_runs.arff
@@ -7,130 +7,135 @@
 @ATTRIBUTE runstatus {ok, timeout, memout, not_applicable, crash, other}
 
 @DATA
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diff --git a/autosklearn/metalearning/files/f1_micro_multiclass.classification_dense/configurations.csv b/autosklearn/metalearning/files/f1_micro_multiclass.classification_dense/configurations.csv
index 14ccf74d94..85e767fd63 100644
--- a/autosklearn/metalearning/files/f1_micro_multiclass.classification_dense/configurations.csv
+++ b/autosklearn/metalearning/files/f1_micro_multiclass.classification_dense/configurations.csv
@@ -1,128 +1,133 @@
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-1,weighting,adaboost,SAMME,0.015984291290723266,7,184,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010000000000000004,median,standardize,,,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.9333242689646983,False,,,,,,,,,,,,,,,
-2,none,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.9525269450493825,None,0.0,4,20,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,standardize,,,,,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-3,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,101,auto,,1.1004704874013712e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.009420818612537619,most_frequent,normalize,,,,,kitchen_sinks,,,,,,,,,,,,,,,,,,,,,,,,0.1495664841849168,2334,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-4,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,23916.78290516781,False,True,1,squared_hinge,ovr,l2,0.0006655607726658531,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.008808715242823137,median,none,,,,,kernel_pca,,,,,,,,,,,,,,,,,,,0.2633153190620261,2,0.004770332714291669,poly,1483,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-5,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,2,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,none,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,94.23876232993393,mutual_info,,,
-6,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.933900673392165,None,0.0,7,4,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,minmax,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-7,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.3700222208089159,None,0.0,14,19,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,minmax,,,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,1.0533068843554318,False,True,1,squared_hinge,ovr,l1,0.00618591414610778,,,,,,,,,,,,,,,,,,,,,,
-8,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5238814331135578,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,median,none,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,28,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-9,none,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13,None,,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,none,,,,,feature_agglomeration,,,,,,,,,,,,,,,euclidean,average,74,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-10,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.5244313211416313,None,0.0,1,2,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.454300236339658,most_frequent,robust_scaler,,,0.7858830918128734,0.2705174720960696,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-11,weighting,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0006294780694492238,most_frequent,standardize,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.063715750591364,fdr,chi2
-12,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.541827838541001,True,True,squared_hinge,0.0001129851063838815,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,standardize,,,,,extra_trees_preproc_for_classification,True,entropy,None,0.5533459595240017,None,0.0,18,4,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-13,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.3652103526812861,None,0.0,6,8,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,quantile_transformer,79,uniform,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.17357047764133562,fwe,chi2
-14,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6185424163558029,False,True,1,squared_hinge,ovr,l2,4.385767395120581e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010397452590017174,median,quantile_transformer,961,normal,,,feature_agglomeration,,,,,,,,,,,,,,,cosine,average,25,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-15,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.643761610990255,None,0.0,20,6,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.039859381630488695,mean,normalize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,
-16,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7947177310891914,None,0.0,13,3,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,mean,robust_scaler,,,0.7681430822715739,0.23411421138225857,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,16.747604337291705,f_classif,,,
-17,none,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.1769332447629957,mean,minmax,,,,,fast_ica,,,,,,,,,,,deflation,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-18,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.07518858116896743,None,0.0,7,10,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0027133920607872518,mean,robust_scaler,,,0.7873452530915046,0.16215139072179952,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-19,none,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,200,None,,0.0017457216340699586,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,none,,,,,kernel_pca,,,,,,,,,,,,,,,,,,,0.0,4,0.0007494648939847078,poly,1996,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-20,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.6039096182414764,None,0.0,2,20,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.1939609823704447,most_frequent,robust_scaler,,,0.7331327008874781,0.29747488162877694,extra_trees_preproc_for_classification,True,entropy,None,0.33401603690569853,None,0.0,4,4,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-21,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.8054434875626825,None,0.0,1,20,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,median,standardize,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4241048505416849,fpr,f_classif
-22,none,adaboost,SAMME,0.011233995624432622,9,477,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.018370622484682127,mean,standardize,,,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6039710338898471,False,,,,,,,,,,,,,,,
-23,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7734.521233796252,,,2.6048661611376747,rbf,-1,True,4.68717607435371e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,none,,,,,feature_agglomeration,,,,,,,,,,,,,,,euclidean,average,25,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-24,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.8367655423352977,None,0.0,1,5,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.01866043949164283,median,standardize,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.3824115704370156,fpr,f_classif
-25,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5010952533165973,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.005838375247601407,mean,normalize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,
-26,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.7297122444166401,False,True,1,squared_hinge,ovr,l2,0.00023416745444541063,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.03941815152197094,most_frequent,normalize,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0973649546833947,fpr,chi2
-27,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.4426097579931897,None,0.0,16,4,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,mean,robust_scaler,,,0.7407323456920554,0.0804756921037867,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,
-28,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5607168729746175,None,0.0,2,3,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.009810589644392154,most_frequent,quantile_transformer,1132,normal,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.06211089008167909,fdr,chi2
-29,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9333237919291875,None,0.0,8,3,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,
-30,none,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.9728532763078892,None,0.0,13,9,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,none,,,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6651071190864879,False,,,,,,,,,,,,,,,
-31,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9561075301148197,None,0.0,6,2,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,
-32,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.557321265077371,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-33,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,1.153931482107762e-10,0.011666190464009397,auto,255,None,29,190,18,loss,1e-07,0.147868307124396,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,
-34,none,adaboost,SAMME,0.6255555220658239,6,446,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.008833097306364968,most_frequent,none,,,,,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,10,None,4,17,1.0,76,,,,,
-35,none,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.563056219822946,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.32793677336996485,most_frequent,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-36,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,3.6797768538445263e-09,0.05511004217375807,auto,255,None,72,62,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,robust_scaler,,,0.7241481576763391,0.0501033899901458,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,36,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-37,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.7521969592385672,None,0.0,9,5,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.020402264943872365,median,robust_scaler,,,0.737227213630259,0.20752747663727789,fast_ica,,,,,,,,,,,parallel,exp,312,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-38,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.887898394612674,None,0.0,10,19,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,robust_scaler,,,0.8350698288864961,0.2347163538066168,extra_trees_preproc_for_classification,False,entropy,None,0.49795108918064074,None,0.0,13,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-39,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,1.6343482806592892e-09,0.19537524572679268,auto,255,None,32,29,13,loss,1e-07,0.05061738224302791,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-40,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7088164344533493,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,mean,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,
-41,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,1.943186107984505e-07,0.08852455702036988,auto,255,None,5,72,19,loss,1e-07,0.039837460762271175,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.01591265419367627,median,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,
-42,weighting,adaboost,SAMME,0.5210651042094924,10,401,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,standardize,,,,,feature_agglomeration,,,,,,,,,,,,,,,euclidean,complete,89,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-43,none,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0686374607394679,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,minmax,,,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.8490260132946401,False,,,,,,,,,,,,,,,
-44,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.6931271368243409,None,0.0,11,10,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.362940004438799,most_frequent,robust_scaler,,,0.9177587796305063,0.17084038458597867,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,30.491514144623952,mutual_info,,,
-45,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.9398869869697849,None,0.0,20,15,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,normalize,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,269,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-46,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,92.66144306787051,,,0.04654639302091653,rbf,-1,True,0.0022231544280610005,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.005194838694455963,mean,none,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,15,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-47,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2681.998807317996,-0.9558745731731211,,0.0011299948421773457,sigmoid,-1,False,1.5292507354478086e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-48,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.8095522998548572,None,0.0,17,10,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.031940595071539304,mean,quantile_transformer,1563,normal,,,extra_trees_preproc_for_classification,False,entropy,None,0.9330145598603196,None,0.0,18,10,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-49,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,26.502259985828186,,,9.475662388405154e-05,rbf,-1,False,0.00019690635968217866,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-50,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,2.5360196477156387e-07,0.011018756408558218,auto,255,None,40,122,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.00011749164080669445,most_frequent,quantile_transformer,757,uniform,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,5.097834697156853,False,True,1,squared_hinge,ovr,l1,0.053877834919477424,,,,,,,,,,,,,,,,,,,,,,
-51,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5255991794296913,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-52,weighting,decision_tree,,,,,,,gini,1.7853447328017091,1.0,None,0.0,13,7,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0001265964225445088,median,normalize,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.26399878867042303,,mutual_info_classif
-53,weighting,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.7776312540090575,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0005253715056715056,mean,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,False,,,,,,,,,,,,
-54,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,1.488333818193668e-10,0.09526676471267805,auto,255,None,258,87,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,normalize,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.3561953264350478,fpr,f_classif
-55,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.9523435078770708,0.8748383232154038,4,0.2195992292488049,poly,-1,True,4.849685143365339e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,minmax,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-56,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.7610308078756552,None,0.0,1,2,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.010000000000000004,mean,robust_scaler,,,0.9205545465103644,0.20272761060743316,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-57,weighting,adaboost,SAMME.R,0.03127273017917347,10,496,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,standardize,,,,,fast_ica,,,,,,,,,,,deflation,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-58,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,151,None,,0.0003283777607180646,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.2510527431514227,mean,normalize,,,,,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,-0.96093182232312,,0.05762510964607661,sigmoid,2632,,,,,,,,,,,,,,,,,
-59,none,bernoulli_nb,,,,,0.6524090653939782,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.005164169126808402,most_frequent,quantile_transformer,16,normal,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.033082145443631535,False,True,1,squared_hinge,ovr,l1,0.002615020898325393,,,,,,,,,,,,,,,,,,,,,,
-60,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.6453555269772124,None,0.0,1,4,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,normalize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-61,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,940.9437776449834,,,1.5839338084625942,rbf,-1,False,0.023072781592565438,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,none,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,77.25763685070707,mutual_info,,,
-62,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,220,auto,,0.0013824567992319003,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00021328932075284388,mean,minmax,,,,,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.039972474835314076,,0.01017810882899714,sigmoid,295,,,,,,,,,,,,,,,,,
-63,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.7326858450533382,0.02513634776687268,2,0.8635510319471909,poly,-1,True,0.0014598272110141993,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.06447266871225255,most_frequent,quantile_transformer,1000,normal,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,79.01961823033078,f_classif,,,
-64,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,29.55731828912983,False,True,1,squared_hinge,ovr,l2,0.0011332486951954968,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,
-65,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,1.1387795876755026e-06,0.12245605190343567,auto,255,None,58,7,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,robust_scaler,,,0.79305915648885,0.1841487406876042,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-66,none,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.563056219822946,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.32793677336996485,most_frequent,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-67,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,0.2638627266631576,0.022360820165005443,auto,255,None,171,6,19,loss,1e-07,0.016209060012924756,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.008763072395677399,mean,standardize,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,78,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-68,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,925.3053743248586,False,True,1,squared_hinge,ovr,l2,0.02764481198395717,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.3850718018366796,most_frequent,quantile_transformer,786,uniform,,,feature_agglomeration,,,,,,,,,,,,,,,cosine,average,156,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-69,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7573078019937971,None,0.0,2,5,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,robust_scaler,,,0.7265312295801113,0.28332515882236425,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.09660564590146253,fpr,f_classif
-70,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.005753626505113041,False,True,squared_hinge,0.00924401505172238,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.3192271696445429,median,quantile_transformer,913,normal,,,kernel_pca,,,,,,,,,,,,,,,,,,,,,,cosine,1963,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-71,none,adaboost,SAMME.R,0.22802213935525836,6,284,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,robust_scaler,,,0.7468645381288924,0.22838821662367073,feature_agglomeration,,,,,,,,,,,,,,,cosine,average,128,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-72,none,adaboost,SAMME,0.1280458968526125,7,125,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,robust_scaler,,,0.7156460334356771,0.2981902862926971,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,65.16171885308417,chi2,,,
-73,none,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9931779675565967,None,0.0,18,17,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,robust_scaler,,,0.75,0.2945917010472818,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,
-74,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.08646118576686276,None,0.0,4,9,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,most_frequent,quantile_transformer,959,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-75,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,1.2430504242164082e-09,0.03359568307446533,auto,255,None,1609,29,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,standardize,,,,,feature_agglomeration,,,,,,,,,,,,,,,cosine,complete,40,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-76,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7.491039171224502,True,True,squared_hinge,2.8058138048327884e-05,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,minmax,,,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.7754372809893839,False,True,1,squared_hinge,ovr,l1,3.9643594596766314e-05,,,,,,,,,,,,,,,,,,,,,,
-77,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.34936025988397124,None,0.0,2,13,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00018601309647406987,median,robust_scaler,,,0.8634309348843472,0.26976989465367607,extra_trees_preproc_for_classification,False,entropy,None,0.48025175288975036,None,0.0,6,17,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-78,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,0.011547155223520492,0.13665554140837183,auto,255,None,13,5,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.1824584492028449,median,minmax,,,,,feature_agglomeration,,,,,,,,,,,,,,,euclidean,average,261,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-79,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.933900673392165,None,0.0,7,4,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,minmax,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-80,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1661.2274747409913,,,0.025821208852387936,rbf,-1,False,0.0017011435104921824,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,none,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,306,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-81,none,adaboost,SAMME,0.011233995624432622,9,477,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.018370622484682127,mean,standardize,,,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6039710338898471,False,,,,,,,,,,,,,,,
-82,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,21.85207494001697,0.18872999589786676,4,1.1863646021868761,poly,-1,False,0.0021338371901294443,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0034080852834770217,mean,normalize,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.11326005109886934,fdr,f_classif
-83,none,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.563056219822946,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.32793677336996485,most_frequent,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-84,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,0.037849720944694,0.1268815770019783,auto,255,None,11,99,11,loss,1e-07,0.10654523627313527,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,
-85,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.593728171200453,None,0.0,7,20,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.02830941310359363,mean,normalize,,,,,extra_trees_preproc_for_classification,False,entropy,None,0.7389446586281172,None,0.0,20,11,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-86,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,1,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.019797877010737826,mean,normalize,,,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.8239678741128165,False,,,,,,,,,,,,,,,
-87,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.46057831591617715,False,True,hinge,0.04557857428827514,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00027457445401600137,median,standardize,,,,,extra_trees_preproc_for_classification,True,gini,None,0.48190346970486964,None,0.0,17,18,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-88,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,38,auto,,0.00019398431171606024,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010000000000000004,median,quantile_transformer,1440,normal,,,extra_trees_preproc_for_classification,True,gini,None,0.5126639956845847,None,0.0,16,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-89,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5722917944959481,None,0.0,1,14,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,
-90,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.26356754187780895,None,0.0,4,3,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010966178401758774,mean,robust_scaler,,,0.7817043861798886,0.24371720112407158,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
-91,weighting,adaboost,SAMME.R,0.13547552520095074,4,62,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.011851011202809002,most_frequent,standardize,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4888776875314595,fpr,chi2
-92,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,80.0028902468169,False,True,1,squared_hinge,ovr,l2,0.0024481999829574722,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.15047970487774687,mean,quantile_transformer,1130,normal,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,1.1912646635544712,False,True,1,squared_hinge,ovr,l1,0.00014314728486450857,,,,,,,,,,,,,,,,,,,,,,
-93,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.22879311080499845,None,0.0,8,4,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.014320022547615783,median,minmax,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.238630703440461,mutual_info,,,
-94,weighting,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,2,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,none,,,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6342574285315175,False,,,,,,,,,,,,,,,
-95,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.107967527639527e-05,True,True,hinge,0.00448294745544775,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,quantile_transformer,953,normal,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,35.00114345405331,chi2,,,
-96,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.14148638702297855,None,0.0,2,6,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.0038499650338378643,most_frequent,quantile_transformer,1759,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-97,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,1.1522414705935405e-10,0.6132800183073315,auto,255,None,1607,67,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010096911249562407,mean,robust_scaler,,,0.7663493326967942,0.136094217337434,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-98,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2746221958279041,False,True,1,squared_hinge,ovr,l2,6.546077664669006e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,quantile_transformer,1054,normal,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,
-99,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.479405900046494,,,0.013340778632154543,rbf,-1,False,6.913586060890114e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,robust_scaler,,,0.9953232616818098,0.06450577886132274,fast_ica,,,,,,,,,,,deflation,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-100,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.825844582229201,None,0.0,12,9,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0008422508222149647,median,robust_scaler,,,0.8658803880857064,0.2528423686293341,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-101,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2574971638664341,True,True,hinge,1.8027411099296446e-05,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.00536396328125103,median,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,
-102,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.7033210835501171,None,0.0,3,19,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,robust_scaler,,,0.7699195245692886,0.29258311638476237,feature_agglomeration,,,,,,,,,,,,,,,euclidean,ward,143,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-103,none,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,128,manual,0.8574560771111307,0.010886139381599514,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.24115007944216788,most_frequent,normalize,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,62.26514170991452,mutual_info,,,
-104,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.6282663389679584,None,0.0,1,3,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,median,minmax,,,,,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-105,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,1.430083734915855e-09,0.08779621406981941,auto,255,None,17,20,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.29749837779115257,mean,robust_scaler,,,0.7236445277814435,0.25,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
-106,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.3294588572341214,None,0.0,1,3,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
-107,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,39.22980757485479,,,0.13597389869623427,rbf,-1,False,0.00011639060052687874,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.006288306814260224,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-108,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.7922837297504705,False,True,1,squared_hinge,ovr,l2,0.0005476597326342352,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.012674631437254894,most_frequent,none,,,,,extra_trees_preproc_for_classification,False,entropy,None,0.23145146971319638,None,0.0,7,20,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-109,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.18060139887649052,None,0.0,10,12,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.010000000000000004,most_frequent,robust_scaler,,,0.9298612094255864,0.1263597946722308,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
-110,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.4111348710613201,None,0.0,16,8,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.01224348527546641,most_frequent,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,
-111,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6427543483503397,True,True,hinge,0.00045318775930916944,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.009338484332306904,median,quantile_transformer,174,normal,,,kernel_pca,,,,,,,,,,,,,,,,,,,,,,cosine,1848,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-112,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.04981080944617379,True,,0.07309386939525211,True,,constant,log,l2,,0.0006721243213777075,no_encoding,no_coalescense,,mean,quantile_transformer,574,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-113,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,9.029473608568458e-09,0.15779990576964315,auto,255,None,37,71,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.018093177195644097,mean,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,False,,,,,,,,,,,,
-114,none,adaboost,SAMME,0.011233995624432622,9,477,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.018370622484682127,mean,standardize,,,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6039710338898471,False,,,,,,,,,,,,,,,
-115,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l2,2.9468189962573666e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.014870368357163507,most_frequent,quantile_transformer,1000,uniform,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,83.43414935094258,f_classif,,,
-116,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.4864284879550203,None,0.0,1,4,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.008665854799630503,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-117,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.43699642289829416,None,0.0,6,20,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,quantile_transformer,1604,uniform,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-118,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.6922752267318475,True,True,squared_hinge,0.06299408576730353,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,quantile_transformer,1742,uniform,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,107,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-119,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.44727252219812963,None,0.0,2,10,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,standardize,,,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l1,8.17533189014765e-05,,,,,,,,,,,,,,,,,,,,,,
-120,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.46057831591617715,False,True,hinge,0.04557857428827514,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00027457445401600137,median,standardize,,,,,extra_trees_preproc_for_classification,True,gini,None,0.48190346970486964,None,0.0,17,18,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-121,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.4210978663047845,None,0.0,5,6,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.006365988550833196,most_frequent,robust_scaler,,,0.8348891074186082,0.00974961635730845,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0695919708890599,fpr,f_classif
-122,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4424195769042466,False,True,hinge,0.02490432909709204,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,mean,quantile_transformer,1000,uniform,,,kernel_pca,,,,,,,,,,,,,,,,,,,,,,cosine,769,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-123,weighting,decision_tree,,,,,,,entropy,1.0435267397863155,1.0,None,0.0,15,3,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.36913659113454333,most_frequent,normalize,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4947786362821451,fpr,f_classif
-124,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1561.1002600303584,,,0.0024048551704194507,rbf,-1,False,0.0001936214667496851,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,minmax,,,,,fast_ica,,,,,,,,,,,parallel,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-125,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.087313242780732,0.0636709914987994,3,0.004481917125024585,poly,-1,False,0.01848880618941367,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.002136015035740733,mean,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-126,weighting,adaboost,SAMME.R,1.696588612003168,9,455,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,minmax,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,82.18726095926662,chi2,,,
-127,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.6466927589470762,None,0.0,11,2,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.009672465311659264,most_frequent,standardize,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,369,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+1,none,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1514.059969351301,False,True,1,squared_hinge,ovr,l2,0.002448814309966599,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,standardize,,,,,extra_trees_preproc_for_classification,True,entropy,None,0.8221481751476923,None,0.0,2,3,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+3,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5707458022222313,None,0.0,3,12,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,minmax,,,,,extra_trees_preproc_for_classification,False,gini,None,0.5651056128131684,None,0.0,9,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+6,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.7283683762613216,None,0.0,12,20,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,median,standardize,,,,,fast_ica,,,,,,,,,,,deflation,exp,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+8,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,114,manual,0.11778631707605758,0.0029112721339713465,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.015582195257240751,median,normalize,,,,,kernel_pca,,,,,,,,,,,,,,,,,,,0.42909623253327767,3,0.25407612000343777,poly,1689,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+11,weighting,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,2,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.3865962574104293,mean,normalize,,,,,fast_ica,,,,,,,,,,,deflation,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+13,weighting,adaboost,SAMME,0.17983222024694556,4,357,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0008898781197407956,mean,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+15,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.21772092350860472,None,0.0,14,17,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.0020488212891693275,median,standardize,,,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,33.74257606530722,False,True,1,squared_hinge,ovr,l1,8.740452966205111e-05,,,,,,,,,,,,,,,,,,,,,,
+18,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,2.4243922131268786e-05,0.07570328162448307,auto,255,None,71,42,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,robust_scaler,,,0.8898729094898769,0.12138152991327475,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,94.96086286583851,chi2,,,
+20,none,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.9866058571747695,None,0.0,16,9,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.1680676743605393,most_frequent,robust_scaler,,,0.8840072797399463,0.08535534311393947,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,False,,,,,,,,,,,,
+23,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,0.00022373024203887573,0.027149343206135476,auto,255,None,49,13,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.00777614320416325,most_frequent,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,
+27,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.41025844265119926,None,0.0,18,15,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,median,quantile_transformer,1911,normal,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,False,,,,,,,,,,,,
+29,none,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.563056219822946,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.32793677336996485,most_frequent,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+31,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,8.757412830636845e-06,0.10095376320667454,auto,255,None,3,2,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.015401766535365336,median,quantile_transformer,647,normal,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,
+32,none,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,174,manual,0.21501892897746921,2.0347110826262054e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.013754893527625522,most_frequent,normalize,,,,,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,-0.019221513778605592,3,0.10190948951339239,poly,2521,,,,,,,,,,,,,,,,,
+35,none,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9.608707084865884,False,True,1,squared_hinge,ovr,l2,0.008086910150491696,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.020901889243957545,median,minmax,,,,,kitchen_sinks,,,,,,,,,,,,,,,,,,,,,,,,0.5694399784333516,3230,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+39,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.6799215694343422,None,0.0,1,5,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.004724310659953132,most_frequent,quantile_transformer,1037,normal,,,fast_ica,,,,,,,,,,,parallel,exp,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+41,none,extra_trees,,,,,,,,,,,,,,,True,gini,None,0.5033866291997137,None,0.0,2,14,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,minmax,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+43,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.9018061166635376,None,0.0,7,9,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,quantile_transformer,1802,normal,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,84.54647012968938,chi2,,,
+44,none,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l2,2.858526169098402e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0007827505640681123,median,standardize,,,,,extra_trees_preproc_for_classification,False,entropy,None,0.3443310459451119,None,0.0,3,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+49,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.049504283446131e-05,False,,0.0004438773852753879,True,2.4110816673432424e-07,constant,log,elasticnet,,0.01321814853186568,one_hot_encoding,minority_coalescer,0.01510416593631535,most_frequent,none,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,66.65285268958851,chi2,,,
+52,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,6.421059381158897e-08,0.11261253191288709,auto,255,None,45,28,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,
+55,none,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.9822179211645446,None,0.0,14,8,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.013966871874533052,most_frequent,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,
+58,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5214197490559657,None,0.0,1,2,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.00826182798588248,mean,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,
+63,none,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,130,manual,0.5446276346297756,0.00846307015171434,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.005736348762561667,median,quantile_transformer,973,uniform,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.08483615095863663,fdr,chi2
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+435,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.44903177399085925,None,0.0,2,17,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,mean,robust_scaler,,,0.7812408516672696,0.12791233577400205,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,
+438,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,6.090138036399552e-10,0.017305568433437588,auto,255,None,202,188,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,robust_scaler,,,0.7526196011313747,0.1563616005702636,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+445,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,68.60833575985336,,,0.03905068757955671,rbf,-1,False,0.02846559747114773,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.04775815990561371,mean,standardize,,,,,extra_trees_preproc_for_classification,False,entropy,None,0.45727987956076366,None,0.0,13,11,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+447,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.3431705351883592,None,0.0,4,16,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.09444471061261189,most_frequent,normalize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+450,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,7.837701348094735e-09,0.05315388879642303,auto,255,None,67,2,10,loss,1e-07,0.11655723047843726,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.001857226018763921,median,none,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.014648191862400293,fdr,f_classif
+458,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,5.581658271288433e-10,0.06171988234311011,auto,255,None,25,6,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.014859405637537319,most_frequent,robust_scaler,,,0.7983900845251328,0.2545046978458648,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,1.380085213170737,False,True,1,squared_hinge,ovr,l1,0.0061450732015354784,,,,,,,,,,,,,,,,,,,,,,
+462,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6,1,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.017471096998391875,median,none,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,67.7801145492284,f_classif,,,
diff --git a/autosklearn/metalearning/files/f1_micro_multiclass.classification_dense/description.txt b/autosklearn/metalearning/files/f1_micro_multiclass.classification_dense/description.txt
index 0ae8a491a5..e175cf7325 100644
--- a/autosklearn/metalearning/files/f1_micro_multiclass.classification_dense/description.txt
+++ b/autosklearn/metalearning/files/f1_micro_multiclass.classification_dense/description.txt
@@ -1,8 +1,9 @@
 features_cutoff_time: 3600
 features_cutoff_memory: 3072
-number_of_feature_steps: 35
+number_of_feature_steps: 42
 feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances
 feature_step LogNumberOfInstances: LogNumberOfInstances
+feature_step NumberOfClasses: NumberOfClasses
 feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures
 feature_step LogNumberOfFeatures: LogNumberOfFeatures
 feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues
@@ -20,6 +21,11 @@ feature_step DatasetRatio: DatasetRatio, LogDatasetRatio
 feature_step LogDatasetRatio: LogDatasetRatio
 feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio
 feature_step LogInverseDatasetRatio: LogInverseDatasetRatio
+feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD
+feature_step ClassProbabilityMin: ClassProbabilityMin
+feature_step ClassProbabilityMax: ClassProbabilityMax
+feature_step ClassProbabilityMean: ClassProbabilityMean
+feature_step ClassProbabilitySTD: ClassProbabilitySTD
 feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum
 feature_step SymbolsMin: SymbolsMin
 feature_step SymbolsMax: SymbolsMax
@@ -36,11 +42,12 @@ feature_step SkewnessMin: SkewnessMin
 feature_step SkewnessMax: SkewnessMax
 feature_step SkewnessMean: SkewnessMean
 feature_step SkewnessSTD: SkewnessSTD
-features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
+feature_step ClassEntropy: ClassEntropy
+features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy
 features_stochastic: 
-default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
+default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy
 
-algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127
+algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235,236,237,238,239,240,241,242,243,244,245,246,247,248,249,250,251,252,253,254,255,256,257,258,259,260,261,262,263,264,265,266,267,268,269,270,271,272,273,274,275,276,277,278,279,280,281,282,283,284,285,286,287,288,289,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,312,313,314,315,316,317,318,319,320,321,322,323,324,325,326,327,328,329,330,331,332,333,334,335,336,337,338,339,340,341,342,343,344,345,346,347,348,349,350,351,352,353,354,355,356,357,358,359,360,361,362,363,364,365,366,367,368,369,370,371,372,373,374,375,376,377,378,379,380,381,382,383,384,385,386,387,388,389,390,391,392,393,394,395,396,397,398,399,400,401,402,403,404,405,406,407,408,409,410,411,412,413,414,415,416,417,418,419,420,421,422,423,424,425,426,427,428,429,430,431,432,433,434,435,436,437,438,439,440,441,442,443,444,445,446,447,448,449,450,451,452,453,454,455,456,457,458,459,460,461,462,463,464
 algorithms_stochastic: 
 performance_measures: f1_micro
 performance_type: solution_quality
diff --git a/autosklearn/metalearning/files/f1_micro_multiclass.classification_dense/feature_costs.arff b/autosklearn/metalearning/files/f1_micro_multiclass.classification_dense/feature_costs.arff
index 0e17e20d22..8512fd9275 100644
--- a/autosklearn/metalearning/files/f1_micro_multiclass.classification_dense/feature_costs.arff
+++ b/autosklearn/metalearning/files/f1_micro_multiclass.classification_dense/feature_costs.arff
@@ -4,6 +4,7 @@
 @ATTRIBUTE repetition NUMERIC
 @ATTRIBUTE NumberOfInstances NUMERIC
 @ATTRIBUTE LogNumberOfInstances NUMERIC
+@ATTRIBUTE NumberOfClasses NUMERIC
 @ATTRIBUTE NumberOfFeatures NUMERIC
 @ATTRIBUTE LogNumberOfFeatures NUMERIC
 @ATTRIBUTE MissingValues NUMERIC
@@ -21,6 +22,11 @@
 @ATTRIBUTE LogDatasetRatio NUMERIC
 @ATTRIBUTE InverseDatasetRatio NUMERIC
 @ATTRIBUTE LogInverseDatasetRatio NUMERIC
+@ATTRIBUTE ClassOccurences NUMERIC
+@ATTRIBUTE ClassProbabilityMin NUMERIC
+@ATTRIBUTE ClassProbabilityMax NUMERIC
+@ATTRIBUTE ClassProbabilityMean NUMERIC
+@ATTRIBUTE ClassProbabilitySTD NUMERIC
 @ATTRIBUTE NumSymbols NUMERIC
 @ATTRIBUTE SymbolsMin NUMERIC
 @ATTRIBUTE SymbolsMax NUMERIC
@@ -37,162 +43,138 @@
 @ATTRIBUTE SkewnessMax NUMERIC
 @ATTRIBUTE SkewnessMean NUMERIC
 @ATTRIBUTE SkewnessSTD NUMERIC
+@ATTRIBUTE ClassEntropy NUMERIC
 
 @DATA
-75123,1.0,1e-05,1e-05,1e-05,1e-05,0.00253,0.00221,1e-05,0.00024,3e-05,0.00011,0.0,2e-05,2e-05,5e-05,8e-05,2e-05,1e-05,7e-05,1e-05,0.00072,1e-05,1e-05,0.00019,0.00041,0.00011,0.00061,0.00012,9e-05,0.00015,0.00025,0.00067,0.00014,0.00011,0.00016,0.00027
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diff --git a/autosklearn/metalearning/files/f1_micro_multiclass.classification_dense/feature_runstatus.arff b/autosklearn/metalearning/files/f1_micro_multiclass.classification_dense/feature_runstatus.arff
index 0034ad2bd6..5fff2dfed3 100644
--- a/autosklearn/metalearning/files/f1_micro_multiclass.classification_dense/feature_runstatus.arff
+++ b/autosklearn/metalearning/files/f1_micro_multiclass.classification_dense/feature_runstatus.arff
@@ -4,6 +4,7 @@
 @ATTRIBUTE repetition NUMERIC
 @ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other}
@@ -21,6 +22,11 @@
 @ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other}
@@ -37,162 +43,138 @@
 @ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other}
 
 @DATA
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diff --git a/autosklearn/metalearning/files/f1_micro_multiclass.classification_dense/feature_values.arff b/autosklearn/metalearning/files/f1_micro_multiclass.classification_dense/feature_values.arff
index a60400e3ce..736dc64321 100644
--- a/autosklearn/metalearning/files/f1_micro_multiclass.classification_dense/feature_values.arff
+++ b/autosklearn/metalearning/files/f1_micro_multiclass.classification_dense/feature_values.arff
@@ -13,7 +13,6 @@
 @ATTRIBUTE KurtosisMean NUMERIC
 @ATTRIBUTE KurtosisMin NUMERIC
 @ATTRIBUTE KurtosisSTD NUMERIC
-@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC
 @ATTRIBUTE LogDatasetRatio NUMERIC
 @ATTRIBUTE LogInverseDatasetRatio NUMERIC
 @ATTRIBUTE LogNumberOfFeatures NUMERIC
@@ -42,160 +41,135 @@
 @ATTRIBUTE SymbolsSum NUMERIC
 
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diff --git a/autosklearn/metalearning/files/f1_micro_multiclass.classification_sparse/algorithm_runs.arff b/autosklearn/metalearning/files/f1_micro_multiclass.classification_sparse/algorithm_runs.arff
index ecc90a2d27..d9f6fad13f 100644
--- a/autosklearn/metalearning/files/f1_micro_multiclass.classification_sparse/algorithm_runs.arff
+++ b/autosklearn/metalearning/files/f1_micro_multiclass.classification_sparse/algorithm_runs.arff
@@ -7,95 +7,133 @@
 @ATTRIBUTE runstatus {ok, timeout, memout, not_applicable, crash, other}
 
 @DATA
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diff --git a/autosklearn/metalearning/files/f1_micro_multiclass.classification_sparse/configurations.csv b/autosklearn/metalearning/files/f1_micro_multiclass.classification_sparse/configurations.csv
index 75662496d2..36fd991e13 100644
--- a/autosklearn/metalearning/files/f1_micro_multiclass.classification_sparse/configurations.csv
+++ b/autosklearn/metalearning/files/f1_micro_multiclass.classification_sparse/configurations.csv
@@ -1,93 +1,131 @@
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-91,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.087313242780732,0.0636709914987994,3,0.004481917125024585,poly,-1,False,0.01848880618941367,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.002136015035740733,mean,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-92,weighting,adaboost,SAMME,0.029932847627447023,8,483,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,quantile_transformer,1290,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+1,none,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1514.059969351301,False,True,1,squared_hinge,ovr,l2,0.002448814309966599,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,standardize,,,,,extra_trees_preproc_for_classification,True,entropy,None,0.8221481751476923,None,0.0,2,3,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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+287,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.3850609070926397,None,0.0,1,2,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.013992309764275707,median,normalize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,,
+289,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.818514683854827,None,0.0,15,20,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.4928285185491301,most_frequent,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,,
+292,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.0479982468066316,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,quantile_transformer,1097,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+293,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7040496889211508,None,0.0,17,19,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.016450784709133564,mean,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+300,none,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.7010137052424128,None,0.0,7,10,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.2761813291805052,most_frequent,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,,
+306,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2677516008848513,True,True,hinge,0.05752557410901496,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0010152857217605206,median,quantile_transformer,1000,uniform,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,51.60974796845029,chi2,,,,
+307,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,33,None,,0.006687546229372618,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.06231343712708962,mean,none,,,,,kernel_pca,,,,,,,,,,,-0.7000777299811654,3,0.008780219231661734,poly,498,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+312,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,60.6761602925822,,,0.038224035513907226,rbf,-1,False,0.0018214343969776353,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.421025538999516,median,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+313,weighting,adaboost,SAMME,1.2423888874490505,5,284,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.01582563952987033,most_frequent,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+316,weighting,adaboost,SAMME,1.2615483379313783,8,190,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.00017789451703497617,mean,robust_scaler,,,0.7345811776810758,0.054715769043996304,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+317,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.4572679693563934,None,0.0,1,11,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,mean,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,,
+318,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.843098530645432,None,0.0,1,4,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,mean,quantile_transformer,930,normal,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,,
+321,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.5864398404787432,None,0.0,1,14,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0070663750850608895,most_frequent,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,,
+326,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0001329242404968632,False,True,squared_hinge,0.000416219546803132,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.007568009633214092,median,quantile_transformer,1126,normal,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,29.06367291960815,chi2,,,,
+329,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.6642356723981304,False,True,1,squared_hinge,ovr,l2,7.912577426155621e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.08464913801800422,most_frequent,normalize,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,53.35800942135187,chi2,,,,
+331,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.5,None,0.0,1,5,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.15244281596303316,mean,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+332,none,adaboost,SAMME,0.22805500898886877,8,172,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.019649593891454008,mean,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+334,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7516795829719571,None,0.0,14,7,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0010013681780844724,mean,none,,,,,extra_trees_preproc_for_classification,True,entropy,None,0.6123323873969666,None,0.0,17,7,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+339,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.36374112587320206,None,0.0,2,10,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010870084126959173,most_frequent,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,,
+343,none,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9062834897256731,None,0.0,20,9,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.005187379382274718,mean,quantile_transformer,349,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+345,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5321814610661173,None,0.0,1,3,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,standardize,,,,,extra_trees_preproc_for_classification,True,gini,None,0.5,None,0.0,1,15,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+350,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.44903177399085925,None,0.0,2,17,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,mean,robust_scaler,,,0.7812408516672696,0.12791233577400205,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,,
+352,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5988524108452234,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,mean,robust_scaler,,,0.75,0.26961884796358077,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+355,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,68.60833575985336,,,0.03905068757955671,rbf,-1,False,0.02846559747114773,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.04775815990561371,mean,standardize,,,,,extra_trees_preproc_for_classification,False,entropy,None,0.45727987956076366,None,0.0,13,11,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+357,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.3431705351883592,None,0.0,4,16,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.09444471061261189,most_frequent,normalize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+359,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.9282407375709691,None,0.0,16,3,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.015995805068365877,mean,standardize,,,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.10042232633120375,False,True,1,squared_hinge,ovr,l1,0.004451009526629745,,,,,,,,,,,,,,,,,,,,,
+367,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.938372431886818e-05,True,True,hinge,0.0016143408148969647,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00024949978971758427,median,quantile_transformer,754,uniform,,,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,-0.22339839702350717,2,0.5718982387915436,poly,5260,,,,,,,,,,,,,,,,
+371,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4,1,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.01020427226149318,median,none,,,,,extra_trees_preproc_for_classification,True,entropy,None,0.3663460889081009,None,0.0,2,7,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
diff --git a/autosklearn/metalearning/files/f1_micro_multiclass.classification_sparse/description.txt b/autosklearn/metalearning/files/f1_micro_multiclass.classification_sparse/description.txt
index 4646f7cc6f..87b98e89d3 100644
--- a/autosklearn/metalearning/files/f1_micro_multiclass.classification_sparse/description.txt
+++ b/autosklearn/metalearning/files/f1_micro_multiclass.classification_sparse/description.txt
@@ -1,8 +1,9 @@
 features_cutoff_time: 3600
 features_cutoff_memory: 3072
-number_of_feature_steps: 35
+number_of_feature_steps: 42
 feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances
 feature_step LogNumberOfInstances: LogNumberOfInstances
+feature_step NumberOfClasses: NumberOfClasses
 feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures
 feature_step LogNumberOfFeatures: LogNumberOfFeatures
 feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues
@@ -20,6 +21,11 @@ feature_step DatasetRatio: DatasetRatio, LogDatasetRatio
 feature_step LogDatasetRatio: LogDatasetRatio
 feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio
 feature_step LogInverseDatasetRatio: LogInverseDatasetRatio
+feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD
+feature_step ClassProbabilityMin: ClassProbabilityMin
+feature_step ClassProbabilityMax: ClassProbabilityMax
+feature_step ClassProbabilityMean: ClassProbabilityMean
+feature_step ClassProbabilitySTD: ClassProbabilitySTD
 feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum
 feature_step SymbolsMin: SymbolsMin
 feature_step SymbolsMax: SymbolsMax
@@ -36,11 +42,12 @@ feature_step SkewnessMin: SkewnessMin
 feature_step SkewnessMax: SkewnessMax
 feature_step SkewnessMean: SkewnessMean
 feature_step SkewnessSTD: SkewnessSTD
-features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
+feature_step ClassEntropy: ClassEntropy
+features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy
 features_stochastic: 
-default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
+default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy
 
-algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92
+algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235,236,237,238,239,240,241,242,243,244,245,246,247,248,249,250,251,252,253,254,255,256,257,258,259,260,261,262,263,264,265,266,267,268,269,270,271,272,273,274,275,276,277,278,279,280,281,282,283,284,285,286,287,288,289,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,312,313,314,315,316,317,318,319,320,321,322,323,324,325,326,327,328,329,330,331,332,333,334,335,336,337,338,339,340,341,342,343,344,345,346,347,348,349,350,351,352,353,354,355,356,357,358,359,360,361,362,363,364,365,366,367,368,369,370,371,372
 algorithms_stochastic: 
 performance_measures: f1_micro
 performance_type: solution_quality
diff --git a/autosklearn/metalearning/files/f1_micro_multiclass.classification_sparse/feature_costs.arff b/autosklearn/metalearning/files/f1_micro_multiclass.classification_sparse/feature_costs.arff
index 0e17e20d22..8512fd9275 100644
--- a/autosklearn/metalearning/files/f1_micro_multiclass.classification_sparse/feature_costs.arff
+++ b/autosklearn/metalearning/files/f1_micro_multiclass.classification_sparse/feature_costs.arff
@@ -4,6 +4,7 @@
 @ATTRIBUTE repetition NUMERIC
 @ATTRIBUTE NumberOfInstances NUMERIC
 @ATTRIBUTE LogNumberOfInstances NUMERIC
+@ATTRIBUTE NumberOfClasses NUMERIC
 @ATTRIBUTE NumberOfFeatures NUMERIC
 @ATTRIBUTE LogNumberOfFeatures NUMERIC
 @ATTRIBUTE MissingValues NUMERIC
@@ -21,6 +22,11 @@
 @ATTRIBUTE LogDatasetRatio NUMERIC
 @ATTRIBUTE InverseDatasetRatio NUMERIC
 @ATTRIBUTE LogInverseDatasetRatio NUMERIC
+@ATTRIBUTE ClassOccurences NUMERIC
+@ATTRIBUTE ClassProbabilityMin NUMERIC
+@ATTRIBUTE ClassProbabilityMax NUMERIC
+@ATTRIBUTE ClassProbabilityMean NUMERIC
+@ATTRIBUTE ClassProbabilitySTD NUMERIC
 @ATTRIBUTE NumSymbols NUMERIC
 @ATTRIBUTE SymbolsMin NUMERIC
 @ATTRIBUTE SymbolsMax NUMERIC
@@ -37,162 +43,138 @@
 @ATTRIBUTE SkewnessMax NUMERIC
 @ATTRIBUTE SkewnessMean NUMERIC
 @ATTRIBUTE SkewnessSTD NUMERIC
+@ATTRIBUTE ClassEntropy NUMERIC
 
 @DATA
-75123,1.0,1e-05,1e-05,1e-05,1e-05,0.00253,0.00221,1e-05,0.00024,3e-05,0.00011,0.0,2e-05,2e-05,5e-05,8e-05,2e-05,1e-05,7e-05,1e-05,0.00072,1e-05,1e-05,0.00019,0.00041,0.00011,0.00061,0.00012,9e-05,0.00015,0.00025,0.00067,0.00014,0.00011,0.00016,0.00027
-75090,1.0,1e-05,1e-05,2e-05,2e-05,0.00815,0.00368,7e-05,0.00228,9e-05,0.00235,0.0,5e-05,5e-05,0.0001,0.00013,0.00013,1e-05,0.00021,2e-05,0.00064,0.0,0.0,0.00014,0.00038,0.00011,0.00066,0.00011,0.00014,0.00014,0.00026,0.00066,0.00011,0.00013,0.00015,0.00027
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+75236,1.0,1e-05,1e-05,0.0001,1e-05,1e-05,0.00218,0.00118,2e-05,0.00065,3e-05,0.00041,1e-05,3e-05,3e-05,9e-05,9e-05,4e-05,1e-05,7e-05,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.0006,1e-05,1e-05,0.00021,0.00032,6e-05,0.00043,7e-05,7e-05,9e-05,0.00019,0.00062,7e-05,8e-05,0.0001,0.00037,0.00048
+75237,1.0,2e-05,1e-05,0.00785,2e-05,1e-05,0.31031,0.27854,2e-05,0.01161,3e-05,0.02022,1e-05,4e-05,3e-05,9e-05,0.00024,6e-05,2e-05,9e-05,2e-05,0.00062,7e-05,3e-05,0.00017,0.00035,0.0008,1e-05,1e-05,0.00028,0.00043,8e-05,0.00043,8e-05,7e-05,9e-05,0.0002,0.00044,6e-05,8e-05,9e-05,0.00021,0.14028
+75239,1.0,1e-05,1e-05,7e-05,1e-05,1e-05,0.00105,0.00084,1e-05,0.00015,1e-05,8e-05,1e-05,2e-05,5e-05,4e-05,8e-05,2e-05,1e-05,3e-05,1e-05,0.00016,2e-05,1e-05,5e-05,9e-05,0.00049,0.0,0.0,0.00012,0.00026,0.00011,0.00046,0.00013,8e-05,8e-05,0.00018,0.00044,6e-05,9e-05,0.00011,0.00018,0.00065
+75240,1.0,2e-05,1e-05,0.00036,5e-05,4e-05,0.00845,0.00644,6e-05,0.00122,4e-05,0.00091,1e-05,4e-05,4e-05,9e-05,0.00013,5e-05,2e-05,9e-05,5e-05,0.00031,3e-05,1e-05,7e-05,0.0002,0.00073,3e-05,2e-05,0.00019,0.0003,0.00018,0.00055,9e-05,9e-05,0.00011,0.00025,0.0005,7e-05,8e-05,0.00015,0.0002,0.00365
+75243,1.0,1e-05,1e-05,0.00029,1e-05,1e-05,0.00461,0.00415,1e-05,0.00035,1e-05,0.00015,0.0,2e-05,2e-05,5e-05,7e-05,2e-05,1e-05,4e-05,1e-05,0.00018,2e-05,1e-05,4e-05,0.00011,0.00022,1e-05,1e-05,5e-05,0.00011,5e-05,0.0004,6e-05,6e-05,8e-05,0.0002,0.00039,6e-05,6e-05,8e-05,0.00019,0.00265
+75244,1.0,3e-05,2e-05,0.0004,3e-05,2e-05,0.0173,0.01298,4e-05,0.00267,4e-05,0.00174,1e-05,7e-05,9e-05,0.00024,0.00027,8e-05,2e-05,0.00013,3e-05,0.00045,7e-05,2e-05,0.00011,0.00025,0.00077,6e-05,2e-05,0.00021,0.00034,0.00013,0.00077,0.00014,0.00013,0.00026,0.00023,0.00044,7e-05,8e-05,0.0001,0.00019,0.00649
+75248,1.0,1e-05,1e-05,0.00017,1e-05,1e-05,0.00416,0.00305,1e-05,0.0006,2e-05,0.00054,1e-05,2e-05,2e-05,5e-05,7e-05,2e-05,1e-05,4e-05,1e-05,0.00017,2e-05,1e-05,4e-05,0.0001,0.00029,5e-05,1e-05,6e-05,0.00012,6e-05,0.00081,0.00011,0.00015,0.00016,0.00038,0.0005,7e-05,8e-05,0.00012,0.00024,0.00184
+75249,1.0,1e-05,1e-05,0.00011,1e-05,1e-05,0.00192,0.00153,1e-05,0.00025,1e-05,0.00017,1e-05,2e-05,2e-05,5e-05,7e-05,2e-05,1e-05,5e-05,3e-05,0.00014,2e-05,1e-05,3e-05,8e-05,0.00029,1e-05,3e-05,6e-05,0.00012,7e-05,0.00041,6e-05,8e-05,0.0001,0.00017,0.00043,6e-05,7e-05,9e-05,0.00019,0.00092
+75250,1.0,1e-05,1e-05,0.00375,1e-05,1e-05,0.04994,0.047,1e-05,0.00247,1e-05,0.00049,1e-05,2e-05,2e-05,6e-05,7e-05,2e-05,1e-05,3e-05,1e-05,0.00022,4e-05,1e-05,4e-05,0.00012,0.00041,0.0,1e-05,0.00011,0.00023,6e-05,0.00076,0.00012,0.00012,0.00019,0.00032,0.00086,0.00012,0.00013,0.00018,0.00043,0.04178
diff --git a/autosklearn/metalearning/files/f1_micro_multiclass.classification_sparse/feature_runstatus.arff b/autosklearn/metalearning/files/f1_micro_multiclass.classification_sparse/feature_runstatus.arff
index 0034ad2bd6..5fff2dfed3 100644
--- a/autosklearn/metalearning/files/f1_micro_multiclass.classification_sparse/feature_runstatus.arff
+++ b/autosklearn/metalearning/files/f1_micro_multiclass.classification_sparse/feature_runstatus.arff
@@ -4,6 +4,7 @@
 @ATTRIBUTE repetition NUMERIC
 @ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other}
@@ -21,6 +22,11 @@
 @ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other}
@@ -37,162 +43,138 @@
 @ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other}
 
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diff --git a/autosklearn/metalearning/files/f1_micro_multiclass.classification_sparse/feature_values.arff b/autosklearn/metalearning/files/f1_micro_multiclass.classification_sparse/feature_values.arff
index a60400e3ce..736dc64321 100644
--- a/autosklearn/metalearning/files/f1_micro_multiclass.classification_sparse/feature_values.arff
+++ b/autosklearn/metalearning/files/f1_micro_multiclass.classification_sparse/feature_values.arff
@@ -13,7 +13,6 @@
 @ATTRIBUTE KurtosisMean NUMERIC
 @ATTRIBUTE KurtosisMin NUMERIC
 @ATTRIBUTE KurtosisSTD NUMERIC
-@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC
 @ATTRIBUTE LogDatasetRatio NUMERIC
 @ATTRIBUTE LogInverseDatasetRatio NUMERIC
 @ATTRIBUTE LogNumberOfFeatures NUMERIC
@@ -42,160 +41,135 @@
 @ATTRIBUTE SymbolsSum NUMERIC
 
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+75232,1.0,0.9201702027239149,0.6647807637906648,0.5,0.3352192362093352,0.16478076379066478,0.05799151343705799,17.24390243902439,585.4510498046875,41.48030315044416,-0.2529125213623047,95.17568446650067,-2.847458599192265,2.8474585991922647,3.713572066704308,6.561030665896573,0.0,2.0,41.0,0.0,707.0,0.0,0.0,41.0,0.0,0.0,0.0,0.0,0.0,23.23834228515625,3.832647103316537,-1.5003634691238403,4.508387764393873,0.0,0.0,0.0,0.0,0.0
+75233,1.0,0.8824680010722367,0.6990344325013663,0.5,0.3009655674986336,0.19903443250136635,0.0038258334851521223,261.3809523809524,619.9618265719739,76.9042121753925,1.1050496101379395,139.01256776590665,-5.565978930826466,5.565978930826466,3.044522437723423,8.610501368549889,0.0,2.0,21.0,0.0,5489.0,0.0,0.0,21.0,0.0,0.0,0.0,0.0,0.0,21.77604866027832,5.3457508484522505,-0.7721167802810669,4.832918145056104,0.0,0.0,0.0,0.0,0.0
+75234,1.0,0.9998393017810485,0.5074626865671642,0.5,0.4925373134328358,0.007462686567164201,0.004033884630899556,247.9,1.9309582710266113,1.5332300662994385,1.3253521919250488,0.13647900129618118,-5.513025439041145,5.513025439041145,2.995732273553991,8.508757712595136,0.0,2.0,20.0,0.0,4958.0,0.0,0.0,20.0,0.0,0.0,0.0,0.0,0.0,-0.1076231524348259,-0.23315090499818325,-0.33683285117149353,0.052567253463639066,0.0,0.0,0.0,0.0,0.0
+75235,1.0,1.7067919449664923,0.4105579868708972,0.25,0.05579868708971553,0.1492903085996621,0.006564551422319475,152.33333333333334,13.76956558227539,2.346758103856697,-1.6145207422400454,4.524616106763882,-5.026071102226095,5.026071102226095,3.1780538303479458,8.204124932574041,0.0,4.0,24.0,0.0,3656.0,0.0,0.0,24.0,0.0,0.0,0.0,0.0,0.0,3.779214382171631,1.515954251983203,0.004355928860604763,1.0657977665170062,0.0,0.0,0.0,0.0,0.0
+75236,1.0,3.3208751934243548,0.10486891385767791,0.1,0.09363295880149813,0.003810297743328803,0.2397003745318352,4.171875,22.422517776489258,-0.9225467168815645,-1.9997756481170654,1.9352856443240884,-1.4283655750405777,1.4283655750405777,5.545177444479562,6.97354301952014,0.0,10.0,256.0,0.0,1068.0,0.0,0.0,256.0,0.0,0.0,0.0,0.0,0.0,4.94191312789917,0.8186543448100565,-0.6194983720779419,0.6381679017395948,0.0,0.0,0.0,0.0,0.0
+75237,1.0,0.7378736147990802,0.7918800894091566,0.5,0.20811991059084348,0.29188008940915655,1.8271625991997027e-05,54729.666666666664,-0.5975548261535093,-0.7926032911766772,-0.9140236377716064,0.13929334047064063,-10.910161193614513,10.910161193614513,1.0986122886681098,12.008773482282624,0.0,2.0,3.0,0.0,164189.0,0.0,0.0,3.0,0.0,0.0,0.0,0.0,0.0,-0.039879728108644485,-0.3348021370669206,-0.6223916411399841,0.2378660138627058,0.0,0.0,0.0,0.0,0.0
+75239,1.0,0.9443547030267275,0.6379707916986933,0.5,0.3620292083013067,0.1379707916986933,0.02536510376633359,39.42424242424242,30.96304702758789,5.229958094222913,-1.8830041885375977,9.159290860687747,-3.674380917046025,3.674380917046025,3.4965075614664802,7.170888478512505,0.0,2.0,33.0,0.0,1301.0,0.0,0.0,33.0,0.0,0.0,0.0,0.0,0.0,5.591684341430664,1.5151810998266393,-0.8763390183448792,1.68213778251707,0.0,0.0,0.0,0.0,0.0
+75240,1.0,0.9473321982231081,0.6342759940463534,0.5,0.3657240059536466,0.13427599404635338,0.01275781416117372,78.38333333333334,1911.103482572295,38.512800597597035,-3.0,145.76440092342054,-4.361611319722948,4.361611319722948,4.0943445622221,8.455955881945048,27.0,2.0,60.0,17.0,4703.0,4703.0,29447.0,33.0,0.2833333333333333,1.0,0.10435537600113402,0.8181818181818182,1.2222222222222223,43.144344696960424,3.913912678196276,-22.79380338813253,4.943976022970944,173.0,22.59259259259259,1.0,43.77997111758117,610.0
+75243,1.0,1.7118844784475225,0.33529140750979036,0.25,0.024648698456576825,0.13045500689907696,0.0009214466712739,1085.25,0.37898291200086787,-0.9656219973073414,-1.9999312246360998,0.7132002663612025,-6.989565654179703,6.989565654179703,2.0794415416798357,9.06900719585954,8.0,4.0,8.0,0.0,8682.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.5423951867147632,0.9342552225820131,-0.008293091335592482,0.40192683634075954,5.0,3.375,2.0,0.8569568250501305,27.0
+75244,1.0,0.33901826511567296,0.9370570367870401,0.5,0.06294296321295983,0.43705703678704017,0.009449881876476544,105.82142857142857,1180.200844451951,40.51648623113333,-3.0,132.43484378998235,-4.661753037398361,4.661753037398361,4.02535169073515,8.68710472813351,41.0,2.0,56.0,14.0,5926.0,5926.0,23694.0,15.0,0.25,1.0,0.0713984378766694,2.7333333333333334,0.36585365853658536,34.38314768097812,3.7769009011075547,-8.659484722748674,5.307279668054912,16.0,5.975609756097561,1.0,3.692363938923924,245.0
+75248,1.0,0.4971128374263667,0.8909272183449651,0.5,0.1090727816550349,0.39092721834496513,0.010967098703888335,91.18181818181819,5010.000199441565,104.23108191482348,-3.0,468.735807382562,-4.512855515163565,4.512855515163565,4.007333185232471,8.520188700396035,39.0,2.0,55.0,12.0,5015.0,4938.0,21515.0,16.0,0.21818181818181817,0.9846460618145564,0.07800235656666364,2.4375,0.41025641025641024,70.79548149028699,4.06998328231248,-50.04498375424548,9.467860234489164,14.0,5.769230769230769,1.0,3.407845687351005,225.0
+75249,1.0,0.3891675492147647,0.9236550632911392,0.5,0.07634493670886076,0.4236550632911392,0.011471518987341773,87.17241379310344,416.3357123975682,46.57141260912883,-3.0,84.69777944086748,-4.467887925280274,4.467887925280274,3.367295829986474,7.8351837552667485,22.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,7.0,0.2413793103448276,1.0,0.056048123090353556,3.142857142857143,0.3181818181818182,20.453256767506936,0.8884855226401125,-20.453256767506936,6.7466910388456185,5.0,2.0454545454545454,1.0,0.7056443043754556,45.0
+75250,1.0,3.992354626842552,0.146342438507591,0.04545454545454545,0.005796927628356971,0.03949259311161838,3.997881123004808e-05,25013.25,2.1476065760596077,1.4719570787187288,0.3045189380645752,0.6973345081454226,-10.127160963449944,10.127160963449944,1.3862943611198906,11.513455324569835,0.0,22.0,4.0,0.0,100053.0,0.0,0.0,4.0,0.0,0.0,0.0,0.0,0.0,1.2135857343673706,0.3996680751442909,-0.3896799087524414,0.7616029272290122,0.0,0.0,0.0,0.0,0.0
diff --git a/autosklearn/metalearning/files/f1_multiclass.classification_dense/algorithm_runs.arff b/autosklearn/metalearning/files/f1_multiclass.classification_dense/algorithm_runs.arff
index da4c911485..e5cb6fc118 100644
--- a/autosklearn/metalearning/files/f1_multiclass.classification_dense/algorithm_runs.arff
+++ b/autosklearn/metalearning/files/f1_multiclass.classification_dense/algorithm_runs.arff
@@ -7,130 +7,135 @@
 @ATTRIBUTE runstatus {ok, timeout, memout, not_applicable, crash, other}
 
 @DATA
-75119,1.0,1,0.016393442622950838,ok
-75212,1.0,2,0.2410714285714286,ok
-252,1.0,3,?,not_applicable
-246,1.0,4,?,not_applicable
-75178,1.0,5,?,not_applicable
-75239,1.0,6,0.0,ok
-75171,1.0,7,0.16427783902976845,ok
-75233,1.0,8,0.04661574927574397,ok
-248,1.0,9,?,not_applicable
-75231,1.0,10,?,not_applicable
-2123,1.0,11,?,not_applicable
-75196,1.0,12,0.009615384615384692,ok
-75188,1.0,13,?,not_applicable
-75092,1.0,14,0.425287356321839,ok
-75248,1.0,15,0.6165540540540541,ok
-75126,1.0,16,0.021324354657688005,ok
-75234,1.0,17,0.02359641985353944,ok
-75150,1.0,18,0.2519893899204243,ok
-258,1.0,19,?,not_applicable
-75168,1.0,20,?,not_applicable
-75235,1.0,21,?,not_applicable
-75159,1.0,22,0.6068376068376068,ok
-244,1.0,23,?,not_applicable
-75221,1.0,24,?,not_applicable
-75219,1.0,25,0.04292527821939596,ok
-75202,1.0,26,?,not_applicable
-3043,1.0,27,0.14942528735632177,ok
-75205,1.0,28,?,not_applicable
-75174,1.0,29,0.19804504887377816,ok
-288,1.0,30,?,not_applicable
-75250,1.0,31,?,not_applicable
-275,1.0,32,?,not_applicable
-75142,1.0,33,0.07069521853916105,ok
-75213,1.0,34,0.14754098360655743,ok
-75099,1.0,35,0.4814814814814815,ok
-75243,1.0,36,?,not_applicable
-75184,1.0,37,0.16477447069653273,ok
-75222,1.0,38,0.3982300884955753,ok
-75175,1.0,39,0.12144420131291034,ok
-233,1.0,40,0.003058103975535076,ok
-75161,1.0,41,0.06075145788735514,ok
-75176,1.0,42,0.014405447878470423,ok
-75090,1.0,43,?,not_applicable
-75114,1.0,44,0.016270337922402955,ok
-260,1.0,45,?,not_applicable
-236,1.0,46,?,not_applicable
-75141,1.0,47,0.06528590724898709,ok
-75107,1.0,48,0.44127597873368773,ok
-262,1.0,49,?,not_applicable
-75146,1.0,50,0.09389760060457208,ok
-75189,1.0,51,0.01519371757012311,ok
-2350,1.0,52,0.5124081136351075,ok
-253,1.0,53,?,not_applicable
-2122,1.0,54,?,not_applicable
-75110,1.0,55,?,not_applicable
-75249,1.0,56,0.015228426395939132,ok
-75108,1.0,57,0.0,ok
-242,1.0,58,?,not_applicable
-75117,1.0,59,0.04281009879253561,ok
-75191,1.0,60,0.13383895433274173,ok
-75226,1.0,61,0.00035984166966529507,ok
-261,1.0,62,0.39495798319327735,ok
-75236,1.0,63,?,not_applicable
-75095,1.0,64,0.15508021390374327,ok
-75148,1.0,65,0.12068965517241381,ok
-75093,1.0,66,0.542528735632184,ok
-75223,1.0,67,?,not_applicable
-75244,1.0,68,0.6586306653809064,ok
-75109,1.0,69,?,not_applicable
-75197,1.0,70,?,not_applicable
-75127,1.0,71,0.37609277769688987,ok
-75143,1.0,72,0.0084283589489339,ok
-75153,1.0,73,0.08062750820868303,ok
-75173,1.0,74,0.11641041732036395,ok
-75215,1.0,75,0.0229191797346201,ok
-75195,1.0,76,0.00012514078338132784,ok
-75207,1.0,77,?,not_applicable
-266,1.0,78,?,not_applicable
-75225,1.0,79,0.5495495495495496,ok
-75166,1.0,80,0.09247472856608019,ok
-75100,1.0,81,0.7777777777777778,ok
-75169,1.0,82,?,not_applicable
-75132,1.0,83,0.855792318434351,ok
-273,1.0,84,0.051914893617021396,ok
-75121,1.0,85,0.0,ok
-75098,1.0,86,?,not_applicable
-75115,1.0,87,0.013844515441959526,ok
-75116,1.0,88,0.007025761124121788,ok
-75185,1.0,89,0.12967459932005843,ok
-2119,1.0,90,?,not_applicable
-75157,1.0,91,0.5551894563426689,ok
-75113,1.0,92,0.04885993485342022,ok
-75134,1.0,93,?,not_applicable
-75096,1.0,94,?,not_applicable
-75203,1.0,95,?,not_applicable
-75182,1.0,96,0.1910112359550561,ok
-251,1.0,97,?,not_applicable
-75123,1.0,98,?,not_applicable
-75237,1.0,99,0.00020259319286874966,ok
-75125,1.0,100,0.020310633213859064,ok
-75232,1.0,101,0.1845493562231758,ok
-75103,1.0,102,0.041009463722397554,ok
-75192,1.0,103,0.5028157683024941,ok
-75230,1.0,104,?,not_applicable
-75139,1.0,105,0.014989293361884481,ok
-75227,1.0,106,0.16682738669238184,ok
-2120,1.0,107,?,not_applicable
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-75240,1.0,109,0.028349082823791005,ok
-75129,1.0,110,0.5874999999999999,ok
-75198,1.0,111,?,not_applicable
-75201,1.0,112,?,not_applicable
-75112,1.0,113,0.1668278529980658,ok
-75133,1.0,114,0.5483870967741935,ok
-75105,1.0,115,0.9094173982442139,ok
-75154,1.0,116,?,not_applicable
-75177,1.0,117,0.15254237288135597,ok
-2117,1.0,118,0.12312601075221818,ok
-75156,1.0,119,0.18863801893663523,ok
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-75172,1.0,122,?,not_applicable
-75106,1.0,123,0.7961218836565097,ok
-75179,1.0,124,0.28075013789299486,ok
-75187,1.0,125,0.016820857863751093,ok
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-75193,1.0,127,?,not_applicable
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+75177,1.0,13,0.06249999999999989,ok
+75092,1.0,38,0.3885350318471338,ok
+75239,1.0,15,0.0,ok
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+75233,1.0,91,0.042766631467793026,ok
+75182,1.0,138,0.18899521531100472,ok
+253,1.0,27,?,not_applicable
+75157,1.0,87,0.4296875000000001,ok
+75187,1.0,267,0.015539689206215956,ok
+75124,1.0,31,0.45247148288973393,ok
+75090,1.0,32,?,not_applicable
+75222,1.0,130,0.23684210526315774,ok
+75231,1.0,34,?,not_applicable
+75185,1.0,325,0.12857836001940803,ok
+2123,1.0,39,?,not_applicable
+75150,1.0,72,0.25668449197860965,ok
+75143,1.0,43,0.006413418845584551,ok
+75196,1.0,44,0.014354066985646008,ok
+75188,1.0,49,?,not_applicable
+75248,1.0,54,0.607843137254902,ok
+75249,1.0,58,0.020408163265306145,ok
+75113,1.0,335,0.031825795644891186,ok
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+288,1.0,64,?,not_applicable
+251,1.0,65,?,not_applicable
+75184,1.0,156,0.1615120274914088,ok
+75234,1.0,68,0.02359641985353944,ok
+258,1.0,73,?,not_applicable
+75166,1.0,281,0.09015777610818954,ok
+75168,1.0,77,?,not_applicable
+75148,1.0,276,0.12393566698202463,ok
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+75159,1.0,84,0.4545454545454546,ok
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+75221,1.0,92,?,not_applicable
+75219,1.0,213,0.021114106019766377,ok
+75202,1.0,96,?,not_applicable
+3043,1.0,99,0.06249999999999989,ok
+75205,1.0,103,?,not_applicable
+75174,1.0,108,0.1862905106085334,ok
+75250,1.0,111,?,not_applicable
+75179,1.0,453,0.2838283828382837,ok
+275,1.0,115,?,not_applicable
+242,1.0,116,?,not_applicable
+75207,1.0,117,?,not_applicable
+75142,1.0,121,0.06886646534580543,ok
+75099,1.0,125,0.47770700636942665,ok
+75243,1.0,126,?,not_applicable
+75175,1.0,259,0.12241191427533604,ok
+233,1.0,136,0.003064351378958219,ok
+75161,1.0,139,0.057576203799145964,ok
+75176,1.0,328,0.014018079392113303,ok
+262,1.0,146,?,not_applicable
+75129,1.0,147,0.5405405405405406,ok
+261,1.0,148,0.38596491228070173,ok
+75114,1.0,154,0.012499999999999956,ok
+75093,1.0,231,0.551433389544688,ok
+260,1.0,158,?,not_applicable
+236,1.0,160,?,not_applicable
+254,1.0,166,0.0,ok
+75107,1.0,169,0.43380855397148677,ok
+75181,1.0,175,?,not_applicable
+75189,1.0,187,0.014132546793966805,ok
+75163,1.0,354,0.06316274781005071,ok
+2350,1.0,191,0.511668230010517,ok
+2122,1.0,195,?,not_applicable
+75110,1.0,198,?,not_applicable
+75213,1.0,424,0.11904761904761907,ok
+75095,1.0,344,0.11111111111111116,ok
+75108,1.0,208,0.0,ok
+75117,1.0,212,0.02444208289054195,ok
+75191,1.0,217,0.13333980142944912,ok
+75226,1.0,219,0.0017972681524083267,ok
+75244,1.0,372,0.5980392156862746,ok
+75236,1.0,223,?,not_applicable
+75169,1.0,227,?,not_applicable
+75116,1.0,320,0.003516998827667206,ok
+75223,1.0,235,?,not_applicable
+75109,1.0,242,?,not_applicable
+75197,1.0,245,?,not_applicable
+75237,1.0,365,0.00021815348656006872,ok
+248,1.0,250,?,not_applicable
+2119,1.0,254,?,not_applicable
+75127,1.0,255,0.3767906126181041,ok
+75153,1.0,263,0.08159531650201235,ok
+75195,1.0,274,0.00018772292096869148,ok
+266,1.0,277,?,not_applicable
+75225,1.0,280,0.569620253164557,ok
+75100,1.0,286,0.7777777777777778,ok
+75132,1.0,296,0.8615541098125012,ok
+75210,1.0,298,0.0,ok
+273,1.0,301,0.051914893617021396,ok
+75133,1.0,413,0.4054054054054055,ok
+75121,1.0,304,0.0,ok
+75098,1.0,310,?,not_applicable
+75115,1.0,317,0.007423117709437932,ok
+75217,1.0,337,?,not_applicable
+75134,1.0,341,?,not_applicable
+75096,1.0,346,?,not_applicable
+75203,1.0,350,?,not_applicable
+75123,1.0,360,?,not_applicable
+75125,1.0,370,0.017878426698450633,ok
+2120,1.0,371,?,not_applicable
+75232,1.0,374,0.1806167400881057,ok
+75103,1.0,380,0.043887147335423204,ok
+75230,1.0,386,?,not_applicable
+75240,1.0,399,0.027808676307007896,ok
+75198,1.0,404,?,not_applicable
+75201,1.0,407,?,not_applicable
+75105,1.0,418,0.8969978032706859,ok
+75154,1.0,420,?,not_applicable
+2117,1.0,428,0.08940568475452193,ok
+75156,1.0,430,0.18486171761280923,ok
+75097,1.0,435,0.025833815307499397,ok
+75101,1.0,438,0.2539724190209627,ok
+75172,1.0,443,?,not_applicable
+75106,1.0,451,0.7721443483447659,ok
+75120,1.0,458,0.016227180527383478,ok
+75193,1.0,461,?,not_applicable
diff --git a/autosklearn/metalearning/files/f1_multiclass.classification_dense/configurations.csv b/autosklearn/metalearning/files/f1_multiclass.classification_dense/configurations.csv
index cc0d063874..512362e6eb 100644
--- a/autosklearn/metalearning/files/f1_multiclass.classification_dense/configurations.csv
+++ b/autosklearn/metalearning/files/f1_multiclass.classification_dense/configurations.csv
@@ -1,128 +1,133 @@
 idx,balancing:strategy,classifier:__choice__,classifier:adaboost:algorithm,classifier:adaboost:learning_rate,classifier:adaboost:max_depth,classifier:adaboost:n_estimators,classifier:bernoulli_nb:alpha,classifier:bernoulli_nb:fit_prior,classifier:decision_tree:criterion,classifier:decision_tree:max_depth_factor,classifier:decision_tree:max_features,classifier:decision_tree:max_leaf_nodes,classifier:decision_tree:min_impurity_decrease,classifier:decision_tree:min_samples_leaf,classifier:decision_tree:min_samples_split,classifier:decision_tree:min_weight_fraction_leaf,classifier:extra_trees:bootstrap,classifier:extra_trees:criterion,classifier:extra_trees:max_depth,classifier:extra_trees:max_features,classifier:extra_trees:max_leaf_nodes,classifier:extra_trees:min_impurity_decrease,classifier:extra_trees:min_samples_leaf,classifier:extra_trees:min_samples_split,classifier:extra_trees:min_weight_fraction_leaf,classifier:gradient_boosting:early_stop,classifier:gradient_boosting:l2_regularization,classifier:gradient_boosting:learning_rate,classifier:gradient_boosting:loss,classifier:gradient_boosting:max_bins,classifier:gradient_boosting:max_depth,classifier:gradient_boosting:max_leaf_nodes,classifier:gradient_boosting:min_samples_leaf,classifier:gradient_boosting:n_iter_no_change,classifier:gradient_boosting:scoring,classifier:gradient_boosting:tol,classifier:gradient_boosting:validation_fraction,classifier:k_nearest_neighbors:n_neighbors,classifier:k_nearest_neighbors:p,classifier:k_nearest_neighbors:weights,classifier:lda:n_components,classifier:lda:shrinkage,classifier:lda:shrinkage_factor,classifier:lda:tol,classifier:liblinear_svc:C,classifier:liblinear_svc:dual,classifier:liblinear_svc:fit_intercept,classifier:liblinear_svc:intercept_scaling,classifier:liblinear_svc:loss,classifier:liblinear_svc:multi_class,classifier:liblinear_svc:penalty,classifier:liblinear_svc:tol,classifier:libsvm_svc:C,classifier:libsvm_svc:coef0,classifier:libsvm_svc:degree,classifier:libsvm_svc:gamma,classifier:libsvm_svc:kernel,classifier:libsvm_svc:max_iter,classifier:libsvm_svc:shrinking,classifier:libsvm_svc:tol,classifier:multinomial_nb:alpha,classifier:multinomial_nb:fit_prior,classifier:passive_aggressive:C,classifier:passive_aggressive:average,classifier:passive_aggressive:fit_intercept,classifier:passive_aggressive:loss,classifier:passive_aggressive:tol,classifier:qda:reg_param,classifier:random_forest:bootstrap,classifier:random_forest:criterion,classifier:random_forest:max_depth,classifier:random_forest:max_features,classifier:random_forest:max_leaf_nodes,classifier:random_forest:min_impurity_decrease,classifier:random_forest:min_samples_leaf,classifier:random_forest:min_samples_split,classifier:random_forest:min_weight_fraction_leaf,classifier:sgd:alpha,classifier:sgd:average,classifier:sgd:epsilon,classifier:sgd:eta0,classifier:sgd:fit_intercept,classifier:sgd:l1_ratio,classifier:sgd:learning_rate,classifier:sgd:loss,classifier:sgd:penalty,classifier:sgd:power_t,classifier:sgd:tol,data_preprocessing:categorical_transformer:categorical_encoding:__choice__,data_preprocessing:categorical_transformer:category_coalescence:__choice__,data_preprocessing:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction,data_preprocessing:numerical_transformer:imputation:strategy,data_preprocessing:numerical_transformer:rescaling:__choice__,data_preprocessing:numerical_transformer:rescaling:quantile_transformer:n_quantiles,data_preprocessing:numerical_transformer:rescaling:quantile_transformer:output_distribution,data_preprocessing:numerical_transformer:rescaling:robust_scaler:q_max,data_preprocessing:numerical_transformer:rescaling:robust_scaler:q_min,feature_preprocessor:__choice__,feature_preprocessor:extra_trees_preproc_for_classification:bootstrap,feature_preprocessor:extra_trees_preproc_for_classification:criterion,feature_preprocessor:extra_trees_preproc_for_classification:max_depth,feature_preprocessor:extra_trees_preproc_for_classification:max_features,feature_preprocessor:extra_trees_preproc_for_classification:max_leaf_nodes,feature_preprocessor:extra_trees_preproc_for_classification:min_impurity_decrease,feature_preprocessor:extra_trees_preproc_for_classification:min_samples_leaf,feature_preprocessor:extra_trees_preproc_for_classification:min_samples_split,feature_preprocessor:extra_trees_preproc_for_classification:min_weight_fraction_leaf,feature_preprocessor:extra_trees_preproc_for_classification:n_estimators,feature_preprocessor:fast_ica:algorithm,feature_preprocessor:fast_ica:fun,feature_preprocessor:fast_ica:n_components,feature_preprocessor:fast_ica:whiten,feature_preprocessor:feature_agglomeration:affinity,feature_preprocessor:feature_agglomeration:linkage,feature_preprocessor:feature_agglomeration:n_clusters,feature_preprocessor:feature_agglomeration:pooling_func,feature_preprocessor:kernel_pca:coef0,feature_preprocessor:kernel_pca:degree,feature_preprocessor:kernel_pca:gamma,feature_preprocessor:kernel_pca:kernel,feature_preprocessor:kernel_pca:n_components,feature_preprocessor:kitchen_sinks:gamma,feature_preprocessor:kitchen_sinks:n_components,feature_preprocessor:liblinear_svc_preprocessor:C,feature_preprocessor:liblinear_svc_preprocessor:dual,feature_preprocessor:liblinear_svc_preprocessor:fit_intercept,feature_preprocessor:liblinear_svc_preprocessor:intercept_scaling,feature_preprocessor:liblinear_svc_preprocessor:loss,feature_preprocessor:liblinear_svc_preprocessor:multi_class,feature_preprocessor:liblinear_svc_preprocessor:penalty,feature_preprocessor:liblinear_svc_preprocessor:tol,feature_preprocessor:nystroem_sampler:coef0,feature_preprocessor:nystroem_sampler:degree,feature_preprocessor:nystroem_sampler:gamma,feature_preprocessor:nystroem_sampler:kernel,feature_preprocessor:nystroem_sampler:n_components,feature_preprocessor:pca:keep_variance,feature_preprocessor:pca:whiten,feature_preprocessor:polynomial:degree,feature_preprocessor:polynomial:include_bias,feature_preprocessor:polynomial:interaction_only,feature_preprocessor:random_trees_embedding:bootstrap,feature_preprocessor:random_trees_embedding:max_depth,feature_preprocessor:random_trees_embedding:max_leaf_nodes,feature_preprocessor:random_trees_embedding:min_samples_leaf,feature_preprocessor:random_trees_embedding:min_samples_split,feature_preprocessor:random_trees_embedding:min_weight_fraction_leaf,feature_preprocessor:random_trees_embedding:n_estimators,feature_preprocessor:select_percentile_classification:percentile,feature_preprocessor:select_percentile_classification:score_func,feature_preprocessor:select_rates_classification:alpha,feature_preprocessor:select_rates_classification:mode,feature_preprocessor:select_rates_classification:score_func
-1,weighting,adaboost,SAMME,0.015984291290723266,7,184,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010000000000000004,median,standardize,,,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.9333242689646983,False,,,,,,,,,,,,,,,
-2,none,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.9525269450493825,None,0.0,4,20,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,standardize,,,,,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-3,none,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,188,None,,0.09976330267940166,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,none,,,,,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,-0.05439674489937307,2,0.051865468486041774,poly,727,,,,,,,,,,,,,,,,,
-4,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,23916.78290516781,False,True,1,squared_hinge,ovr,l2,0.0006655607726658531,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.008808715242823137,median,none,,,,,kernel_pca,,,,,,,,,,,,,,,,,,,0.2633153190620261,2,0.004770332714291669,poly,1483,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-5,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,2,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010091246322497477,median,none,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,95.37976833605768,mutual_info,,,
-6,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.933900673392165,None,0.0,7,4,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,minmax,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-7,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,0.9455840414651977,0.035051597802663065,auto,255,None,6,39,20,loss,1e-07,0.03762410007700571,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,robust_scaler,,,0.8083718526277422,0.24604827304064597,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,1377.6677844231401,False,True,1,squared_hinge,ovr,l1,0.0019190146350085739,,,,,,,,,,,,,,,,,,,,,,
-8,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5238814331135578,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,median,none,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,28,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-9,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1292.7448076998514,0.004489932344506231,2,1.3088036376373702,poly,-1,False,1.7943262139196537e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,minmax,,,,,feature_agglomeration,,,,,,,,,,,,,,,cosine,average,386,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-10,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.5244313211416313,None,0.0,1,2,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.454300236339658,most_frequent,robust_scaler,,,0.7858830918128734,0.2705174720960696,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-11,weighting,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0006294780694492238,most_frequent,standardize,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.063715750591364,fdr,chi2
-12,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.541827838541001,True,True,squared_hinge,0.0001129851063838815,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,standardize,,,,,extra_trees_preproc_for_classification,True,entropy,None,0.5533459595240017,None,0.0,18,4,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-13,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.25657910619102237,None,0.0,6,6,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.021086303324492208,most_frequent,quantile_transformer,1247,uniform,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.16021572815319468,fpr,chi2
-14,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6185424163558029,False,True,1,squared_hinge,ovr,l2,4.385767395120581e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010397452590017174,median,quantile_transformer,961,normal,,,feature_agglomeration,,,,,,,,,,,,,,,cosine,average,25,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-15,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.5890308372885505,None,0.0,19,10,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.02193054860417746,median,robust_scaler,,,0.75,0.25,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,87.97956633784086,f_classif,,,
-16,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7947177310891914,None,0.0,13,3,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,mean,robust_scaler,,,0.7681430822715739,0.23411421138225857,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,16.747604337291705,f_classif,,,
-17,none,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.1769332447629957,mean,minmax,,,,,fast_ica,,,,,,,,,,,deflation,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-18,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,40,2,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,robust_scaler,,,0.8108128057144657,0.1573950644227126,fast_ica,,,,,,,,,,,parallel,exp,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-19,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,199,None,,0.003239674765949011,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,robust_scaler,,,0.7228447795482008,0.25,kernel_pca,,,,,,,,,,,,,,,,,,,0.09440148939740256,3,0.0008114095405154861,poly,1996,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-20,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.6039096182414764,None,0.0,2,20,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.1939609823704447,most_frequent,robust_scaler,,,0.7331327008874781,0.29747488162877694,extra_trees_preproc_for_classification,True,entropy,None,0.33401603690569853,None,0.0,4,4,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-21,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,2.5882079595129472e-05,0.36125906161027654,auto,255,None,341,119,16,loss,1e-07,0.2924550184445239,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.02338478303505337,most_frequent,robust_scaler,,,0.7866731278723753,0.29543475006822734,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,68,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-22,weighting,adaboost,SAMME,0.2353770068926199,1,62,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.007159634821655197,most_frequent,quantile_transformer,1616,normal,,,feature_agglomeration,,,,,,,,,,,,,,,cosine,average,151,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-23,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7725.740943204492,,,3.122840783722913,rbf,-1,True,1.4116065061807418e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.14280178218510184,most_frequent,none,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,27,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-24,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.21794354428393548,None,0.0,2,16,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0025451910134387575,median,quantile_transformer,1477,uniform,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
-25,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5010952533165973,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.005838375247601407,mean,normalize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,
-26,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5514127836113225,None,0.0,4,20,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.010750756228608195,most_frequent,robust_scaler,,,0.7471420685032172,0.2916152663822578,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.22557315057515023,fwe,chi2
-27,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.4426097579931897,None,0.0,16,4,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,mean,robust_scaler,,,0.7407323456920554,0.0804756921037867,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,
-28,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.506204385946851,None,0.0,2,8,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.03433007982751011,mean,none,,,,,extra_trees_preproc_for_classification,False,gini,None,0.32676415738837183,None,0.0,5,6,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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-34,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,22194.242946902177,0.45210849669021247,,0.0005669713702171556,sigmoid,-1,False,0.03542142266081451,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,quantile_transformer,291,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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-43,none,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0686374607394679,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,minmax,,,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.8490260132946401,False,,,,,,,,,,,,,,,
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-46,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,92.66144306787051,,,0.04654639302091653,rbf,-1,True,0.0022231544280610005,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.005194838694455963,mean,none,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,15,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-47,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2681.998807317996,-0.9558745731731211,,0.0011299948421773457,sigmoid,-1,False,1.5292507354478086e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-48,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.8095522998548572,None,0.0,17,10,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.031940595071539304,mean,quantile_transformer,1563,normal,,,extra_trees_preproc_for_classification,False,entropy,None,0.9330145598603196,None,0.0,18,10,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-49,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,26.502259985828186,,,9.475662388405154e-05,rbf,-1,False,0.00019690635968217866,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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-55,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.6109468076475573,0.8748383232154038,4,0.41333070422336576,poly,-1,False,3.591268778393482e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.011804925819789203,median,minmax,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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-64,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,29.55731828912983,False,True,1,squared_hinge,ovr,l2,0.0011332486951954968,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,
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-68,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,925.3053743248586,False,True,1,squared_hinge,ovr,l2,0.02764481198395717,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.3850718018366796,most_frequent,quantile_transformer,786,uniform,,,feature_agglomeration,,,,,,,,,,,,,,,cosine,average,156,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-69,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.8278163313806293,None,0.0,3,5,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,robust_scaler,,,0.725835793765774,0.2571431961341018,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.07784020380291123,fdr,f_classif
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-77,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.34936025988397124,None,0.0,2,13,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00018601309647406987,median,robust_scaler,,,0.8634309348843472,0.26976989465367607,extra_trees_preproc_for_classification,False,entropy,None,0.48025175288975036,None,0.0,6,17,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-78,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,2.925599188129995e-05,0.12560285030651847,auto,255,None,300,193,17,loss,1e-07,0.039442997915546514,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00299400754997757,mean,robust_scaler,,,0.7093292916146515,0.009366983030047793,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-79,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.933900673392165,None,0.0,7,4,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,minmax,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-80,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1661.2274747409913,,,0.025821208852387936,rbf,-1,False,0.0017011435104921824,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,none,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,306,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-81,none,adaboost,SAMME,0.011233995624432622,9,477,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.018370622484682127,mean,standardize,,,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6039710338898471,False,,,,,,,,,,,,,,,
-82,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,21.85207494001697,0.18872999589786676,4,1.1863646021868761,poly,-1,False,0.0021338371901294443,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0034080852834770217,mean,normalize,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.11326005109886934,fdr,f_classif
-83,weighting,adaboost,SAMME,0.38341570193537416,9,250,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,minmax,,,,,feature_agglomeration,,,,,,,,,,,,,,,euclidean,average,13,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-84,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,0.037849720944694,0.1268815770019783,auto,255,None,11,99,11,loss,1e-07,0.10654523627313527,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,
-85,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.593728171200453,None,0.0,7,20,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.02830941310359363,mean,normalize,,,,,extra_trees_preproc_for_classification,False,entropy,None,0.7389446586281172,None,0.0,20,11,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-86,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,1,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.019797877010737826,mean,normalize,,,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.8239678741128165,False,,,,,,,,,,,,,,,
-87,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.46057831591617715,False,True,hinge,0.04557857428827514,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00027457445401600137,median,standardize,,,,,extra_trees_preproc_for_classification,True,gini,None,0.48190346970486964,None,0.0,17,18,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-88,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,38,auto,,0.00019398431171606024,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010000000000000004,median,quantile_transformer,1440,normal,,,extra_trees_preproc_for_classification,True,gini,None,0.5126639956845847,None,0.0,16,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-89,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5722917944959481,None,0.0,1,14,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,
-90,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.26356754187780895,None,0.0,4,3,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010966178401758774,mean,robust_scaler,,,0.7817043861798886,0.24371720112407158,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
-91,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,0.2129867633207542,0.05608094456817426,auto,255,None,14,99,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.000780073452899051,mean,minmax,,,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6093374564219372,True,,,,,,,,,,,,,,,
-92,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,80.0028902468169,False,True,1,squared_hinge,ovr,l2,0.0024481999829574722,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.15047970487774687,mean,quantile_transformer,1130,normal,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,1.1912646635544712,False,True,1,squared_hinge,ovr,l1,0.00014314728486450857,,,,,,,,,,,,,,,,,,,,,,
-93,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.22879311080499845,None,0.0,8,4,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.014320022547615783,median,minmax,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.238630703440461,mutual_info,,,
-94,weighting,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,2,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0073667222552306465,median,robust_scaler,,,0.701595034693991,0.29514640404971515,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6066051105210388,False,,,,,,,,,,,,,,,
-95,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.107967527639527e-05,True,True,hinge,0.00448294745544775,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,quantile_transformer,953,normal,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,35.00114345405331,chi2,,,
-96,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.14148638702297855,None,0.0,2,6,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.0038499650338378643,most_frequent,quantile_transformer,1759,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-97,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,1.1522414705935405e-10,0.6132800183073315,auto,255,None,1607,67,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010096911249562407,mean,robust_scaler,,,0.7663493326967942,0.136094217337434,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-98,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1228.3897711257332,0.6862670093933454,5,1.113863025113022,poly,-1,True,1.9271116749907707e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.018161834200128672,mean,normalize,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.06278549919408385,fpr,chi2
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-100,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.825844582229201,None,0.0,12,9,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0008422508222149647,median,robust_scaler,,,0.8658803880857064,0.2528423686293341,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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-102,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.7033210835501171,None,0.0,3,19,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,robust_scaler,,,0.7699195245692886,0.29258311638476237,feature_agglomeration,,,,,,,,,,,,,,,euclidean,ward,143,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-103,none,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,128,manual,0.8574560771111307,0.010886139381599514,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.24115007944216788,most_frequent,normalize,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,62.26514170991452,mutual_info,,,
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-105,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,1.430083734915855e-09,0.08779621406981941,auto,255,None,17,20,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.29749837779115257,mean,robust_scaler,,,0.7236445277814435,0.25,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
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-107,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,11.046854203258679,,,0.07809571976769347,rbf,-1,False,0.00036193159909053734,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.006445557635410205,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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-112,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.02021502857757827,True,,0.055848937053280076,True,,constant,log,l2,,0.00016356162584832537,no_encoding,no_coalescense,,median,quantile_transformer,27,normal,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.11873149622606233,fwe,chi2
-113,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,9.029473608568458e-09,0.15779990576964315,auto,255,None,37,71,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.018093177195644097,mean,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,False,,,,,,,,,,,,
-114,weighting,adaboost,SAMME.R,0.10000000000000002,3,50,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,robust_scaler,,,0.7639429738274788,0.25,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,67.72501979095493,f_classif,,,
-115,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.129143822880219,True,True,hinge,0.00012612093569084375,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0016222546082911887,most_frequent,quantile_transformer,1309,uniform,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8.816417526015055,f_classif,,,
-116,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.4864284879550203,None,0.0,1,4,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.008665854799630503,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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-121,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.4210978663047845,None,0.0,5,6,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.006365988550833196,most_frequent,robust_scaler,,,0.8348891074186082,0.00974961635730845,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0695919708890599,fpr,f_classif
-122,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.922871100218626,False,True,squared_hinge,0.09957180737451943,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0016563186856411274,median,quantile_transformer,850,uniform,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.09123695282772847,fpr,chi2
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-124,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1561.1002600303584,,,0.0024048551704194507,rbf,-1,False,0.0001936214667496851,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,minmax,,,,,fast_ica,,,,,,,,,,,parallel,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-125,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.087313242780732,0.0636709914987994,3,0.004481917125024585,poly,-1,False,0.01848880618941367,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.002136015035740733,mean,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-126,weighting,adaboost,SAMME.R,1.696588612003168,9,455,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,minmax,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,82.18726095926662,chi2,,,
-127,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.6466927589470762,None,0.0,11,2,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.009672465311659264,most_frequent,standardize,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,369,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+3,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5707458022222313,None,0.0,3,12,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,minmax,,,,,extra_trees_preproc_for_classification,False,gini,None,0.5651056128131684,None,0.0,9,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+7,none,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.1956599819716578,None,0.0,1,7,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,robust_scaler,,,0.92941901988866,0.07591140934892966,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,87.6629358232908,mutual_info,,,
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+68,weighting,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,minmax,,,,,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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+77,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.2309302197434343e-05,False,,0.006503403719704897,True,,constant,log,l1,,1.531583361000749e-05,one_hot_encoding,no_coalescense,,most_frequent,robust_scaler,,,0.7530789965606406,0.2430090971909385,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,cosine,1693,,,,,,,,,,,,,,,,,
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+325,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,1.944632809484799e-07,0.06023679587502036,auto,255,None,28,193,15,loss,1e-07,0.0639715328873465,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,False,,,,,,,,,,,,
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+337,weighting,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,minmax,,,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,227.42750098824558,False,True,1,squared_hinge,ovr,l1,0.0018060472646546995,,,,,,,,,,,,,,,,,,,,,,
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+354,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.47037910442880493,None,0.0,10,3,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.17511596636550678,mean,none,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,87.25836994493127,mutual_info,,,
+360,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.5720469164289109,None,0.0,1,17,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.03310727058091163,median,robust_scaler,,,0.8680123006808074,0.024825227678742904,fast_ica,,,,,,,,,,,parallel,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+365,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.0479982468066316,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,quantile_transformer,1097,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+370,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,161,manual,0.06874267877159168,0.0024806196360048064,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.02346562053758342,median,quantile_transformer,1087,normal,,,extra_trees_preproc_for_classification,True,gini,None,0.6515955878602243,None,0.0,17,7,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+371,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,4.198700105243736e-09,0.011070860038925674,auto,255,None,9,116,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010247554402833578,median,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,
+372,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.5431102417332107,None,0.0,1,16,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010731370069741165,median,none,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.35426195730988774,fdr,chi2
+374,none,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.7304811343030777,None,0.0,2,3,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,robust_scaler,,,0.7202654714984674,0.21555973677200435,feature_agglomeration,,,,,,,,,,,,,,,euclidean,average,149,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+380,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2677516008848513,True,True,hinge,0.05752557410901496,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0010152857217605206,median,quantile_transformer,1000,uniform,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,51.60974796845029,chi2,,,
+384,weighting,adaboost,SAMME,0.0924407457907304,8,61,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.3090968001340013,median,robust_scaler,,,0.75,0.26158375682461943,fast_ica,,,,,,,,,,,deflation,exp,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+386,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9358939105347388,None,0.0,1,5,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,none,,,,,fast_ica,,,,,,,,,,,parallel,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+399,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.5864398404787432,None,0.0,1,14,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0070663750850608895,most_frequent,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
+404,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8.380057363340162,False,True,hinge,0.0020653831067503525,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.05020677662128942,median,quantile_transformer,560,normal,,,kernel_pca,,,,,,,,,,,,,,,,,,,,,,cosine,891,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+407,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.7292011996224884,True,True,hinge,8.162763086383311e-05,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010337761588362885,median,quantile_transformer,877,uniform,,,kernel_pca,,,,,,,,,,,,,,,,,,,,,,cosine,1317,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+413,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,7.194851113809193e-10,0.013882834218943657,auto,255,None,18,2,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0005124238915083231,mean,quantile_transformer,948,normal,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,60.45885911036194,f_classif,,,
+418,weighting,adaboost,SAMME,0.1313487280585511,3,189,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,robust_scaler,,,0.8008090310131288,0.2935856792824304,extra_trees_preproc_for_classification,False,entropy,None,0.7722400064297721,None,0.0,5,4,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+420,weighting,adaboost,SAMME.R,0.05920795019389743,8,313,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,standardize,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,104,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+424,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,2.344883102482138e-07,0.0328548789988159,auto,255,None,42,24,16,loss,1e-07,0.13015466346727866,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,most_frequent,robust_scaler,,,0.7010898774332888,0.2534280821273264,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,
+428,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,3.738096432152635e-10,0.04468500715913894,auto,255,None,31,156,20,loss,1e-07,0.10279783687118692,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010000000000000004,most_frequent,quantile_transformer,1132,normal,,,feature_agglomeration,,,,,,,,,,,,,,,euclidean,ward,43,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+430,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5321814610661173,None,0.0,1,3,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,standardize,,,,,extra_trees_preproc_for_classification,True,gini,None,0.5,None,0.0,1,15,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+435,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.44903177399085925,None,0.0,2,17,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,mean,robust_scaler,,,0.7812408516672696,0.12791233577400205,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,
+438,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,6.090138036399552e-10,0.017305568433437588,auto,255,None,202,188,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,robust_scaler,,,0.7526196011313747,0.1563616005702636,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+443,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.6269076084367177,None,0.0,1,7,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.12943250859943486,most_frequent,standardize,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,62.84526554323969,chi2,,,
+451,weighting,adaboost,SAMME,0.2814550343434438,5,87,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.00018428537998177612,mean,standardize,,,,,feature_agglomeration,,,,,,,,,,,,,,,cosine,average,307,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+453,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,162.34906836693145,,,2.470518094983172,rbf,-1,False,1.5366632541084908e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.33912551828528803,median,none,,,,,fast_ica,,,,,,,,,,,deflation,logcosh,412,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+458,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,5.581658271288433e-10,0.06171988234311011,auto,255,None,25,6,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.014859405637537319,most_frequent,robust_scaler,,,0.7983900845251328,0.2545046978458648,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,1.380085213170737,False,True,1,squared_hinge,ovr,l1,0.0061450732015354784,,,,,,,,,,,,,,,,,,,,,,
+461,none,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.9796199105973851,None,0.0,2,13,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.00845950033601661,mean,robust_scaler,,,0.8275228560550734,0.23814615163106348,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
diff --git a/autosklearn/metalearning/files/f1_multiclass.classification_dense/description.txt b/autosklearn/metalearning/files/f1_multiclass.classification_dense/description.txt
index f7031dc919..d68f4e7291 100644
--- a/autosklearn/metalearning/files/f1_multiclass.classification_dense/description.txt
+++ b/autosklearn/metalearning/files/f1_multiclass.classification_dense/description.txt
@@ -1,8 +1,9 @@
 features_cutoff_time: 3600
 features_cutoff_memory: 3072
-number_of_feature_steps: 35
+number_of_feature_steps: 42
 feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances
 feature_step LogNumberOfInstances: LogNumberOfInstances
+feature_step NumberOfClasses: NumberOfClasses
 feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures
 feature_step LogNumberOfFeatures: LogNumberOfFeatures
 feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues
@@ -20,6 +21,11 @@ feature_step DatasetRatio: DatasetRatio, LogDatasetRatio
 feature_step LogDatasetRatio: LogDatasetRatio
 feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio
 feature_step LogInverseDatasetRatio: LogInverseDatasetRatio
+feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD
+feature_step ClassProbabilityMin: ClassProbabilityMin
+feature_step ClassProbabilityMax: ClassProbabilityMax
+feature_step ClassProbabilityMean: ClassProbabilityMean
+feature_step ClassProbabilitySTD: ClassProbabilitySTD
 feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum
 feature_step SymbolsMin: SymbolsMin
 feature_step SymbolsMax: SymbolsMax
@@ -36,11 +42,12 @@ feature_step SkewnessMin: SkewnessMin
 feature_step SkewnessMax: SkewnessMax
 feature_step SkewnessMean: SkewnessMean
 feature_step SkewnessSTD: SkewnessSTD
-features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
+feature_step ClassEntropy: ClassEntropy
+features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy
 features_stochastic: 
-default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
+default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy
 
-algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127
+algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235,236,237,238,239,240,241,242,243,244,245,246,247,248,249,250,251,252,253,254,255,256,257,258,259,260,261,262,263,264,265,266,267,268,269,270,271,272,273,274,275,276,277,278,279,280,281,282,283,284,285,286,287,288,289,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,312,313,314,315,316,317,318,319,320,321,322,323,324,325,326,327,328,329,330,331,332,333,334,335,336,337,338,339,340,341,342,343,344,345,346,347,348,349,350,351,352,353,354,355,356,357,358,359,360,361,362,363,364,365,366,367,368,369,370,371,372,373,374,375,376,377,378,379,380,381,382,383,384,385,386,387,388,389,390,391,392,393,394,395,396,397,398,399,400,401,402,403,404,405,406,407,408,409,410,411,412,413,414,415,416,417,418,419,420,421,422,423,424,425,426,427,428,429,430,431,432,433,434,435,436,437,438,439,440,441,442,443,444,445,446,447,448,449,450,451,452,453,454,455,456,457,458,459,460,461,462,463,464
 algorithms_stochastic: 
 performance_measures: f1
 performance_type: solution_quality
diff --git a/autosklearn/metalearning/files/f1_multiclass.classification_dense/feature_costs.arff b/autosklearn/metalearning/files/f1_multiclass.classification_dense/feature_costs.arff
index 0e17e20d22..8512fd9275 100644
--- a/autosklearn/metalearning/files/f1_multiclass.classification_dense/feature_costs.arff
+++ b/autosklearn/metalearning/files/f1_multiclass.classification_dense/feature_costs.arff
@@ -4,6 +4,7 @@
 @ATTRIBUTE repetition NUMERIC
 @ATTRIBUTE NumberOfInstances NUMERIC
 @ATTRIBUTE LogNumberOfInstances NUMERIC
+@ATTRIBUTE NumberOfClasses NUMERIC
 @ATTRIBUTE NumberOfFeatures NUMERIC
 @ATTRIBUTE LogNumberOfFeatures NUMERIC
 @ATTRIBUTE MissingValues NUMERIC
@@ -21,6 +22,11 @@
 @ATTRIBUTE LogDatasetRatio NUMERIC
 @ATTRIBUTE InverseDatasetRatio NUMERIC
 @ATTRIBUTE LogInverseDatasetRatio NUMERIC
+@ATTRIBUTE ClassOccurences NUMERIC
+@ATTRIBUTE ClassProbabilityMin NUMERIC
+@ATTRIBUTE ClassProbabilityMax NUMERIC
+@ATTRIBUTE ClassProbabilityMean NUMERIC
+@ATTRIBUTE ClassProbabilitySTD NUMERIC
 @ATTRIBUTE NumSymbols NUMERIC
 @ATTRIBUTE SymbolsMin NUMERIC
 @ATTRIBUTE SymbolsMax NUMERIC
@@ -37,162 +43,138 @@
 @ATTRIBUTE SkewnessMax NUMERIC
 @ATTRIBUTE SkewnessMean NUMERIC
 @ATTRIBUTE SkewnessSTD NUMERIC
+@ATTRIBUTE ClassEntropy NUMERIC
 
 @DATA
-75123,1.0,1e-05,1e-05,1e-05,1e-05,0.00253,0.00221,1e-05,0.00024,3e-05,0.00011,0.0,2e-05,2e-05,5e-05,8e-05,2e-05,1e-05,7e-05,1e-05,0.00072,1e-05,1e-05,0.00019,0.00041,0.00011,0.00061,0.00012,9e-05,0.00015,0.00025,0.00067,0.00014,0.00011,0.00016,0.00027
-75090,1.0,1e-05,1e-05,2e-05,2e-05,0.00815,0.00368,7e-05,0.00228,9e-05,0.00235,0.0,5e-05,5e-05,0.0001,0.00013,0.00013,1e-05,0.00021,2e-05,0.00064,0.0,0.0,0.00014,0.00038,0.00011,0.00066,0.00011,0.00014,0.00014,0.00026,0.00066,0.00011,0.00013,0.00015,0.00027
-2123,1.0,1e-05,1e-05,1e-05,1e-05,0.00097,0.00079,1e-05,0.00013,1e-05,7e-05,0.0,2e-05,2e-05,5e-05,8e-05,2e-05,1e-05,5e-05,1e-05,0.0007,1e-05,0.0,0.00017,0.00042,0.0001,0.00052,6e-05,8e-05,0.00012,0.00026,0.00049,6e-05,0.00011,9e-05,0.00023
-248,1.0,1e-05,1e-05,1e-05,1e-05,0.00088,0.00077,1e-05,0.0001,1e-05,4e-05,0.0,1e-05,1e-05,4e-05,5e-05,2e-05,1e-05,4e-05,1e-05,0.00041,1e-05,0.0,9e-05,0.00022,9e-05,0.00046,6e-05,8e-05,9e-05,0.00023,0.00049,7e-05,0.0001,0.0001,0.00021
-75101,1.0,1e-05,1e-05,1e-05,1e-05,0.0418,0.03546,1e-05,0.00389,1e-05,0.00248,0.0,2e-05,2e-05,4e-05,6e-05,2e-05,1e-05,5e-05,2e-05,0.00045,1e-05,0.0,0.00011,0.00023,0.0001,0.00041,6e-05,8e-05,0.0001,0.00018,0.00047,6e-05,0.0001,8e-05,0.00023
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-75217,1.0,1e-05,1e-05,2e-05,1e-05,0.00196,0.00158,1e-05,0.00026,2e-05,0.00016,1e-05,3e-05,3e-05,7e-05,0.0001,3e-05,1e-05,7e-05,2e-05,0.00089,1e-05,1e-05,0.00022,0.00053,0.00013,0.00074,9e-05,0.0001,0.00019,0.00036,0.00065,0.00013,0.0001,0.00011,0.00031
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-2117,1.0,1e-05,1e-05,1e-05,1e-05,0.01735,0.0154,1e-05,0.00141,1e-05,0.00057,0.0,1e-05,1e-05,3e-05,6e-05,2e-05,1e-05,5e-05,1e-05,0.0004,1e-05,0.0,8e-05,0.00021,0.0001,0.00065,0.00012,0.00011,0.00015,0.00026,0.00055,7e-05,0.00014,9e-05,0.00025
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-75103,1.0,1e-05,1e-05,1e-05,1e-05,0.01302,0.00819,3e-05,0.00234,3e-05,0.00255,0.0,2e-05,2e-05,6e-05,0.00012,5e-05,1e-05,0.00011,1e-05,0.0005,1e-05,0.0,9e-05,0.00029,0.00011,0.00054,8e-05,0.00011,0.00011,0.00024,0.00066,7e-05,0.00015,0.00011,0.00032
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-75157,1.0,1e-05,1e-05,1e-05,1e-05,0.00125,0.00106,1e-05,0.00015,2e-05,7e-05,0.0,2e-05,2e-05,5e-05,6e-05,2e-05,1e-05,4e-05,1e-05,0.00048,2e-05,0.0,0.0001,0.00026,0.0001,0.00039,6e-05,7e-05,8e-05,0.00019,0.00043,6e-05,9e-05,8e-05,0.0002
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+75235,1.0,1e-05,1e-05,0.0003,2e-05,1e-05,0.00461,0.00385,2e-05,0.00055,2e-05,0.00026,1e-05,3e-05,3e-05,0.0001,0.00011,3e-05,1e-05,5e-05,2e-05,0.00026,3e-05,1e-05,8e-05,0.00014,0.00075,1e-05,1e-05,0.0002,0.00043,0.00011,0.00127,0.00017,0.0002,0.00029,0.00061,0.0008,0.00015,0.00012,0.00016,0.00038,0.00242
+75236,1.0,1e-05,1e-05,0.0001,1e-05,1e-05,0.00218,0.00118,2e-05,0.00065,3e-05,0.00041,1e-05,3e-05,3e-05,9e-05,9e-05,4e-05,1e-05,7e-05,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.0006,1e-05,1e-05,0.00021,0.00032,6e-05,0.00043,7e-05,7e-05,9e-05,0.00019,0.00062,7e-05,8e-05,0.0001,0.00037,0.00048
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+75249,1.0,1e-05,1e-05,0.00011,1e-05,1e-05,0.00192,0.00153,1e-05,0.00025,1e-05,0.00017,1e-05,2e-05,2e-05,5e-05,7e-05,2e-05,1e-05,5e-05,3e-05,0.00014,2e-05,1e-05,3e-05,8e-05,0.00029,1e-05,3e-05,6e-05,0.00012,7e-05,0.00041,6e-05,8e-05,0.0001,0.00017,0.00043,6e-05,7e-05,9e-05,0.00019,0.00092
+75250,1.0,1e-05,1e-05,0.00375,1e-05,1e-05,0.04994,0.047,1e-05,0.00247,1e-05,0.00049,1e-05,2e-05,2e-05,6e-05,7e-05,2e-05,1e-05,3e-05,1e-05,0.00022,4e-05,1e-05,4e-05,0.00012,0.00041,0.0,1e-05,0.00011,0.00023,6e-05,0.00076,0.00012,0.00012,0.00019,0.00032,0.00086,0.00012,0.00013,0.00018,0.00043,0.04178
diff --git a/autosklearn/metalearning/files/f1_multiclass.classification_dense/feature_runstatus.arff b/autosklearn/metalearning/files/f1_multiclass.classification_dense/feature_runstatus.arff
index 0034ad2bd6..5fff2dfed3 100644
--- a/autosklearn/metalearning/files/f1_multiclass.classification_dense/feature_runstatus.arff
+++ b/autosklearn/metalearning/files/f1_multiclass.classification_dense/feature_runstatus.arff
@@ -4,6 +4,7 @@
 @ATTRIBUTE repetition NUMERIC
 @ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other}
@@ -21,6 +22,11 @@
 @ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other}
@@ -37,162 +43,138 @@
 @ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other}
 
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diff --git a/autosklearn/metalearning/files/f1_multiclass.classification_dense/feature_values.arff b/autosklearn/metalearning/files/f1_multiclass.classification_dense/feature_values.arff
index a60400e3ce..736dc64321 100644
--- a/autosklearn/metalearning/files/f1_multiclass.classification_dense/feature_values.arff
+++ b/autosklearn/metalearning/files/f1_multiclass.classification_dense/feature_values.arff
@@ -13,7 +13,6 @@
 @ATTRIBUTE KurtosisMean NUMERIC
 @ATTRIBUTE KurtosisMin NUMERIC
 @ATTRIBUTE KurtosisSTD NUMERIC
-@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC
 @ATTRIBUTE LogDatasetRatio NUMERIC
 @ATTRIBUTE LogInverseDatasetRatio NUMERIC
 @ATTRIBUTE LogNumberOfFeatures NUMERIC
@@ -42,160 +41,135 @@
 @ATTRIBUTE SymbolsSum NUMERIC
 
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diff --git a/autosklearn/metalearning/files/f1_multiclass.classification_sparse/algorithm_runs.arff b/autosklearn/metalearning/files/f1_multiclass.classification_sparse/algorithm_runs.arff
index 45809e172b..cefe06bb72 100644
--- a/autosklearn/metalearning/files/f1_multiclass.classification_sparse/algorithm_runs.arff
+++ b/autosklearn/metalearning/files/f1_multiclass.classification_sparse/algorithm_runs.arff
@@ -7,95 +7,133 @@
 @ATTRIBUTE runstatus {ok, timeout, memout, not_applicable, crash, other}
 
 @DATA
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-246,1.0,2,?,not_applicable
-75178,1.0,3,?,not_applicable
-75171,1.0,4,0.16716417910447756,ok
-248,1.0,5,?,not_applicable
-75231,1.0,6,?,not_applicable
-75196,1.0,7,0.009615384615384692,ok
-75188,1.0,8,?,not_applicable
-75248,1.0,9,0.6215177713736791,ok
-75126,1.0,10,0.0305775764439411,ok
-75234,1.0,11,0.04135649296939625,ok
-75150,1.0,12,0.2549019607843137,ok
-258,1.0,13,?,not_applicable
-75168,1.0,14,?,not_applicable
-75235,1.0,15,?,not_applicable
-244,1.0,16,?,not_applicable
-75221,1.0,17,?,not_applicable
-75219,1.0,18,0.04292527821939596,ok
-75202,1.0,19,?,not_applicable
-3043,1.0,20,0.14942528735632177,ok
-75205,1.0,21,?,not_applicable
-75174,1.0,22,0.1995599559955995,ok
-275,1.0,23,?,not_applicable
-75213,1.0,24,0.14754098360655743,ok
-75099,1.0,25,0.5384615384615385,ok
-75184,1.0,26,0.2218203737191078,ok
-75222,1.0,27,0.3982300884955753,ok
-233,1.0,28,0.003058103975535076,ok
-75114,1.0,29,0.02244389027431415,ok
-236,1.0,30,?,not_applicable
-75141,1.0,31,0.06528590724898709,ok
-75107,1.0,32,0.44127597873368773,ok
-262,1.0,33,?,not_applicable
-75146,1.0,34,0.10646460771149047,ok
-75189,1.0,35,0.01519371757012311,ok
-2350,1.0,36,0.5124081136351075,ok
-75249,1.0,37,0.015228426395939132,ok
-242,1.0,38,?,not_applicable
-75117,1.0,39,0.04281009879253561,ok
-75191,1.0,40,0.13383895433274173,ok
-261,1.0,41,0.39495798319327735,ok
-75236,1.0,42,?,not_applicable
-75095,1.0,43,0.15508021390374327,ok
-75093,1.0,44,0.5832426550598477,ok
-75223,1.0,45,?,not_applicable
-75109,1.0,46,?,not_applicable
-75197,1.0,47,?,not_applicable
-75127,1.0,48,0.37609277769688987,ok
-75143,1.0,49,0.0084283589489339,ok
-75153,1.0,50,0.08062750820868303,ok
-75173,1.0,51,0.1164987405541561,ok
-75215,1.0,52,0.023660067600193124,ok
-75195,1.0,53,0.0007510326699211589,ok
-75207,1.0,54,?,not_applicable
-75225,1.0,55,0.608695652173913,ok
-75166,1.0,56,0.15290519877675834,ok
-75100,1.0,57,0.9621212121212122,ok
-75169,1.0,58,?,not_applicable
-75121,1.0,59,0.0,ok
-75098,1.0,60,?,not_applicable
-75115,1.0,61,0.013844515441959526,ok
-75116,1.0,62,0.011820330969267157,ok
-75185,1.0,63,0.14093959731543626,ok
-2119,1.0,64,?,not_applicable
-75157,1.0,65,0.6242171189979123,ok
-75113,1.0,66,0.04885993485342022,ok
-75203,1.0,67,?,not_applicable
-75182,1.0,68,0.1910112359550561,ok
-251,1.0,69,?,not_applicable
-75123,1.0,70,?,not_applicable
-75125,1.0,71,0.020310633213859064,ok
-75232,1.0,72,0.18644067796610175,ok
-75103,1.0,73,0.07858243451463787,ok
-75192,1.0,74,0.5266932270916335,ok
-75230,1.0,75,?,not_applicable
-75139,1.0,76,0.019908116385911057,ok
-75227,1.0,77,0.16682738669238184,ok
-2120,1.0,78,?,not_applicable
-75124,1.0,79,0.5010752688172044,ok
-75240,1.0,80,0.028349082823791005,ok
-75198,1.0,81,?,not_applicable
-75201,1.0,82,?,not_applicable
-75133,1.0,83,0.5517241379310345,ok
-75154,1.0,84,?,not_applicable
-75177,1.0,85,0.16167664670658688,ok
-2117,1.0,86,0.12507005820219885,ok
-75156,1.0,87,0.18863801893663523,ok
-75097,1.0,88,0.03273424394919722,ok
-75172,1.0,89,?,not_applicable
-75106,1.0,90,0.8017386427369602,ok
-75187,1.0,91,0.016820857863751093,ok
-75120,1.0,92,0.020325203252032464,ok
+75192,1.0,308,0.4695652173913044,ok
+75119,1.0,2,0.01836734693877551,ok
+75212,1.0,319,0.2584269662921348,ok
+246,1.0,6,?,not_applicable
+252,1.0,7,?,not_applicable
+75178,1.0,8,?,not_applicable
+75177,1.0,11,0.06249999999999989,ok
+75092,1.0,31,0.3885350318471338,ok
+75239,1.0,13,0.0,ok
+75215,1.0,287,0.02388419782870932,ok
+75171,1.0,17,0.16186915887850462,ok
+75227,1.0,151,0.16197866149369544,ok
+75233,1.0,83,0.045621394861038334,ok
+75182,1.0,22,0.19539730785931397,ok
+253,1.0,23,?,not_applicable
+75157,1.0,70,0.4296875000000001,ok
+75124,1.0,202,0.5073529411764706,ok
+75222,1.0,27,0.267605633802817,ok
+75231,1.0,28,?,not_applicable
+75185,1.0,259,0.13307240704500978,ok
+2123,1.0,32,?,not_applicable
+75150,1.0,33,0.2586666666666667,ok
+75143,1.0,34,0.006419753086419733,ok
+75196,1.0,35,0.014354066985646008,ok
+75188,1.0,40,?,not_applicable
+75248,1.0,45,0.607843137254902,ok
+75249,1.0,49,0.020408163265306145,ok
+75113,1.0,50,0.04234527687296408,ok
+75126,1.0,51,0.022271714922049046,ok
+251,1.0,53,?,not_applicable
+75184,1.0,122,0.16530373831775702,ok
+75234,1.0,56,0.030995106035889064,ok
+258,1.0,60,?,not_applicable
+75166,1.0,227,0.09015777610818954,ok
+75168,1.0,63,?,not_applicable
+75148,1.0,183,0.13452492944496708,ok
+75235,1.0,67,?,not_applicable
+75159,1.0,186,0.6160000000000001,ok
+244,1.0,71,?,not_applicable
+75141,1.0,131,0.06294964028776973,ok
+75221,1.0,74,?,not_applicable
+75219,1.0,82,0.026528776978417268,ok
+75202,1.0,77,?,not_applicable
+3043,1.0,80,0.06249999999999989,ok
+75205,1.0,84,?,not_applicable
+75174,1.0,88,0.19286801478582305,ok
+288,1.0,89,?,not_applicable
+75250,1.0,90,?,not_applicable
+75179,1.0,363,0.2897696839850028,ok
+275,1.0,92,?,not_applicable
+75207,1.0,93,?,not_applicable
+75142,1.0,94,0.06979143636229634,ok
+75099,1.0,98,0.5319148936170213,ok
+75243,1.0,99,?,not_applicable
+75175,1.0,211,0.1333333333333333,ok
+233,1.0,107,0.004073319755600768,ok
+75161,1.0,109,0.06113118501585424,ok
+75176,1.0,110,0.014139827179890041,ok
+262,1.0,113,?,not_applicable
+75129,1.0,114,0.5614973262032086,ok
+261,1.0,115,0.38596491228070173,ok
+75090,1.0,116,?,not_applicable
+75114,1.0,120,0.012499999999999956,ok
+75093,1.0,187,0.551433389544688,ok
+260,1.0,124,?,not_applicable
+236,1.0,126,?,not_applicable
+254,1.0,132,0.0,ok
+75107,1.0,133,0.4384577360355908,ok
+75139,1.0,313,0.01593137254901955,ok
+75146,1.0,140,0.09667558272831489,ok
+75189,1.0,145,0.012421433500568901,ok
+75163,1.0,150,0.06384933394579695,ok
+2350,1.0,152,0.511668230010517,ok
+2122,1.0,156,?,not_applicable
+75110,1.0,159,?,not_applicable
+75213,1.0,162,0.12994350282485867,ok
+75095,1.0,163,0.1165644171779141,ok
+75108,1.0,167,0.0,ok
+75117,1.0,170,0.02444208289054195,ok
+75191,1.0,175,0.13333980142944912,ok
+75226,1.0,264,0.0023385500989385744,ok
+75244,1.0,298,0.5980392156862746,ok
+75236,1.0,181,?,not_applicable
+75169,1.0,184,?,not_applicable
+75116,1.0,185,0.005847953216374324,ok
+75223,1.0,189,?,not_applicable
+75109,1.0,196,?,not_applicable
+75197,1.0,199,?,not_applicable
+248,1.0,203,?,not_applicable
+2119,1.0,206,?,not_applicable
+75127,1.0,207,0.3767906126181041,ok
+75153,1.0,215,0.08159531650201235,ok
+75173,1.0,216,0.11768407803650094,ok
+75187,1.0,218,0.016365925304238305,ok
+75195,1.0,223,0.0005633802816901179,ok
+75225,1.0,266,0.5897435897435896,ok
+75100,1.0,232,0.9166666666666666,ok
+75132,1.0,237,0.8615541098125012,ok
+75210,1.0,239,0.0,ok
+273,1.0,241,0.055698371893744714,ok
+75133,1.0,332,0.48571428571428577,ok
+75121,1.0,244,0.0010256410256410664,ok
+75098,1.0,248,?,not_applicable
+75115,1.0,254,0.007423117709437932,ok
+266,1.0,258,?,not_applicable
+75134,1.0,274,?,not_applicable
+75096,1.0,277,?,not_applicable
+75203,1.0,280,?,not_applicable
+75123,1.0,288,?,not_applicable
+75237,1.0,292,0.00021815348656006872,ok
+75125,1.0,293,0.02026221692491048,ok
+2120,1.0,297,?,not_applicable
+75232,1.0,301,0.219298245614035,ok
+75103,1.0,306,0.043887147335423204,ok
+242,1.0,307,?,not_applicable
+75230,1.0,312,?,not_applicable
+75240,1.0,321,0.027808676307007896,ok
+75198,1.0,325,?,not_applicable
+75201,1.0,328,?,not_applicable
+75112,1.0,331,0.18627450980392168,ok
+75105,1.0,336,0.8969978032706859,ok
+75154,1.0,338,?,not_applicable
+2117,1.0,343,0.09187697160883279,ok
+75156,1.0,345,0.18486171761280923,ok
+75097,1.0,350,0.025833815307499397,ok
+75101,1.0,352,0.26037383722625607,ok
+75172,1.0,353,?,not_applicable
+75106,1.0,358,0.7728826433725788,ok
+75120,1.0,367,0.018218623481781382,ok
+75193,1.0,369,?,not_applicable
diff --git a/autosklearn/metalearning/files/f1_multiclass.classification_sparse/configurations.csv b/autosklearn/metalearning/files/f1_multiclass.classification_sparse/configurations.csv
index ce7ec55100..08474b4111 100644
--- a/autosklearn/metalearning/files/f1_multiclass.classification_sparse/configurations.csv
+++ b/autosklearn/metalearning/files/f1_multiclass.classification_sparse/configurations.csv
@@ -1,93 +1,131 @@
 idx,balancing:strategy,classifier:__choice__,classifier:adaboost:algorithm,classifier:adaboost:learning_rate,classifier:adaboost:max_depth,classifier:adaboost:n_estimators,classifier:bernoulli_nb:alpha,classifier:bernoulli_nb:fit_prior,classifier:decision_tree:criterion,classifier:decision_tree:max_depth_factor,classifier:decision_tree:max_features,classifier:decision_tree:max_leaf_nodes,classifier:decision_tree:min_impurity_decrease,classifier:decision_tree:min_samples_leaf,classifier:decision_tree:min_samples_split,classifier:decision_tree:min_weight_fraction_leaf,classifier:extra_trees:bootstrap,classifier:extra_trees:criterion,classifier:extra_trees:max_depth,classifier:extra_trees:max_features,classifier:extra_trees:max_leaf_nodes,classifier:extra_trees:min_impurity_decrease,classifier:extra_trees:min_samples_leaf,classifier:extra_trees:min_samples_split,classifier:extra_trees:min_weight_fraction_leaf,classifier:gradient_boosting:early_stop,classifier:gradient_boosting:l2_regularization,classifier:gradient_boosting:learning_rate,classifier:gradient_boosting:loss,classifier:gradient_boosting:max_bins,classifier:gradient_boosting:max_depth,classifier:gradient_boosting:max_leaf_nodes,classifier:gradient_boosting:min_samples_leaf,classifier:gradient_boosting:n_iter_no_change,classifier:gradient_boosting:scoring,classifier:gradient_boosting:tol,classifier:gradient_boosting:validation_fraction,classifier:k_nearest_neighbors:n_neighbors,classifier:k_nearest_neighbors:p,classifier:k_nearest_neighbors:weights,classifier:lda:n_components,classifier:lda:shrinkage,classifier:lda:shrinkage_factor,classifier:lda:tol,classifier:liblinear_svc:C,classifier:liblinear_svc:dual,classifier:liblinear_svc:fit_intercept,classifier:liblinear_svc:intercept_scaling,classifier:liblinear_svc:loss,classifier:liblinear_svc:multi_class,classifier:liblinear_svc:penalty,classifier:liblinear_svc:tol,classifier:libsvm_svc:C,classifier:libsvm_svc:coef0,classifier:libsvm_svc:degree,classifier:libsvm_svc:gamma,classifier:libsvm_svc:kernel,classifier:libsvm_svc:max_iter,classifier:libsvm_svc:shrinking,classifier:libsvm_svc:tol,classifier:multinomial_nb:alpha,classifier:multinomial_nb:fit_prior,classifier:passive_aggressive:C,classifier:passive_aggressive:average,classifier:passive_aggressive:fit_intercept,classifier:passive_aggressive:loss,classifier:passive_aggressive:tol,classifier:qda:reg_param,classifier:random_forest:bootstrap,classifier:random_forest:criterion,classifier:random_forest:max_depth,classifier:random_forest:max_features,classifier:random_forest:max_leaf_nodes,classifier:random_forest:min_impurity_decrease,classifier:random_forest:min_samples_leaf,classifier:random_forest:min_samples_split,classifier:random_forest:min_weight_fraction_leaf,classifier:sgd:alpha,classifier:sgd:average,classifier:sgd:epsilon,classifier:sgd:eta0,classifier:sgd:fit_intercept,classifier:sgd:l1_ratio,classifier:sgd:learning_rate,classifier:sgd:loss,classifier:sgd:penalty,classifier:sgd:power_t,classifier:sgd:tol,data_preprocessing:categorical_transformer:categorical_encoding:__choice__,data_preprocessing:categorical_transformer:category_coalescence:__choice__,data_preprocessing:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction,data_preprocessing:numerical_transformer:imputation:strategy,data_preprocessing:numerical_transformer:rescaling:__choice__,data_preprocessing:numerical_transformer:rescaling:quantile_transformer:n_quantiles,data_preprocessing:numerical_transformer:rescaling:quantile_transformer:output_distribution,data_preprocessing:numerical_transformer:rescaling:robust_scaler:q_max,data_preprocessing:numerical_transformer:rescaling:robust_scaler:q_min,feature_preprocessor:__choice__,feature_preprocessor:extra_trees_preproc_for_classification:bootstrap,feature_preprocessor:extra_trees_preproc_for_classification:criterion,feature_preprocessor:extra_trees_preproc_for_classification:max_depth,feature_preprocessor:extra_trees_preproc_for_classification:max_features,feature_preprocessor:extra_trees_preproc_for_classification:max_leaf_nodes,feature_preprocessor:extra_trees_preproc_for_classification:min_impurity_decrease,feature_preprocessor:extra_trees_preproc_for_classification:min_samples_leaf,feature_preprocessor:extra_trees_preproc_for_classification:min_samples_split,feature_preprocessor:extra_trees_preproc_for_classification:min_weight_fraction_leaf,feature_preprocessor:extra_trees_preproc_for_classification:n_estimators,feature_preprocessor:kernel_pca:coef0,feature_preprocessor:kernel_pca:degree,feature_preprocessor:kernel_pca:gamma,feature_preprocessor:kernel_pca:kernel,feature_preprocessor:kernel_pca:n_components,feature_preprocessor:kitchen_sinks:gamma,feature_preprocessor:kitchen_sinks:n_components,feature_preprocessor:liblinear_svc_preprocessor:C,feature_preprocessor:liblinear_svc_preprocessor:dual,feature_preprocessor:liblinear_svc_preprocessor:fit_intercept,feature_preprocessor:liblinear_svc_preprocessor:intercept_scaling,feature_preprocessor:liblinear_svc_preprocessor:loss,feature_preprocessor:liblinear_svc_preprocessor:multi_class,feature_preprocessor:liblinear_svc_preprocessor:penalty,feature_preprocessor:liblinear_svc_preprocessor:tol,feature_preprocessor:nystroem_sampler:coef0,feature_preprocessor:nystroem_sampler:degree,feature_preprocessor:nystroem_sampler:gamma,feature_preprocessor:nystroem_sampler:kernel,feature_preprocessor:nystroem_sampler:n_components,feature_preprocessor:polynomial:degree,feature_preprocessor:polynomial:include_bias,feature_preprocessor:polynomial:interaction_only,feature_preprocessor:random_trees_embedding:bootstrap,feature_preprocessor:random_trees_embedding:max_depth,feature_preprocessor:random_trees_embedding:max_leaf_nodes,feature_preprocessor:random_trees_embedding:min_samples_leaf,feature_preprocessor:random_trees_embedding:min_samples_split,feature_preprocessor:random_trees_embedding:min_weight_fraction_leaf,feature_preprocessor:random_trees_embedding:n_estimators,feature_preprocessor:select_percentile_classification:percentile,feature_preprocessor:select_percentile_classification:score_func,feature_preprocessor:select_rates_classification:alpha,feature_preprocessor:select_rates_classification:mode,feature_preprocessor:select_rates_classification:score_func,feature_preprocessor:truncatedSVD:target_dim
-1,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.3163020577549344,None,0.0,15,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.01322981552715978,most_frequent,robust_scaler,,,0.9746667009556722,0.1974876581478311,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-2,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,23916.78290516781,False,True,1,squared_hinge,ovr,l2,0.0006655607726658531,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.008808715242823137,median,none,,,,,kernel_pca,,,,,,,,,,,0.2633153190620261,2,0.004770332714291669,poly,1483,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-3,weighting,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,88,2,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.254263872179305,median,normalize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-4,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.18985287343431662,0.6308014265183048,4,0.00803156980050639,poly,-1,True,0.0009345282234674084,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,robust_scaler,,,0.7664607090972055,0.16965101213172026,extra_trees_preproc_for_classification,False,entropy,None,0.5,None,0.0,16,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-5,none,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00011009762574324903,True,,0.031641726094190054,True,,invscaling,hinge,l2,0.2208821470267501,0.007895311091089785,one_hot_encoding,minority_coalescer,0.13807502937372682,mean,quantile_transformer,1340,normal,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,,
-6,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.5244313211416313,None,0.0,1,2,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.454300236339658,most_frequent,robust_scaler,,,0.7858830918128734,0.2705174720960696,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-7,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.541827838541001,True,True,squared_hinge,0.0001129851063838815,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,standardize,,,,,extra_trees_preproc_for_classification,True,entropy,None,0.5533459595240017,None,0.0,18,4,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-8,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.25657910619102237,None,0.0,6,6,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.021086303324492208,most_frequent,quantile_transformer,1247,uniform,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.16021572815319468,fpr,chi2,
-9,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.643761610990255,None,0.0,20,6,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.039859381630488695,mean,normalize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,,
-10,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.8253905225980761,None,0.0,18,20,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.003725255016700434,mean,robust_scaler,,,0.7701705977765594,0.2396520157991559,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.3579038776690903,False,True,1,squared_hinge,ovr,l1,4.994232534734023e-05,,,,,,,,,,,,,,,,,,,,,
-11,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.06263739023781334,None,0.0,18,8,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.02206379295100344,most_frequent,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,,
-12,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.07518858116896743,None,0.0,7,10,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0027133920607872518,mean,robust_scaler,,,0.7873452530915046,0.16215139072179952,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-13,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,199,None,,0.003239674765949011,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,robust_scaler,,,0.7228447795482008,0.25,kernel_pca,,,,,,,,,,,0.09440148939740256,3,0.0008114095405154861,poly,1996,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-14,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.6039096182414764,None,0.0,2,20,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.1939609823704447,most_frequent,robust_scaler,,,0.7331327008874781,0.29747488162877694,extra_trees_preproc_for_classification,True,entropy,None,0.33401603690569853,None,0.0,4,4,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-15,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7865652301288171,None,0.0,1,4,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,median,robust_scaler,,,0.8623635641193496,0.13298297278730625,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-16,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.905401147837377,-0.32832122529048147,3,1.001896309040499,poly,-1,False,5.1523484258030785e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-17,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.21794354428393548,None,0.0,2,16,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0025451910134387575,median,quantile_transformer,1477,uniform,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,,
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-25,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.7772914874049878,None,0.0,10,15,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00015285482513309234,mean,robust_scaler,,,0.8373404000074934,0.14303151080827242,extra_trees_preproc_for_classification,True,entropy,None,0.5021713909357703,None,0.0,13,16,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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-29,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,10,1,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0022606253047811435,median,quantile_transformer,1247,normal,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,25.327793585266683,chi2,,,,
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-32,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.8095522998548572,None,0.0,17,10,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.031940595071539304,mean,quantile_transformer,1563,normal,,,extra_trees_preproc_for_classification,False,entropy,None,0.9330145598603196,None,0.0,18,10,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-33,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,26.502259985828186,,,9.475662388405154e-05,rbf,-1,False,0.00019690635968217866,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-34,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.339600708038189,False,True,1,squared_hinge,ovr,l2,9.572641158475304e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,quantile_transformer,1193,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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+369,none,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.9796199105973851,None,0.0,2,13,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.00845950033601661,mean,robust_scaler,,,0.8275228560550734,0.23814615163106348,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
diff --git a/autosklearn/metalearning/files/f1_multiclass.classification_sparse/description.txt b/autosklearn/metalearning/files/f1_multiclass.classification_sparse/description.txt
index c826b0b627..6faf309c30 100644
--- a/autosklearn/metalearning/files/f1_multiclass.classification_sparse/description.txt
+++ b/autosklearn/metalearning/files/f1_multiclass.classification_sparse/description.txt
@@ -1,8 +1,9 @@
 features_cutoff_time: 3600
 features_cutoff_memory: 3072
-number_of_feature_steps: 35
+number_of_feature_steps: 42
 feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances
 feature_step LogNumberOfInstances: LogNumberOfInstances
+feature_step NumberOfClasses: NumberOfClasses
 feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures
 feature_step LogNumberOfFeatures: LogNumberOfFeatures
 feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues
@@ -20,6 +21,11 @@ feature_step DatasetRatio: DatasetRatio, LogDatasetRatio
 feature_step LogDatasetRatio: LogDatasetRatio
 feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio
 feature_step LogInverseDatasetRatio: LogInverseDatasetRatio
+feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD
+feature_step ClassProbabilityMin: ClassProbabilityMin
+feature_step ClassProbabilityMax: ClassProbabilityMax
+feature_step ClassProbabilityMean: ClassProbabilityMean
+feature_step ClassProbabilitySTD: ClassProbabilitySTD
 feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum
 feature_step SymbolsMin: SymbolsMin
 feature_step SymbolsMax: SymbolsMax
@@ -36,11 +42,12 @@ feature_step SkewnessMin: SkewnessMin
 feature_step SkewnessMax: SkewnessMax
 feature_step SkewnessMean: SkewnessMean
 feature_step SkewnessSTD: SkewnessSTD
-features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
+feature_step ClassEntropy: ClassEntropy
+features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy
 features_stochastic: 
-default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
+default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy
 
-algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92
+algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235,236,237,238,239,240,241,242,243,244,245,246,247,248,249,250,251,252,253,254,255,256,257,258,259,260,261,262,263,264,265,266,267,268,269,270,271,272,273,274,275,276,277,278,279,280,281,282,283,284,285,286,287,288,289,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,312,313,314,315,316,317,318,319,320,321,322,323,324,325,326,327,328,329,330,331,332,333,334,335,336,337,338,339,340,341,342,343,344,345,346,347,348,349,350,351,352,353,354,355,356,357,358,359,360,361,362,363,364,365,366,367,368,369,370,371,372
 algorithms_stochastic: 
 performance_measures: f1
 performance_type: solution_quality
diff --git a/autosklearn/metalearning/files/f1_multiclass.classification_sparse/feature_costs.arff b/autosklearn/metalearning/files/f1_multiclass.classification_sparse/feature_costs.arff
index 0e17e20d22..8512fd9275 100644
--- a/autosklearn/metalearning/files/f1_multiclass.classification_sparse/feature_costs.arff
+++ b/autosklearn/metalearning/files/f1_multiclass.classification_sparse/feature_costs.arff
@@ -4,6 +4,7 @@
 @ATTRIBUTE repetition NUMERIC
 @ATTRIBUTE NumberOfInstances NUMERIC
 @ATTRIBUTE LogNumberOfInstances NUMERIC
+@ATTRIBUTE NumberOfClasses NUMERIC
 @ATTRIBUTE NumberOfFeatures NUMERIC
 @ATTRIBUTE LogNumberOfFeatures NUMERIC
 @ATTRIBUTE MissingValues NUMERIC
@@ -21,6 +22,11 @@
 @ATTRIBUTE LogDatasetRatio NUMERIC
 @ATTRIBUTE InverseDatasetRatio NUMERIC
 @ATTRIBUTE LogInverseDatasetRatio NUMERIC
+@ATTRIBUTE ClassOccurences NUMERIC
+@ATTRIBUTE ClassProbabilityMin NUMERIC
+@ATTRIBUTE ClassProbabilityMax NUMERIC
+@ATTRIBUTE ClassProbabilityMean NUMERIC
+@ATTRIBUTE ClassProbabilitySTD NUMERIC
 @ATTRIBUTE NumSymbols NUMERIC
 @ATTRIBUTE SymbolsMin NUMERIC
 @ATTRIBUTE SymbolsMax NUMERIC
@@ -37,162 +43,138 @@
 @ATTRIBUTE SkewnessMax NUMERIC
 @ATTRIBUTE SkewnessMean NUMERIC
 @ATTRIBUTE SkewnessSTD NUMERIC
+@ATTRIBUTE ClassEntropy NUMERIC
 
 @DATA
-75123,1.0,1e-05,1e-05,1e-05,1e-05,0.00253,0.00221,1e-05,0.00024,3e-05,0.00011,0.0,2e-05,2e-05,5e-05,8e-05,2e-05,1e-05,7e-05,1e-05,0.00072,1e-05,1e-05,0.00019,0.00041,0.00011,0.00061,0.00012,9e-05,0.00015,0.00025,0.00067,0.00014,0.00011,0.00016,0.00027
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-75101,1.0,1e-05,1e-05,1e-05,1e-05,0.0418,0.03546,1e-05,0.00389,1e-05,0.00248,0.0,2e-05,2e-05,4e-05,6e-05,2e-05,1e-05,5e-05,2e-05,0.00045,1e-05,0.0,0.00011,0.00023,0.0001,0.00041,6e-05,8e-05,0.0001,0.00018,0.00047,6e-05,0.0001,8e-05,0.00023
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-75187,1.0,1e-05,1e-05,2e-05,1e-05,0.00548,0.00473,1e-05,0.0005,2e-05,0.00029,1e-05,2e-05,2e-05,7e-05,9e-05,3e-05,1e-05,6e-05,1e-05,0.00062,1e-05,1e-05,0.00014,0.00033,0.00013,0.00038,7e-05,7e-05,8e-05,0.00016,0.00047,6e-05,9e-05,9e-05,0.00023
-266,1.0,1e-05,1e-05,1e-05,1e-05,0.0018,0.00146,1e-05,0.00023,2e-05,0.00015,2e-05,2e-05,2e-05,6e-05,8e-05,3e-05,1e-05,6e-05,1e-05,0.00059,1e-05,1e-05,0.00014,0.00031,0.00013,0.0004,5e-05,7e-05,8e-05,0.0002,0.00044,8e-05,8e-05,8e-05,0.0002
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-75217,1.0,1e-05,1e-05,2e-05,1e-05,0.00196,0.00158,1e-05,0.00026,2e-05,0.00016,1e-05,3e-05,3e-05,7e-05,0.0001,3e-05,1e-05,7e-05,2e-05,0.00089,1e-05,1e-05,0.00022,0.00053,0.00013,0.00074,9e-05,0.0001,0.00019,0.00036,0.00065,0.00013,0.0001,0.00011,0.00031
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-75103,1.0,1e-05,1e-05,1e-05,1e-05,0.01302,0.00819,3e-05,0.00234,3e-05,0.00255,0.0,2e-05,2e-05,6e-05,0.00012,5e-05,1e-05,0.00011,1e-05,0.0005,1e-05,0.0,9e-05,0.00029,0.00011,0.00054,8e-05,0.00011,0.00011,0.00024,0.00066,7e-05,0.00015,0.00011,0.00032
-75168,1.0,1e-05,0.0,1e-05,1e-05,0.02353,0.00601,0.00016,0.01754,0.00018,0.00031,0.0,0.00013,0.00013,0.00027,0.0003,0.00032,1e-05,0.00042,1e-05,0.0004,1e-05,0.0,8e-05,0.00021,0.0001,0.0011,0.00022,0.00025,0.00026,0.00037,0.00124,0.00022,0.00025,0.00028,0.00049
-75157,1.0,1e-05,1e-05,1e-05,1e-05,0.00125,0.00106,1e-05,0.00015,2e-05,7e-05,0.0,2e-05,2e-05,5e-05,6e-05,2e-05,1e-05,4e-05,1e-05,0.00048,2e-05,0.0,0.0001,0.00026,0.0001,0.00039,6e-05,7e-05,8e-05,0.00019,0.00043,6e-05,9e-05,8e-05,0.0002
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-75236,1.0,1e-05,1e-05,1e-05,1e-05,0.00208,0.00115,2e-05,0.00054,3e-05,0.00044,0.0,3e-05,3e-05,7e-05,8e-05,5e-05,1e-05,8e-05,1e-05,0.00062,0.0,0.0,0.00016,0.00036,0.0001,0.00068,0.0001,0.00014,0.00016,0.00029,0.00072,0.0001,0.00015,0.00015,0.00032
-75119,1.0,1e-05,1e-05,2e-05,1e-05,0.04185,0.01398,0.00063,0.01614,0.0007,0.01306,0.0,0.00046,0.00045,0.00091,0.00097,0.0012,1e-05,0.0015,1e-05,0.00047,1e-05,0.0,8e-05,0.00026,0.00012,0.00421,0.00091,0.00097,0.00103,0.0013,0.00402,0.00082,0.0008,0.00097,0.00144
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+75235,1.0,1e-05,1e-05,0.0003,2e-05,1e-05,0.00461,0.00385,2e-05,0.00055,2e-05,0.00026,1e-05,3e-05,3e-05,0.0001,0.00011,3e-05,1e-05,5e-05,2e-05,0.00026,3e-05,1e-05,8e-05,0.00014,0.00075,1e-05,1e-05,0.0002,0.00043,0.00011,0.00127,0.00017,0.0002,0.00029,0.00061,0.0008,0.00015,0.00012,0.00016,0.00038,0.00242
+75236,1.0,1e-05,1e-05,0.0001,1e-05,1e-05,0.00218,0.00118,2e-05,0.00065,3e-05,0.00041,1e-05,3e-05,3e-05,9e-05,9e-05,4e-05,1e-05,7e-05,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.0006,1e-05,1e-05,0.00021,0.00032,6e-05,0.00043,7e-05,7e-05,9e-05,0.00019,0.00062,7e-05,8e-05,0.0001,0.00037,0.00048
+75237,1.0,2e-05,1e-05,0.00785,2e-05,1e-05,0.31031,0.27854,2e-05,0.01161,3e-05,0.02022,1e-05,4e-05,3e-05,9e-05,0.00024,6e-05,2e-05,9e-05,2e-05,0.00062,7e-05,3e-05,0.00017,0.00035,0.0008,1e-05,1e-05,0.00028,0.00043,8e-05,0.00043,8e-05,7e-05,9e-05,0.0002,0.00044,6e-05,8e-05,9e-05,0.00021,0.14028
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+75243,1.0,1e-05,1e-05,0.00029,1e-05,1e-05,0.00461,0.00415,1e-05,0.00035,1e-05,0.00015,0.0,2e-05,2e-05,5e-05,7e-05,2e-05,1e-05,4e-05,1e-05,0.00018,2e-05,1e-05,4e-05,0.00011,0.00022,1e-05,1e-05,5e-05,0.00011,5e-05,0.0004,6e-05,6e-05,8e-05,0.0002,0.00039,6e-05,6e-05,8e-05,0.00019,0.00265
+75244,1.0,3e-05,2e-05,0.0004,3e-05,2e-05,0.0173,0.01298,4e-05,0.00267,4e-05,0.00174,1e-05,7e-05,9e-05,0.00024,0.00027,8e-05,2e-05,0.00013,3e-05,0.00045,7e-05,2e-05,0.00011,0.00025,0.00077,6e-05,2e-05,0.00021,0.00034,0.00013,0.00077,0.00014,0.00013,0.00026,0.00023,0.00044,7e-05,8e-05,0.0001,0.00019,0.00649
+75248,1.0,1e-05,1e-05,0.00017,1e-05,1e-05,0.00416,0.00305,1e-05,0.0006,2e-05,0.00054,1e-05,2e-05,2e-05,5e-05,7e-05,2e-05,1e-05,4e-05,1e-05,0.00017,2e-05,1e-05,4e-05,0.0001,0.00029,5e-05,1e-05,6e-05,0.00012,6e-05,0.00081,0.00011,0.00015,0.00016,0.00038,0.0005,7e-05,8e-05,0.00012,0.00024,0.00184
+75249,1.0,1e-05,1e-05,0.00011,1e-05,1e-05,0.00192,0.00153,1e-05,0.00025,1e-05,0.00017,1e-05,2e-05,2e-05,5e-05,7e-05,2e-05,1e-05,5e-05,3e-05,0.00014,2e-05,1e-05,3e-05,8e-05,0.00029,1e-05,3e-05,6e-05,0.00012,7e-05,0.00041,6e-05,8e-05,0.0001,0.00017,0.00043,6e-05,7e-05,9e-05,0.00019,0.00092
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diff --git a/autosklearn/metalearning/files/f1_multiclass.classification_sparse/feature_runstatus.arff b/autosklearn/metalearning/files/f1_multiclass.classification_sparse/feature_runstatus.arff
index 0034ad2bd6..5fff2dfed3 100644
--- a/autosklearn/metalearning/files/f1_multiclass.classification_sparse/feature_runstatus.arff
+++ b/autosklearn/metalearning/files/f1_multiclass.classification_sparse/feature_runstatus.arff
@@ -4,6 +4,7 @@
 @ATTRIBUTE repetition NUMERIC
 @ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other}
@@ -21,6 +22,11 @@
 @ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other}
@@ -37,162 +43,138 @@
 @ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other}
 
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+75249,1.0,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,other,ok,ok,ok,ok,ok
+75250,1.0,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,other,ok,ok,ok,ok,ok
diff --git a/autosklearn/metalearning/files/f1_multiclass.classification_sparse/feature_values.arff b/autosklearn/metalearning/files/f1_multiclass.classification_sparse/feature_values.arff
index a60400e3ce..736dc64321 100644
--- a/autosklearn/metalearning/files/f1_multiclass.classification_sparse/feature_values.arff
+++ b/autosklearn/metalearning/files/f1_multiclass.classification_sparse/feature_values.arff
@@ -13,7 +13,6 @@
 @ATTRIBUTE KurtosisMean NUMERIC
 @ATTRIBUTE KurtosisMin NUMERIC
 @ATTRIBUTE KurtosisSTD NUMERIC
-@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC
 @ATTRIBUTE LogDatasetRatio NUMERIC
 @ATTRIBUTE LogInverseDatasetRatio NUMERIC
 @ATTRIBUTE LogNumberOfFeatures NUMERIC
@@ -42,160 +41,135 @@
 @ATTRIBUTE SymbolsSum NUMERIC
 
 @DATA
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diff --git a/autosklearn/metalearning/files/f1_samples_binary.classification_dense/algorithm_runs.arff b/autosklearn/metalearning/files/f1_samples_binary.classification_dense/algorithm_runs.arff
index bb9281cde2..4824cf8886 100644
--- a/autosklearn/metalearning/files/f1_samples_binary.classification_dense/algorithm_runs.arff
+++ b/autosklearn/metalearning/files/f1_samples_binary.classification_dense/algorithm_runs.arff
@@ -7,130 +7,135 @@
 @ATTRIBUTE runstatus {ok, timeout, memout, not_applicable, crash, other}
 
 @DATA
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-75212,1.0,2,?,not_applicable
-252,1.0,3,?,not_applicable
-246,1.0,4,?,not_applicable
-75178,1.0,5,?,not_applicable
-75239,1.0,6,?,not_applicable
-75171,1.0,7,?,not_applicable
-75233,1.0,8,?,not_applicable
-248,1.0,9,?,not_applicable
-75231,1.0,10,?,not_applicable
-2123,1.0,11,?,not_applicable
-75196,1.0,12,?,not_applicable
-75188,1.0,13,?,not_applicable
+75192,1.0,1,?,not_applicable
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+75139,1.0,5,?,not_applicable
+75212,1.0,6,?,not_applicable
+246,1.0,8,?,not_applicable
+252,1.0,9,?,not_applicable
+75178,1.0,10,?,not_applicable
+75177,1.0,13,?,not_applicable
 75092,1.0,14,?,not_applicable
-75248,1.0,15,?,not_applicable
-75126,1.0,16,?,not_applicable
-75234,1.0,17,?,not_applicable
-75150,1.0,18,?,not_applicable
-258,1.0,19,?,not_applicable
-75168,1.0,20,?,not_applicable
-75235,1.0,21,?,not_applicable
-75159,1.0,22,?,not_applicable
-244,1.0,23,?,not_applicable
-75221,1.0,24,?,not_applicable
-75219,1.0,25,?,not_applicable
-75202,1.0,26,?,not_applicable
-3043,1.0,27,?,not_applicable
-75205,1.0,28,?,not_applicable
-75174,1.0,29,?,not_applicable
-288,1.0,30,?,not_applicable
-75250,1.0,31,?,not_applicable
-275,1.0,32,?,not_applicable
-75142,1.0,33,?,not_applicable
-75213,1.0,34,?,not_applicable
-75099,1.0,35,?,not_applicable
-75243,1.0,36,?,not_applicable
-75184,1.0,37,?,not_applicable
-75222,1.0,38,?,not_applicable
-75175,1.0,39,?,not_applicable
-233,1.0,40,?,not_applicable
-75161,1.0,41,?,not_applicable
-75176,1.0,42,?,not_applicable
-75090,1.0,43,?,not_applicable
-75114,1.0,44,?,not_applicable
-260,1.0,45,?,not_applicable
-236,1.0,46,?,not_applicable
-75141,1.0,47,?,not_applicable
-75107,1.0,48,?,not_applicable
-262,1.0,49,?,not_applicable
-75146,1.0,50,?,not_applicable
-75189,1.0,51,?,not_applicable
-2350,1.0,52,?,not_applicable
-253,1.0,53,?,not_applicable
-2122,1.0,54,?,not_applicable
-75110,1.0,55,?,not_applicable
-75249,1.0,56,?,not_applicable
-75108,1.0,57,?,not_applicable
-242,1.0,58,?,not_applicable
-75117,1.0,59,?,not_applicable
-75191,1.0,60,?,not_applicable
-75226,1.0,61,?,not_applicable
-261,1.0,62,?,not_applicable
-75236,1.0,63,?,not_applicable
-75095,1.0,64,?,not_applicable
-75148,1.0,65,?,not_applicable
-75093,1.0,66,?,not_applicable
-75223,1.0,67,?,not_applicable
-75244,1.0,68,?,not_applicable
-75109,1.0,69,?,not_applicable
-75197,1.0,70,?,not_applicable
-75127,1.0,71,?,not_applicable
-75143,1.0,72,?,not_applicable
-75153,1.0,73,?,not_applicable
-75173,1.0,74,?,not_applicable
-75215,1.0,75,?,not_applicable
-75195,1.0,76,?,not_applicable
-75207,1.0,77,?,not_applicable
-266,1.0,78,?,not_applicable
-75225,1.0,79,?,not_applicable
-75166,1.0,80,?,not_applicable
-75100,1.0,81,?,not_applicable
-75169,1.0,82,?,not_applicable
-75132,1.0,83,?,not_applicable
-273,1.0,84,?,not_applicable
-75121,1.0,85,?,not_applicable
-75098,1.0,86,?,not_applicable
-75115,1.0,87,?,not_applicable
-75116,1.0,88,?,not_applicable
-75185,1.0,89,?,not_applicable
-2119,1.0,90,?,not_applicable
-75157,1.0,91,?,not_applicable
-75113,1.0,92,?,not_applicable
-75134,1.0,93,?,not_applicable
-75096,1.0,94,?,not_applicable
-75203,1.0,95,?,not_applicable
-75182,1.0,96,?,not_applicable
-251,1.0,97,?,not_applicable
-75123,1.0,98,?,not_applicable
-75237,1.0,99,?,not_applicable
-75125,1.0,100,?,not_applicable
-75232,1.0,101,?,not_applicable
-75103,1.0,102,?,not_applicable
-75192,1.0,103,?,not_applicable
-75230,1.0,104,?,not_applicable
-75139,1.0,105,?,not_applicable
-75227,1.0,106,?,not_applicable
-2120,1.0,107,?,not_applicable
-75124,1.0,108,?,not_applicable
-75240,1.0,109,?,not_applicable
-75129,1.0,110,?,not_applicable
-75198,1.0,111,?,not_applicable
-75201,1.0,112,?,not_applicable
-75112,1.0,113,?,not_applicable
-75133,1.0,114,?,not_applicable
-75105,1.0,115,?,not_applicable
-75154,1.0,116,?,not_applicable
-75177,1.0,117,?,not_applicable
-2117,1.0,118,?,not_applicable
-75156,1.0,119,?,not_applicable
-75097,1.0,120,?,not_applicable
-75101,1.0,121,?,not_applicable
-75172,1.0,122,?,not_applicable
-75106,1.0,123,?,not_applicable
-75179,1.0,124,?,not_applicable
-75187,1.0,125,?,not_applicable
-75120,1.0,126,?,not_applicable
-75193,1.0,127,?,not_applicable
+75239,1.0,15,?,not_applicable
+75173,1.0,17,?,not_applicable
+75215,1.0,18,?,not_applicable
+75171,1.0,19,?,not_applicable
+75112,1.0,23,?,not_applicable
+75227,1.0,24,?,not_applicable
+75233,1.0,25,?,not_applicable
+75182,1.0,26,?,not_applicable
+253,1.0,27,?,not_applicable
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+75124,1.0,31,?,not_applicable
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+75222,1.0,33,?,not_applicable
+75231,1.0,34,?,not_applicable
+75185,1.0,37,?,not_applicable
+2123,1.0,39,?,not_applicable
+75150,1.0,41,?,not_applicable
+75143,1.0,43,?,not_applicable
+75196,1.0,44,?,not_applicable
+75188,1.0,49,?,not_applicable
+75248,1.0,54,?,not_applicable
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+288,1.0,64,?,not_applicable
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+75184,1.0,66,?,not_applicable
+75234,1.0,67,?,not_applicable
+258,1.0,73,?,not_applicable
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+75148,1.0,80,?,not_applicable
+75235,1.0,81,?,not_applicable
+75159,1.0,84,?,not_applicable
+75146,1.0,88,?,not_applicable
+244,1.0,89,?,not_applicable
+75141,1.0,90,?,not_applicable
+75221,1.0,92,?,not_applicable
+75219,1.0,95,?,not_applicable
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+3043,1.0,98,?,not_applicable
+75205,1.0,103,?,not_applicable
+75174,1.0,106,?,not_applicable
+75250,1.0,111,?,not_applicable
+75179,1.0,114,?,not_applicable
+275,1.0,115,?,not_applicable
+242,1.0,116,?,not_applicable
+75207,1.0,117,?,not_applicable
+75142,1.0,118,?,not_applicable
+75099,1.0,123,?,not_applicable
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+75175,1.0,132,?,not_applicable
+233,1.0,134,?,not_applicable
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+262,1.0,146,?,not_applicable
+75129,1.0,147,?,not_applicable
+261,1.0,148,?,not_applicable
+75114,1.0,152,?,not_applicable
+75093,1.0,157,?,not_applicable
+260,1.0,158,?,not_applicable
+236,1.0,160,?,not_applicable
+254,1.0,166,?,not_applicable
+75107,1.0,167,?,not_applicable
+75181,1.0,175,?,not_applicable
+75189,1.0,184,?,not_applicable
+75163,1.0,189,?,not_applicable
+2350,1.0,191,?,not_applicable
+2122,1.0,195,?,not_applicable
+75110,1.0,198,?,not_applicable
+75213,1.0,201,?,not_applicable
+75095,1.0,203,?,not_applicable
+75108,1.0,207,?,not_applicable
+75117,1.0,211,?,not_applicable
+75191,1.0,216,?,not_applicable
+75226,1.0,219,?,not_applicable
+75244,1.0,222,?,not_applicable
+75236,1.0,223,?,not_applicable
+75169,1.0,227,?,not_applicable
+75116,1.0,228,?,not_applicable
+75223,1.0,235,?,not_applicable
+75109,1.0,242,?,not_applicable
+75197,1.0,245,?,not_applicable
+75237,1.0,248,?,not_applicable
+248,1.0,250,?,not_applicable
+2119,1.0,254,?,not_applicable
+75127,1.0,255,?,not_applicable
+75153,1.0,260,?,not_applicable
+75195,1.0,271,?,not_applicable
+266,1.0,277,?,not_applicable
+75225,1.0,279,?,not_applicable
+75100,1.0,286,?,not_applicable
+75132,1.0,293,?,not_applicable
+75210,1.0,298,?,not_applicable
+273,1.0,300,?,not_applicable
+75133,1.0,303,?,not_applicable
+75121,1.0,304,?,not_applicable
+75098,1.0,310,?,not_applicable
+75115,1.0,315,?,not_applicable
+75217,1.0,337,?,not_applicable
+75134,1.0,341,?,not_applicable
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+75203,1.0,350,?,not_applicable
+75123,1.0,360,?,not_applicable
+75125,1.0,367,?,not_applicable
+2120,1.0,371,?,not_applicable
+75232,1.0,374,?,not_applicable
+75103,1.0,378,?,not_applicable
+75230,1.0,386,?,not_applicable
+75240,1.0,398,?,not_applicable
+75198,1.0,404,?,not_applicable
+75201,1.0,407,?,not_applicable
+75105,1.0,415,?,not_applicable
+75154,1.0,420,?,not_applicable
+2117,1.0,426,?,not_applicable
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+75097,1.0,434,?,not_applicable
+75101,1.0,438,?,not_applicable
+75172,1.0,443,?,not_applicable
+75106,1.0,448,?,not_applicable
+75120,1.0,456,?,not_applicable
+75193,1.0,461,?,not_applicable
diff --git a/autosklearn/metalearning/files/f1_samples_binary.classification_dense/configurations.csv b/autosklearn/metalearning/files/f1_samples_binary.classification_dense/configurations.csv
index 8d94f66c45..03d7f03e43 100644
--- a/autosklearn/metalearning/files/f1_samples_binary.classification_dense/configurations.csv
+++ b/autosklearn/metalearning/files/f1_samples_binary.classification_dense/configurations.csv
@@ -1,128 +1,133 @@
 idx,balancing:strategy,classifier:__choice__,classifier:adaboost:algorithm,classifier:adaboost:learning_rate,classifier:adaboost:max_depth,classifier:adaboost:n_estimators,classifier:bernoulli_nb:alpha,classifier:bernoulli_nb:fit_prior,classifier:decision_tree:criterion,classifier:decision_tree:max_depth_factor,classifier:decision_tree:max_features,classifier:decision_tree:max_leaf_nodes,classifier:decision_tree:min_impurity_decrease,classifier:decision_tree:min_samples_leaf,classifier:decision_tree:min_samples_split,classifier:decision_tree:min_weight_fraction_leaf,classifier:extra_trees:bootstrap,classifier:extra_trees:criterion,classifier:extra_trees:max_depth,classifier:extra_trees:max_features,classifier:extra_trees:max_leaf_nodes,classifier:extra_trees:min_impurity_decrease,classifier:extra_trees:min_samples_leaf,classifier:extra_trees:min_samples_split,classifier:extra_trees:min_weight_fraction_leaf,classifier:gradient_boosting:early_stop,classifier:gradient_boosting:l2_regularization,classifier:gradient_boosting:learning_rate,classifier:gradient_boosting:loss,classifier:gradient_boosting:max_bins,classifier:gradient_boosting:max_depth,classifier:gradient_boosting:max_leaf_nodes,classifier:gradient_boosting:min_samples_leaf,classifier:gradient_boosting:n_iter_no_change,classifier:gradient_boosting:scoring,classifier:gradient_boosting:tol,classifier:gradient_boosting:validation_fraction,classifier:k_nearest_neighbors:n_neighbors,classifier:k_nearest_neighbors:p,classifier:k_nearest_neighbors:weights,classifier:lda:n_components,classifier:lda:shrinkage,classifier:lda:shrinkage_factor,classifier:lda:tol,classifier:liblinear_svc:C,classifier:liblinear_svc:dual,classifier:liblinear_svc:fit_intercept,classifier:liblinear_svc:intercept_scaling,classifier:liblinear_svc:loss,classifier:liblinear_svc:multi_class,classifier:liblinear_svc:penalty,classifier:liblinear_svc:tol,classifier:libsvm_svc:C,classifier:libsvm_svc:coef0,classifier:libsvm_svc:degree,classifier:libsvm_svc:gamma,classifier:libsvm_svc:kernel,classifier:libsvm_svc:max_iter,classifier:libsvm_svc:shrinking,classifier:libsvm_svc:tol,classifier:multinomial_nb:alpha,classifier:multinomial_nb:fit_prior,classifier:passive_aggressive:C,classifier:passive_aggressive:average,classifier:passive_aggressive:fit_intercept,classifier:passive_aggressive:loss,classifier:passive_aggressive:tol,classifier:qda:reg_param,classifier:random_forest:bootstrap,classifier:random_forest:criterion,classifier:random_forest:max_depth,classifier:random_forest:max_features,classifier:random_forest:max_leaf_nodes,classifier:random_forest:min_impurity_decrease,classifier:random_forest:min_samples_leaf,classifier:random_forest:min_samples_split,classifier:random_forest:min_weight_fraction_leaf,classifier:sgd:alpha,classifier:sgd:average,classifier:sgd:epsilon,classifier:sgd:eta0,classifier:sgd:fit_intercept,classifier:sgd:l1_ratio,classifier:sgd:learning_rate,classifier:sgd:loss,classifier:sgd:penalty,classifier:sgd:power_t,classifier:sgd:tol,data_preprocessing:categorical_transformer:categorical_encoding:__choice__,data_preprocessing:categorical_transformer:category_coalescence:__choice__,data_preprocessing:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction,data_preprocessing:numerical_transformer:imputation:strategy,data_preprocessing:numerical_transformer:rescaling:__choice__,data_preprocessing:numerical_transformer:rescaling:quantile_transformer:n_quantiles,data_preprocessing:numerical_transformer:rescaling:quantile_transformer:output_distribution,data_preprocessing:numerical_transformer:rescaling:robust_scaler:q_max,data_preprocessing:numerical_transformer:rescaling:robust_scaler:q_min,feature_preprocessor:__choice__,feature_preprocessor:extra_trees_preproc_for_classification:bootstrap,feature_preprocessor:extra_trees_preproc_for_classification:criterion,feature_preprocessor:extra_trees_preproc_for_classification:max_depth,feature_preprocessor:extra_trees_preproc_for_classification:max_features,feature_preprocessor:extra_trees_preproc_for_classification:max_leaf_nodes,feature_preprocessor:extra_trees_preproc_for_classification:min_impurity_decrease,feature_preprocessor:extra_trees_preproc_for_classification:min_samples_leaf,feature_preprocessor:extra_trees_preproc_for_classification:min_samples_split,feature_preprocessor:extra_trees_preproc_for_classification:min_weight_fraction_leaf,feature_preprocessor:extra_trees_preproc_for_classification:n_estimators,feature_preprocessor:fast_ica:algorithm,feature_preprocessor:fast_ica:fun,feature_preprocessor:fast_ica:n_components,feature_preprocessor:fast_ica:whiten,feature_preprocessor:feature_agglomeration:affinity,feature_preprocessor:feature_agglomeration:linkage,feature_preprocessor:feature_agglomeration:n_clusters,feature_preprocessor:feature_agglomeration:pooling_func,feature_preprocessor:kernel_pca:coef0,feature_preprocessor:kernel_pca:degree,feature_preprocessor:kernel_pca:gamma,feature_preprocessor:kernel_pca:kernel,feature_preprocessor:kernel_pca:n_components,feature_preprocessor:kitchen_sinks:gamma,feature_preprocessor:kitchen_sinks:n_components,feature_preprocessor:liblinear_svc_preprocessor:C,feature_preprocessor:liblinear_svc_preprocessor:dual,feature_preprocessor:liblinear_svc_preprocessor:fit_intercept,feature_preprocessor:liblinear_svc_preprocessor:intercept_scaling,feature_preprocessor:liblinear_svc_preprocessor:loss,feature_preprocessor:liblinear_svc_preprocessor:multi_class,feature_preprocessor:liblinear_svc_preprocessor:penalty,feature_preprocessor:liblinear_svc_preprocessor:tol,feature_preprocessor:nystroem_sampler:coef0,feature_preprocessor:nystroem_sampler:degree,feature_preprocessor:nystroem_sampler:gamma,feature_preprocessor:nystroem_sampler:kernel,feature_preprocessor:nystroem_sampler:n_components,feature_preprocessor:pca:keep_variance,feature_preprocessor:pca:whiten,feature_preprocessor:polynomial:degree,feature_preprocessor:polynomial:include_bias,feature_preprocessor:polynomial:interaction_only,feature_preprocessor:random_trees_embedding:bootstrap,feature_preprocessor:random_trees_embedding:max_depth,feature_preprocessor:random_trees_embedding:max_leaf_nodes,feature_preprocessor:random_trees_embedding:min_samples_leaf,feature_preprocessor:random_trees_embedding:min_samples_split,feature_preprocessor:random_trees_embedding:min_weight_fraction_leaf,feature_preprocessor:random_trees_embedding:n_estimators,feature_preprocessor:select_percentile_classification:percentile,feature_preprocessor:select_percentile_classification:score_func,feature_preprocessor:select_rates_classification:alpha,feature_preprocessor:select_rates_classification:mode,feature_preprocessor:select_rates_classification:score_func
-1,none,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,standardize,,,,,extra_trees_preproc_for_classification,False,entropy,None,0.8629651625821806,None,0.0,10,10,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-2,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9.971526339963626,False,True,1,squared_hinge,ovr,l2,1.2119829083478464e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.10211544986888581,most_frequent,robust_scaler,,,0.7878089656793734,0.28746519719167807,fast_ica,,,,,,,,,,,parallel,cube,30,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-3,none,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,188,None,,0.09976330267940166,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,none,,,,,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,-0.05439674489937307,2,0.051865468486041774,poly,727,,,,,,,,,,,,,,,,,
-4,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,23916.78290516781,False,True,1,squared_hinge,ovr,l2,0.0006655607726658531,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.008808715242823137,median,none,,,,,kernel_pca,,,,,,,,,,,,,,,,,,,0.2633153190620261,2,0.004770332714291669,poly,1483,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-5,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,2,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010091246322497477,median,none,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,95.37976833605768,mutual_info,,,
-6,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.933900673392165,None,0.0,7,4,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,minmax,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-7,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,170.37509684994612,0.6734219115470694,3,0.08998608839429428,poly,-1,True,0.024147312348267988,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,none,,,,,extra_trees_preproc_for_classification,False,entropy,None,0.9449154369109399,None,0.0,1,14,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-8,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2981.609927782268,0.12126372002542518,2,0.0009954412712187496,poly,-1,False,9.701828055863241e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,mean,standardize,,,,,feature_agglomeration,,,,,,,,,,,,,,,euclidean,ward,37,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-9,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1292.7448076998514,0.004489932344506231,2,1.3088036376373702,poly,-1,False,1.7943262139196537e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,minmax,,,,,feature_agglomeration,,,,,,,,,,,,,,,cosine,average,386,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-10,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.5244313211416313,None,0.0,1,2,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.454300236339658,most_frequent,robust_scaler,,,0.7858830918128734,0.2705174720960696,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-11,weighting,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0006294780694492238,most_frequent,standardize,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.063715750591364,fdr,chi2
-12,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.02299691847304223,False,True,squared_hinge,2.16045124689341e-05,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010708146395792527,most_frequent,standardize,,,,,extra_trees_preproc_for_classification,False,entropy,None,0.5298549662385138,None,0.0,12,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-13,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.25657910619102237,None,0.0,6,6,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.021086303324492208,most_frequent,quantile_transformer,1247,uniform,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.16021572815319468,fpr,chi2
-14,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.4897130288457967,False,True,1,squared_hinge,ovr,l2,0.00023610948867176137,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.22014262985890154,median,quantile_transformer,1499,normal,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,331,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-15,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.643761610990255,None,0.0,20,6,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.039859381630488695,mean,normalize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,
-16,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.8172226998998798,None,0.0,17,3,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.006497124806317511,median,robust_scaler,,,0.7090070294600064,0.2287445390747399,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.478031402559985,mutual_info,,,
-17,weighting,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.06330379669963868,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.054434091133046744,most_frequent,robust_scaler,,,0.7480112557523932,0.17257870788958515,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-18,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00335225820194075,True,True,squared_hinge,2.9136221277715572e-05,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.00021598139496857644,median,minmax,,,,,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,4,None,4,9,1.0,14,,,,,
-19,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,199,None,,0.003239674765949011,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,robust_scaler,,,0.7228447795482008,0.25,kernel_pca,,,,,,,,,,,,,,,,,,,0.09440148939740256,3,0.0008114095405154861,poly,1996,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-20,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.6039096182414764,None,0.0,2,20,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.1939609823704447,most_frequent,robust_scaler,,,0.7331327008874781,0.29747488162877694,extra_trees_preproc_for_classification,True,entropy,None,0.33401603690569853,None,0.0,4,4,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-21,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,2.5882079595129472e-05,0.36125906161027654,auto,255,None,341,119,16,loss,1e-07,0.2924550184445239,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.02338478303505337,most_frequent,robust_scaler,,,0.7866731278723753,0.29543475006822734,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,68,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-22,weighting,adaboost,SAMME,0.24425611020065865,2,50,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0014011297758596516,most_frequent,quantile_transformer,1324,normal,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,338,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-23,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7725.740943204492,,,3.122840783722913,rbf,-1,True,1.4116065061807418e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.14280178218510184,most_frequent,none,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,27,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-24,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.21794354428393548,None,0.0,2,16,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0025451910134387575,median,quantile_transformer,1477,uniform,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
-25,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5010952533165973,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.005838375247601407,mean,normalize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,
-26,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5514127836113225,None,0.0,4,20,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.010750756228608195,most_frequent,robust_scaler,,,0.7471420685032172,0.2916152663822578,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.22557315057515023,fwe,chi2
-27,weighting,decision_tree,,,,,,,gini,1.0778738481485166,1.0,None,0.0,14,7,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.002474357425774638,mean,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,
-28,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.506204385946851,None,0.0,2,8,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.03433007982751011,mean,none,,,,,extra_trees_preproc_for_classification,False,gini,None,0.32676415738837183,None,0.0,5,6,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-29,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9333237919291875,None,0.0,8,3,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,
-30,none,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.9728532763078892,None,0.0,13,9,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,none,,,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6651071190864879,False,,,,,,,,,,,,,,,
-31,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9561075301148197,None,0.0,6,2,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,
-32,none,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,184,manual,0.5022041786628263,2.3064966255468173e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,most_frequent,standardize,,,,,kernel_pca,,,,,,,,,,,,,,,,,,,-0.32023224593255817,2,0.09479590655633498,poly,1382,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-33,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,2.7220799337966617e-08,0.011194310743518529,auto,255,None,38,187,3,loss,1e-07,0.13581959545340272,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0036603728729535916,mean,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,False,,,,,,,,,,,,
-34,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,22194.242946902177,0.45210849669021247,,0.0005669713702171556,sigmoid,-1,False,0.03542142266081451,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,quantile_transformer,291,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-35,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.2697794350876887,None,0.0,20,19,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.01857832166651019,median,robust_scaler,,,0.746850014013939,0.26258045325454815,fast_ica,,,,,,,,,,,deflation,cube,67,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-36,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,0.032732094595234795,0.09918078053098744,auto,255,None,97,54,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.019135985037746533,median,quantile_transformer,1000,uniform,,,feature_agglomeration,,,,,,,,,,,,,,,euclidean,average,37,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-37,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7360659359578277,None,0.0,18,5,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.0032431851910047368,mean,robust_scaler,,,0.7204949497300925,0.2277786352264519,fast_ica,,,,,,,,,,,deflation,exp,307,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-38,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.756167892711991e-05,False,True,squared_hinge,5.235791933868702e-05,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0001632172987320906,most_frequent,standardize,,,,,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,5,None,19,12,1.0,70,,,,,
-39,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,3.6796787331285204e-07,0.14399102828508656,auto,255,None,18,12,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,quantile_transformer,965,normal,,,feature_agglomeration,,,,,,,,,,,,,,,cosine,average,365,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-40,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7005303455027131,None,0.0,1,5,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.027348815457551995,mean,quantile_transformer,789,uniform,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,
-41,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,1.943186107984505e-07,0.08852455702036988,auto,255,None,5,72,19,loss,1e-07,0.039837460762271175,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.01591265419367627,median,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,
-42,weighting,adaboost,SAMME,0.34205296521106404,10,89,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.02025205191860567,most_frequent,minmax,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-43,none,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0686374607394679,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,minmax,,,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.8490260132946401,False,,,,,,,,,,,,,,,
-44,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,10,1,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0022606253047811435,median,quantile_transformer,1247,normal,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,25.327793585266683,chi2,,,
-45,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.3663040729445102,None,0.0,20,19,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.010000000000000004,median,robust_scaler,,,0.7338553275791654,0.25,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,331,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-46,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,92.66144306787051,,,0.04654639302091653,rbf,-1,True,0.0022231544280610005,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.005194838694455963,mean,none,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,15,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-47,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1550807214391849,True,True,hinge,0.00019875748182257,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.05786688075604592,mean,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,
-48,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.4467384469668449,None,0.0,19,19,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.00033708405018786203,median,quantile_transformer,711,normal,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,7.744829113696808,False,True,1,squared_hinge,ovr,l1,0.0001869446320464273,,,,,,,,,,,,,,,,,,,,,,
-49,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,26.502259985828186,,,9.475662388405154e-05,rbf,-1,False,0.00019690635968217866,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-50,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,2.3520913174804126e-07,0.052918150906133596,auto,255,None,16,185,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.00790855487341428,mean,robust_scaler,,,0.746273035018099,0.21296174657639108,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,1.298440997922375,False,True,1,squared_hinge,ovr,l1,0.0018510248972884416,,,,,,,,,,,,,,,,,,,,,,
-51,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5255991794296913,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-52,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.34697723213923637,None,0.0,15,18,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.1923594385988973,mean,normalize,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,25.77499302945827,chi2,,,
-53,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1252.7997374154156,0.20811431041897355,5,0.0018140593074227453,poly,-1,False,0.03149782358621565,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.001930822730474197,most_frequent,quantile_transformer,233,normal,,,feature_agglomeration,,,,,,,,,,,,,,,euclidean,ward,29,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-54,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,1.5661823810486637e-10,0.08996863554015262,auto,255,None,156,89,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.009331521286159971,median,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,
-55,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.6109468076475573,0.8748383232154038,4,0.41333070422336576,poly,-1,False,3.591268778393482e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.011804925819789203,median,minmax,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-56,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.7388247096873266,None,0.0,1,2,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.02005334514549439,most_frequent,robust_scaler,,,0.9347422252540531,0.2909868620945952,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,
-57,weighting,adaboost,SAMME.R,0.03127273017917347,10,496,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,standardize,,,,,fast_ica,,,,,,,,,,,deflation,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-58,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,151,None,,0.0003283777607180646,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.2510527431514227,mean,normalize,,,,,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,-0.96093182232312,,0.05762510964607661,sigmoid,2632,,,,,,,,,,,,,,,,,
-59,none,bernoulli_nb,,,,,1.9593357170002308,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.005109073482613361,mean,quantile_transformer,10,normal,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.03495379340060141,False,True,1,squared_hinge,ovr,l1,0.0033386410851928994,,,,,,,,,,,,,,,,,,,,,,
-60,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.6453555269772124,None,0.0,1,4,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,normalize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-61,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5363.189119763322,,,0.4294659312024404,rbf,-1,True,0.06935839422940453,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.019860129781307266,most_frequent,none,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,92.1980997621979,mutual_info,,,
-62,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.20911875507660596,None,0.0,12,8,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,median,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,
-63,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.34534888916828577,0.02513634776687268,2,1.0733091880109409,poly,-1,True,0.0006974263047918307,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,quantile_transformer,1000,normal,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,77.99934218022861,mutual_info,,,
-64,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,29.55731828912983,False,True,1,squared_hinge,ovr,l2,0.0011332486951954968,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,
-65,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,1.1387795876755026e-06,0.12245605190343567,auto,255,None,58,7,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,robust_scaler,,,0.79305915648885,0.1841487406876042,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-66,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.8425560317492612,None,0.0,8,15,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.015276988502366128,median,none,,,,,fast_ica,,,,,,,,,,,parallel,cube,17,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-67,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,0.2638627266631576,0.022360820165005443,auto,255,None,171,6,19,loss,1e-07,0.016209060012924756,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.008763072395677399,mean,standardize,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,78,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-68,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5664.233390385957,0.3496807683995542,4,0.00017689402501527962,poly,-1,True,0.01756282016660122,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.13312139731266243,median,quantile_transformer,1362,uniform,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,72.39572310756226,mutual_info,,,
-69,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.8278163313806293,None,0.0,3,5,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,robust_scaler,,,0.725835793765774,0.2571431961341018,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.07784020380291123,fdr,f_classif
-70,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0023391956112474004,False,True,squared_hinge,0.011792446147470903,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.08295440990272292,most_frequent,quantile_transformer,962,normal,,,kernel_pca,,,,,,,,,,,,,,,,,,,,,,cosine,1778,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-71,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.4264370333817712,None,0.0,13,3,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,median,minmax,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.14972073341699849,fpr,chi2
-72,none,adaboost,SAMME,0.07025975715693651,5,339,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,median,robust_scaler,,,0.7117466125338451,0.2984609070131515,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.21351971922962878,fdr,chi2
-73,none,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9931779675565967,None,0.0,18,17,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,robust_scaler,,,0.75,0.2945917010472818,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,
-74,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2611084157414942,True,True,hinge,0.0009587835023329391,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.14664248075999733,most_frequent,minmax,,,,,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.509603573308092,rbf,198,,,,,,,,,,,,,,,,,
-75,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,3.044019499065853e-08,0.030966538899129755,auto,255,None,64,50,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.00860619823050038,median,minmax,,,,,feature_agglomeration,,,,,,,,,,,,,,,cosine,average,36,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-76,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7.667945934601232,True,True,squared_hinge,1.5995631297928604e-05,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.004775063434540409,most_frequent,minmax,,,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.28455344994291587,False,True,1,squared_hinge,ovr,l1,6.230255219574715e-05,,,,,,,,,,,,,,,,,,,,,,
-77,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.34936025988397124,None,0.0,2,13,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00018601309647406987,median,robust_scaler,,,0.8634309348843472,0.26976989465367607,extra_trees_preproc_for_classification,False,entropy,None,0.48025175288975036,None,0.0,6,17,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-78,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,2.925599188129995e-05,0.12560285030651847,auto,255,None,300,193,17,loss,1e-07,0.039442997915546514,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00299400754997757,mean,robust_scaler,,,0.7093292916146515,0.009366983030047793,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-79,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.642370445299362,None,0.0,15,20,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.014823484140711048,median,robust_scaler,,,0.780144337652783,0.2309544712129402,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,2.2123747341894098,False,True,1,squared_hinge,ovr,l1,0.0033125984161268183,,,,,,,,,,,,,,,,,,,,,,
-80,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2471.298583654142,,,0.025866749427709803,rbf,-1,False,0.0009481287482516433,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,none,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,323,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-81,weighting,adaboost,SAMME,0.028939256953011736,2,368,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,quantile_transformer,1954,uniform,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,41.04018582009321,mutual_info,,,
-82,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,21.85207494001697,0.18872999589786676,4,1.1863646021868761,poly,-1,False,0.0021338371901294443,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0034080852834770217,mean,normalize,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.11326005109886934,fdr,f_classif
-83,weighting,adaboost,SAMME,0.38341570193537416,9,250,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,minmax,,,,,feature_agglomeration,,,,,,,,,,,,,,,euclidean,average,13,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-84,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,0.023106206735923184,0.12762814679231496,auto,255,None,24,151,16,loss,1e-07,0.08978875361280989,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.007723510861056437,median,quantile_transformer,1217,normal,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,
-85,weighting,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.00012847697769902057,mean,minmax,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,10.088989224596698,mutual_info,,,
-86,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,1,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.019797877010737826,mean,normalize,,,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.8239678741128165,False,,,,,,,,,,,,,,,
-87,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.46057831591617715,False,True,hinge,0.04557857428827514,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00027457445401600137,median,standardize,,,,,extra_trees_preproc_for_classification,True,gini,None,0.48190346970486964,None,0.0,17,18,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-88,none,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,230,auto,,0.0014037334852669801,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,quantile_transformer,848,uniform,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,61.06963527679606,f_classif,,,
-89,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,64,auto,,0.00021332620906594865,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.026032882800756493,median,standardize,,,,,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,-0.9513223792709335,,0.00029881582960753004,sigmoid,543,,,,,,,,,,,,,,,,,
-90,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.26356754187780895,None,0.0,4,3,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010966178401758774,mean,robust_scaler,,,0.7817043861798886,0.24371720112407158,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
-91,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,3.7287807266408084e-10,0.15544439749103187,auto,255,None,28,16,18,loss,1e-07,0.10835808508958217,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0207886310256421,mean,quantile_transformer,1245,normal,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.39383167612281667,fpr,f_classif
-92,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,130.2903387607578,False,True,1,squared_hinge,ovr,l2,0.0020820269897903502,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.08534605541971435,mean,quantile_transformer,613,normal,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,12.936259313956771,False,True,1,squared_hinge,ovr,l1,9.225567050752251e-05,,,,,,,,,,,,,,,,,,,,,,
-93,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.22879311080499845,None,0.0,8,4,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.014320022547615783,median,minmax,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.238630703440461,mutual_info,,,
-94,weighting,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,2,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0073667222552306465,median,robust_scaler,,,0.701595034693991,0.29514640404971515,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6066051105210388,False,,,,,,,,,,,,,,,
-95,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.107967527639527e-05,True,True,hinge,0.00448294745544775,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,quantile_transformer,953,normal,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,35.00114345405331,chi2,,,
-96,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.457928416564218,None,0.0,12,2,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,
-97,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,1.1522414705935405e-10,0.6132800183073315,auto,255,None,1607,67,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010096911249562407,mean,robust_scaler,,,0.7663493326967942,0.136094217337434,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-98,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1228.3897711257332,0.6862670093933454,5,1.113863025113022,poly,-1,True,1.9271116749907707e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.018161834200128672,mean,normalize,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.06278549919408385,fpr,chi2
-99,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.479405900046494,,,0.013340778632154543,rbf,-1,False,6.913586060890114e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,robust_scaler,,,0.9953232616818098,0.06450577886132274,fast_ica,,,,,,,,,,,deflation,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-100,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.9815182201168626,None,0.0,15,19,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,robust_scaler,,,0.8878662977383482,0.25666364929970126,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,44.15748219826322,mutual_info,,,
-101,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1584.207754377407,,,0.020660207249591715,rbf,-1,False,0.03773359340731028,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,quantile_transformer,1055,uniform,,,fast_ica,,,,,,,,,,,parallel,exp,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-102,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.7285597583341324,None,0.0,3,20,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.011700861722450575,most_frequent,robust_scaler,,,0.7911119178158159,0.2999762250828743,feature_agglomeration,,,,,,,,,,,,,,,euclidean,ward,151,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-103,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2897.996028838154,,,0.04621527412391808,rbf,-1,False,0.0017262380676728777,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.07770313303106262,mean,robust_scaler,,,0.7510736797641198,0.23032782852489117,extra_trees_preproc_for_classification,False,gini,None,0.9588902962283966,None,0.0,13,12,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-104,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.6282663389679584,None,0.0,1,3,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,median,minmax,,,,,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-105,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,8.14394583618027e-09,0.1314274861878701,auto,255,None,27,15,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.3598067419611515,mean,robust_scaler,,,0.7494615433236279,0.260649229829574,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
-106,weighting,adaboost,SAMME.R,0.46962472583666215,8,455,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.03708420001141539,most_frequent,robust_scaler,,,0.7364857891944228,0.23021698558282877,feature_agglomeration,,,,,,,,,,,,,,,euclidean,complete,5,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-107,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,11.046854203258679,,,0.07809571976769347,rbf,-1,False,0.00036193159909053734,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.006445557635410205,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-108,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.6728827282646637,None,0.0,18,8,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,robust_scaler,,,0.8512985751386942,0.14092824896182807,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,
-109,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.18060139887649052,None,0.0,10,12,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.010000000000000004,most_frequent,robust_scaler,,,0.9298612094255864,0.1263597946722308,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
-110,none,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.16236660803443026,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,quantile_transformer,377,normal,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2611168895496939,fpr,f_classif
-111,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6427543483503397,True,True,hinge,0.00045318775930916944,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.009338484332306904,median,quantile_transformer,174,normal,,,kernel_pca,,,,,,,,,,,,,,,,,,,,,,cosine,1848,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-112,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.02021502857757827,True,,0.055848937053280076,True,,constant,log,l2,,0.00016356162584832537,no_encoding,no_coalescense,,median,quantile_transformer,27,normal,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.11873149622606233,fwe,chi2
-113,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,9.029473608568458e-09,0.15779990576964315,auto,255,None,37,71,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.018093177195644097,mean,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,False,,,,,,,,,,,,
-114,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8.24807027221455,-0.23458710218209156,,0.0001030034352044511,sigmoid,-1,True,0.0002726309373020207,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,quantile_transformer,1974,normal,,,extra_trees_preproc_for_classification,True,entropy,None,0.9395220389606214,None,0.0,2,20,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-115,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.3747863651224668,True,True,hinge,0.00019106780876479388,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.002442315142014378,median,quantile_transformer,1850,uniform,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.570194774022733,f_classif,,,
-116,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.4864284879550203,None,0.0,1,4,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.008665854799630503,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-117,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.8785519499336585,None,0.0,13,17,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.05156816034494817,most_frequent,quantile_transformer,1243,uniform,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.11082264321263885,fdr,f_classif
-118,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.536709062860521e-07,True,,,True,,optimal,squared_hinge,l1,,1.6934515188687678e-05,one_hot_encoding,no_coalescense,,most_frequent,normalize,,,,,feature_agglomeration,,,,,,,,,,,,,,,cosine,complete,306,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-119,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.6905196223806276,None,0.0,6,5,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,minmax,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-120,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.6154724467600127,None,0.0,19,12,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,mean,minmax,,,,,extra_trees_preproc_for_classification,True,entropy,None,0.7630778936392856,None,0.0,11,20,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-121,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5836006490230695,None,0.0,1,19,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.02127792104027424,most_frequent,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,
-122,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.922871100218626,False,True,squared_hinge,0.09957180737451943,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0016563186856411274,median,quantile_transformer,850,uniform,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.09123695282772847,fpr,chi2
-123,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.06571678518052461,False,True,1,squared_hinge,ovr,l2,3.5551445493853517e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0007406565537349627,median,quantile_transformer,1114,normal,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.13436517316012828,fwe,f_classif
-124,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1561.1002600303584,,,0.0024048551704194507,rbf,-1,False,0.0001936214667496851,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,minmax,,,,,fast_ica,,,,,,,,,,,parallel,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-125,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.087313242780732,0.0636709914987994,3,0.004481917125024585,poly,-1,False,0.01848880618941367,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.002136015035740733,mean,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-126,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.03204653439894958,True,True,squared_hinge,0.00013006393401112688,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.013506575694382031,most_frequent,normalize,,,,,feature_agglomeration,,,,,,,,,,,,,,,euclidean,ward,356,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-127,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.6466927589470762,None,0.0,11,2,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.009672465311659264,most_frequent,standardize,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,369,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+1,none,decision_tree,,,,,,,entropy,1.877536950957045,1.0,None,0.0,10,13,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,quantile_transformer,1275,uniform,,,extra_trees_preproc_for_classification,True,gini,None,0.04910706843318435,None,0.0,12,12,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+2,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.8905944749638223,None,0.0,5,4,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.15463552048633444,most_frequent,robust_scaler,,,0.7545842296967857,0.22898551665984185,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.24780801481682346,fwe,chi2
+5,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,9.83838677007206e-09,0.09269618726619562,auto,255,None,37,16,14,loss,1e-07,0.04208303685387712,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
+6,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,4.267426422904654e-06,0.011555121209024403,auto,255,None,121,34,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010064483887441845,most_frequent,quantile_transformer,1194,uniform,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,
+8,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,114,manual,0.11778631707605758,0.0029112721339713465,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.015582195257240751,median,normalize,,,,,kernel_pca,,,,,,,,,,,,,,,,,,,0.42909623253327767,3,0.25407612000343777,poly,1689,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+9,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.685918004243461,True,True,squared_hinge,1.7784504877380735e-05,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010335606354842895,most_frequent,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,
+10,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.7396493120555564,None,0.0,1,18,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010000000000000004,median,robust_scaler,,,0.8908334215036855,0.08666907249785906,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,
+13,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,0.9907352051102745,0.018115439174701313,auto,255,None,84,5,3,loss,1e-07,0.13697766901072808,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.036524416007602305,mean,robust_scaler,,,0.9824116214519696,0.2911868066464925,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,
+14,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,4.629265598705481e-10,0.03894546521791516,auto,255,None,5,22,18,loss,1e-07,0.0837137336215462,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.008397749525484112,mean,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
+15,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.21772092350860472,None,0.0,14,17,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.0020488212891693275,median,standardize,,,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,33.74257606530722,False,True,1,squared_hinge,ovr,l1,8.740452966205111e-05,,,,,,,,,,,,,,,,,,,,,,
+17,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.6591033863118033,None,0.0,10,10,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,none,,,,,fast_ica,,,,,,,,,,,deflation,exp,1506,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+18,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,1.9110915129429343e-09,0.06380311481632683,auto,255,None,20,62,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,robust_scaler,,,0.7307407504330838,0.2660206566992171,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,
+19,weighting,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,99,2,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010033505607662804,median,none,,,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.069760700053376,False,True,1,squared_hinge,ovr,l1,0.06611177025130774,,,,,,,,,,,,,,,,,,,,,,
+23,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,2.0447842449942555e-09,0.04257026709693251,auto,255,None,47,13,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010000000000000004,median,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,
+24,weighting,adaboost,SAMME.R,0.44886160948796244,9,493,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.031007103864624616,mean,robust_scaler,,,0.7575180367976682,0.26457274298753825,feature_agglomeration,,,,,,,,,,,,,,,cosine,complete,211,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+25,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,0.008942041103505539,0.05229567301622349,auto,255,None,6,24,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.01936143670847451,mean,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,
+26,weighting,adaboost,SAMME.R,0.01561270559347964,4,417,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,quantile_transformer,1617,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+27,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.41025844265119926,None,0.0,18,15,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,median,quantile_transformer,1911,normal,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,False,,,,,,,,,,,,
+29,none,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.9034033923099005,False,True,1,squared_hinge,ovr,l2,1.2159485299684885e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.027586423552745792,most_frequent,none,,,,,kernel_pca,,,,,,,,,,,,,,,,,,,,,0.4034250963681743,rbf,735,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+30,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,490.8047233341048,0.13853827450882505,,0.004044309450174347,sigmoid,-1,False,0.005958796387567885,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.008446690235218424,median,minmax,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,229,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+31,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,3.0538220042587883e-07,0.0704056758356995,auto,255,None,9,3,19,loss,1e-07,0.01927933154167906,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.019804174585488932,median,quantile_transformer,921,normal,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,
+32,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,101,auto,,1.553086088086431e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.01137059177001425,most_frequent,normalize,,,,,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,-0.021621751597165284,4,0.7468597264810062,poly,5078,,,,,,,,,,,,,,,,,
+33,none,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.5769165522851386,None,0.0,1,6,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,none,,,,,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,7,None,9,14,1.0,74,,,,,
+34,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,0.06671977293714751,0.04456236025167353,auto,255,None,3,163,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.4333293305857194,mean,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+37,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0618160212153551,True,True,squared_hinge,1.2370453081062464e-05,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.01448339838697973,mean,standardize,,,,,kitchen_sinks,,,,,,,,,,,,,,,,,,,,,,,,0.018879929475513826,1637,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+39,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.6799215694343422,None,0.0,1,5,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.004724310659953132,most_frequent,quantile_transformer,1037,normal,,,fast_ica,,,,,,,,,,,parallel,exp,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+41,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.6965935192702026,None,0.0,12,16,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,none,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,67.60637688434707,f_classif,,,
+43,none,decision_tree,,,,,,,gini,1.1110863621017264,1.0,None,0.0,16,19,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.00043339385698342055,mean,none,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.20957492283649354,fwe,f_classif
+44,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,2.6159836259985015e-05,0.05532294838858921,auto,255,None,116,4,14,loss,1e-07,0.20422881335187132,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,
+49,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00010123414317660679,False,,0.04011567850168966,True,,constant,log,l1,,0.00014179758438189507,one_hot_encoding,minority_coalescer,0.008920898694379564,mean,robust_scaler,,,0.7712963178784032,0.2702398922671455,kernel_pca,,,,,,,,,,,,,,,,,,,,,,cosine,1090,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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+456,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0322540554793962e-05,False,,0.015117861103299655,True,,invscaling,log,l1,0.558781568719232,0.00016877274953233003,one_hot_encoding,no_coalescense,,median,quantile_transformer,681,normal,,,extra_trees_preproc_for_classification,False,gini,None,0.6320249932267078,None,0.0,17,18,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+461,none,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.9796199105973851,None,0.0,2,13,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.00845950033601661,mean,robust_scaler,,,0.8275228560550734,0.23814615163106348,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
diff --git a/autosklearn/metalearning/files/f1_samples_binary.classification_dense/description.txt b/autosklearn/metalearning/files/f1_samples_binary.classification_dense/description.txt
index 233d7b2bb6..be246911cb 100644
--- a/autosklearn/metalearning/files/f1_samples_binary.classification_dense/description.txt
+++ b/autosklearn/metalearning/files/f1_samples_binary.classification_dense/description.txt
@@ -1,8 +1,9 @@
 features_cutoff_time: 3600
 features_cutoff_memory: 3072
-number_of_feature_steps: 35
+number_of_feature_steps: 42
 feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances
 feature_step LogNumberOfInstances: LogNumberOfInstances
+feature_step NumberOfClasses: NumberOfClasses
 feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures
 feature_step LogNumberOfFeatures: LogNumberOfFeatures
 feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues
@@ -20,6 +21,11 @@ feature_step DatasetRatio: DatasetRatio, LogDatasetRatio
 feature_step LogDatasetRatio: LogDatasetRatio
 feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio
 feature_step LogInverseDatasetRatio: LogInverseDatasetRatio
+feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD
+feature_step ClassProbabilityMin: ClassProbabilityMin
+feature_step ClassProbabilityMax: ClassProbabilityMax
+feature_step ClassProbabilityMean: ClassProbabilityMean
+feature_step ClassProbabilitySTD: ClassProbabilitySTD
 feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum
 feature_step SymbolsMin: SymbolsMin
 feature_step SymbolsMax: SymbolsMax
@@ -36,11 +42,12 @@ feature_step SkewnessMin: SkewnessMin
 feature_step SkewnessMax: SkewnessMax
 feature_step SkewnessMean: SkewnessMean
 feature_step SkewnessSTD: SkewnessSTD
-features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
+feature_step ClassEntropy: ClassEntropy
+features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy
 features_stochastic: 
-default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
+default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy
 
-algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127
+algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235,236,237,238,239,240,241,242,243,244,245,246,247,248,249,250,251,252,253,254,255,256,257,258,259,260,261,262,263,264,265,266,267,268,269,270,271,272,273,274,275,276,277,278,279,280,281,282,283,284,285,286,287,288,289,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,312,313,314,315,316,317,318,319,320,321,322,323,324,325,326,327,328,329,330,331,332,333,334,335,336,337,338,339,340,341,342,343,344,345,346,347,348,349,350,351,352,353,354,355,356,357,358,359,360,361,362,363,364,365,366,367,368,369,370,371,372,373,374,375,376,377,378,379,380,381,382,383,384,385,386,387,388,389,390,391,392,393,394,395,396,397,398,399,400,401,402,403,404,405,406,407,408,409,410,411,412,413,414,415,416,417,418,419,420,421,422,423,424,425,426,427,428,429,430,431,432,433,434,435,436,437,438,439,440,441,442,443,444,445,446,447,448,449,450,451,452,453,454,455,456,457,458,459,460,461,462,463,464
 algorithms_stochastic: 
 performance_measures: f1_samples
 performance_type: solution_quality
diff --git a/autosklearn/metalearning/files/f1_samples_binary.classification_dense/feature_costs.arff b/autosklearn/metalearning/files/f1_samples_binary.classification_dense/feature_costs.arff
index 0e17e20d22..8512fd9275 100644
--- a/autosklearn/metalearning/files/f1_samples_binary.classification_dense/feature_costs.arff
+++ b/autosklearn/metalearning/files/f1_samples_binary.classification_dense/feature_costs.arff
@@ -4,6 +4,7 @@
 @ATTRIBUTE repetition NUMERIC
 @ATTRIBUTE NumberOfInstances NUMERIC
 @ATTRIBUTE LogNumberOfInstances NUMERIC
+@ATTRIBUTE NumberOfClasses NUMERIC
 @ATTRIBUTE NumberOfFeatures NUMERIC
 @ATTRIBUTE LogNumberOfFeatures NUMERIC
 @ATTRIBUTE MissingValues NUMERIC
@@ -21,6 +22,11 @@
 @ATTRIBUTE LogDatasetRatio NUMERIC
 @ATTRIBUTE InverseDatasetRatio NUMERIC
 @ATTRIBUTE LogInverseDatasetRatio NUMERIC
+@ATTRIBUTE ClassOccurences NUMERIC
+@ATTRIBUTE ClassProbabilityMin NUMERIC
+@ATTRIBUTE ClassProbabilityMax NUMERIC
+@ATTRIBUTE ClassProbabilityMean NUMERIC
+@ATTRIBUTE ClassProbabilitySTD NUMERIC
 @ATTRIBUTE NumSymbols NUMERIC
 @ATTRIBUTE SymbolsMin NUMERIC
 @ATTRIBUTE SymbolsMax NUMERIC
@@ -37,162 +43,138 @@
 @ATTRIBUTE SkewnessMax NUMERIC
 @ATTRIBUTE SkewnessMean NUMERIC
 @ATTRIBUTE SkewnessSTD NUMERIC
+@ATTRIBUTE ClassEntropy NUMERIC
 
 @DATA
-75123,1.0,1e-05,1e-05,1e-05,1e-05,0.00253,0.00221,1e-05,0.00024,3e-05,0.00011,0.0,2e-05,2e-05,5e-05,8e-05,2e-05,1e-05,7e-05,1e-05,0.00072,1e-05,1e-05,0.00019,0.00041,0.00011,0.00061,0.00012,9e-05,0.00015,0.00025,0.00067,0.00014,0.00011,0.00016,0.00027
-75090,1.0,1e-05,1e-05,2e-05,2e-05,0.00815,0.00368,7e-05,0.00228,9e-05,0.00235,0.0,5e-05,5e-05,0.0001,0.00013,0.00013,1e-05,0.00021,2e-05,0.00064,0.0,0.0,0.00014,0.00038,0.00011,0.00066,0.00011,0.00014,0.00014,0.00026,0.00066,0.00011,0.00013,0.00015,0.00027
-2123,1.0,1e-05,1e-05,1e-05,1e-05,0.00097,0.00079,1e-05,0.00013,1e-05,7e-05,0.0,2e-05,2e-05,5e-05,8e-05,2e-05,1e-05,5e-05,1e-05,0.0007,1e-05,0.0,0.00017,0.00042,0.0001,0.00052,6e-05,8e-05,0.00012,0.00026,0.00049,6e-05,0.00011,9e-05,0.00023
-248,1.0,1e-05,1e-05,1e-05,1e-05,0.00088,0.00077,1e-05,0.0001,1e-05,4e-05,0.0,1e-05,1e-05,4e-05,5e-05,2e-05,1e-05,4e-05,1e-05,0.00041,1e-05,0.0,9e-05,0.00022,9e-05,0.00046,6e-05,8e-05,9e-05,0.00023,0.00049,7e-05,0.0001,0.0001,0.00021
-75101,1.0,1e-05,1e-05,1e-05,1e-05,0.0418,0.03546,1e-05,0.00389,1e-05,0.00248,0.0,2e-05,2e-05,4e-05,6e-05,2e-05,1e-05,5e-05,2e-05,0.00045,1e-05,0.0,0.00011,0.00023,0.0001,0.00041,6e-05,8e-05,0.0001,0.00018,0.00047,6e-05,0.0001,8e-05,0.00023
-75191,1.0,1e-05,1e-05,3e-05,1e-05,0.53436,0.40777,2e-05,0.09655,3e-05,0.0301,1e-05,2e-05,3e-05,5e-05,0.00011,7e-05,1e-05,0.00011,3e-05,0.00084,5e-05,7e-05,0.00017,0.00033,0.00023,0.00088,0.00012,0.00017,0.00032,0.00026,0.00068,9e-05,0.00012,0.00013,0.00035
-75187,1.0,1e-05,1e-05,2e-05,1e-05,0.00548,0.00473,1e-05,0.0005,2e-05,0.00029,1e-05,2e-05,2e-05,7e-05,9e-05,3e-05,1e-05,6e-05,1e-05,0.00062,1e-05,1e-05,0.00014,0.00033,0.00013,0.00038,7e-05,7e-05,8e-05,0.00016,0.00047,6e-05,9e-05,9e-05,0.00023
-266,1.0,1e-05,1e-05,1e-05,1e-05,0.0018,0.00146,1e-05,0.00023,2e-05,0.00015,2e-05,2e-05,2e-05,6e-05,8e-05,3e-05,1e-05,6e-05,1e-05,0.00059,1e-05,1e-05,0.00014,0.00031,0.00013,0.0004,5e-05,7e-05,8e-05,0.0002,0.00044,8e-05,8e-05,8e-05,0.0002
-253,1.0,1e-05,1e-05,1e-05,1e-05,0.00082,0.00063,1e-05,0.00016,2e-05,5e-05,0.0,1e-05,1e-05,4e-05,5e-05,2e-05,1e-05,5e-05,2e-05,0.00046,1e-05,0.0,9e-05,0.00024,0.00012,0.00044,6e-05,7e-05,8e-05,0.00023,0.00043,6e-05,0.0001,8e-05,0.00019
-75217,1.0,1e-05,1e-05,2e-05,1e-05,0.00196,0.00158,1e-05,0.00026,2e-05,0.00016,1e-05,3e-05,3e-05,7e-05,0.0001,3e-05,1e-05,7e-05,2e-05,0.00089,1e-05,1e-05,0.00022,0.00053,0.00013,0.00074,9e-05,0.0001,0.00019,0.00036,0.00065,0.00013,0.0001,0.00011,0.00031
-75176,1.0,1e-05,1e-05,2e-05,1e-05,0.01406,0.01285,1e-05,0.00094,2e-05,0.00031,1e-05,3e-05,3e-05,6e-05,9e-05,3e-05,1e-05,7e-05,2e-05,0.00069,1e-05,1e-05,0.00017,0.00035,0.00015,0.00085,0.00018,0.00011,0.0002,0.00036,0.0011,0.00022,0.00023,0.00023,0.00042
-2117,1.0,1e-05,1e-05,1e-05,1e-05,0.01735,0.0154,1e-05,0.00141,1e-05,0.00057,0.0,1e-05,1e-05,3e-05,6e-05,2e-05,1e-05,5e-05,1e-05,0.0004,1e-05,0.0,8e-05,0.00021,0.0001,0.00065,0.00012,0.00011,0.00015,0.00026,0.00055,7e-05,0.00014,9e-05,0.00025
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-75103,1.0,1e-05,1e-05,1e-05,1e-05,0.01302,0.00819,3e-05,0.00234,3e-05,0.00255,0.0,2e-05,2e-05,6e-05,0.00012,5e-05,1e-05,0.00011,1e-05,0.0005,1e-05,0.0,9e-05,0.00029,0.00011,0.00054,8e-05,0.00011,0.00011,0.00024,0.00066,7e-05,0.00015,0.00011,0.00032
-75168,1.0,1e-05,0.0,1e-05,1e-05,0.02353,0.00601,0.00016,0.01754,0.00018,0.00031,0.0,0.00013,0.00013,0.00027,0.0003,0.00032,1e-05,0.00042,1e-05,0.0004,1e-05,0.0,8e-05,0.00021,0.0001,0.0011,0.00022,0.00025,0.00026,0.00037,0.00124,0.00022,0.00025,0.00028,0.00049
-75157,1.0,1e-05,1e-05,1e-05,1e-05,0.00125,0.00106,1e-05,0.00015,2e-05,7e-05,0.0,2e-05,2e-05,5e-05,6e-05,2e-05,1e-05,4e-05,1e-05,0.00048,2e-05,0.0,0.0001,0.00026,0.0001,0.00039,6e-05,7e-05,8e-05,0.00019,0.00043,6e-05,9e-05,8e-05,0.0002
-75092,1.0,1e-05,1e-05,1e-05,1e-05,0.00099,0.00072,1e-05,0.00018,1e-05,0.00011,0.0,2e-05,2e-05,4e-05,6e-05,2e-05,1e-05,5e-05,1e-05,0.00058,1e-05,0.0,0.00014,0.00034,9e-05,0.00041,6e-05,8e-05,9e-05,0.00017,0.00045,7e-05,0.0001,9e-05,0.00019
-75213,1.0,1e-05,1e-05,1e-05,1e-05,0.00096,0.00079,1e-05,0.00013,1e-05,7e-05,1e-05,2e-05,2e-05,5e-05,7e-05,2e-05,1e-05,6e-05,1e-05,0.00056,1e-05,1e-05,0.00013,0.00029,0.00012,0.00056,6e-05,8e-05,9e-05,0.00033,0.00063,8e-05,0.00014,0.00013,0.00029
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-75119,1.0,1e-05,1e-05,2e-05,1e-05,0.04185,0.01398,0.00063,0.01614,0.0007,0.01306,0.0,0.00046,0.00045,0.00091,0.00097,0.0012,1e-05,0.0015,1e-05,0.00047,1e-05,0.0,8e-05,0.00026,0.00012,0.00421,0.00091,0.00097,0.00103,0.0013,0.00402,0.00082,0.0008,0.00097,0.00144
-75227,1.0,1e-05,1e-05,2e-05,1e-05,0.00416,0.00373,1e-05,0.00038,2e-05,9e-05,1e-05,2e-05,2e-05,6e-05,0.0001,3e-05,1e-05,7e-05,2e-05,0.00063,1e-05,1e-05,0.00015,0.00034,0.00013,0.00037,6e-05,8e-05,8e-05,0.00016,0.00043,6e-05,9e-05,9e-05,0.0002
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diff --git a/autosklearn/metalearning/files/f1_samples_binary.classification_dense/feature_runstatus.arff b/autosklearn/metalearning/files/f1_samples_binary.classification_dense/feature_runstatus.arff
index 0034ad2bd6..5fff2dfed3 100644
--- a/autosklearn/metalearning/files/f1_samples_binary.classification_dense/feature_runstatus.arff
+++ b/autosklearn/metalearning/files/f1_samples_binary.classification_dense/feature_runstatus.arff
@@ -4,6 +4,7 @@
 @ATTRIBUTE repetition NUMERIC
 @ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other}
@@ -21,6 +22,11 @@
 @ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other}
@@ -37,162 +43,138 @@
 @ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other}
 
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diff --git a/autosklearn/metalearning/files/f1_samples_binary.classification_dense/feature_values.arff b/autosklearn/metalearning/files/f1_samples_binary.classification_dense/feature_values.arff
index a60400e3ce..736dc64321 100644
--- a/autosklearn/metalearning/files/f1_samples_binary.classification_dense/feature_values.arff
+++ b/autosklearn/metalearning/files/f1_samples_binary.classification_dense/feature_values.arff
@@ -13,7 +13,6 @@
 @ATTRIBUTE KurtosisMean NUMERIC
 @ATTRIBUTE KurtosisMin NUMERIC
 @ATTRIBUTE KurtosisSTD NUMERIC
-@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC
 @ATTRIBUTE LogDatasetRatio NUMERIC
 @ATTRIBUTE LogInverseDatasetRatio NUMERIC
 @ATTRIBUTE LogNumberOfFeatures NUMERIC
@@ -42,160 +41,135 @@
 @ATTRIBUTE SymbolsSum NUMERIC
 
 @DATA
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diff --git a/autosklearn/metalearning/files/f1_samples_binary.classification_sparse/algorithm_runs.arff b/autosklearn/metalearning/files/f1_samples_binary.classification_sparse/algorithm_runs.arff
index 5c15e37f6a..70c99dcef1 100644
--- a/autosklearn/metalearning/files/f1_samples_binary.classification_sparse/algorithm_runs.arff
+++ b/autosklearn/metalearning/files/f1_samples_binary.classification_sparse/algorithm_runs.arff
@@ -7,95 +7,133 @@
 @ATTRIBUTE runstatus {ok, timeout, memout, not_applicable, crash, other}
 
 @DATA
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-246,1.0,2,?,not_applicable
-75178,1.0,3,?,not_applicable
-75171,1.0,4,?,not_applicable
-248,1.0,5,?,not_applicable
-75231,1.0,6,?,not_applicable
-75196,1.0,7,?,not_applicable
-75188,1.0,8,?,not_applicable
-75248,1.0,9,?,not_applicable
-75126,1.0,10,?,not_applicable
-75234,1.0,11,?,not_applicable
-75150,1.0,12,?,not_applicable
-258,1.0,13,?,not_applicable
-75168,1.0,14,?,not_applicable
-75235,1.0,15,?,not_applicable
-244,1.0,16,?,not_applicable
-75221,1.0,17,?,not_applicable
-75219,1.0,18,?,not_applicable
-75202,1.0,19,?,not_applicable
-3043,1.0,20,?,not_applicable
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 75222,1.0,27,?,not_applicable
-233,1.0,28,?,not_applicable
-75114,1.0,29,?,not_applicable
-236,1.0,30,?,not_applicable
-75141,1.0,31,?,not_applicable
-75107,1.0,32,?,not_applicable
-262,1.0,33,?,not_applicable
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-2350,1.0,36,?,not_applicable
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-242,1.0,38,?,not_applicable
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-75121,1.0,59,?,not_applicable
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-2119,1.0,64,?,not_applicable
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-75113,1.0,66,?,not_applicable
-75203,1.0,67,?,not_applicable
-75182,1.0,68,?,not_applicable
-251,1.0,69,?,not_applicable
-75123,1.0,70,?,not_applicable
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-75232,1.0,72,?,not_applicable
-75103,1.0,73,?,not_applicable
-75192,1.0,74,?,not_applicable
-75230,1.0,75,?,not_applicable
-75139,1.0,76,?,not_applicable
-75227,1.0,77,?,not_applicable
-2120,1.0,78,?,not_applicable
-75124,1.0,79,?,not_applicable
-75240,1.0,80,?,not_applicable
-75198,1.0,81,?,not_applicable
-75201,1.0,82,?,not_applicable
-75133,1.0,83,?,not_applicable
-75154,1.0,84,?,not_applicable
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-2117,1.0,86,?,not_applicable
-75156,1.0,87,?,not_applicable
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-75172,1.0,89,?,not_applicable
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diff --git a/autosklearn/metalearning/files/f1_samples_binary.classification_sparse/configurations.csv b/autosklearn/metalearning/files/f1_samples_binary.classification_sparse/configurations.csv
index 888e169980..c3d201ba5c 100644
--- a/autosklearn/metalearning/files/f1_samples_binary.classification_sparse/configurations.csv
+++ b/autosklearn/metalearning/files/f1_samples_binary.classification_sparse/configurations.csv
@@ -1,93 +1,131 @@
 idx,balancing:strategy,classifier:__choice__,classifier:adaboost:algorithm,classifier:adaboost:learning_rate,classifier:adaboost:max_depth,classifier:adaboost:n_estimators,classifier:bernoulli_nb:alpha,classifier:bernoulli_nb:fit_prior,classifier:decision_tree:criterion,classifier:decision_tree:max_depth_factor,classifier:decision_tree:max_features,classifier:decision_tree:max_leaf_nodes,classifier:decision_tree:min_impurity_decrease,classifier:decision_tree:min_samples_leaf,classifier:decision_tree:min_samples_split,classifier:decision_tree:min_weight_fraction_leaf,classifier:extra_trees:bootstrap,classifier:extra_trees:criterion,classifier:extra_trees:max_depth,classifier:extra_trees:max_features,classifier:extra_trees:max_leaf_nodes,classifier:extra_trees:min_impurity_decrease,classifier:extra_trees:min_samples_leaf,classifier:extra_trees:min_samples_split,classifier:extra_trees:min_weight_fraction_leaf,classifier:gradient_boosting:early_stop,classifier:gradient_boosting:l2_regularization,classifier:gradient_boosting:learning_rate,classifier:gradient_boosting:loss,classifier:gradient_boosting:max_bins,classifier:gradient_boosting:max_depth,classifier:gradient_boosting:max_leaf_nodes,classifier:gradient_boosting:min_samples_leaf,classifier:gradient_boosting:n_iter_no_change,classifier:gradient_boosting:scoring,classifier:gradient_boosting:tol,classifier:gradient_boosting:validation_fraction,classifier:k_nearest_neighbors:n_neighbors,classifier:k_nearest_neighbors:p,classifier:k_nearest_neighbors:weights,classifier:lda:n_components,classifier:lda:shrinkage,classifier:lda:shrinkage_factor,classifier:lda:tol,classifier:liblinear_svc:C,classifier:liblinear_svc:dual,classifier:liblinear_svc:fit_intercept,classifier:liblinear_svc:intercept_scaling,classifier:liblinear_svc:loss,classifier:liblinear_svc:multi_class,classifier:liblinear_svc:penalty,classifier:liblinear_svc:tol,classifier:libsvm_svc:C,classifier:libsvm_svc:coef0,classifier:libsvm_svc:degree,classifier:libsvm_svc:gamma,classifier:libsvm_svc:kernel,classifier:libsvm_svc:max_iter,classifier:libsvm_svc:shrinking,classifier:libsvm_svc:tol,classifier:multinomial_nb:alpha,classifier:multinomial_nb:fit_prior,classifier:passive_aggressive:C,classifier:passive_aggressive:average,classifier:passive_aggressive:fit_intercept,classifier:passive_aggressive:loss,classifier:passive_aggressive:tol,classifier:qda:reg_param,classifier:random_forest:bootstrap,classifier:random_forest:criterion,classifier:random_forest:max_depth,classifier:random_forest:max_features,classifier:random_forest:max_leaf_nodes,classifier:random_forest:min_impurity_decrease,classifier:random_forest:min_samples_leaf,classifier:random_forest:min_samples_split,classifier:random_forest:min_weight_fraction_leaf,classifier:sgd:alpha,classifier:sgd:average,classifier:sgd:epsilon,classifier:sgd:eta0,classifier:sgd:fit_intercept,classifier:sgd:l1_ratio,classifier:sgd:learning_rate,classifier:sgd:loss,classifier:sgd:penalty,classifier:sgd:power_t,classifier:sgd:tol,data_preprocessing:categorical_transformer:categorical_encoding:__choice__,data_preprocessing:categorical_transformer:category_coalescence:__choice__,data_preprocessing:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction,data_preprocessing:numerical_transformer:imputation:strategy,data_preprocessing:numerical_transformer:rescaling:__choice__,data_preprocessing:numerical_transformer:rescaling:quantile_transformer:n_quantiles,data_preprocessing:numerical_transformer:rescaling:quantile_transformer:output_distribution,data_preprocessing:numerical_transformer:rescaling:robust_scaler:q_max,data_preprocessing:numerical_transformer:rescaling:robust_scaler:q_min,feature_preprocessor:__choice__,feature_preprocessor:extra_trees_preproc_for_classification:bootstrap,feature_preprocessor:extra_trees_preproc_for_classification:criterion,feature_preprocessor:extra_trees_preproc_for_classification:max_depth,feature_preprocessor:extra_trees_preproc_for_classification:max_features,feature_preprocessor:extra_trees_preproc_for_classification:max_leaf_nodes,feature_preprocessor:extra_trees_preproc_for_classification:min_impurity_decrease,feature_preprocessor:extra_trees_preproc_for_classification:min_samples_leaf,feature_preprocessor:extra_trees_preproc_for_classification:min_samples_split,feature_preprocessor:extra_trees_preproc_for_classification:min_weight_fraction_leaf,feature_preprocessor:extra_trees_preproc_for_classification:n_estimators,feature_preprocessor:kernel_pca:coef0,feature_preprocessor:kernel_pca:degree,feature_preprocessor:kernel_pca:gamma,feature_preprocessor:kernel_pca:kernel,feature_preprocessor:kernel_pca:n_components,feature_preprocessor:kitchen_sinks:gamma,feature_preprocessor:kitchen_sinks:n_components,feature_preprocessor:liblinear_svc_preprocessor:C,feature_preprocessor:liblinear_svc_preprocessor:dual,feature_preprocessor:liblinear_svc_preprocessor:fit_intercept,feature_preprocessor:liblinear_svc_preprocessor:intercept_scaling,feature_preprocessor:liblinear_svc_preprocessor:loss,feature_preprocessor:liblinear_svc_preprocessor:multi_class,feature_preprocessor:liblinear_svc_preprocessor:penalty,feature_preprocessor:liblinear_svc_preprocessor:tol,feature_preprocessor:nystroem_sampler:coef0,feature_preprocessor:nystroem_sampler:degree,feature_preprocessor:nystroem_sampler:gamma,feature_preprocessor:nystroem_sampler:kernel,feature_preprocessor:nystroem_sampler:n_components,feature_preprocessor:polynomial:degree,feature_preprocessor:polynomial:include_bias,feature_preprocessor:polynomial:interaction_only,feature_preprocessor:random_trees_embedding:bootstrap,feature_preprocessor:random_trees_embedding:max_depth,feature_preprocessor:random_trees_embedding:max_leaf_nodes,feature_preprocessor:random_trees_embedding:min_samples_leaf,feature_preprocessor:random_trees_embedding:min_samples_split,feature_preprocessor:random_trees_embedding:min_weight_fraction_leaf,feature_preprocessor:random_trees_embedding:n_estimators,feature_preprocessor:select_percentile_classification:percentile,feature_preprocessor:select_percentile_classification:score_func,feature_preprocessor:select_rates_classification:alpha,feature_preprocessor:select_rates_classification:mode,feature_preprocessor:select_rates_classification:score_func,feature_preprocessor:truncatedSVD:target_dim
-1,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.3163020577549344,None,0.0,15,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.01322981552715978,most_frequent,robust_scaler,,,0.9746667009556722,0.1974876581478311,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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-3,weighting,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,88,2,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.254263872179305,median,normalize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-4,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,170.37509684994612,0.6734219115470694,3,0.08998608839429428,poly,-1,True,0.024147312348267988,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,none,,,,,extra_trees_preproc_for_classification,False,entropy,None,0.9449154369109399,None,0.0,1,14,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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-7,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.02299691847304223,False,True,squared_hinge,2.16045124689341e-05,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010708146395792527,most_frequent,standardize,,,,,extra_trees_preproc_for_classification,False,entropy,None,0.5298549662385138,None,0.0,12,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-8,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.25657910619102237,None,0.0,6,6,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.021086303324492208,most_frequent,quantile_transformer,1247,uniform,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.16021572815319468,fpr,chi2,
-9,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.643761610990255,None,0.0,20,6,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.039859381630488695,mean,normalize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,,
-10,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.8253905225980761,None,0.0,18,20,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.003725255016700434,mean,robust_scaler,,,0.7701705977765594,0.2396520157991559,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.3579038776690903,False,True,1,squared_hinge,ovr,l1,4.994232534734023e-05,,,,,,,,,,,,,,,,,,,,,
-11,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.06263739023781334,None,0.0,18,8,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.02206379295100344,most_frequent,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,,
-12,none,decision_tree,,,,,,,entropy,0.9175027108465073,1.0,None,0.0,18,13,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0005144268441601888,most_frequent,none,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.36269138268682594,fdr,chi2,
-13,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,199,None,,0.003239674765949011,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,robust_scaler,,,0.7228447795482008,0.25,kernel_pca,,,,,,,,,,,0.09440148939740256,3,0.0008114095405154861,poly,1996,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-14,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.6039096182414764,None,0.0,2,20,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.1939609823704447,most_frequent,robust_scaler,,,0.7331327008874781,0.29747488162877694,extra_trees_preproc_for_classification,True,entropy,None,0.33401603690569853,None,0.0,4,4,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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-18,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5010952533165973,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.005838375247601407,mean,normalize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,,
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-22,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9333237919291875,None,0.0,8,3,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,,
-23,none,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,184,manual,0.5022041786628263,2.3064966255468173e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,most_frequent,standardize,,,,,kernel_pca,,,,,,,,,,,-0.32023224593255817,2,0.09479590655633498,poly,1382,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-24,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,22194.242946902177,0.45210849669021247,,0.0005669713702171556,sigmoid,-1,False,0.03542142266081451,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,quantile_transformer,291,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-25,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.4752532930653711,None,0.0,9,17,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,robust_scaler,,,0.8538731695642808,0.11963446560467883,extra_trees_preproc_for_classification,True,entropy,None,0.5642223456125496,None,0.0,6,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-26,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7422641456052917,None,0.0,8,15,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.005561351266169067,most_frequent,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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-29,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,10,1,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0022606253047811435,median,quantile_transformer,1247,normal,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,25.327793585266683,chi2,,,,
-30,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,45.824472650016915,,,0.04733593018751384,rbf,-1,False,0.0001002563992422035,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.007882457896645694,most_frequent,robust_scaler,,,0.7360778699898849,0.27800774875566153,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-31,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,21.69482701422382,-0.9439355912923322,,0.012708037096662675,sigmoid,-1,True,1.3900680173654052e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-32,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.4467384469668449,None,0.0,19,19,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.00033708405018786203,median,quantile_transformer,711,normal,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,7.744829113696808,False,True,1,squared_hinge,ovr,l1,0.0001869446320464273,,,,,,,,,,,,,,,,,,,,,
-33,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,26.502259985828186,,,9.475662388405154e-05,rbf,-1,False,0.00019690635968217866,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-34,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.339600708038189,False,True,1,squared_hinge,ovr,l2,9.572641158475304e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,quantile_transformer,1193,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-35,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5255991794296913,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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-37,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.7388247096873266,None,0.0,1,2,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.02005334514549439,most_frequent,robust_scaler,,,0.9347422252540531,0.2909868620945952,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,,
-38,none,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4994182104906286,False,True,1,squared_hinge,ovr,l2,0.006324920556381277,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.06848370232187935,mean,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-39,none,bernoulli_nb,,,,,1.9593357170002308,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.005109073482613361,mean,quantile_transformer,10,normal,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.03495379340060141,False,True,1,squared_hinge,ovr,l1,0.0033386410851928994,,,,,,,,,,,,,,,,,,,,,
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-42,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.49918755377415064,None,0.0,1,2,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.36992315774620843,mean,robust_scaler,,,0.7410494147895569,0.29895918805929733,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-43,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,29.55731828912983,False,True,1,squared_hinge,ovr,l2,0.0011332486951954968,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,,
-44,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.37801720673725603,True,True,squared_hinge,0.0001255132393635067,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.025993178344125793,most_frequent,standardize,,,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,1.4421737618429475,False,True,1,squared_hinge,ovr,l1,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,
-45,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.10581257327556097,None,0.0,7,12,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.004219392676189224,median,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,,
-46,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.4188764793561401,None,0.0,5,5,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.010589444903605501,mean,robust_scaler,,,0.9796997860990453,0.27262832749694405,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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-48,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.41019363453549706,None,0.0,15,2,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.0003507389224306018,median,none,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.20614098085343638,fdr,chi2,
-49,none,adaboost,SAMME,0.07025975715693651,5,339,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,median,robust_scaler,,,0.7117466125338451,0.2984609070131515,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.21351971922962878,fdr,chi2,
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-52,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.6271213423927893,None,0.0,1,4,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.009403509495453675,mean,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,,
-53,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8.85547546146731,True,True,squared_hinge,4.0111304608748804e-05,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-54,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.34936025988397124,None,0.0,2,13,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00018601309647406987,median,robust_scaler,,,0.8634309348843472,0.26976989465367607,extra_trees_preproc_for_classification,False,entropy,None,0.48025175288975036,None,0.0,6,17,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-55,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.642370445299362,None,0.0,15,20,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.014823484140711048,median,robust_scaler,,,0.780144337652783,0.2309544712129402,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,2.2123747341894098,False,True,1,squared_hinge,ovr,l1,0.0033125984161268183,,,,,,,,,,,,,,,,,,,,,
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-57,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0016941428709087083,True,,,True,,optimal,squared_hinge,l1,,5.124265599423001e-05,no_encoding,no_coalescense,,most_frequent,quantile_transformer,461,uniform,,,extra_trees_preproc_for_classification,True,gini,None,0.31371431180257103,None,0.0,5,17,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-58,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.7591396576940418,-0.02447104417007273,2,0.7325180366342191,poly,-1,True,0.00045922794962860523,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,standardize,,,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,2.057731506094293,False,True,1,squared_hinge,ovr,l1,0.03575998247131604,,,,,,,,,,,,,,,,,,,,,
-59,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,116.26983401226964,False,True,1,squared_hinge,ovr,l2,0.08512673221446286,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-60,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5542793596955801,None,0.0,1,3,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,most_frequent,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-61,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.46057831591617715,False,True,hinge,0.04557857428827514,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00027457445401600137,median,standardize,,,,,extra_trees_preproc_for_classification,True,gini,None,0.48190346970486964,None,0.0,17,18,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-62,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.46057831591617715,False,True,hinge,0.04557857428827514,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00027457445401600137,median,standardize,,,,,extra_trees_preproc_for_classification,True,gini,None,0.48190346970486964,None,0.0,17,18,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-63,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.8494314157539654,None,0.0,3,19,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,mean,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,,
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+365,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0322540554793962e-05,False,,0.015117861103299655,True,,invscaling,log,l1,0.558781568719232,0.00016877274953233003,one_hot_encoding,no_coalescense,,median,quantile_transformer,681,normal,,,extra_trees_preproc_for_classification,False,gini,None,0.6320249932267078,None,0.0,17,18,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+369,none,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.9796199105973851,None,0.0,2,13,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.00845950033601661,mean,robust_scaler,,,0.8275228560550734,0.23814615163106348,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
diff --git a/autosklearn/metalearning/files/f1_samples_binary.classification_sparse/description.txt b/autosklearn/metalearning/files/f1_samples_binary.classification_sparse/description.txt
index e1c169d0eb..f4e707605b 100644
--- a/autosklearn/metalearning/files/f1_samples_binary.classification_sparse/description.txt
+++ b/autosklearn/metalearning/files/f1_samples_binary.classification_sparse/description.txt
@@ -1,8 +1,9 @@
 features_cutoff_time: 3600
 features_cutoff_memory: 3072
-number_of_feature_steps: 35
+number_of_feature_steps: 42
 feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances
 feature_step LogNumberOfInstances: LogNumberOfInstances
+feature_step NumberOfClasses: NumberOfClasses
 feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures
 feature_step LogNumberOfFeatures: LogNumberOfFeatures
 feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues
@@ -20,6 +21,11 @@ feature_step DatasetRatio: DatasetRatio, LogDatasetRatio
 feature_step LogDatasetRatio: LogDatasetRatio
 feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio
 feature_step LogInverseDatasetRatio: LogInverseDatasetRatio
+feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD
+feature_step ClassProbabilityMin: ClassProbabilityMin
+feature_step ClassProbabilityMax: ClassProbabilityMax
+feature_step ClassProbabilityMean: ClassProbabilityMean
+feature_step ClassProbabilitySTD: ClassProbabilitySTD
 feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum
 feature_step SymbolsMin: SymbolsMin
 feature_step SymbolsMax: SymbolsMax
@@ -36,11 +42,12 @@ feature_step SkewnessMin: SkewnessMin
 feature_step SkewnessMax: SkewnessMax
 feature_step SkewnessMean: SkewnessMean
 feature_step SkewnessSTD: SkewnessSTD
-features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
+feature_step ClassEntropy: ClassEntropy
+features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy
 features_stochastic: 
-default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
+default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy
 
-algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92
+algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235,236,237,238,239,240,241,242,243,244,245,246,247,248,249,250,251,252,253,254,255,256,257,258,259,260,261,262,263,264,265,266,267,268,269,270,271,272,273,274,275,276,277,278,279,280,281,282,283,284,285,286,287,288,289,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,312,313,314,315,316,317,318,319,320,321,322,323,324,325,326,327,328,329,330,331,332,333,334,335,336,337,338,339,340,341,342,343,344,345,346,347,348,349,350,351,352,353,354,355,356,357,358,359,360,361,362,363,364,365,366,367,368,369,370,371,372
 algorithms_stochastic: 
 performance_measures: f1_samples
 performance_type: solution_quality
diff --git a/autosklearn/metalearning/files/f1_samples_binary.classification_sparse/feature_costs.arff b/autosklearn/metalearning/files/f1_samples_binary.classification_sparse/feature_costs.arff
index 0e17e20d22..8512fd9275 100644
--- a/autosklearn/metalearning/files/f1_samples_binary.classification_sparse/feature_costs.arff
+++ b/autosklearn/metalearning/files/f1_samples_binary.classification_sparse/feature_costs.arff
@@ -4,6 +4,7 @@
 @ATTRIBUTE repetition NUMERIC
 @ATTRIBUTE NumberOfInstances NUMERIC
 @ATTRIBUTE LogNumberOfInstances NUMERIC
+@ATTRIBUTE NumberOfClasses NUMERIC
 @ATTRIBUTE NumberOfFeatures NUMERIC
 @ATTRIBUTE LogNumberOfFeatures NUMERIC
 @ATTRIBUTE MissingValues NUMERIC
@@ -21,6 +22,11 @@
 @ATTRIBUTE LogDatasetRatio NUMERIC
 @ATTRIBUTE InverseDatasetRatio NUMERIC
 @ATTRIBUTE LogInverseDatasetRatio NUMERIC
+@ATTRIBUTE ClassOccurences NUMERIC
+@ATTRIBUTE ClassProbabilityMin NUMERIC
+@ATTRIBUTE ClassProbabilityMax NUMERIC
+@ATTRIBUTE ClassProbabilityMean NUMERIC
+@ATTRIBUTE ClassProbabilitySTD NUMERIC
 @ATTRIBUTE NumSymbols NUMERIC
 @ATTRIBUTE SymbolsMin NUMERIC
 @ATTRIBUTE SymbolsMax NUMERIC
@@ -37,162 +43,138 @@
 @ATTRIBUTE SkewnessMax NUMERIC
 @ATTRIBUTE SkewnessMean NUMERIC
 @ATTRIBUTE SkewnessSTD NUMERIC
+@ATTRIBUTE ClassEntropy NUMERIC
 
 @DATA
-75123,1.0,1e-05,1e-05,1e-05,1e-05,0.00253,0.00221,1e-05,0.00024,3e-05,0.00011,0.0,2e-05,2e-05,5e-05,8e-05,2e-05,1e-05,7e-05,1e-05,0.00072,1e-05,1e-05,0.00019,0.00041,0.00011,0.00061,0.00012,9e-05,0.00015,0.00025,0.00067,0.00014,0.00011,0.00016,0.00027
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-75101,1.0,1e-05,1e-05,1e-05,1e-05,0.0418,0.03546,1e-05,0.00389,1e-05,0.00248,0.0,2e-05,2e-05,4e-05,6e-05,2e-05,1e-05,5e-05,2e-05,0.00045,1e-05,0.0,0.00011,0.00023,0.0001,0.00041,6e-05,8e-05,0.0001,0.00018,0.00047,6e-05,0.0001,8e-05,0.00023
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-75187,1.0,1e-05,1e-05,2e-05,1e-05,0.00548,0.00473,1e-05,0.0005,2e-05,0.00029,1e-05,2e-05,2e-05,7e-05,9e-05,3e-05,1e-05,6e-05,1e-05,0.00062,1e-05,1e-05,0.00014,0.00033,0.00013,0.00038,7e-05,7e-05,8e-05,0.00016,0.00047,6e-05,9e-05,9e-05,0.00023
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-75103,1.0,1e-05,1e-05,1e-05,1e-05,0.01302,0.00819,3e-05,0.00234,3e-05,0.00255,0.0,2e-05,2e-05,6e-05,0.00012,5e-05,1e-05,0.00011,1e-05,0.0005,1e-05,0.0,9e-05,0.00029,0.00011,0.00054,8e-05,0.00011,0.00011,0.00024,0.00066,7e-05,0.00015,0.00011,0.00032
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-75157,1.0,1e-05,1e-05,1e-05,1e-05,0.00125,0.00106,1e-05,0.00015,2e-05,7e-05,0.0,2e-05,2e-05,5e-05,6e-05,2e-05,1e-05,4e-05,1e-05,0.00048,2e-05,0.0,0.0001,0.00026,0.0001,0.00039,6e-05,7e-05,8e-05,0.00019,0.00043,6e-05,9e-05,8e-05,0.0002
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diff --git a/autosklearn/metalearning/files/f1_samples_binary.classification_sparse/feature_runstatus.arff b/autosklearn/metalearning/files/f1_samples_binary.classification_sparse/feature_runstatus.arff
index 0034ad2bd6..5fff2dfed3 100644
--- a/autosklearn/metalearning/files/f1_samples_binary.classification_sparse/feature_runstatus.arff
+++ b/autosklearn/metalearning/files/f1_samples_binary.classification_sparse/feature_runstatus.arff
@@ -4,6 +4,7 @@
 @ATTRIBUTE repetition NUMERIC
 @ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other}
@@ -21,6 +22,11 @@
 @ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other}
@@ -37,162 +43,138 @@
 @ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other}
 
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diff --git a/autosklearn/metalearning/files/f1_samples_binary.classification_sparse/feature_values.arff b/autosklearn/metalearning/files/f1_samples_binary.classification_sparse/feature_values.arff
index a60400e3ce..736dc64321 100644
--- a/autosklearn/metalearning/files/f1_samples_binary.classification_sparse/feature_values.arff
+++ b/autosklearn/metalearning/files/f1_samples_binary.classification_sparse/feature_values.arff
@@ -13,7 +13,6 @@
 @ATTRIBUTE KurtosisMean NUMERIC
 @ATTRIBUTE KurtosisMin NUMERIC
 @ATTRIBUTE KurtosisSTD NUMERIC
-@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC
 @ATTRIBUTE LogDatasetRatio NUMERIC
 @ATTRIBUTE LogInverseDatasetRatio NUMERIC
 @ATTRIBUTE LogNumberOfFeatures NUMERIC
@@ -42,160 +41,135 @@
 @ATTRIBUTE SymbolsSum NUMERIC
 
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diff --git a/autosklearn/metalearning/files/f1_samples_multiclass.classification_dense/algorithm_runs.arff b/autosklearn/metalearning/files/f1_samples_multiclass.classification_dense/algorithm_runs.arff
index bb9281cde2..4824cf8886 100644
--- a/autosklearn/metalearning/files/f1_samples_multiclass.classification_dense/algorithm_runs.arff
+++ b/autosklearn/metalearning/files/f1_samples_multiclass.classification_dense/algorithm_runs.arff
@@ -7,130 +7,135 @@
 @ATTRIBUTE runstatus {ok, timeout, memout, not_applicable, crash, other}
 
 @DATA
-75119,1.0,1,?,not_applicable
-75212,1.0,2,?,not_applicable
-252,1.0,3,?,not_applicable
-246,1.0,4,?,not_applicable
-75178,1.0,5,?,not_applicable
-75239,1.0,6,?,not_applicable
-75171,1.0,7,?,not_applicable
-75233,1.0,8,?,not_applicable
-248,1.0,9,?,not_applicable
-75231,1.0,10,?,not_applicable
-2123,1.0,11,?,not_applicable
-75196,1.0,12,?,not_applicable
-75188,1.0,13,?,not_applicable
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+75139,1.0,5,?,not_applicable
+75212,1.0,6,?,not_applicable
+246,1.0,8,?,not_applicable
+252,1.0,9,?,not_applicable
+75178,1.0,10,?,not_applicable
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 75092,1.0,14,?,not_applicable
-75248,1.0,15,?,not_applicable
-75126,1.0,16,?,not_applicable
-75234,1.0,17,?,not_applicable
-75150,1.0,18,?,not_applicable
-258,1.0,19,?,not_applicable
-75168,1.0,20,?,not_applicable
-75235,1.0,21,?,not_applicable
-75159,1.0,22,?,not_applicable
-244,1.0,23,?,not_applicable
-75221,1.0,24,?,not_applicable
-75219,1.0,25,?,not_applicable
-75202,1.0,26,?,not_applicable
-3043,1.0,27,?,not_applicable
-75205,1.0,28,?,not_applicable
-75174,1.0,29,?,not_applicable
-288,1.0,30,?,not_applicable
-75250,1.0,31,?,not_applicable
-275,1.0,32,?,not_applicable
-75142,1.0,33,?,not_applicable
-75213,1.0,34,?,not_applicable
-75099,1.0,35,?,not_applicable
-75243,1.0,36,?,not_applicable
-75184,1.0,37,?,not_applicable
-75222,1.0,38,?,not_applicable
-75175,1.0,39,?,not_applicable
-233,1.0,40,?,not_applicable
-75161,1.0,41,?,not_applicable
-75176,1.0,42,?,not_applicable
-75090,1.0,43,?,not_applicable
-75114,1.0,44,?,not_applicable
-260,1.0,45,?,not_applicable
-236,1.0,46,?,not_applicable
-75141,1.0,47,?,not_applicable
-75107,1.0,48,?,not_applicable
-262,1.0,49,?,not_applicable
-75146,1.0,50,?,not_applicable
-75189,1.0,51,?,not_applicable
-2350,1.0,52,?,not_applicable
-253,1.0,53,?,not_applicable
-2122,1.0,54,?,not_applicable
-75110,1.0,55,?,not_applicable
-75249,1.0,56,?,not_applicable
-75108,1.0,57,?,not_applicable
-242,1.0,58,?,not_applicable
-75117,1.0,59,?,not_applicable
-75191,1.0,60,?,not_applicable
-75226,1.0,61,?,not_applicable
-261,1.0,62,?,not_applicable
-75236,1.0,63,?,not_applicable
-75095,1.0,64,?,not_applicable
-75148,1.0,65,?,not_applicable
-75093,1.0,66,?,not_applicable
-75223,1.0,67,?,not_applicable
-75244,1.0,68,?,not_applicable
-75109,1.0,69,?,not_applicable
-75197,1.0,70,?,not_applicable
-75127,1.0,71,?,not_applicable
-75143,1.0,72,?,not_applicable
-75153,1.0,73,?,not_applicable
-75173,1.0,74,?,not_applicable
-75215,1.0,75,?,not_applicable
-75195,1.0,76,?,not_applicable
-75207,1.0,77,?,not_applicable
-266,1.0,78,?,not_applicable
-75225,1.0,79,?,not_applicable
-75166,1.0,80,?,not_applicable
-75100,1.0,81,?,not_applicable
-75169,1.0,82,?,not_applicable
-75132,1.0,83,?,not_applicable
-273,1.0,84,?,not_applicable
-75121,1.0,85,?,not_applicable
-75098,1.0,86,?,not_applicable
-75115,1.0,87,?,not_applicable
-75116,1.0,88,?,not_applicable
-75185,1.0,89,?,not_applicable
-2119,1.0,90,?,not_applicable
-75157,1.0,91,?,not_applicable
-75113,1.0,92,?,not_applicable
-75134,1.0,93,?,not_applicable
-75096,1.0,94,?,not_applicable
-75203,1.0,95,?,not_applicable
-75182,1.0,96,?,not_applicable
-251,1.0,97,?,not_applicable
-75123,1.0,98,?,not_applicable
-75237,1.0,99,?,not_applicable
-75125,1.0,100,?,not_applicable
-75232,1.0,101,?,not_applicable
-75103,1.0,102,?,not_applicable
-75192,1.0,103,?,not_applicable
-75230,1.0,104,?,not_applicable
-75139,1.0,105,?,not_applicable
-75227,1.0,106,?,not_applicable
-2120,1.0,107,?,not_applicable
-75124,1.0,108,?,not_applicable
-75240,1.0,109,?,not_applicable
-75129,1.0,110,?,not_applicable
-75198,1.0,111,?,not_applicable
-75201,1.0,112,?,not_applicable
-75112,1.0,113,?,not_applicable
-75133,1.0,114,?,not_applicable
-75105,1.0,115,?,not_applicable
-75154,1.0,116,?,not_applicable
-75177,1.0,117,?,not_applicable
-2117,1.0,118,?,not_applicable
-75156,1.0,119,?,not_applicable
-75097,1.0,120,?,not_applicable
-75101,1.0,121,?,not_applicable
-75172,1.0,122,?,not_applicable
-75106,1.0,123,?,not_applicable
-75179,1.0,124,?,not_applicable
-75187,1.0,125,?,not_applicable
-75120,1.0,126,?,not_applicable
-75193,1.0,127,?,not_applicable
+75239,1.0,15,?,not_applicable
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+75171,1.0,19,?,not_applicable
+75112,1.0,23,?,not_applicable
+75227,1.0,24,?,not_applicable
+75233,1.0,25,?,not_applicable
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+253,1.0,27,?,not_applicable
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+2123,1.0,39,?,not_applicable
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+288,1.0,64,?,not_applicable
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+258,1.0,73,?,not_applicable
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+75146,1.0,88,?,not_applicable
+244,1.0,89,?,not_applicable
+75141,1.0,90,?,not_applicable
+75221,1.0,92,?,not_applicable
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+3043,1.0,98,?,not_applicable
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+75174,1.0,106,?,not_applicable
+75250,1.0,111,?,not_applicable
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+275,1.0,115,?,not_applicable
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+236,1.0,160,?,not_applicable
+254,1.0,166,?,not_applicable
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+75181,1.0,175,?,not_applicable
+75189,1.0,184,?,not_applicable
+75163,1.0,189,?,not_applicable
+2350,1.0,191,?,not_applicable
+2122,1.0,195,?,not_applicable
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+75213,1.0,201,?,not_applicable
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+75117,1.0,211,?,not_applicable
+75191,1.0,216,?,not_applicable
+75226,1.0,219,?,not_applicable
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+75223,1.0,235,?,not_applicable
+75109,1.0,242,?,not_applicable
+75197,1.0,245,?,not_applicable
+75237,1.0,248,?,not_applicable
+248,1.0,250,?,not_applicable
+2119,1.0,254,?,not_applicable
+75127,1.0,255,?,not_applicable
+75153,1.0,260,?,not_applicable
+75195,1.0,271,?,not_applicable
+266,1.0,277,?,not_applicable
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+75100,1.0,286,?,not_applicable
+75132,1.0,293,?,not_applicable
+75210,1.0,298,?,not_applicable
+273,1.0,300,?,not_applicable
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+75217,1.0,337,?,not_applicable
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+75123,1.0,360,?,not_applicable
+75125,1.0,367,?,not_applicable
+2120,1.0,371,?,not_applicable
+75232,1.0,374,?,not_applicable
+75103,1.0,378,?,not_applicable
+75230,1.0,386,?,not_applicable
+75240,1.0,398,?,not_applicable
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+75106,1.0,448,?,not_applicable
+75120,1.0,456,?,not_applicable
+75193,1.0,461,?,not_applicable
diff --git a/autosklearn/metalearning/files/f1_samples_multiclass.classification_dense/configurations.csv b/autosklearn/metalearning/files/f1_samples_multiclass.classification_dense/configurations.csv
index 8d94f66c45..03d7f03e43 100644
--- a/autosklearn/metalearning/files/f1_samples_multiclass.classification_dense/configurations.csv
+++ b/autosklearn/metalearning/files/f1_samples_multiclass.classification_dense/configurations.csv
@@ -1,128 +1,133 @@
 idx,balancing:strategy,classifier:__choice__,classifier:adaboost:algorithm,classifier:adaboost:learning_rate,classifier:adaboost:max_depth,classifier:adaboost:n_estimators,classifier:bernoulli_nb:alpha,classifier:bernoulli_nb:fit_prior,classifier:decision_tree:criterion,classifier:decision_tree:max_depth_factor,classifier:decision_tree:max_features,classifier:decision_tree:max_leaf_nodes,classifier:decision_tree:min_impurity_decrease,classifier:decision_tree:min_samples_leaf,classifier:decision_tree:min_samples_split,classifier:decision_tree:min_weight_fraction_leaf,classifier:extra_trees:bootstrap,classifier:extra_trees:criterion,classifier:extra_trees:max_depth,classifier:extra_trees:max_features,classifier:extra_trees:max_leaf_nodes,classifier:extra_trees:min_impurity_decrease,classifier:extra_trees:min_samples_leaf,classifier:extra_trees:min_samples_split,classifier:extra_trees:min_weight_fraction_leaf,classifier:gradient_boosting:early_stop,classifier:gradient_boosting:l2_regularization,classifier:gradient_boosting:learning_rate,classifier:gradient_boosting:loss,classifier:gradient_boosting:max_bins,classifier:gradient_boosting:max_depth,classifier:gradient_boosting:max_leaf_nodes,classifier:gradient_boosting:min_samples_leaf,classifier:gradient_boosting:n_iter_no_change,classifier:gradient_boosting:scoring,classifier:gradient_boosting:tol,classifier:gradient_boosting:validation_fraction,classifier:k_nearest_neighbors:n_neighbors,classifier:k_nearest_neighbors:p,classifier:k_nearest_neighbors:weights,classifier:lda:n_components,classifier:lda:shrinkage,classifier:lda:shrinkage_factor,classifier:lda:tol,classifier:liblinear_svc:C,classifier:liblinear_svc:dual,classifier:liblinear_svc:fit_intercept,classifier:liblinear_svc:intercept_scaling,classifier:liblinear_svc:loss,classifier:liblinear_svc:multi_class,classifier:liblinear_svc:penalty,classifier:liblinear_svc:tol,classifier:libsvm_svc:C,classifier:libsvm_svc:coef0,classifier:libsvm_svc:degree,classifier:libsvm_svc:gamma,classifier:libsvm_svc:kernel,classifier:libsvm_svc:max_iter,classifier:libsvm_svc:shrinking,classifier:libsvm_svc:tol,classifier:multinomial_nb:alpha,classifier:multinomial_nb:fit_prior,classifier:passive_aggressive:C,classifier:passive_aggressive:average,classifier:passive_aggressive:fit_intercept,classifier:passive_aggressive:loss,classifier:passive_aggressive:tol,classifier:qda:reg_param,classifier:random_forest:bootstrap,classifier:random_forest:criterion,classifier:random_forest:max_depth,classifier:random_forest:max_features,classifier:random_forest:max_leaf_nodes,classifier:random_forest:min_impurity_decrease,classifier:random_forest:min_samples_leaf,classifier:random_forest:min_samples_split,classifier:random_forest:min_weight_fraction_leaf,classifier:sgd:alpha,classifier:sgd:average,classifier:sgd:epsilon,classifier:sgd:eta0,classifier:sgd:fit_intercept,classifier:sgd:l1_ratio,classifier:sgd:learning_rate,classifier:sgd:loss,classifier:sgd:penalty,classifier:sgd:power_t,classifier:sgd:tol,data_preprocessing:categorical_transformer:categorical_encoding:__choice__,data_preprocessing:categorical_transformer:category_coalescence:__choice__,data_preprocessing:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction,data_preprocessing:numerical_transformer:imputation:strategy,data_preprocessing:numerical_transformer:rescaling:__choice__,data_preprocessing:numerical_transformer:rescaling:quantile_transformer:n_quantiles,data_preprocessing:numerical_transformer:rescaling:quantile_transformer:output_distribution,data_preprocessing:numerical_transformer:rescaling:robust_scaler:q_max,data_preprocessing:numerical_transformer:rescaling:robust_scaler:q_min,feature_preprocessor:__choice__,feature_preprocessor:extra_trees_preproc_for_classification:bootstrap,feature_preprocessor:extra_trees_preproc_for_classification:criterion,feature_preprocessor:extra_trees_preproc_for_classification:max_depth,feature_preprocessor:extra_trees_preproc_for_classification:max_features,feature_preprocessor:extra_trees_preproc_for_classification:max_leaf_nodes,feature_preprocessor:extra_trees_preproc_for_classification:min_impurity_decrease,feature_preprocessor:extra_trees_preproc_for_classification:min_samples_leaf,feature_preprocessor:extra_trees_preproc_for_classification:min_samples_split,feature_preprocessor:extra_trees_preproc_for_classification:min_weight_fraction_leaf,feature_preprocessor:extra_trees_preproc_for_classification:n_estimators,feature_preprocessor:fast_ica:algorithm,feature_preprocessor:fast_ica:fun,feature_preprocessor:fast_ica:n_components,feature_preprocessor:fast_ica:whiten,feature_preprocessor:feature_agglomeration:affinity,feature_preprocessor:feature_agglomeration:linkage,feature_preprocessor:feature_agglomeration:n_clusters,feature_preprocessor:feature_agglomeration:pooling_func,feature_preprocessor:kernel_pca:coef0,feature_preprocessor:kernel_pca:degree,feature_preprocessor:kernel_pca:gamma,feature_preprocessor:kernel_pca:kernel,feature_preprocessor:kernel_pca:n_components,feature_preprocessor:kitchen_sinks:gamma,feature_preprocessor:kitchen_sinks:n_components,feature_preprocessor:liblinear_svc_preprocessor:C,feature_preprocessor:liblinear_svc_preprocessor:dual,feature_preprocessor:liblinear_svc_preprocessor:fit_intercept,feature_preprocessor:liblinear_svc_preprocessor:intercept_scaling,feature_preprocessor:liblinear_svc_preprocessor:loss,feature_preprocessor:liblinear_svc_preprocessor:multi_class,feature_preprocessor:liblinear_svc_preprocessor:penalty,feature_preprocessor:liblinear_svc_preprocessor:tol,feature_preprocessor:nystroem_sampler:coef0,feature_preprocessor:nystroem_sampler:degree,feature_preprocessor:nystroem_sampler:gamma,feature_preprocessor:nystroem_sampler:kernel,feature_preprocessor:nystroem_sampler:n_components,feature_preprocessor:pca:keep_variance,feature_preprocessor:pca:whiten,feature_preprocessor:polynomial:degree,feature_preprocessor:polynomial:include_bias,feature_preprocessor:polynomial:interaction_only,feature_preprocessor:random_trees_embedding:bootstrap,feature_preprocessor:random_trees_embedding:max_depth,feature_preprocessor:random_trees_embedding:max_leaf_nodes,feature_preprocessor:random_trees_embedding:min_samples_leaf,feature_preprocessor:random_trees_embedding:min_samples_split,feature_preprocessor:random_trees_embedding:min_weight_fraction_leaf,feature_preprocessor:random_trees_embedding:n_estimators,feature_preprocessor:select_percentile_classification:percentile,feature_preprocessor:select_percentile_classification:score_func,feature_preprocessor:select_rates_classification:alpha,feature_preprocessor:select_rates_classification:mode,feature_preprocessor:select_rates_classification:score_func
-1,none,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,standardize,,,,,extra_trees_preproc_for_classification,False,entropy,None,0.8629651625821806,None,0.0,10,10,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-2,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9.971526339963626,False,True,1,squared_hinge,ovr,l2,1.2119829083478464e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.10211544986888581,most_frequent,robust_scaler,,,0.7878089656793734,0.28746519719167807,fast_ica,,,,,,,,,,,parallel,cube,30,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-3,none,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,188,None,,0.09976330267940166,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,none,,,,,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,-0.05439674489937307,2,0.051865468486041774,poly,727,,,,,,,,,,,,,,,,,
-4,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,23916.78290516781,False,True,1,squared_hinge,ovr,l2,0.0006655607726658531,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.008808715242823137,median,none,,,,,kernel_pca,,,,,,,,,,,,,,,,,,,0.2633153190620261,2,0.004770332714291669,poly,1483,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-5,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,2,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010091246322497477,median,none,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,95.37976833605768,mutual_info,,,
-6,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.933900673392165,None,0.0,7,4,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,minmax,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-7,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,170.37509684994612,0.6734219115470694,3,0.08998608839429428,poly,-1,True,0.024147312348267988,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,none,,,,,extra_trees_preproc_for_classification,False,entropy,None,0.9449154369109399,None,0.0,1,14,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-8,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2981.609927782268,0.12126372002542518,2,0.0009954412712187496,poly,-1,False,9.701828055863241e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,mean,standardize,,,,,feature_agglomeration,,,,,,,,,,,,,,,euclidean,ward,37,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-9,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1292.7448076998514,0.004489932344506231,2,1.3088036376373702,poly,-1,False,1.7943262139196537e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,minmax,,,,,feature_agglomeration,,,,,,,,,,,,,,,cosine,average,386,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-10,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.5244313211416313,None,0.0,1,2,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.454300236339658,most_frequent,robust_scaler,,,0.7858830918128734,0.2705174720960696,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-11,weighting,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0006294780694492238,most_frequent,standardize,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.063715750591364,fdr,chi2
-12,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.02299691847304223,False,True,squared_hinge,2.16045124689341e-05,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010708146395792527,most_frequent,standardize,,,,,extra_trees_preproc_for_classification,False,entropy,None,0.5298549662385138,None,0.0,12,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-13,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.25657910619102237,None,0.0,6,6,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.021086303324492208,most_frequent,quantile_transformer,1247,uniform,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.16021572815319468,fpr,chi2
-14,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.4897130288457967,False,True,1,squared_hinge,ovr,l2,0.00023610948867176137,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.22014262985890154,median,quantile_transformer,1499,normal,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,331,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-15,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.643761610990255,None,0.0,20,6,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.039859381630488695,mean,normalize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,
-16,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.8172226998998798,None,0.0,17,3,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.006497124806317511,median,robust_scaler,,,0.7090070294600064,0.2287445390747399,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.478031402559985,mutual_info,,,
-17,weighting,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.06330379669963868,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.054434091133046744,most_frequent,robust_scaler,,,0.7480112557523932,0.17257870788958515,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-18,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00335225820194075,True,True,squared_hinge,2.9136221277715572e-05,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.00021598139496857644,median,minmax,,,,,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,4,None,4,9,1.0,14,,,,,
-19,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,199,None,,0.003239674765949011,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,robust_scaler,,,0.7228447795482008,0.25,kernel_pca,,,,,,,,,,,,,,,,,,,0.09440148939740256,3,0.0008114095405154861,poly,1996,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-20,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.6039096182414764,None,0.0,2,20,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.1939609823704447,most_frequent,robust_scaler,,,0.7331327008874781,0.29747488162877694,extra_trees_preproc_for_classification,True,entropy,None,0.33401603690569853,None,0.0,4,4,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-21,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,2.5882079595129472e-05,0.36125906161027654,auto,255,None,341,119,16,loss,1e-07,0.2924550184445239,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.02338478303505337,most_frequent,robust_scaler,,,0.7866731278723753,0.29543475006822734,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,68,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-22,weighting,adaboost,SAMME,0.24425611020065865,2,50,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0014011297758596516,most_frequent,quantile_transformer,1324,normal,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,338,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-23,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7725.740943204492,,,3.122840783722913,rbf,-1,True,1.4116065061807418e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.14280178218510184,most_frequent,none,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,27,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-24,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.21794354428393548,None,0.0,2,16,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0025451910134387575,median,quantile_transformer,1477,uniform,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
-25,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5010952533165973,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.005838375247601407,mean,normalize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,
-26,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5514127836113225,None,0.0,4,20,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.010750756228608195,most_frequent,robust_scaler,,,0.7471420685032172,0.2916152663822578,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.22557315057515023,fwe,chi2
-27,weighting,decision_tree,,,,,,,gini,1.0778738481485166,1.0,None,0.0,14,7,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.002474357425774638,mean,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,
-28,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.506204385946851,None,0.0,2,8,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.03433007982751011,mean,none,,,,,extra_trees_preproc_for_classification,False,gini,None,0.32676415738837183,None,0.0,5,6,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-29,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9333237919291875,None,0.0,8,3,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,
-30,none,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.9728532763078892,None,0.0,13,9,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,none,,,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6651071190864879,False,,,,,,,,,,,,,,,
-31,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9561075301148197,None,0.0,6,2,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,
-32,none,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,184,manual,0.5022041786628263,2.3064966255468173e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,most_frequent,standardize,,,,,kernel_pca,,,,,,,,,,,,,,,,,,,-0.32023224593255817,2,0.09479590655633498,poly,1382,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-33,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,2.7220799337966617e-08,0.011194310743518529,auto,255,None,38,187,3,loss,1e-07,0.13581959545340272,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0036603728729535916,mean,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,False,,,,,,,,,,,,
-34,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,22194.242946902177,0.45210849669021247,,0.0005669713702171556,sigmoid,-1,False,0.03542142266081451,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,quantile_transformer,291,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-35,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.2697794350876887,None,0.0,20,19,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.01857832166651019,median,robust_scaler,,,0.746850014013939,0.26258045325454815,fast_ica,,,,,,,,,,,deflation,cube,67,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-36,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,0.032732094595234795,0.09918078053098744,auto,255,None,97,54,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.019135985037746533,median,quantile_transformer,1000,uniform,,,feature_agglomeration,,,,,,,,,,,,,,,euclidean,average,37,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-37,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7360659359578277,None,0.0,18,5,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.0032431851910047368,mean,robust_scaler,,,0.7204949497300925,0.2277786352264519,fast_ica,,,,,,,,,,,deflation,exp,307,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-38,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.756167892711991e-05,False,True,squared_hinge,5.235791933868702e-05,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0001632172987320906,most_frequent,standardize,,,,,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,5,None,19,12,1.0,70,,,,,
-39,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,3.6796787331285204e-07,0.14399102828508656,auto,255,None,18,12,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,quantile_transformer,965,normal,,,feature_agglomeration,,,,,,,,,,,,,,,cosine,average,365,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-40,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7005303455027131,None,0.0,1,5,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.027348815457551995,mean,quantile_transformer,789,uniform,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,
-41,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,1.943186107984505e-07,0.08852455702036988,auto,255,None,5,72,19,loss,1e-07,0.039837460762271175,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.01591265419367627,median,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,
-42,weighting,adaboost,SAMME,0.34205296521106404,10,89,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.02025205191860567,most_frequent,minmax,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-43,none,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0686374607394679,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,minmax,,,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.8490260132946401,False,,,,,,,,,,,,,,,
-44,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,10,1,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0022606253047811435,median,quantile_transformer,1247,normal,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,25.327793585266683,chi2,,,
-45,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.3663040729445102,None,0.0,20,19,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.010000000000000004,median,robust_scaler,,,0.7338553275791654,0.25,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,331,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-46,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,92.66144306787051,,,0.04654639302091653,rbf,-1,True,0.0022231544280610005,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.005194838694455963,mean,none,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,15,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-47,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1550807214391849,True,True,hinge,0.00019875748182257,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.05786688075604592,mean,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,
-48,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.4467384469668449,None,0.0,19,19,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.00033708405018786203,median,quantile_transformer,711,normal,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,7.744829113696808,False,True,1,squared_hinge,ovr,l1,0.0001869446320464273,,,,,,,,,,,,,,,,,,,,,,
-49,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,26.502259985828186,,,9.475662388405154e-05,rbf,-1,False,0.00019690635968217866,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-50,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,2.3520913174804126e-07,0.052918150906133596,auto,255,None,16,185,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.00790855487341428,mean,robust_scaler,,,0.746273035018099,0.21296174657639108,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,1.298440997922375,False,True,1,squared_hinge,ovr,l1,0.0018510248972884416,,,,,,,,,,,,,,,,,,,,,,
-51,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5255991794296913,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-52,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.34697723213923637,None,0.0,15,18,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.1923594385988973,mean,normalize,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,25.77499302945827,chi2,,,
-53,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1252.7997374154156,0.20811431041897355,5,0.0018140593074227453,poly,-1,False,0.03149782358621565,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.001930822730474197,most_frequent,quantile_transformer,233,normal,,,feature_agglomeration,,,,,,,,,,,,,,,euclidean,ward,29,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-54,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,1.5661823810486637e-10,0.08996863554015262,auto,255,None,156,89,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.009331521286159971,median,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,
-55,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.6109468076475573,0.8748383232154038,4,0.41333070422336576,poly,-1,False,3.591268778393482e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.011804925819789203,median,minmax,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-56,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.7388247096873266,None,0.0,1,2,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.02005334514549439,most_frequent,robust_scaler,,,0.9347422252540531,0.2909868620945952,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,
-57,weighting,adaboost,SAMME.R,0.03127273017917347,10,496,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,standardize,,,,,fast_ica,,,,,,,,,,,deflation,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-58,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,151,None,,0.0003283777607180646,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.2510527431514227,mean,normalize,,,,,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,-0.96093182232312,,0.05762510964607661,sigmoid,2632,,,,,,,,,,,,,,,,,
-59,none,bernoulli_nb,,,,,1.9593357170002308,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.005109073482613361,mean,quantile_transformer,10,normal,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.03495379340060141,False,True,1,squared_hinge,ovr,l1,0.0033386410851928994,,,,,,,,,,,,,,,,,,,,,,
-60,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.6453555269772124,None,0.0,1,4,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,normalize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-61,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5363.189119763322,,,0.4294659312024404,rbf,-1,True,0.06935839422940453,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.019860129781307266,most_frequent,none,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,92.1980997621979,mutual_info,,,
-62,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.20911875507660596,None,0.0,12,8,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,median,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,
-63,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.34534888916828577,0.02513634776687268,2,1.0733091880109409,poly,-1,True,0.0006974263047918307,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,quantile_transformer,1000,normal,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,77.99934218022861,mutual_info,,,
-64,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,29.55731828912983,False,True,1,squared_hinge,ovr,l2,0.0011332486951954968,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,
-65,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,1.1387795876755026e-06,0.12245605190343567,auto,255,None,58,7,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,robust_scaler,,,0.79305915648885,0.1841487406876042,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-66,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.8425560317492612,None,0.0,8,15,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.015276988502366128,median,none,,,,,fast_ica,,,,,,,,,,,parallel,cube,17,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-67,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,0.2638627266631576,0.022360820165005443,auto,255,None,171,6,19,loss,1e-07,0.016209060012924756,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.008763072395677399,mean,standardize,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,78,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-68,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5664.233390385957,0.3496807683995542,4,0.00017689402501527962,poly,-1,True,0.01756282016660122,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.13312139731266243,median,quantile_transformer,1362,uniform,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,72.39572310756226,mutual_info,,,
-69,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.8278163313806293,None,0.0,3,5,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,robust_scaler,,,0.725835793765774,0.2571431961341018,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.07784020380291123,fdr,f_classif
-70,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0023391956112474004,False,True,squared_hinge,0.011792446147470903,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.08295440990272292,most_frequent,quantile_transformer,962,normal,,,kernel_pca,,,,,,,,,,,,,,,,,,,,,,cosine,1778,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-71,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.4264370333817712,None,0.0,13,3,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,median,minmax,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.14972073341699849,fpr,chi2
-72,none,adaboost,SAMME,0.07025975715693651,5,339,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,median,robust_scaler,,,0.7117466125338451,0.2984609070131515,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.21351971922962878,fdr,chi2
-73,none,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9931779675565967,None,0.0,18,17,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,robust_scaler,,,0.75,0.2945917010472818,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,
-74,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2611084157414942,True,True,hinge,0.0009587835023329391,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.14664248075999733,most_frequent,minmax,,,,,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.509603573308092,rbf,198,,,,,,,,,,,,,,,,,
-75,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,3.044019499065853e-08,0.030966538899129755,auto,255,None,64,50,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.00860619823050038,median,minmax,,,,,feature_agglomeration,,,,,,,,,,,,,,,cosine,average,36,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-76,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7.667945934601232,True,True,squared_hinge,1.5995631297928604e-05,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.004775063434540409,most_frequent,minmax,,,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.28455344994291587,False,True,1,squared_hinge,ovr,l1,6.230255219574715e-05,,,,,,,,,,,,,,,,,,,,,,
-77,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.34936025988397124,None,0.0,2,13,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00018601309647406987,median,robust_scaler,,,0.8634309348843472,0.26976989465367607,extra_trees_preproc_for_classification,False,entropy,None,0.48025175288975036,None,0.0,6,17,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-78,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,2.925599188129995e-05,0.12560285030651847,auto,255,None,300,193,17,loss,1e-07,0.039442997915546514,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00299400754997757,mean,robust_scaler,,,0.7093292916146515,0.009366983030047793,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-79,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.642370445299362,None,0.0,15,20,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.014823484140711048,median,robust_scaler,,,0.780144337652783,0.2309544712129402,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,2.2123747341894098,False,True,1,squared_hinge,ovr,l1,0.0033125984161268183,,,,,,,,,,,,,,,,,,,,,,
-80,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2471.298583654142,,,0.025866749427709803,rbf,-1,False,0.0009481287482516433,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,none,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,323,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-81,weighting,adaboost,SAMME,0.028939256953011736,2,368,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,quantile_transformer,1954,uniform,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,41.04018582009321,mutual_info,,,
-82,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,21.85207494001697,0.18872999589786676,4,1.1863646021868761,poly,-1,False,0.0021338371901294443,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0034080852834770217,mean,normalize,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.11326005109886934,fdr,f_classif
-83,weighting,adaboost,SAMME,0.38341570193537416,9,250,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,minmax,,,,,feature_agglomeration,,,,,,,,,,,,,,,euclidean,average,13,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-84,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,0.023106206735923184,0.12762814679231496,auto,255,None,24,151,16,loss,1e-07,0.08978875361280989,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.007723510861056437,median,quantile_transformer,1217,normal,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,
-85,weighting,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.00012847697769902057,mean,minmax,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,10.088989224596698,mutual_info,,,
-86,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,1,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.019797877010737826,mean,normalize,,,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.8239678741128165,False,,,,,,,,,,,,,,,
-87,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.46057831591617715,False,True,hinge,0.04557857428827514,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00027457445401600137,median,standardize,,,,,extra_trees_preproc_for_classification,True,gini,None,0.48190346970486964,None,0.0,17,18,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-88,none,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,230,auto,,0.0014037334852669801,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,quantile_transformer,848,uniform,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,61.06963527679606,f_classif,,,
-89,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,64,auto,,0.00021332620906594865,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.026032882800756493,median,standardize,,,,,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,-0.9513223792709335,,0.00029881582960753004,sigmoid,543,,,,,,,,,,,,,,,,,
-90,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.26356754187780895,None,0.0,4,3,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010966178401758774,mean,robust_scaler,,,0.7817043861798886,0.24371720112407158,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
-91,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,3.7287807266408084e-10,0.15544439749103187,auto,255,None,28,16,18,loss,1e-07,0.10835808508958217,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0207886310256421,mean,quantile_transformer,1245,normal,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.39383167612281667,fpr,f_classif
-92,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,130.2903387607578,False,True,1,squared_hinge,ovr,l2,0.0020820269897903502,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.08534605541971435,mean,quantile_transformer,613,normal,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,12.936259313956771,False,True,1,squared_hinge,ovr,l1,9.225567050752251e-05,,,,,,,,,,,,,,,,,,,,,,
-93,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.22879311080499845,None,0.0,8,4,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.014320022547615783,median,minmax,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.238630703440461,mutual_info,,,
-94,weighting,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,2,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0073667222552306465,median,robust_scaler,,,0.701595034693991,0.29514640404971515,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6066051105210388,False,,,,,,,,,,,,,,,
-95,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.107967527639527e-05,True,True,hinge,0.00448294745544775,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,quantile_transformer,953,normal,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,35.00114345405331,chi2,,,
-96,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.457928416564218,None,0.0,12,2,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,
-97,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,1.1522414705935405e-10,0.6132800183073315,auto,255,None,1607,67,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010096911249562407,mean,robust_scaler,,,0.7663493326967942,0.136094217337434,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-98,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1228.3897711257332,0.6862670093933454,5,1.113863025113022,poly,-1,True,1.9271116749907707e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.018161834200128672,mean,normalize,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.06278549919408385,fpr,chi2
-99,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.479405900046494,,,0.013340778632154543,rbf,-1,False,6.913586060890114e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,robust_scaler,,,0.9953232616818098,0.06450577886132274,fast_ica,,,,,,,,,,,deflation,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-100,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.9815182201168626,None,0.0,15,19,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,robust_scaler,,,0.8878662977383482,0.25666364929970126,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,44.15748219826322,mutual_info,,,
-101,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1584.207754377407,,,0.020660207249591715,rbf,-1,False,0.03773359340731028,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,quantile_transformer,1055,uniform,,,fast_ica,,,,,,,,,,,parallel,exp,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-102,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.7285597583341324,None,0.0,3,20,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.011700861722450575,most_frequent,robust_scaler,,,0.7911119178158159,0.2999762250828743,feature_agglomeration,,,,,,,,,,,,,,,euclidean,ward,151,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-103,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2897.996028838154,,,0.04621527412391808,rbf,-1,False,0.0017262380676728777,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.07770313303106262,mean,robust_scaler,,,0.7510736797641198,0.23032782852489117,extra_trees_preproc_for_classification,False,gini,None,0.9588902962283966,None,0.0,13,12,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-104,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.6282663389679584,None,0.0,1,3,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,median,minmax,,,,,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-105,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,8.14394583618027e-09,0.1314274861878701,auto,255,None,27,15,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.3598067419611515,mean,robust_scaler,,,0.7494615433236279,0.260649229829574,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
-106,weighting,adaboost,SAMME.R,0.46962472583666215,8,455,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.03708420001141539,most_frequent,robust_scaler,,,0.7364857891944228,0.23021698558282877,feature_agglomeration,,,,,,,,,,,,,,,euclidean,complete,5,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-107,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,11.046854203258679,,,0.07809571976769347,rbf,-1,False,0.00036193159909053734,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.006445557635410205,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-108,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.6728827282646637,None,0.0,18,8,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,robust_scaler,,,0.8512985751386942,0.14092824896182807,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,
-109,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.18060139887649052,None,0.0,10,12,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.010000000000000004,most_frequent,robust_scaler,,,0.9298612094255864,0.1263597946722308,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
-110,none,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.16236660803443026,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,quantile_transformer,377,normal,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2611168895496939,fpr,f_classif
-111,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6427543483503397,True,True,hinge,0.00045318775930916944,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.009338484332306904,median,quantile_transformer,174,normal,,,kernel_pca,,,,,,,,,,,,,,,,,,,,,,cosine,1848,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-112,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.02021502857757827,True,,0.055848937053280076,True,,constant,log,l2,,0.00016356162584832537,no_encoding,no_coalescense,,median,quantile_transformer,27,normal,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.11873149622606233,fwe,chi2
-113,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,9.029473608568458e-09,0.15779990576964315,auto,255,None,37,71,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.018093177195644097,mean,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,False,,,,,,,,,,,,
-114,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8.24807027221455,-0.23458710218209156,,0.0001030034352044511,sigmoid,-1,True,0.0002726309373020207,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,quantile_transformer,1974,normal,,,extra_trees_preproc_for_classification,True,entropy,None,0.9395220389606214,None,0.0,2,20,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-115,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.3747863651224668,True,True,hinge,0.00019106780876479388,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.002442315142014378,median,quantile_transformer,1850,uniform,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.570194774022733,f_classif,,,
-116,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.4864284879550203,None,0.0,1,4,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.008665854799630503,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-117,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.8785519499336585,None,0.0,13,17,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.05156816034494817,most_frequent,quantile_transformer,1243,uniform,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.11082264321263885,fdr,f_classif
-118,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.536709062860521e-07,True,,,True,,optimal,squared_hinge,l1,,1.6934515188687678e-05,one_hot_encoding,no_coalescense,,most_frequent,normalize,,,,,feature_agglomeration,,,,,,,,,,,,,,,cosine,complete,306,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-119,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.6905196223806276,None,0.0,6,5,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,minmax,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-120,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.6154724467600127,None,0.0,19,12,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,mean,minmax,,,,,extra_trees_preproc_for_classification,True,entropy,None,0.7630778936392856,None,0.0,11,20,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-121,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5836006490230695,None,0.0,1,19,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.02127792104027424,most_frequent,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,
-122,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.922871100218626,False,True,squared_hinge,0.09957180737451943,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0016563186856411274,median,quantile_transformer,850,uniform,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.09123695282772847,fpr,chi2
-123,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.06571678518052461,False,True,1,squared_hinge,ovr,l2,3.5551445493853517e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0007406565537349627,median,quantile_transformer,1114,normal,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.13436517316012828,fwe,f_classif
-124,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1561.1002600303584,,,0.0024048551704194507,rbf,-1,False,0.0001936214667496851,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,minmax,,,,,fast_ica,,,,,,,,,,,parallel,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-125,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.087313242780732,0.0636709914987994,3,0.004481917125024585,poly,-1,False,0.01848880618941367,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.002136015035740733,mean,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-126,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.03204653439894958,True,True,squared_hinge,0.00013006393401112688,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.013506575694382031,most_frequent,normalize,,,,,feature_agglomeration,,,,,,,,,,,,,,,euclidean,ward,356,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-127,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.6466927589470762,None,0.0,11,2,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.009672465311659264,most_frequent,standardize,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,369,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+1,none,decision_tree,,,,,,,entropy,1.877536950957045,1.0,None,0.0,10,13,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,quantile_transformer,1275,uniform,,,extra_trees_preproc_for_classification,True,gini,None,0.04910706843318435,None,0.0,12,12,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+2,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.8905944749638223,None,0.0,5,4,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.15463552048633444,most_frequent,robust_scaler,,,0.7545842296967857,0.22898551665984185,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.24780801481682346,fwe,chi2
+5,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,9.83838677007206e-09,0.09269618726619562,auto,255,None,37,16,14,loss,1e-07,0.04208303685387712,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
+6,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,4.267426422904654e-06,0.011555121209024403,auto,255,None,121,34,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010064483887441845,most_frequent,quantile_transformer,1194,uniform,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,
+8,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,114,manual,0.11778631707605758,0.0029112721339713465,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.015582195257240751,median,normalize,,,,,kernel_pca,,,,,,,,,,,,,,,,,,,0.42909623253327767,3,0.25407612000343777,poly,1689,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+9,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.685918004243461,True,True,squared_hinge,1.7784504877380735e-05,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010335606354842895,most_frequent,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,
+10,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.7396493120555564,None,0.0,1,18,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010000000000000004,median,robust_scaler,,,0.8908334215036855,0.08666907249785906,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,
+13,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,0.9907352051102745,0.018115439174701313,auto,255,None,84,5,3,loss,1e-07,0.13697766901072808,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.036524416007602305,mean,robust_scaler,,,0.9824116214519696,0.2911868066464925,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,
+14,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,4.629265598705481e-10,0.03894546521791516,auto,255,None,5,22,18,loss,1e-07,0.0837137336215462,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.008397749525484112,mean,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
+15,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.21772092350860472,None,0.0,14,17,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.0020488212891693275,median,standardize,,,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,33.74257606530722,False,True,1,squared_hinge,ovr,l1,8.740452966205111e-05,,,,,,,,,,,,,,,,,,,,,,
+17,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.6591033863118033,None,0.0,10,10,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,none,,,,,fast_ica,,,,,,,,,,,deflation,exp,1506,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+18,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,1.9110915129429343e-09,0.06380311481632683,auto,255,None,20,62,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,robust_scaler,,,0.7307407504330838,0.2660206566992171,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,
+19,weighting,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,99,2,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010033505607662804,median,none,,,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.069760700053376,False,True,1,squared_hinge,ovr,l1,0.06611177025130774,,,,,,,,,,,,,,,,,,,,,,
+23,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,2.0447842449942555e-09,0.04257026709693251,auto,255,None,47,13,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010000000000000004,median,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,
+24,weighting,adaboost,SAMME.R,0.44886160948796244,9,493,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.031007103864624616,mean,robust_scaler,,,0.7575180367976682,0.26457274298753825,feature_agglomeration,,,,,,,,,,,,,,,cosine,complete,211,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+25,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,0.008942041103505539,0.05229567301622349,auto,255,None,6,24,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.01936143670847451,mean,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,
+26,weighting,adaboost,SAMME.R,0.01561270559347964,4,417,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,quantile_transformer,1617,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+27,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.41025844265119926,None,0.0,18,15,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,median,quantile_transformer,1911,normal,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,False,,,,,,,,,,,,
+29,none,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.9034033923099005,False,True,1,squared_hinge,ovr,l2,1.2159485299684885e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.027586423552745792,most_frequent,none,,,,,kernel_pca,,,,,,,,,,,,,,,,,,,,,0.4034250963681743,rbf,735,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+30,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,490.8047233341048,0.13853827450882505,,0.004044309450174347,sigmoid,-1,False,0.005958796387567885,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.008446690235218424,median,minmax,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,229,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+31,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,3.0538220042587883e-07,0.0704056758356995,auto,255,None,9,3,19,loss,1e-07,0.01927933154167906,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.019804174585488932,median,quantile_transformer,921,normal,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,
+32,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,101,auto,,1.553086088086431e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.01137059177001425,most_frequent,normalize,,,,,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,-0.021621751597165284,4,0.7468597264810062,poly,5078,,,,,,,,,,,,,,,,,
+33,none,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.5769165522851386,None,0.0,1,6,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,none,,,,,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,7,None,9,14,1.0,74,,,,,
+34,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,0.06671977293714751,0.04456236025167353,auto,255,None,3,163,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.4333293305857194,mean,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+37,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0618160212153551,True,True,squared_hinge,1.2370453081062464e-05,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.01448339838697973,mean,standardize,,,,,kitchen_sinks,,,,,,,,,,,,,,,,,,,,,,,,0.018879929475513826,1637,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+39,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.6799215694343422,None,0.0,1,5,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.004724310659953132,most_frequent,quantile_transformer,1037,normal,,,fast_ica,,,,,,,,,,,parallel,exp,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+41,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.6965935192702026,None,0.0,12,16,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,none,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,67.60637688434707,f_classif,,,
+43,none,decision_tree,,,,,,,gini,1.1110863621017264,1.0,None,0.0,16,19,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.00043339385698342055,mean,none,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.20957492283649354,fwe,f_classif
+44,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,2.6159836259985015e-05,0.05532294838858921,auto,255,None,116,4,14,loss,1e-07,0.20422881335187132,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,
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+448,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.7463662441763204,None,0.0,20,18,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0004507212632016269,mean,none,,,,,extra_trees_preproc_for_classification,True,gini,None,0.4279327206778048,None,0.0,3,9,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+456,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0322540554793962e-05,False,,0.015117861103299655,True,,invscaling,log,l1,0.558781568719232,0.00016877274953233003,one_hot_encoding,no_coalescense,,median,quantile_transformer,681,normal,,,extra_trees_preproc_for_classification,False,gini,None,0.6320249932267078,None,0.0,17,18,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+461,none,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.9796199105973851,None,0.0,2,13,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.00845950033601661,mean,robust_scaler,,,0.8275228560550734,0.23814615163106348,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
diff --git a/autosklearn/metalearning/files/f1_samples_multiclass.classification_dense/description.txt b/autosklearn/metalearning/files/f1_samples_multiclass.classification_dense/description.txt
index 233d7b2bb6..be246911cb 100644
--- a/autosklearn/metalearning/files/f1_samples_multiclass.classification_dense/description.txt
+++ b/autosklearn/metalearning/files/f1_samples_multiclass.classification_dense/description.txt
@@ -1,8 +1,9 @@
 features_cutoff_time: 3600
 features_cutoff_memory: 3072
-number_of_feature_steps: 35
+number_of_feature_steps: 42
 feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances
 feature_step LogNumberOfInstances: LogNumberOfInstances
+feature_step NumberOfClasses: NumberOfClasses
 feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures
 feature_step LogNumberOfFeatures: LogNumberOfFeatures
 feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues
@@ -20,6 +21,11 @@ feature_step DatasetRatio: DatasetRatio, LogDatasetRatio
 feature_step LogDatasetRatio: LogDatasetRatio
 feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio
 feature_step LogInverseDatasetRatio: LogInverseDatasetRatio
+feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD
+feature_step ClassProbabilityMin: ClassProbabilityMin
+feature_step ClassProbabilityMax: ClassProbabilityMax
+feature_step ClassProbabilityMean: ClassProbabilityMean
+feature_step ClassProbabilitySTD: ClassProbabilitySTD
 feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum
 feature_step SymbolsMin: SymbolsMin
 feature_step SymbolsMax: SymbolsMax
@@ -36,11 +42,12 @@ feature_step SkewnessMin: SkewnessMin
 feature_step SkewnessMax: SkewnessMax
 feature_step SkewnessMean: SkewnessMean
 feature_step SkewnessSTD: SkewnessSTD
-features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
+feature_step ClassEntropy: ClassEntropy
+features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy
 features_stochastic: 
-default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
+default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy
 
-algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127
+algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235,236,237,238,239,240,241,242,243,244,245,246,247,248,249,250,251,252,253,254,255,256,257,258,259,260,261,262,263,264,265,266,267,268,269,270,271,272,273,274,275,276,277,278,279,280,281,282,283,284,285,286,287,288,289,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,312,313,314,315,316,317,318,319,320,321,322,323,324,325,326,327,328,329,330,331,332,333,334,335,336,337,338,339,340,341,342,343,344,345,346,347,348,349,350,351,352,353,354,355,356,357,358,359,360,361,362,363,364,365,366,367,368,369,370,371,372,373,374,375,376,377,378,379,380,381,382,383,384,385,386,387,388,389,390,391,392,393,394,395,396,397,398,399,400,401,402,403,404,405,406,407,408,409,410,411,412,413,414,415,416,417,418,419,420,421,422,423,424,425,426,427,428,429,430,431,432,433,434,435,436,437,438,439,440,441,442,443,444,445,446,447,448,449,450,451,452,453,454,455,456,457,458,459,460,461,462,463,464
 algorithms_stochastic: 
 performance_measures: f1_samples
 performance_type: solution_quality
diff --git a/autosklearn/metalearning/files/f1_samples_multiclass.classification_dense/feature_costs.arff b/autosklearn/metalearning/files/f1_samples_multiclass.classification_dense/feature_costs.arff
index 0e17e20d22..8512fd9275 100644
--- a/autosklearn/metalearning/files/f1_samples_multiclass.classification_dense/feature_costs.arff
+++ b/autosklearn/metalearning/files/f1_samples_multiclass.classification_dense/feature_costs.arff
@@ -4,6 +4,7 @@
 @ATTRIBUTE repetition NUMERIC
 @ATTRIBUTE NumberOfInstances NUMERIC
 @ATTRIBUTE LogNumberOfInstances NUMERIC
+@ATTRIBUTE NumberOfClasses NUMERIC
 @ATTRIBUTE NumberOfFeatures NUMERIC
 @ATTRIBUTE LogNumberOfFeatures NUMERIC
 @ATTRIBUTE MissingValues NUMERIC
@@ -21,6 +22,11 @@
 @ATTRIBUTE LogDatasetRatio NUMERIC
 @ATTRIBUTE InverseDatasetRatio NUMERIC
 @ATTRIBUTE LogInverseDatasetRatio NUMERIC
+@ATTRIBUTE ClassOccurences NUMERIC
+@ATTRIBUTE ClassProbabilityMin NUMERIC
+@ATTRIBUTE ClassProbabilityMax NUMERIC
+@ATTRIBUTE ClassProbabilityMean NUMERIC
+@ATTRIBUTE ClassProbabilitySTD NUMERIC
 @ATTRIBUTE NumSymbols NUMERIC
 @ATTRIBUTE SymbolsMin NUMERIC
 @ATTRIBUTE SymbolsMax NUMERIC
@@ -37,162 +43,138 @@
 @ATTRIBUTE SkewnessMax NUMERIC
 @ATTRIBUTE SkewnessMean NUMERIC
 @ATTRIBUTE SkewnessSTD NUMERIC
+@ATTRIBUTE ClassEntropy NUMERIC
 
 @DATA
-75123,1.0,1e-05,1e-05,1e-05,1e-05,0.00253,0.00221,1e-05,0.00024,3e-05,0.00011,0.0,2e-05,2e-05,5e-05,8e-05,2e-05,1e-05,7e-05,1e-05,0.00072,1e-05,1e-05,0.00019,0.00041,0.00011,0.00061,0.00012,9e-05,0.00015,0.00025,0.00067,0.00014,0.00011,0.00016,0.00027
-75090,1.0,1e-05,1e-05,2e-05,2e-05,0.00815,0.00368,7e-05,0.00228,9e-05,0.00235,0.0,5e-05,5e-05,0.0001,0.00013,0.00013,1e-05,0.00021,2e-05,0.00064,0.0,0.0,0.00014,0.00038,0.00011,0.00066,0.00011,0.00014,0.00014,0.00026,0.00066,0.00011,0.00013,0.00015,0.00027
-2123,1.0,1e-05,1e-05,1e-05,1e-05,0.00097,0.00079,1e-05,0.00013,1e-05,7e-05,0.0,2e-05,2e-05,5e-05,8e-05,2e-05,1e-05,5e-05,1e-05,0.0007,1e-05,0.0,0.00017,0.00042,0.0001,0.00052,6e-05,8e-05,0.00012,0.00026,0.00049,6e-05,0.00011,9e-05,0.00023
-248,1.0,1e-05,1e-05,1e-05,1e-05,0.00088,0.00077,1e-05,0.0001,1e-05,4e-05,0.0,1e-05,1e-05,4e-05,5e-05,2e-05,1e-05,4e-05,1e-05,0.00041,1e-05,0.0,9e-05,0.00022,9e-05,0.00046,6e-05,8e-05,9e-05,0.00023,0.00049,7e-05,0.0001,0.0001,0.00021
-75101,1.0,1e-05,1e-05,1e-05,1e-05,0.0418,0.03546,1e-05,0.00389,1e-05,0.00248,0.0,2e-05,2e-05,4e-05,6e-05,2e-05,1e-05,5e-05,2e-05,0.00045,1e-05,0.0,0.00011,0.00023,0.0001,0.00041,6e-05,8e-05,0.0001,0.00018,0.00047,6e-05,0.0001,8e-05,0.00023
-75191,1.0,1e-05,1e-05,3e-05,1e-05,0.53436,0.40777,2e-05,0.09655,3e-05,0.0301,1e-05,2e-05,3e-05,5e-05,0.00011,7e-05,1e-05,0.00011,3e-05,0.00084,5e-05,7e-05,0.00017,0.00033,0.00023,0.00088,0.00012,0.00017,0.00032,0.00026,0.00068,9e-05,0.00012,0.00013,0.00035
-75187,1.0,1e-05,1e-05,2e-05,1e-05,0.00548,0.00473,1e-05,0.0005,2e-05,0.00029,1e-05,2e-05,2e-05,7e-05,9e-05,3e-05,1e-05,6e-05,1e-05,0.00062,1e-05,1e-05,0.00014,0.00033,0.00013,0.00038,7e-05,7e-05,8e-05,0.00016,0.00047,6e-05,9e-05,9e-05,0.00023
-266,1.0,1e-05,1e-05,1e-05,1e-05,0.0018,0.00146,1e-05,0.00023,2e-05,0.00015,2e-05,2e-05,2e-05,6e-05,8e-05,3e-05,1e-05,6e-05,1e-05,0.00059,1e-05,1e-05,0.00014,0.00031,0.00013,0.0004,5e-05,7e-05,8e-05,0.0002,0.00044,8e-05,8e-05,8e-05,0.0002
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-75217,1.0,1e-05,1e-05,2e-05,1e-05,0.00196,0.00158,1e-05,0.00026,2e-05,0.00016,1e-05,3e-05,3e-05,7e-05,0.0001,3e-05,1e-05,7e-05,2e-05,0.00089,1e-05,1e-05,0.00022,0.00053,0.00013,0.00074,9e-05,0.0001,0.00019,0.00036,0.00065,0.00013,0.0001,0.00011,0.00031
-75176,1.0,1e-05,1e-05,2e-05,1e-05,0.01406,0.01285,1e-05,0.00094,2e-05,0.00031,1e-05,3e-05,3e-05,6e-05,9e-05,3e-05,1e-05,7e-05,2e-05,0.00069,1e-05,1e-05,0.00017,0.00035,0.00015,0.00085,0.00018,0.00011,0.0002,0.00036,0.0011,0.00022,0.00023,0.00023,0.00042
-2117,1.0,1e-05,1e-05,1e-05,1e-05,0.01735,0.0154,1e-05,0.00141,1e-05,0.00057,0.0,1e-05,1e-05,3e-05,6e-05,2e-05,1e-05,5e-05,1e-05,0.0004,1e-05,0.0,8e-05,0.00021,0.0001,0.00065,0.00012,0.00011,0.00015,0.00026,0.00055,7e-05,0.00014,9e-05,0.00025
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-75103,1.0,1e-05,1e-05,1e-05,1e-05,0.01302,0.00819,3e-05,0.00234,3e-05,0.00255,0.0,2e-05,2e-05,6e-05,0.00012,5e-05,1e-05,0.00011,1e-05,0.0005,1e-05,0.0,9e-05,0.00029,0.00011,0.00054,8e-05,0.00011,0.00011,0.00024,0.00066,7e-05,0.00015,0.00011,0.00032
-75168,1.0,1e-05,0.0,1e-05,1e-05,0.02353,0.00601,0.00016,0.01754,0.00018,0.00031,0.0,0.00013,0.00013,0.00027,0.0003,0.00032,1e-05,0.00042,1e-05,0.0004,1e-05,0.0,8e-05,0.00021,0.0001,0.0011,0.00022,0.00025,0.00026,0.00037,0.00124,0.00022,0.00025,0.00028,0.00049
-75157,1.0,1e-05,1e-05,1e-05,1e-05,0.00125,0.00106,1e-05,0.00015,2e-05,7e-05,0.0,2e-05,2e-05,5e-05,6e-05,2e-05,1e-05,4e-05,1e-05,0.00048,2e-05,0.0,0.0001,0.00026,0.0001,0.00039,6e-05,7e-05,8e-05,0.00019,0.00043,6e-05,9e-05,8e-05,0.0002
-75092,1.0,1e-05,1e-05,1e-05,1e-05,0.00099,0.00072,1e-05,0.00018,1e-05,0.00011,0.0,2e-05,2e-05,4e-05,6e-05,2e-05,1e-05,5e-05,1e-05,0.00058,1e-05,0.0,0.00014,0.00034,9e-05,0.00041,6e-05,8e-05,9e-05,0.00017,0.00045,7e-05,0.0001,9e-05,0.00019
-75213,1.0,1e-05,1e-05,1e-05,1e-05,0.00096,0.00079,1e-05,0.00013,1e-05,7e-05,1e-05,2e-05,2e-05,5e-05,7e-05,2e-05,1e-05,6e-05,1e-05,0.00056,1e-05,1e-05,0.00013,0.00029,0.00012,0.00056,6e-05,8e-05,9e-05,0.00033,0.00063,8e-05,0.00014,0.00013,0.00029
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-75119,1.0,1e-05,1e-05,2e-05,1e-05,0.04185,0.01398,0.00063,0.01614,0.0007,0.01306,0.0,0.00046,0.00045,0.00091,0.00097,0.0012,1e-05,0.0015,1e-05,0.00047,1e-05,0.0,8e-05,0.00026,0.00012,0.00421,0.00091,0.00097,0.00103,0.0013,0.00402,0.00082,0.0008,0.00097,0.00144
-75227,1.0,1e-05,1e-05,2e-05,1e-05,0.00416,0.00373,1e-05,0.00038,2e-05,9e-05,1e-05,2e-05,2e-05,6e-05,0.0001,3e-05,1e-05,7e-05,2e-05,0.00063,1e-05,1e-05,0.00015,0.00034,0.00013,0.00037,6e-05,8e-05,8e-05,0.00016,0.00043,6e-05,9e-05,9e-05,0.0002
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diff --git a/autosklearn/metalearning/files/f1_samples_multiclass.classification_dense/feature_runstatus.arff b/autosklearn/metalearning/files/f1_samples_multiclass.classification_dense/feature_runstatus.arff
index 0034ad2bd6..5fff2dfed3 100644
--- a/autosklearn/metalearning/files/f1_samples_multiclass.classification_dense/feature_runstatus.arff
+++ b/autosklearn/metalearning/files/f1_samples_multiclass.classification_dense/feature_runstatus.arff
@@ -4,6 +4,7 @@
 @ATTRIBUTE repetition NUMERIC
 @ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other}
@@ -21,6 +22,11 @@
 @ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other}
@@ -37,162 +43,138 @@
 @ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other}
 
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diff --git a/autosklearn/metalearning/files/f1_samples_multiclass.classification_dense/feature_values.arff b/autosklearn/metalearning/files/f1_samples_multiclass.classification_dense/feature_values.arff
index a60400e3ce..736dc64321 100644
--- a/autosklearn/metalearning/files/f1_samples_multiclass.classification_dense/feature_values.arff
+++ b/autosklearn/metalearning/files/f1_samples_multiclass.classification_dense/feature_values.arff
@@ -13,7 +13,6 @@
 @ATTRIBUTE KurtosisMean NUMERIC
 @ATTRIBUTE KurtosisMin NUMERIC
 @ATTRIBUTE KurtosisSTD NUMERIC
-@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC
 @ATTRIBUTE LogDatasetRatio NUMERIC
 @ATTRIBUTE LogInverseDatasetRatio NUMERIC
 @ATTRIBUTE LogNumberOfFeatures NUMERIC
@@ -42,160 +41,135 @@
 @ATTRIBUTE SymbolsSum NUMERIC
 
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diff --git a/autosklearn/metalearning/files/f1_samples_multiclass.classification_sparse/algorithm_runs.arff b/autosklearn/metalearning/files/f1_samples_multiclass.classification_sparse/algorithm_runs.arff
index 5c15e37f6a..70c99dcef1 100644
--- a/autosklearn/metalearning/files/f1_samples_multiclass.classification_sparse/algorithm_runs.arff
+++ b/autosklearn/metalearning/files/f1_samples_multiclass.classification_sparse/algorithm_runs.arff
@@ -7,95 +7,133 @@
 @ATTRIBUTE runstatus {ok, timeout, memout, not_applicable, crash, other}
 
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diff --git a/autosklearn/metalearning/files/f1_samples_multiclass.classification_sparse/configurations.csv b/autosklearn/metalearning/files/f1_samples_multiclass.classification_sparse/configurations.csv
index 888e169980..c3d201ba5c 100644
--- a/autosklearn/metalearning/files/f1_samples_multiclass.classification_sparse/configurations.csv
+++ b/autosklearn/metalearning/files/f1_samples_multiclass.classification_sparse/configurations.csv
@@ -1,93 +1,131 @@
 idx,balancing:strategy,classifier:__choice__,classifier:adaboost:algorithm,classifier:adaboost:learning_rate,classifier:adaboost:max_depth,classifier:adaboost:n_estimators,classifier:bernoulli_nb:alpha,classifier:bernoulli_nb:fit_prior,classifier:decision_tree:criterion,classifier:decision_tree:max_depth_factor,classifier:decision_tree:max_features,classifier:decision_tree:max_leaf_nodes,classifier:decision_tree:min_impurity_decrease,classifier:decision_tree:min_samples_leaf,classifier:decision_tree:min_samples_split,classifier:decision_tree:min_weight_fraction_leaf,classifier:extra_trees:bootstrap,classifier:extra_trees:criterion,classifier:extra_trees:max_depth,classifier:extra_trees:max_features,classifier:extra_trees:max_leaf_nodes,classifier:extra_trees:min_impurity_decrease,classifier:extra_trees:min_samples_leaf,classifier:extra_trees:min_samples_split,classifier:extra_trees:min_weight_fraction_leaf,classifier:gradient_boosting:early_stop,classifier:gradient_boosting:l2_regularization,classifier:gradient_boosting:learning_rate,classifier:gradient_boosting:loss,classifier:gradient_boosting:max_bins,classifier:gradient_boosting:max_depth,classifier:gradient_boosting:max_leaf_nodes,classifier:gradient_boosting:min_samples_leaf,classifier:gradient_boosting:n_iter_no_change,classifier:gradient_boosting:scoring,classifier:gradient_boosting:tol,classifier:gradient_boosting:validation_fraction,classifier:k_nearest_neighbors:n_neighbors,classifier:k_nearest_neighbors:p,classifier:k_nearest_neighbors:weights,classifier:lda:n_components,classifier:lda:shrinkage,classifier:lda:shrinkage_factor,classifier:lda:tol,classifier:liblinear_svc:C,classifier:liblinear_svc:dual,classifier:liblinear_svc:fit_intercept,classifier:liblinear_svc:intercept_scaling,classifier:liblinear_svc:loss,classifier:liblinear_svc:multi_class,classifier:liblinear_svc:penalty,classifier:liblinear_svc:tol,classifier:libsvm_svc:C,classifier:libsvm_svc:coef0,classifier:libsvm_svc:degree,classifier:libsvm_svc:gamma,classifier:libsvm_svc:kernel,classifier:libsvm_svc:max_iter,classifier:libsvm_svc:shrinking,classifier:libsvm_svc:tol,classifier:multinomial_nb:alpha,classifier:multinomial_nb:fit_prior,classifier:passive_aggressive:C,classifier:passive_aggressive:average,classifier:passive_aggressive:fit_intercept,classifier:passive_aggressive:loss,classifier:passive_aggressive:tol,classifier:qda:reg_param,classifier:random_forest:bootstrap,classifier:random_forest:criterion,classifier:random_forest:max_depth,classifier:random_forest:max_features,classifier:random_forest:max_leaf_nodes,classifier:random_forest:min_impurity_decrease,classifier:random_forest:min_samples_leaf,classifier:random_forest:min_samples_split,classifier:random_forest:min_weight_fraction_leaf,classifier:sgd:alpha,classifier:sgd:average,classifier:sgd:epsilon,classifier:sgd:eta0,classifier:sgd:fit_intercept,classifier:sgd:l1_ratio,classifier:sgd:learning_rate,classifier:sgd:loss,classifier:sgd:penalty,classifier:sgd:power_t,classifier:sgd:tol,data_preprocessing:categorical_transformer:categorical_encoding:__choice__,data_preprocessing:categorical_transformer:category_coalescence:__choice__,data_preprocessing:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction,data_preprocessing:numerical_transformer:imputation:strategy,data_preprocessing:numerical_transformer:rescaling:__choice__,data_preprocessing:numerical_transformer:rescaling:quantile_transformer:n_quantiles,data_preprocessing:numerical_transformer:rescaling:quantile_transformer:output_distribution,data_preprocessing:numerical_transformer:rescaling:robust_scaler:q_max,data_preprocessing:numerical_transformer:rescaling:robust_scaler:q_min,feature_preprocessor:__choice__,feature_preprocessor:extra_trees_preproc_for_classification:bootstrap,feature_preprocessor:extra_trees_preproc_for_classification:criterion,feature_preprocessor:extra_trees_preproc_for_classification:max_depth,feature_preprocessor:extra_trees_preproc_for_classification:max_features,feature_preprocessor:extra_trees_preproc_for_classification:max_leaf_nodes,feature_preprocessor:extra_trees_preproc_for_classification:min_impurity_decrease,feature_preprocessor:extra_trees_preproc_for_classification:min_samples_leaf,feature_preprocessor:extra_trees_preproc_for_classification:min_samples_split,feature_preprocessor:extra_trees_preproc_for_classification:min_weight_fraction_leaf,feature_preprocessor:extra_trees_preproc_for_classification:n_estimators,feature_preprocessor:kernel_pca:coef0,feature_preprocessor:kernel_pca:degree,feature_preprocessor:kernel_pca:gamma,feature_preprocessor:kernel_pca:kernel,feature_preprocessor:kernel_pca:n_components,feature_preprocessor:kitchen_sinks:gamma,feature_preprocessor:kitchen_sinks:n_components,feature_preprocessor:liblinear_svc_preprocessor:C,feature_preprocessor:liblinear_svc_preprocessor:dual,feature_preprocessor:liblinear_svc_preprocessor:fit_intercept,feature_preprocessor:liblinear_svc_preprocessor:intercept_scaling,feature_preprocessor:liblinear_svc_preprocessor:loss,feature_preprocessor:liblinear_svc_preprocessor:multi_class,feature_preprocessor:liblinear_svc_preprocessor:penalty,feature_preprocessor:liblinear_svc_preprocessor:tol,feature_preprocessor:nystroem_sampler:coef0,feature_preprocessor:nystroem_sampler:degree,feature_preprocessor:nystroem_sampler:gamma,feature_preprocessor:nystroem_sampler:kernel,feature_preprocessor:nystroem_sampler:n_components,feature_preprocessor:polynomial:degree,feature_preprocessor:polynomial:include_bias,feature_preprocessor:polynomial:interaction_only,feature_preprocessor:random_trees_embedding:bootstrap,feature_preprocessor:random_trees_embedding:max_depth,feature_preprocessor:random_trees_embedding:max_leaf_nodes,feature_preprocessor:random_trees_embedding:min_samples_leaf,feature_preprocessor:random_trees_embedding:min_samples_split,feature_preprocessor:random_trees_embedding:min_weight_fraction_leaf,feature_preprocessor:random_trees_embedding:n_estimators,feature_preprocessor:select_percentile_classification:percentile,feature_preprocessor:select_percentile_classification:score_func,feature_preprocessor:select_rates_classification:alpha,feature_preprocessor:select_rates_classification:mode,feature_preprocessor:select_rates_classification:score_func,feature_preprocessor:truncatedSVD:target_dim
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-10,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.8253905225980761,None,0.0,18,20,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.003725255016700434,mean,robust_scaler,,,0.7701705977765594,0.2396520157991559,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.3579038776690903,False,True,1,squared_hinge,ovr,l1,4.994232534734023e-05,,,,,,,,,,,,,,,,,,,,,
-11,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.06263739023781334,None,0.0,18,8,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.02206379295100344,most_frequent,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,,
-12,none,decision_tree,,,,,,,entropy,0.9175027108465073,1.0,None,0.0,18,13,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0005144268441601888,most_frequent,none,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.36269138268682594,fdr,chi2,
-13,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,199,None,,0.003239674765949011,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,robust_scaler,,,0.7228447795482008,0.25,kernel_pca,,,,,,,,,,,0.09440148939740256,3,0.0008114095405154861,poly,1996,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-14,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.6039096182414764,None,0.0,2,20,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.1939609823704447,most_frequent,robust_scaler,,,0.7331327008874781,0.29747488162877694,extra_trees_preproc_for_classification,True,entropy,None,0.33401603690569853,None,0.0,4,4,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-15,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7865652301288171,None,0.0,1,4,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,median,robust_scaler,,,0.8623635641193496,0.13298297278730625,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-16,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.905401147837377,-0.32832122529048147,3,1.001896309040499,poly,-1,False,5.1523484258030785e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-17,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.21794354428393548,None,0.0,2,16,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0025451910134387575,median,quantile_transformer,1477,uniform,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,,
-18,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5010952533165973,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.005838375247601407,mean,normalize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,,
-19,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5514127836113225,None,0.0,4,20,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.010750756228608195,most_frequent,robust_scaler,,,0.7471420685032172,0.2916152663822578,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.22557315057515023,fwe,chi2,
-20,weighting,decision_tree,,,,,,,gini,1.0778738481485166,1.0,None,0.0,14,7,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.002474357425774638,mean,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,,
-21,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.506204385946851,None,0.0,2,8,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.03433007982751011,mean,none,,,,,extra_trees_preproc_for_classification,False,gini,None,0.32676415738837183,None,0.0,5,6,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-22,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9333237919291875,None,0.0,8,3,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,,
-23,none,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,184,manual,0.5022041786628263,2.3064966255468173e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,most_frequent,standardize,,,,,kernel_pca,,,,,,,,,,,-0.32023224593255817,2,0.09479590655633498,poly,1382,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-24,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,22194.242946902177,0.45210849669021247,,0.0005669713702171556,sigmoid,-1,False,0.03542142266081451,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,quantile_transformer,291,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-25,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.4752532930653711,None,0.0,9,17,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,robust_scaler,,,0.8538731695642808,0.11963446560467883,extra_trees_preproc_for_classification,True,entropy,None,0.5642223456125496,None,0.0,6,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-26,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7422641456052917,None,0.0,8,15,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.005561351266169067,most_frequent,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-27,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.756167892711991e-05,False,True,squared_hinge,5.235791933868702e-05,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0001632172987320906,most_frequent,standardize,,,,,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,5,None,19,12,1.0,70,,,,,,
-28,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7005303455027131,None,0.0,1,5,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.027348815457551995,mean,quantile_transformer,789,uniform,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,,
-29,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,10,1,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0022606253047811435,median,quantile_transformer,1247,normal,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,25.327793585266683,chi2,,,,
-30,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,45.824472650016915,,,0.04733593018751384,rbf,-1,False,0.0001002563992422035,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.007882457896645694,most_frequent,robust_scaler,,,0.7360778699898849,0.27800774875566153,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-31,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,21.69482701422382,-0.9439355912923322,,0.012708037096662675,sigmoid,-1,True,1.3900680173654052e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-32,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.4467384469668449,None,0.0,19,19,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.00033708405018786203,median,quantile_transformer,711,normal,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,7.744829113696808,False,True,1,squared_hinge,ovr,l1,0.0001869446320464273,,,,,,,,,,,,,,,,,,,,,
-33,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,26.502259985828186,,,9.475662388405154e-05,rbf,-1,False,0.00019690635968217866,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-34,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.339600708038189,False,True,1,squared_hinge,ovr,l2,9.572641158475304e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,quantile_transformer,1193,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-35,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5255991794296913,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-36,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.34697723213923637,None,0.0,15,18,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.1923594385988973,mean,normalize,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,25.77499302945827,chi2,,,,
-37,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.7388247096873266,None,0.0,1,2,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.02005334514549439,most_frequent,robust_scaler,,,0.9347422252540531,0.2909868620945952,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,,
-38,none,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4994182104906286,False,True,1,squared_hinge,ovr,l2,0.006324920556381277,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.06848370232187935,mean,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-39,none,bernoulli_nb,,,,,1.9593357170002308,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.005109073482613361,mean,quantile_transformer,10,normal,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.03495379340060141,False,True,1,squared_hinge,ovr,l1,0.0033386410851928994,,,,,,,,,,,,,,,,,,,,,
-40,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.6453555269772124,None,0.0,1,4,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,normalize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-41,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.20911875507660596,None,0.0,12,8,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,median,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,,
-42,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.49918755377415064,None,0.0,1,2,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.36992315774620843,mean,robust_scaler,,,0.7410494147895569,0.29895918805929733,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-43,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,29.55731828912983,False,True,1,squared_hinge,ovr,l2,0.0011332486951954968,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,,
-44,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.37801720673725603,True,True,squared_hinge,0.0001255132393635067,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.025993178344125793,most_frequent,standardize,,,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,1.4421737618429475,False,True,1,squared_hinge,ovr,l1,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,
-45,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.10581257327556097,None,0.0,7,12,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.004219392676189224,median,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,,
-46,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.4188764793561401,None,0.0,5,5,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.010589444903605501,mean,robust_scaler,,,0.9796997860990453,0.27262832749694405,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-47,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0023391956112474004,False,True,squared_hinge,0.011792446147470903,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.08295440990272292,most_frequent,quantile_transformer,962,normal,,,kernel_pca,,,,,,,,,,,,,,cosine,1778,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-48,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.41019363453549706,None,0.0,15,2,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.0003507389224306018,median,none,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.20614098085343638,fdr,chi2,
-49,none,adaboost,SAMME,0.07025975715693651,5,339,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,median,robust_scaler,,,0.7117466125338451,0.2984609070131515,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.21351971922962878,fdr,chi2,
-50,none,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9931779675565967,None,0.0,18,17,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,robust_scaler,,,0.75,0.2945917010472818,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,,
-51,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.08646118576686276,None,0.0,4,9,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,most_frequent,quantile_transformer,959,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-52,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.6271213423927893,None,0.0,1,4,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.009403509495453675,mean,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,,
-53,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8.85547546146731,True,True,squared_hinge,4.0111304608748804e-05,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-54,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.34936025988397124,None,0.0,2,13,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00018601309647406987,median,robust_scaler,,,0.8634309348843472,0.26976989465367607,extra_trees_preproc_for_classification,False,entropy,None,0.48025175288975036,None,0.0,6,17,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-55,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.642370445299362,None,0.0,15,20,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.014823484140711048,median,robust_scaler,,,0.780144337652783,0.2309544712129402,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,2.2123747341894098,False,True,1,squared_hinge,ovr,l1,0.0033125984161268183,,,,,,,,,,,,,,,,,,,,,
-56,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.014911374426433241,None,0.0,6,2,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,,
-57,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0016941428709087083,True,,,True,,optimal,squared_hinge,l1,,5.124265599423001e-05,no_encoding,no_coalescense,,most_frequent,quantile_transformer,461,uniform,,,extra_trees_preproc_for_classification,True,gini,None,0.31371431180257103,None,0.0,5,17,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-58,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.7591396576940418,-0.02447104417007273,2,0.7325180366342191,poly,-1,True,0.00045922794962860523,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,standardize,,,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,2.057731506094293,False,True,1,squared_hinge,ovr,l1,0.03575998247131604,,,,,,,,,,,,,,,,,,,,,
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-82,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.02021502857757827,True,,0.055848937053280076,True,,constant,log,l2,,0.00016356162584832537,no_encoding,no_coalescense,,median,quantile_transformer,27,normal,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.11873149622606233,fwe,chi2,
-83,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8.24807027221455,-0.23458710218209156,,0.0001030034352044511,sigmoid,-1,True,0.0002726309373020207,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,quantile_transformer,1974,normal,,,extra_trees_preproc_for_classification,True,entropy,None,0.9395220389606214,None,0.0,2,20,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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+341,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5287741993191233,None,0.0,12,17,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,normalize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,False,,,,,,,,,,,,,
+345,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.4385746943443949,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,median,none,,,,,extra_trees_preproc_for_classification,False,gini,None,0.4961402916688301,None,0.0,1,18,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+349,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.4438263804770306,None,0.0,2,10,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,mean,robust_scaler,,,0.9824945141547571,0.24967364012300372,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+352,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5746384738423225,None,0.0,1,2,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.014830459445672814,mean,robust_scaler,,,0.7814390720260649,0.2588734594830571,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+353,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.6269076084367177,None,0.0,1,7,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.12943250859943486,most_frequent,standardize,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,62.84526554323969,chi2,,,,
+358,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.7463662441763204,None,0.0,20,18,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0004507212632016269,mean,none,,,,,extra_trees_preproc_for_classification,True,gini,None,0.4279327206778048,None,0.0,3,9,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+365,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0322540554793962e-05,False,,0.015117861103299655,True,,invscaling,log,l1,0.558781568719232,0.00016877274953233003,one_hot_encoding,no_coalescense,,median,quantile_transformer,681,normal,,,extra_trees_preproc_for_classification,False,gini,None,0.6320249932267078,None,0.0,17,18,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+369,none,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.9796199105973851,None,0.0,2,13,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.00845950033601661,mean,robust_scaler,,,0.8275228560550734,0.23814615163106348,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
diff --git a/autosklearn/metalearning/files/f1_samples_multiclass.classification_sparse/description.txt b/autosklearn/metalearning/files/f1_samples_multiclass.classification_sparse/description.txt
index e1c169d0eb..f4e707605b 100644
--- a/autosklearn/metalearning/files/f1_samples_multiclass.classification_sparse/description.txt
+++ b/autosklearn/metalearning/files/f1_samples_multiclass.classification_sparse/description.txt
@@ -1,8 +1,9 @@
 features_cutoff_time: 3600
 features_cutoff_memory: 3072
-number_of_feature_steps: 35
+number_of_feature_steps: 42
 feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances
 feature_step LogNumberOfInstances: LogNumberOfInstances
+feature_step NumberOfClasses: NumberOfClasses
 feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures
 feature_step LogNumberOfFeatures: LogNumberOfFeatures
 feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues
@@ -20,6 +21,11 @@ feature_step DatasetRatio: DatasetRatio, LogDatasetRatio
 feature_step LogDatasetRatio: LogDatasetRatio
 feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio
 feature_step LogInverseDatasetRatio: LogInverseDatasetRatio
+feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD
+feature_step ClassProbabilityMin: ClassProbabilityMin
+feature_step ClassProbabilityMax: ClassProbabilityMax
+feature_step ClassProbabilityMean: ClassProbabilityMean
+feature_step ClassProbabilitySTD: ClassProbabilitySTD
 feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum
 feature_step SymbolsMin: SymbolsMin
 feature_step SymbolsMax: SymbolsMax
@@ -36,11 +42,12 @@ feature_step SkewnessMin: SkewnessMin
 feature_step SkewnessMax: SkewnessMax
 feature_step SkewnessMean: SkewnessMean
 feature_step SkewnessSTD: SkewnessSTD
-features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
+feature_step ClassEntropy: ClassEntropy
+features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy
 features_stochastic: 
-default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
+default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy
 
-algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92
+algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235,236,237,238,239,240,241,242,243,244,245,246,247,248,249,250,251,252,253,254,255,256,257,258,259,260,261,262,263,264,265,266,267,268,269,270,271,272,273,274,275,276,277,278,279,280,281,282,283,284,285,286,287,288,289,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,312,313,314,315,316,317,318,319,320,321,322,323,324,325,326,327,328,329,330,331,332,333,334,335,336,337,338,339,340,341,342,343,344,345,346,347,348,349,350,351,352,353,354,355,356,357,358,359,360,361,362,363,364,365,366,367,368,369,370,371,372
 algorithms_stochastic: 
 performance_measures: f1_samples
 performance_type: solution_quality
diff --git a/autosklearn/metalearning/files/f1_samples_multiclass.classification_sparse/feature_costs.arff b/autosklearn/metalearning/files/f1_samples_multiclass.classification_sparse/feature_costs.arff
index 0e17e20d22..8512fd9275 100644
--- a/autosklearn/metalearning/files/f1_samples_multiclass.classification_sparse/feature_costs.arff
+++ b/autosklearn/metalearning/files/f1_samples_multiclass.classification_sparse/feature_costs.arff
@@ -4,6 +4,7 @@
 @ATTRIBUTE repetition NUMERIC
 @ATTRIBUTE NumberOfInstances NUMERIC
 @ATTRIBUTE LogNumberOfInstances NUMERIC
+@ATTRIBUTE NumberOfClasses NUMERIC
 @ATTRIBUTE NumberOfFeatures NUMERIC
 @ATTRIBUTE LogNumberOfFeatures NUMERIC
 @ATTRIBUTE MissingValues NUMERIC
@@ -21,6 +22,11 @@
 @ATTRIBUTE LogDatasetRatio NUMERIC
 @ATTRIBUTE InverseDatasetRatio NUMERIC
 @ATTRIBUTE LogInverseDatasetRatio NUMERIC
+@ATTRIBUTE ClassOccurences NUMERIC
+@ATTRIBUTE ClassProbabilityMin NUMERIC
+@ATTRIBUTE ClassProbabilityMax NUMERIC
+@ATTRIBUTE ClassProbabilityMean NUMERIC
+@ATTRIBUTE ClassProbabilitySTD NUMERIC
 @ATTRIBUTE NumSymbols NUMERIC
 @ATTRIBUTE SymbolsMin NUMERIC
 @ATTRIBUTE SymbolsMax NUMERIC
@@ -37,162 +43,138 @@
 @ATTRIBUTE SkewnessMax NUMERIC
 @ATTRIBUTE SkewnessMean NUMERIC
 @ATTRIBUTE SkewnessSTD NUMERIC
+@ATTRIBUTE ClassEntropy NUMERIC
 
 @DATA
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diff --git a/autosklearn/metalearning/files/f1_samples_multiclass.classification_sparse/feature_runstatus.arff b/autosklearn/metalearning/files/f1_samples_multiclass.classification_sparse/feature_runstatus.arff
index 0034ad2bd6..5fff2dfed3 100644
--- a/autosklearn/metalearning/files/f1_samples_multiclass.classification_sparse/feature_runstatus.arff
+++ b/autosklearn/metalearning/files/f1_samples_multiclass.classification_sparse/feature_runstatus.arff
@@ -4,6 +4,7 @@
 @ATTRIBUTE repetition NUMERIC
 @ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other}
@@ -21,6 +22,11 @@
 @ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other}
@@ -37,162 +43,138 @@
 @ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other}
 
 @DATA
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-2119,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok
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-252,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok
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diff --git a/autosklearn/metalearning/files/f1_samples_multiclass.classification_sparse/feature_values.arff b/autosklearn/metalearning/files/f1_samples_multiclass.classification_sparse/feature_values.arff
index a60400e3ce..736dc64321 100644
--- a/autosklearn/metalearning/files/f1_samples_multiclass.classification_sparse/feature_values.arff
+++ b/autosklearn/metalearning/files/f1_samples_multiclass.classification_sparse/feature_values.arff
@@ -13,7 +13,6 @@
 @ATTRIBUTE KurtosisMean NUMERIC
 @ATTRIBUTE KurtosisMin NUMERIC
 @ATTRIBUTE KurtosisSTD NUMERIC
-@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC
 @ATTRIBUTE LogDatasetRatio NUMERIC
 @ATTRIBUTE LogInverseDatasetRatio NUMERIC
 @ATTRIBUTE LogNumberOfFeatures NUMERIC
@@ -42,160 +41,135 @@
 @ATTRIBUTE SymbolsSum NUMERIC
 
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diff --git a/autosklearn/metalearning/files/f1_weighted_binary.classification_dense/algorithm_runs.arff b/autosklearn/metalearning/files/f1_weighted_binary.classification_dense/algorithm_runs.arff
index 5cfefb0721..456e6c3497 100644
--- a/autosklearn/metalearning/files/f1_weighted_binary.classification_dense/algorithm_runs.arff
+++ b/autosklearn/metalearning/files/f1_weighted_binary.classification_dense/algorithm_runs.arff
@@ -7,130 +7,135 @@
 @ATTRIBUTE runstatus {ok, timeout, memout, not_applicable, crash, other}
 
 @DATA
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diff --git a/autosklearn/metalearning/files/f1_weighted_binary.classification_dense/configurations.csv b/autosklearn/metalearning/files/f1_weighted_binary.classification_dense/configurations.csv
index 78ef3538b9..aa53e0cde8 100644
--- a/autosklearn/metalearning/files/f1_weighted_binary.classification_dense/configurations.csv
+++ b/autosklearn/metalearning/files/f1_weighted_binary.classification_dense/configurations.csv
@@ -1,128 +1,133 @@
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-46,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,92.66144306787051,,,0.04654639302091653,rbf,-1,True,0.0022231544280610005,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.005194838694455963,mean,none,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,15,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-47,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2681.998807317996,-0.9558745731731211,,0.0011299948421773457,sigmoid,-1,False,1.5292507354478086e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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-49,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,26.502259985828186,,,9.475662388405154e-05,rbf,-1,False,0.00019690635968217866,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-50,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,2.5360196477156387e-07,0.011018756408558218,auto,255,None,40,122,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.00011749164080669445,most_frequent,quantile_transformer,757,uniform,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,5.097834697156853,False,True,1,squared_hinge,ovr,l1,0.053877834919477424,,,,,,,,,,,,,,,,,,,,,,
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-54,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,1.488333818193668e-10,0.09526676471267805,auto,255,None,258,87,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,normalize,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.3561953264350478,fpr,f_classif
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-58,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,151,None,,0.0003283777607180646,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.2510527431514227,mean,normalize,,,,,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,-0.96093182232312,,0.05762510964607661,sigmoid,2632,,,,,,,,,,,,,,,,,
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-64,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,29.55731828912983,False,True,1,squared_hinge,ovr,l2,0.0011332486951954968,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,
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-69,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7573078019937971,None,0.0,2,5,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,robust_scaler,,,0.7265312295801113,0.28332515882236425,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.09660564590146253,fpr,f_classif
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-72,none,adaboost,SAMME,0.1280458968526125,7,125,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,robust_scaler,,,0.7156460334356771,0.2981902862926971,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,65.16171885308417,chi2,,,
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-79,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.933900673392165,None,0.0,7,4,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,minmax,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-80,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1661.2274747409913,,,0.025821208852387936,rbf,-1,False,0.0017011435104921824,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,none,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,306,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-81,none,adaboost,SAMME,0.011233995624432622,9,477,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.018370622484682127,mean,standardize,,,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6039710338898471,False,,,,,,,,,,,,,,,
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-85,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.593728171200453,None,0.0,7,20,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.02830941310359363,mean,normalize,,,,,extra_trees_preproc_for_classification,False,entropy,None,0.7389446586281172,None,0.0,20,11,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-86,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,1,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.019797877010737826,mean,normalize,,,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.8239678741128165,False,,,,,,,,,,,,,,,
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-89,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5722917944959481,None,0.0,1,14,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,
-90,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.26356754187780895,None,0.0,4,3,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010966178401758774,mean,robust_scaler,,,0.7817043861798886,0.24371720112407158,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
-91,none,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.563056219822946,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.32793677336996485,most_frequent,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-92,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,80.0028902468169,False,True,1,squared_hinge,ovr,l2,0.0024481999829574722,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.15047970487774687,mean,quantile_transformer,1130,normal,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,1.1912646635544712,False,True,1,squared_hinge,ovr,l1,0.00014314728486450857,,,,,,,,,,,,,,,,,,,,,,
-93,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.22879311080499845,None,0.0,8,4,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.014320022547615783,median,minmax,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.238630703440461,mutual_info,,,
-94,weighting,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,2,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,none,,,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6342574285315175,False,,,,,,,,,,,,,,,
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-96,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.14148638702297855,None,0.0,2,6,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.0038499650338378643,most_frequent,quantile_transformer,1759,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-97,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,1.1522414705935405e-10,0.6132800183073315,auto,255,None,1607,67,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010096911249562407,mean,robust_scaler,,,0.7663493326967942,0.136094217337434,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-98,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.35863733679484333,None,0.0,1,11,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.0009777923655842618,median,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-99,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.479405900046494,,,0.013340778632154543,rbf,-1,False,6.913586060890114e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,robust_scaler,,,0.9953232616818098,0.06450577886132274,fast_ica,,,,,,,,,,,deflation,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-100,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.825844582229201,None,0.0,12,9,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0008422508222149647,median,robust_scaler,,,0.8658803880857064,0.2528423686293341,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-101,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2574971638664341,True,True,hinge,1.8027411099296446e-05,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.00536396328125103,median,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,
-102,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.7033210835501171,None,0.0,3,19,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,robust_scaler,,,0.7699195245692886,0.29258311638476237,feature_agglomeration,,,,,,,,,,,,,,,euclidean,ward,143,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-103,none,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,128,manual,0.8574560771111307,0.010886139381599514,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.24115007944216788,most_frequent,normalize,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,62.26514170991452,mutual_info,,,
-104,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.6282663389679584,None,0.0,1,3,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,median,minmax,,,,,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-105,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,1.430083734915855e-09,0.08779621406981941,auto,255,None,17,20,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.29749837779115257,mean,robust_scaler,,,0.7236445277814435,0.25,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
-106,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.3294588572341214,None,0.0,1,3,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
-107,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,39.22980757485479,,,0.13597389869623427,rbf,-1,False,0.00011639060052687874,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.006288306814260224,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-108,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.7922837297504705,False,True,1,squared_hinge,ovr,l2,0.0005476597326342352,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.012674631437254894,most_frequent,none,,,,,extra_trees_preproc_for_classification,False,entropy,None,0.23145146971319638,None,0.0,7,20,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-109,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.18060139887649052,None,0.0,10,12,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.010000000000000004,most_frequent,robust_scaler,,,0.9298612094255864,0.1263597946722308,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
-110,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.4111348710613201,None,0.0,16,8,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.01224348527546641,most_frequent,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,
-111,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6427543483503397,True,True,hinge,0.00045318775930916944,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.009338484332306904,median,quantile_transformer,174,normal,,,kernel_pca,,,,,,,,,,,,,,,,,,,,,,cosine,1848,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-112,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.044449043853721795,True,,,True,,optimal,log,l2,,0.0005323728333290294,no_encoding,no_coalescense,,most_frequent,quantile_transformer,588,normal,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,47.34146395379382,chi2,,,
-113,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,9.029473608568458e-09,0.15779990576964315,auto,255,None,37,71,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.018093177195644097,mean,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,False,,,,,,,,,,,,
-114,weighting,adaboost,SAMME.R,0.23766684501868082,10,97,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.004737885672001703,median,none,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50.0,chi2,,,
-115,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l2,2.9468189962573666e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.014870368357163507,most_frequent,quantile_transformer,1000,uniform,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,83.43414935094258,f_classif,,,
-116,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.4864284879550203,None,0.0,1,4,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.008665854799630503,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-117,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.8851946632713493,None,0.0,2,20,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.006095662608922393,median,quantile_transformer,1861,normal,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.10486328449423876,fdr,f_classif
-118,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.6922752267318475,True,True,squared_hinge,0.06299408576730353,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,quantile_transformer,1742,uniform,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,107,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-119,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.44727252219812963,None,0.0,2,10,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,standardize,,,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l1,8.17533189014765e-05,,,,,,,,,,,,,,,,,,,,,,
-120,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5479616732130552,None,0.0,16,4,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.0002189699211089695,mean,quantile_transformer,1290,normal,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.9999,False,,,,,,,,,,,,,,,
-121,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.4210978663047845,None,0.0,5,6,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.006365988550833196,most_frequent,robust_scaler,,,0.8348891074186082,0.00974961635730845,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0695919708890599,fpr,f_classif
-122,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.3341112040803983,False,True,hinge,0.003045682798589668,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.005225537488332702,median,quantile_transformer,1225,uniform,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.10310163232668391,fpr,chi2
-123,weighting,decision_tree,,,,,,,entropy,1.0435267397863155,1.0,None,0.0,15,3,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.36913659113454333,most_frequent,normalize,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4947786362821451,fpr,f_classif
-124,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1561.1002600303584,,,0.0024048551704194507,rbf,-1,False,0.0001936214667496851,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,minmax,,,,,fast_ica,,,,,,,,,,,parallel,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-125,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.087313242780732,0.0636709914987994,3,0.004481917125024585,poly,-1,False,0.01848880618941367,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.002136015035740733,mean,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-126,weighting,adaboost,SAMME,0.029932847627447023,8,483,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,quantile_transformer,1290,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-127,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.6466927589470762,None,0.0,11,2,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.009672465311659264,most_frequent,standardize,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,369,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+1,weighting,bernoulli_nb,,,,,3.80819075993021,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,quantile_transformer,1649,normal,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.06432376826473184,,mutual_info_classif
+3,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5707458022222313,None,0.0,3,12,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,minmax,,,,,extra_trees_preproc_for_classification,False,gini,None,0.5651056128131684,None,0.0,9,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+6,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.7283683762613216,None,0.0,12,20,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,median,standardize,,,,,fast_ica,,,,,,,,,,,deflation,exp,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+11,weighting,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,2,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.3865962574104293,mean,normalize,,,,,fast_ica,,,,,,,,,,,deflation,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+13,weighting,adaboost,SAMME,0.17983222024694556,4,357,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0008898781197407956,mean,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+15,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.21772092350860472,None,0.0,14,17,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.0020488212891693275,median,standardize,,,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,33.74257606530722,False,True,1,squared_hinge,ovr,l1,8.740452966205111e-05,,,,,,,,,,,,,,,,,,,,,,
+18,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,2.4243922131268786e-05,0.07570328162448307,auto,255,None,71,42,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,robust_scaler,,,0.8898729094898769,0.12138152991327475,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,94.96086286583851,chi2,,,
+20,none,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.9866058571747695,None,0.0,16,9,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.1680676743605393,most_frequent,robust_scaler,,,0.8840072797399463,0.08535534311393947,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,False,,,,,,,,,,,,
+23,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,0.00022373024203887573,0.027149343206135476,auto,255,None,49,13,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.00777614320416325,most_frequent,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,
+27,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.41025844265119926,None,0.0,18,15,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,median,quantile_transformer,1911,normal,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,False,,,,,,,,,,,,
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+385,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.8280243811834952,None,0.0,9,14,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.08913760502443781,median,none,,,,,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+388,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,53.67009896349757,,,0.03687753298495132,rbf,-1,False,0.028791286245157307,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.047859755696856916,mean,standardize,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,70,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+392,none,adaboost,SAMME,0.7387448781351068,8,450,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0473229636375385,median,standardize,,,,,feature_agglomeration,,,,,,,,,,,,,,,euclidean,ward,344,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+399,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.5864398404787432,None,0.0,1,14,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0070663750850608895,most_frequent,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
+403,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1866.9172613016979,,,0.012606336762631641,rbf,-1,True,0.000504574634300575,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,
+405,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0001329242404968632,False,True,squared_hinge,0.000416219546803132,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.007568009633214092,median,quantile_transformer,1126,normal,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,29.06367291960815,chi2,,,
+408,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.426289271149965,False,True,1,squared_hinge,ovr,l2,0.00012788535194515696,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,normalize,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,51.45463995444033,chi2,,,
+413,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,7.194851113809193e-10,0.013882834218943657,auto,255,None,18,2,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0005124238915083231,mean,quantile_transformer,948,normal,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,60.45885911036194,f_classif,,,
+415,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,5.215923689311285e-05,0.01709129738847392,auto,255,None,10,19,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0004854193510398933,most_frequent,quantile_transformer,709,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+422,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.36374112587320206,None,0.0,2,10,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010870084126959173,most_frequent,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,
+424,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,2.344883102482138e-07,0.0328548789988159,auto,255,None,42,24,16,loss,1e-07,0.13015466346727866,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,most_frequent,robust_scaler,,,0.7010898774332888,0.2534280821273264,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,
+428,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,3.738096432152635e-10,0.04468500715913894,auto,255,None,31,156,20,loss,1e-07,0.10279783687118692,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010000000000000004,most_frequent,quantile_transformer,1132,normal,,,feature_agglomeration,,,,,,,,,,,,,,,euclidean,ward,43,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+430,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5321814610661173,None,0.0,1,3,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,standardize,,,,,extra_trees_preproc_for_classification,True,gini,None,0.5,None,0.0,1,15,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+435,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.44903177399085925,None,0.0,2,17,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,mean,robust_scaler,,,0.7812408516672696,0.12791233577400205,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,
+438,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,6.090138036399552e-10,0.017305568433437588,auto,255,None,202,188,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,robust_scaler,,,0.7526196011313747,0.1563616005702636,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+445,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,68.60833575985336,,,0.03905068757955671,rbf,-1,False,0.02846559747114773,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.04775815990561371,mean,standardize,,,,,extra_trees_preproc_for_classification,False,entropy,None,0.45727987956076366,None,0.0,13,11,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+447,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.3431705351883592,None,0.0,4,16,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.09444471061261189,most_frequent,normalize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+450,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,7.837701348094735e-09,0.05315388879642303,auto,255,None,67,2,10,loss,1e-07,0.11655723047843726,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.001857226018763921,median,none,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.014648191862400293,fdr,f_classif
+458,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,5.581658271288433e-10,0.06171988234311011,auto,255,None,25,6,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.014859405637537319,most_frequent,robust_scaler,,,0.7983900845251328,0.2545046978458648,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,1.380085213170737,False,True,1,squared_hinge,ovr,l1,0.0061450732015354784,,,,,,,,,,,,,,,,,,,,,,
+462,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6,1,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.017471096998391875,median,none,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,67.7801145492284,f_classif,,,
diff --git a/autosklearn/metalearning/files/f1_weighted_binary.classification_dense/description.txt b/autosklearn/metalearning/files/f1_weighted_binary.classification_dense/description.txt
index 11ad5da820..72d787b6f3 100644
--- a/autosklearn/metalearning/files/f1_weighted_binary.classification_dense/description.txt
+++ b/autosklearn/metalearning/files/f1_weighted_binary.classification_dense/description.txt
@@ -1,8 +1,9 @@
 features_cutoff_time: 3600
 features_cutoff_memory: 3072
-number_of_feature_steps: 35
+number_of_feature_steps: 42
 feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances
 feature_step LogNumberOfInstances: LogNumberOfInstances
+feature_step NumberOfClasses: NumberOfClasses
 feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures
 feature_step LogNumberOfFeatures: LogNumberOfFeatures
 feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues
@@ -20,6 +21,11 @@ feature_step DatasetRatio: DatasetRatio, LogDatasetRatio
 feature_step LogDatasetRatio: LogDatasetRatio
 feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio
 feature_step LogInverseDatasetRatio: LogInverseDatasetRatio
+feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD
+feature_step ClassProbabilityMin: ClassProbabilityMin
+feature_step ClassProbabilityMax: ClassProbabilityMax
+feature_step ClassProbabilityMean: ClassProbabilityMean
+feature_step ClassProbabilitySTD: ClassProbabilitySTD
 feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum
 feature_step SymbolsMin: SymbolsMin
 feature_step SymbolsMax: SymbolsMax
@@ -36,11 +42,12 @@ feature_step SkewnessMin: SkewnessMin
 feature_step SkewnessMax: SkewnessMax
 feature_step SkewnessMean: SkewnessMean
 feature_step SkewnessSTD: SkewnessSTD
-features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
+feature_step ClassEntropy: ClassEntropy
+features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy
 features_stochastic: 
-default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
+default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy
 
-algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127
+algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235,236,237,238,239,240,241,242,243,244,245,246,247,248,249,250,251,252,253,254,255,256,257,258,259,260,261,262,263,264,265,266,267,268,269,270,271,272,273,274,275,276,277,278,279,280,281,282,283,284,285,286,287,288,289,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,312,313,314,315,316,317,318,319,320,321,322,323,324,325,326,327,328,329,330,331,332,333,334,335,336,337,338,339,340,341,342,343,344,345,346,347,348,349,350,351,352,353,354,355,356,357,358,359,360,361,362,363,364,365,366,367,368,369,370,371,372,373,374,375,376,377,378,379,380,381,382,383,384,385,386,387,388,389,390,391,392,393,394,395,396,397,398,399,400,401,402,403,404,405,406,407,408,409,410,411,412,413,414,415,416,417,418,419,420,421,422,423,424,425,426,427,428,429,430,431,432,433,434,435,436,437,438,439,440,441,442,443,444,445,446,447,448,449,450,451,452,453,454,455,456,457,458,459,460,461,462,463,464
 algorithms_stochastic: 
 performance_measures: f1_weighted
 performance_type: solution_quality
diff --git a/autosklearn/metalearning/files/f1_weighted_binary.classification_dense/feature_costs.arff b/autosklearn/metalearning/files/f1_weighted_binary.classification_dense/feature_costs.arff
index 0e17e20d22..8512fd9275 100644
--- a/autosklearn/metalearning/files/f1_weighted_binary.classification_dense/feature_costs.arff
+++ b/autosklearn/metalearning/files/f1_weighted_binary.classification_dense/feature_costs.arff
@@ -4,6 +4,7 @@
 @ATTRIBUTE repetition NUMERIC
 @ATTRIBUTE NumberOfInstances NUMERIC
 @ATTRIBUTE LogNumberOfInstances NUMERIC
+@ATTRIBUTE NumberOfClasses NUMERIC
 @ATTRIBUTE NumberOfFeatures NUMERIC
 @ATTRIBUTE LogNumberOfFeatures NUMERIC
 @ATTRIBUTE MissingValues NUMERIC
@@ -21,6 +22,11 @@
 @ATTRIBUTE LogDatasetRatio NUMERIC
 @ATTRIBUTE InverseDatasetRatio NUMERIC
 @ATTRIBUTE LogInverseDatasetRatio NUMERIC
+@ATTRIBUTE ClassOccurences NUMERIC
+@ATTRIBUTE ClassProbabilityMin NUMERIC
+@ATTRIBUTE ClassProbabilityMax NUMERIC
+@ATTRIBUTE ClassProbabilityMean NUMERIC
+@ATTRIBUTE ClassProbabilitySTD NUMERIC
 @ATTRIBUTE NumSymbols NUMERIC
 @ATTRIBUTE SymbolsMin NUMERIC
 @ATTRIBUTE SymbolsMax NUMERIC
@@ -37,162 +43,138 @@
 @ATTRIBUTE SkewnessMax NUMERIC
 @ATTRIBUTE SkewnessMean NUMERIC
 @ATTRIBUTE SkewnessSTD NUMERIC
+@ATTRIBUTE ClassEntropy NUMERIC
 
 @DATA
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+75240,1.0,2e-05,1e-05,0.00036,5e-05,4e-05,0.00845,0.00644,6e-05,0.00122,4e-05,0.00091,1e-05,4e-05,4e-05,9e-05,0.00013,5e-05,2e-05,9e-05,5e-05,0.00031,3e-05,1e-05,7e-05,0.0002,0.00073,3e-05,2e-05,0.00019,0.0003,0.00018,0.00055,9e-05,9e-05,0.00011,0.00025,0.0005,7e-05,8e-05,0.00015,0.0002,0.00365
+75243,1.0,1e-05,1e-05,0.00029,1e-05,1e-05,0.00461,0.00415,1e-05,0.00035,1e-05,0.00015,0.0,2e-05,2e-05,5e-05,7e-05,2e-05,1e-05,4e-05,1e-05,0.00018,2e-05,1e-05,4e-05,0.00011,0.00022,1e-05,1e-05,5e-05,0.00011,5e-05,0.0004,6e-05,6e-05,8e-05,0.0002,0.00039,6e-05,6e-05,8e-05,0.00019,0.00265
+75244,1.0,3e-05,2e-05,0.0004,3e-05,2e-05,0.0173,0.01298,4e-05,0.00267,4e-05,0.00174,1e-05,7e-05,9e-05,0.00024,0.00027,8e-05,2e-05,0.00013,3e-05,0.00045,7e-05,2e-05,0.00011,0.00025,0.00077,6e-05,2e-05,0.00021,0.00034,0.00013,0.00077,0.00014,0.00013,0.00026,0.00023,0.00044,7e-05,8e-05,0.0001,0.00019,0.00649
+75248,1.0,1e-05,1e-05,0.00017,1e-05,1e-05,0.00416,0.00305,1e-05,0.0006,2e-05,0.00054,1e-05,2e-05,2e-05,5e-05,7e-05,2e-05,1e-05,4e-05,1e-05,0.00017,2e-05,1e-05,4e-05,0.0001,0.00029,5e-05,1e-05,6e-05,0.00012,6e-05,0.00081,0.00011,0.00015,0.00016,0.00038,0.0005,7e-05,8e-05,0.00012,0.00024,0.00184
+75249,1.0,1e-05,1e-05,0.00011,1e-05,1e-05,0.00192,0.00153,1e-05,0.00025,1e-05,0.00017,1e-05,2e-05,2e-05,5e-05,7e-05,2e-05,1e-05,5e-05,3e-05,0.00014,2e-05,1e-05,3e-05,8e-05,0.00029,1e-05,3e-05,6e-05,0.00012,7e-05,0.00041,6e-05,8e-05,0.0001,0.00017,0.00043,6e-05,7e-05,9e-05,0.00019,0.00092
+75250,1.0,1e-05,1e-05,0.00375,1e-05,1e-05,0.04994,0.047,1e-05,0.00247,1e-05,0.00049,1e-05,2e-05,2e-05,6e-05,7e-05,2e-05,1e-05,3e-05,1e-05,0.00022,4e-05,1e-05,4e-05,0.00012,0.00041,0.0,1e-05,0.00011,0.00023,6e-05,0.00076,0.00012,0.00012,0.00019,0.00032,0.00086,0.00012,0.00013,0.00018,0.00043,0.04178
diff --git a/autosklearn/metalearning/files/f1_weighted_binary.classification_dense/feature_runstatus.arff b/autosklearn/metalearning/files/f1_weighted_binary.classification_dense/feature_runstatus.arff
index 0034ad2bd6..5fff2dfed3 100644
--- a/autosklearn/metalearning/files/f1_weighted_binary.classification_dense/feature_runstatus.arff
+++ b/autosklearn/metalearning/files/f1_weighted_binary.classification_dense/feature_runstatus.arff
@@ -4,6 +4,7 @@
 @ATTRIBUTE repetition NUMERIC
 @ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other}
@@ -21,6 +22,11 @@
 @ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other}
@@ -37,162 +43,138 @@
 @ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other}
 
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diff --git a/autosklearn/metalearning/files/f1_weighted_binary.classification_dense/feature_values.arff b/autosklearn/metalearning/files/f1_weighted_binary.classification_dense/feature_values.arff
index a60400e3ce..736dc64321 100644
--- a/autosklearn/metalearning/files/f1_weighted_binary.classification_dense/feature_values.arff
+++ b/autosklearn/metalearning/files/f1_weighted_binary.classification_dense/feature_values.arff
@@ -13,7 +13,6 @@
 @ATTRIBUTE KurtosisMean NUMERIC
 @ATTRIBUTE KurtosisMin NUMERIC
 @ATTRIBUTE KurtosisSTD NUMERIC
-@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC
 @ATTRIBUTE LogDatasetRatio NUMERIC
 @ATTRIBUTE LogInverseDatasetRatio NUMERIC
 @ATTRIBUTE LogNumberOfFeatures NUMERIC
@@ -42,160 +41,135 @@
 @ATTRIBUTE SymbolsSum NUMERIC
 
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diff --git a/autosklearn/metalearning/files/f1_weighted_binary.classification_sparse/algorithm_runs.arff b/autosklearn/metalearning/files/f1_weighted_binary.classification_sparse/algorithm_runs.arff
index 8e10d2e6f6..40fbaf20fb 100644
--- a/autosklearn/metalearning/files/f1_weighted_binary.classification_sparse/algorithm_runs.arff
+++ b/autosklearn/metalearning/files/f1_weighted_binary.classification_sparse/algorithm_runs.arff
@@ -7,95 +7,133 @@
 @ATTRIBUTE runstatus {ok, timeout, memout, not_applicable, crash, other}
 
 @DATA
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diff --git a/autosklearn/metalearning/files/f1_weighted_binary.classification_sparse/configurations.csv b/autosklearn/metalearning/files/f1_weighted_binary.classification_sparse/configurations.csv
index 9e3b1a7294..7e1f113687 100644
--- a/autosklearn/metalearning/files/f1_weighted_binary.classification_sparse/configurations.csv
+++ b/autosklearn/metalearning/files/f1_weighted_binary.classification_sparse/configurations.csv
@@ -1,93 +1,131 @@
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-87,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.44727252219812963,None,0.0,2,10,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,standardize,,,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l1,8.17533189014765e-05,,,,,,,,,,,,,,,,,,,,,
-88,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.46057831591617715,False,True,hinge,0.04557857428827514,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00027457445401600137,median,standardize,,,,,extra_trees_preproc_for_classification,True,gini,None,0.48190346970486964,None,0.0,17,18,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-89,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.3341112040803983,False,True,hinge,0.003045682798589668,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.005225537488332702,median,quantile_transformer,1225,uniform,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.10310163232668391,fpr,chi2,
-90,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4834106677905965,False,True,1,squared_hinge,ovr,l2,0.00017625027586147415,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0009157492873444966,most_frequent,quantile_transformer,1150,normal,,,extra_trees_preproc_for_classification,False,entropy,None,0.4981700453792278,None,0.0,9,15,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-91,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.087313242780732,0.0636709914987994,3,0.004481917125024585,poly,-1,False,0.01848880618941367,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.002136015035740733,mean,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-92,weighting,adaboost,SAMME,0.029932847627447023,8,483,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,quantile_transformer,1290,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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+284,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5609113236355627,None,0.0,2,2,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.47917908217902894,mean,quantile_transformer,673,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+286,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,33.440072270881174,-0.22811742088286835,3,0.05010299776905627,poly,-1,False,2.275627187537573e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,normalize,,,,,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,2,None,5,14,1.0,52,,,,,,
+287,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.3850609070926397,None,0.0,1,2,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.013992309764275707,median,normalize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,,
+288,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.5719833791553314,None,0.0,16,3,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.13804007452389966,mean,normalize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+292,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.0479982468066316,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,quantile_transformer,1097,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+293,weighting,adaboost,SAMME,0.2540331297425094,2,187,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,robust_scaler,,,0.9867887266047534,0.041631156927011324,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50.70828969428249,chi2,,,,
+298,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.5431102417332107,None,0.0,1,16,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010731370069741165,median,none,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.35426195730988774,fdr,chi2,
+301,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.5,None,0.0,2,4,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.008039249201522535,most_frequent,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,,
+306,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2677516008848513,True,True,hinge,0.05752557410901496,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0010152857217605206,median,quantile_transformer,1000,uniform,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,51.60974796845029,chi2,,,,
+307,none,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,249,None,,0.0005516162390755682,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010000000000000004,mean,quantile_transformer,1350,uniform,,,kernel_pca,,,,,,,,,,,0.162692683181769,3,1.0,poly,487,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+312,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,60.6761602925822,,,0.038224035513907226,rbf,-1,False,0.0018214343969776353,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.421025538999516,median,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+313,weighting,adaboost,SAMME,1.2423888874490505,5,284,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.01582563952987033,most_frequent,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+316,weighting,adaboost,SAMME,1.2615483379313783,8,190,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.00017789451703497617,mean,robust_scaler,,,0.7345811776810758,0.054715769043996304,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+317,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.4572679693563934,None,0.0,1,11,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,mean,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,,
+318,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.843098530645432,None,0.0,1,4,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,mean,quantile_transformer,930,normal,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,,
+321,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.5864398404787432,None,0.0,1,14,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0070663750850608895,most_frequent,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,,
+324,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.5240435604277579,None,0.0,1,3,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.05089089351168927,median,robust_scaler,,,0.8952718567345662,0.24647973407695942,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,,
+326,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0001329242404968632,False,True,squared_hinge,0.000416219546803132,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.007568009633214092,median,quantile_transformer,1126,normal,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,29.06367291960815,chi2,,,,
+329,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.426289271149965,False,True,1,squared_hinge,ovr,l2,0.00012788535194515696,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,normalize,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,51.45463995444033,chi2,,,,
+331,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.5,None,0.0,1,5,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.15244281596303316,mean,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+332,none,adaboost,SAMME,0.22805500898886877,8,172,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.019649593891454008,mean,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+334,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7516795829719571,None,0.0,14,7,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0010013681780844724,mean,none,,,,,extra_trees_preproc_for_classification,True,entropy,None,0.6123323873969666,None,0.0,17,7,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+339,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.36374112587320206,None,0.0,2,10,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010870084126959173,most_frequent,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,,
+343,none,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9062834897256731,None,0.0,20,9,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.005187379382274718,mean,quantile_transformer,349,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+345,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5321814610661173,None,0.0,1,3,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,standardize,,,,,extra_trees_preproc_for_classification,True,gini,None,0.5,None,0.0,1,15,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+350,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.44903177399085925,None,0.0,2,17,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,mean,robust_scaler,,,0.7812408516672696,0.12791233577400205,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,,
+352,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5988524108452234,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,mean,robust_scaler,,,0.75,0.26961884796358077,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+355,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,68.60833575985336,,,0.03905068757955671,rbf,-1,False,0.02846559747114773,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.04775815990561371,mean,standardize,,,,,extra_trees_preproc_for_classification,False,entropy,None,0.45727987956076366,None,0.0,13,11,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+357,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.3431705351883592,None,0.0,4,16,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.09444471061261189,most_frequent,normalize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+358,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.8028142492855044,None,0.0,18,13,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.11658640167924614,mean,standardize,,,,,extra_trees_preproc_for_classification,False,entropy,None,0.19215209539619693,None,0.0,15,18,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+367,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.938372431886818e-05,True,True,hinge,0.0016143408148969647,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00024949978971758427,median,quantile_transformer,754,uniform,,,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,-0.22339839702350717,2,0.5718982387915436,poly,5260,,,,,,,,,,,,,,,,
+371,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4,1,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.01020427226149318,median,none,,,,,extra_trees_preproc_for_classification,True,entropy,None,0.3663460889081009,None,0.0,2,7,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
diff --git a/autosklearn/metalearning/files/f1_weighted_binary.classification_sparse/description.txt b/autosklearn/metalearning/files/f1_weighted_binary.classification_sparse/description.txt
index ab0061e897..8915e41e6b 100644
--- a/autosklearn/metalearning/files/f1_weighted_binary.classification_sparse/description.txt
+++ b/autosklearn/metalearning/files/f1_weighted_binary.classification_sparse/description.txt
@@ -1,8 +1,9 @@
 features_cutoff_time: 3600
 features_cutoff_memory: 3072
-number_of_feature_steps: 35
+number_of_feature_steps: 42
 feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances
 feature_step LogNumberOfInstances: LogNumberOfInstances
+feature_step NumberOfClasses: NumberOfClasses
 feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures
 feature_step LogNumberOfFeatures: LogNumberOfFeatures
 feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues
@@ -20,6 +21,11 @@ feature_step DatasetRatio: DatasetRatio, LogDatasetRatio
 feature_step LogDatasetRatio: LogDatasetRatio
 feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio
 feature_step LogInverseDatasetRatio: LogInverseDatasetRatio
+feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD
+feature_step ClassProbabilityMin: ClassProbabilityMin
+feature_step ClassProbabilityMax: ClassProbabilityMax
+feature_step ClassProbabilityMean: ClassProbabilityMean
+feature_step ClassProbabilitySTD: ClassProbabilitySTD
 feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum
 feature_step SymbolsMin: SymbolsMin
 feature_step SymbolsMax: SymbolsMax
@@ -36,11 +42,12 @@ feature_step SkewnessMin: SkewnessMin
 feature_step SkewnessMax: SkewnessMax
 feature_step SkewnessMean: SkewnessMean
 feature_step SkewnessSTD: SkewnessSTD
-features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
+feature_step ClassEntropy: ClassEntropy
+features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy
 features_stochastic: 
-default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
+default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy
 
-algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92
+algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235,236,237,238,239,240,241,242,243,244,245,246,247,248,249,250,251,252,253,254,255,256,257,258,259,260,261,262,263,264,265,266,267,268,269,270,271,272,273,274,275,276,277,278,279,280,281,282,283,284,285,286,287,288,289,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,312,313,314,315,316,317,318,319,320,321,322,323,324,325,326,327,328,329,330,331,332,333,334,335,336,337,338,339,340,341,342,343,344,345,346,347,348,349,350,351,352,353,354,355,356,357,358,359,360,361,362,363,364,365,366,367,368,369,370,371,372
 algorithms_stochastic: 
 performance_measures: f1_weighted
 performance_type: solution_quality
diff --git a/autosklearn/metalearning/files/f1_weighted_binary.classification_sparse/feature_costs.arff b/autosklearn/metalearning/files/f1_weighted_binary.classification_sparse/feature_costs.arff
index 0e17e20d22..8512fd9275 100644
--- a/autosklearn/metalearning/files/f1_weighted_binary.classification_sparse/feature_costs.arff
+++ b/autosklearn/metalearning/files/f1_weighted_binary.classification_sparse/feature_costs.arff
@@ -4,6 +4,7 @@
 @ATTRIBUTE repetition NUMERIC
 @ATTRIBUTE NumberOfInstances NUMERIC
 @ATTRIBUTE LogNumberOfInstances NUMERIC
+@ATTRIBUTE NumberOfClasses NUMERIC
 @ATTRIBUTE NumberOfFeatures NUMERIC
 @ATTRIBUTE LogNumberOfFeatures NUMERIC
 @ATTRIBUTE MissingValues NUMERIC
@@ -21,6 +22,11 @@
 @ATTRIBUTE LogDatasetRatio NUMERIC
 @ATTRIBUTE InverseDatasetRatio NUMERIC
 @ATTRIBUTE LogInverseDatasetRatio NUMERIC
+@ATTRIBUTE ClassOccurences NUMERIC
+@ATTRIBUTE ClassProbabilityMin NUMERIC
+@ATTRIBUTE ClassProbabilityMax NUMERIC
+@ATTRIBUTE ClassProbabilityMean NUMERIC
+@ATTRIBUTE ClassProbabilitySTD NUMERIC
 @ATTRIBUTE NumSymbols NUMERIC
 @ATTRIBUTE SymbolsMin NUMERIC
 @ATTRIBUTE SymbolsMax NUMERIC
@@ -37,162 +43,138 @@
 @ATTRIBUTE SkewnessMax NUMERIC
 @ATTRIBUTE SkewnessMean NUMERIC
 @ATTRIBUTE SkewnessSTD NUMERIC
+@ATTRIBUTE ClassEntropy NUMERIC
 
 @DATA
-75123,1.0,1e-05,1e-05,1e-05,1e-05,0.00253,0.00221,1e-05,0.00024,3e-05,0.00011,0.0,2e-05,2e-05,5e-05,8e-05,2e-05,1e-05,7e-05,1e-05,0.00072,1e-05,1e-05,0.00019,0.00041,0.00011,0.00061,0.00012,9e-05,0.00015,0.00025,0.00067,0.00014,0.00011,0.00016,0.00027
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+75234,1.0,1e-05,1e-05,0.00015,1e-05,1e-05,0.0058,0.00534,1e-05,0.00033,1e-05,0.00016,0.0,2e-05,2e-05,7e-05,0.00204,3e-05,1e-05,5e-05,1e-05,0.00024,3e-05,2e-05,0.0001,9e-05,0.00043,0.0,0.0,0.00012,0.00024,6e-05,0.00156,6e-05,7e-05,0.00121,0.00021,0.00054,6e-05,7e-05,9e-05,0.00032,0.00501
+75235,1.0,1e-05,1e-05,0.0003,2e-05,1e-05,0.00461,0.00385,2e-05,0.00055,2e-05,0.00026,1e-05,3e-05,3e-05,0.0001,0.00011,3e-05,1e-05,5e-05,2e-05,0.00026,3e-05,1e-05,8e-05,0.00014,0.00075,1e-05,1e-05,0.0002,0.00043,0.00011,0.00127,0.00017,0.0002,0.00029,0.00061,0.0008,0.00015,0.00012,0.00016,0.00038,0.00242
+75236,1.0,1e-05,1e-05,0.0001,1e-05,1e-05,0.00218,0.00118,2e-05,0.00065,3e-05,0.00041,1e-05,3e-05,3e-05,9e-05,9e-05,4e-05,1e-05,7e-05,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.0006,1e-05,1e-05,0.00021,0.00032,6e-05,0.00043,7e-05,7e-05,9e-05,0.00019,0.00062,7e-05,8e-05,0.0001,0.00037,0.00048
+75237,1.0,2e-05,1e-05,0.00785,2e-05,1e-05,0.31031,0.27854,2e-05,0.01161,3e-05,0.02022,1e-05,4e-05,3e-05,9e-05,0.00024,6e-05,2e-05,9e-05,2e-05,0.00062,7e-05,3e-05,0.00017,0.00035,0.0008,1e-05,1e-05,0.00028,0.00043,8e-05,0.00043,8e-05,7e-05,9e-05,0.0002,0.00044,6e-05,8e-05,9e-05,0.00021,0.14028
+75239,1.0,1e-05,1e-05,7e-05,1e-05,1e-05,0.00105,0.00084,1e-05,0.00015,1e-05,8e-05,1e-05,2e-05,5e-05,4e-05,8e-05,2e-05,1e-05,3e-05,1e-05,0.00016,2e-05,1e-05,5e-05,9e-05,0.00049,0.0,0.0,0.00012,0.00026,0.00011,0.00046,0.00013,8e-05,8e-05,0.00018,0.00044,6e-05,9e-05,0.00011,0.00018,0.00065
+75240,1.0,2e-05,1e-05,0.00036,5e-05,4e-05,0.00845,0.00644,6e-05,0.00122,4e-05,0.00091,1e-05,4e-05,4e-05,9e-05,0.00013,5e-05,2e-05,9e-05,5e-05,0.00031,3e-05,1e-05,7e-05,0.0002,0.00073,3e-05,2e-05,0.00019,0.0003,0.00018,0.00055,9e-05,9e-05,0.00011,0.00025,0.0005,7e-05,8e-05,0.00015,0.0002,0.00365
+75243,1.0,1e-05,1e-05,0.00029,1e-05,1e-05,0.00461,0.00415,1e-05,0.00035,1e-05,0.00015,0.0,2e-05,2e-05,5e-05,7e-05,2e-05,1e-05,4e-05,1e-05,0.00018,2e-05,1e-05,4e-05,0.00011,0.00022,1e-05,1e-05,5e-05,0.00011,5e-05,0.0004,6e-05,6e-05,8e-05,0.0002,0.00039,6e-05,6e-05,8e-05,0.00019,0.00265
+75244,1.0,3e-05,2e-05,0.0004,3e-05,2e-05,0.0173,0.01298,4e-05,0.00267,4e-05,0.00174,1e-05,7e-05,9e-05,0.00024,0.00027,8e-05,2e-05,0.00013,3e-05,0.00045,7e-05,2e-05,0.00011,0.00025,0.00077,6e-05,2e-05,0.00021,0.00034,0.00013,0.00077,0.00014,0.00013,0.00026,0.00023,0.00044,7e-05,8e-05,0.0001,0.00019,0.00649
+75248,1.0,1e-05,1e-05,0.00017,1e-05,1e-05,0.00416,0.00305,1e-05,0.0006,2e-05,0.00054,1e-05,2e-05,2e-05,5e-05,7e-05,2e-05,1e-05,4e-05,1e-05,0.00017,2e-05,1e-05,4e-05,0.0001,0.00029,5e-05,1e-05,6e-05,0.00012,6e-05,0.00081,0.00011,0.00015,0.00016,0.00038,0.0005,7e-05,8e-05,0.00012,0.00024,0.00184
+75249,1.0,1e-05,1e-05,0.00011,1e-05,1e-05,0.00192,0.00153,1e-05,0.00025,1e-05,0.00017,1e-05,2e-05,2e-05,5e-05,7e-05,2e-05,1e-05,5e-05,3e-05,0.00014,2e-05,1e-05,3e-05,8e-05,0.00029,1e-05,3e-05,6e-05,0.00012,7e-05,0.00041,6e-05,8e-05,0.0001,0.00017,0.00043,6e-05,7e-05,9e-05,0.00019,0.00092
+75250,1.0,1e-05,1e-05,0.00375,1e-05,1e-05,0.04994,0.047,1e-05,0.00247,1e-05,0.00049,1e-05,2e-05,2e-05,6e-05,7e-05,2e-05,1e-05,3e-05,1e-05,0.00022,4e-05,1e-05,4e-05,0.00012,0.00041,0.0,1e-05,0.00011,0.00023,6e-05,0.00076,0.00012,0.00012,0.00019,0.00032,0.00086,0.00012,0.00013,0.00018,0.00043,0.04178
diff --git a/autosklearn/metalearning/files/f1_weighted_binary.classification_sparse/feature_runstatus.arff b/autosklearn/metalearning/files/f1_weighted_binary.classification_sparse/feature_runstatus.arff
index 0034ad2bd6..5fff2dfed3 100644
--- a/autosklearn/metalearning/files/f1_weighted_binary.classification_sparse/feature_runstatus.arff
+++ b/autosklearn/metalearning/files/f1_weighted_binary.classification_sparse/feature_runstatus.arff
@@ -4,6 +4,7 @@
 @ATTRIBUTE repetition NUMERIC
 @ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other}
@@ -21,6 +22,11 @@
 @ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other}
@@ -37,162 +43,138 @@
 @ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other}
 
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diff --git a/autosklearn/metalearning/files/f1_weighted_binary.classification_sparse/feature_values.arff b/autosklearn/metalearning/files/f1_weighted_binary.classification_sparse/feature_values.arff
index a60400e3ce..736dc64321 100644
--- a/autosklearn/metalearning/files/f1_weighted_binary.classification_sparse/feature_values.arff
+++ b/autosklearn/metalearning/files/f1_weighted_binary.classification_sparse/feature_values.arff
@@ -13,7 +13,6 @@
 @ATTRIBUTE KurtosisMean NUMERIC
 @ATTRIBUTE KurtosisMin NUMERIC
 @ATTRIBUTE KurtosisSTD NUMERIC
-@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC
 @ATTRIBUTE LogDatasetRatio NUMERIC
 @ATTRIBUTE LogInverseDatasetRatio NUMERIC
 @ATTRIBUTE LogNumberOfFeatures NUMERIC
@@ -42,160 +41,135 @@
 @ATTRIBUTE SymbolsSum NUMERIC
 
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diff --git a/autosklearn/metalearning/files/f1_weighted_multiclass.classification_dense/algorithm_runs.arff b/autosklearn/metalearning/files/f1_weighted_multiclass.classification_dense/algorithm_runs.arff
index 5cfefb0721..456e6c3497 100644
--- a/autosklearn/metalearning/files/f1_weighted_multiclass.classification_dense/algorithm_runs.arff
+++ b/autosklearn/metalearning/files/f1_weighted_multiclass.classification_dense/algorithm_runs.arff
@@ -7,130 +7,135 @@
 @ATTRIBUTE runstatus {ok, timeout, memout, not_applicable, crash, other}
 
 @DATA
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diff --git a/autosklearn/metalearning/files/f1_weighted_multiclass.classification_dense/configurations.csv b/autosklearn/metalearning/files/f1_weighted_multiclass.classification_dense/configurations.csv
index 78ef3538b9..aa53e0cde8 100644
--- a/autosklearn/metalearning/files/f1_weighted_multiclass.classification_dense/configurations.csv
+++ b/autosklearn/metalearning/files/f1_weighted_multiclass.classification_dense/configurations.csv
@@ -1,128 +1,133 @@
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-23,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7734.521233796252,,,2.6048661611376747,rbf,-1,True,4.68717607435371e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,none,,,,,feature_agglomeration,,,,,,,,,,,,,,,euclidean,average,25,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-24,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.8367655423352977,None,0.0,1,5,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.01866043949164283,median,standardize,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.3824115704370156,fpr,f_classif
-25,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5010952533165973,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.005838375247601407,mean,normalize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,
-26,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.7297122444166401,False,True,1,squared_hinge,ovr,l2,0.00023416745444541063,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.03941815152197094,most_frequent,normalize,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0973649546833947,fpr,chi2
-27,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.4426097579931897,None,0.0,16,4,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,mean,robust_scaler,,,0.7407323456920554,0.0804756921037867,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,
-28,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5607168729746175,None,0.0,2,3,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.009810589644392154,most_frequent,quantile_transformer,1132,normal,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.06211089008167909,fdr,chi2
-29,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9333237919291875,None,0.0,8,3,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,
-30,none,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.9728532763078892,None,0.0,13,9,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,none,,,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6651071190864879,False,,,,,,,,,,,,,,,
-31,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9561075301148197,None,0.0,6,2,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,
-32,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.557321265077371,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-33,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,1.153931482107762e-10,0.011666190464009397,auto,255,None,29,190,18,loss,1e-07,0.147868307124396,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,
-34,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,22194.242946902177,0.45210849669021247,,0.0005669713702171556,sigmoid,-1,False,0.03542142266081451,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,quantile_transformer,291,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-35,none,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.563056219822946,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.32793677336996485,most_frequent,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-36,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,3.6797768538445263e-09,0.05511004217375807,auto,255,None,72,62,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,robust_scaler,,,0.7241481576763391,0.0501033899901458,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,36,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-37,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.7521969592385672,None,0.0,9,5,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.020402264943872365,median,robust_scaler,,,0.737227213630259,0.20752747663727789,fast_ica,,,,,,,,,,,parallel,exp,312,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-38,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.887898394612674,None,0.0,10,19,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,robust_scaler,,,0.8350698288864961,0.2347163538066168,extra_trees_preproc_for_classification,False,entropy,None,0.49795108918064074,None,0.0,13,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-39,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,1.6343482806592892e-09,0.19537524572679268,auto,255,None,32,29,13,loss,1e-07,0.05061738224302791,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-40,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7088164344533493,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,mean,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,
-41,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,1.943186107984505e-07,0.08852455702036988,auto,255,None,5,72,19,loss,1e-07,0.039837460762271175,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.01591265419367627,median,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,
-42,weighting,adaboost,SAMME,0.5210651042094924,10,401,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,standardize,,,,,feature_agglomeration,,,,,,,,,,,,,,,euclidean,complete,89,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-43,none,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0686374607394679,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,minmax,,,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.8490260132946401,False,,,,,,,,,,,,,,,
-44,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.6931271368243409,None,0.0,11,10,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.362940004438799,most_frequent,robust_scaler,,,0.9177587796305063,0.17084038458597867,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,30.491514144623952,mutual_info,,,
-45,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.9398869869697849,None,0.0,20,15,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,normalize,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,269,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-46,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,92.66144306787051,,,0.04654639302091653,rbf,-1,True,0.0022231544280610005,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.005194838694455963,mean,none,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,15,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-47,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2681.998807317996,-0.9558745731731211,,0.0011299948421773457,sigmoid,-1,False,1.5292507354478086e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-48,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.8095522998548572,None,0.0,17,10,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.031940595071539304,mean,quantile_transformer,1563,normal,,,extra_trees_preproc_for_classification,False,entropy,None,0.9330145598603196,None,0.0,18,10,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-49,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,26.502259985828186,,,9.475662388405154e-05,rbf,-1,False,0.00019690635968217866,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-50,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,2.5360196477156387e-07,0.011018756408558218,auto,255,None,40,122,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.00011749164080669445,most_frequent,quantile_transformer,757,uniform,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,5.097834697156853,False,True,1,squared_hinge,ovr,l1,0.053877834919477424,,,,,,,,,,,,,,,,,,,,,,
-51,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5255991794296913,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-52,weighting,decision_tree,,,,,,,gini,1.7853447328017091,1.0,None,0.0,13,7,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0001265964225445088,median,normalize,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.26399878867042303,,mutual_info_classif
-53,weighting,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.7776312540090575,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0005253715056715056,mean,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,False,,,,,,,,,,,,
-54,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,1.488333818193668e-10,0.09526676471267805,auto,255,None,258,87,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,normalize,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.3561953264350478,fpr,f_classif
-55,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.9523435078770708,0.8748383232154038,4,0.2195992292488049,poly,-1,True,4.849685143365339e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,minmax,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-56,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.7610308078756552,None,0.0,1,2,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.010000000000000004,mean,robust_scaler,,,0.9205545465103644,0.20272761060743316,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-57,weighting,adaboost,SAMME.R,0.03127273017917347,10,496,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,standardize,,,,,fast_ica,,,,,,,,,,,deflation,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-58,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,151,None,,0.0003283777607180646,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.2510527431514227,mean,normalize,,,,,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,-0.96093182232312,,0.05762510964607661,sigmoid,2632,,,,,,,,,,,,,,,,,
-59,none,bernoulli_nb,,,,,0.6524090653939782,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.005164169126808402,most_frequent,quantile_transformer,16,normal,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.033082145443631535,False,True,1,squared_hinge,ovr,l1,0.002615020898325393,,,,,,,,,,,,,,,,,,,,,,
-60,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.6453555269772124,None,0.0,1,4,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,normalize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-61,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,940.9437776449834,,,1.5839338084625942,rbf,-1,False,0.023072781592565438,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,none,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,77.25763685070707,mutual_info,,,
-62,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,220,auto,,0.0013824567992319003,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00021328932075284388,mean,minmax,,,,,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.039972474835314076,,0.01017810882899714,sigmoid,295,,,,,,,,,,,,,,,,,
-63,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.34534888916828577,0.02513634776687268,2,1.0733091880109409,poly,-1,True,0.0006974263047918307,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,quantile_transformer,1000,normal,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,77.99934218022861,mutual_info,,,
-64,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,29.55731828912983,False,True,1,squared_hinge,ovr,l2,0.0011332486951954968,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,
-65,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,1.1387795876755026e-06,0.12245605190343567,auto,255,None,58,7,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,robust_scaler,,,0.79305915648885,0.1841487406876042,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-66,none,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.563056219822946,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.32793677336996485,most_frequent,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-67,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,0.2638627266631576,0.022360820165005443,auto,255,None,171,6,19,loss,1e-07,0.016209060012924756,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.008763072395677399,mean,standardize,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,78,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-68,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,925.3053743248586,False,True,1,squared_hinge,ovr,l2,0.02764481198395717,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.3850718018366796,most_frequent,quantile_transformer,786,uniform,,,feature_agglomeration,,,,,,,,,,,,,,,cosine,average,156,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-69,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7573078019937971,None,0.0,2,5,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,robust_scaler,,,0.7265312295801113,0.28332515882236425,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.09660564590146253,fpr,f_classif
-70,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0057657098053164255,False,True,squared_hinge,0.013468519636061941,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.484357187898904,mean,quantile_transformer,675,normal,,,kernel_pca,,,,,,,,,,,,,,,,,,,,,,cosine,1876,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-71,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.41019363453549706,None,0.0,15,2,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.0003507389224306018,median,none,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.20614098085343638,fdr,chi2
-72,none,adaboost,SAMME,0.1280458968526125,7,125,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,robust_scaler,,,0.7156460334356771,0.2981902862926971,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,65.16171885308417,chi2,,,
-73,none,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9931779675565967,None,0.0,18,17,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,robust_scaler,,,0.75,0.2945917010472818,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,
-74,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.08646118576686276,None,0.0,4,9,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,most_frequent,quantile_transformer,959,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-75,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,1.2430504242164082e-09,0.03359568307446533,auto,255,None,1609,29,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,standardize,,,,,feature_agglomeration,,,,,,,,,,,,,,,cosine,complete,40,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-76,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7.491039171224502,True,True,squared_hinge,2.8058138048327884e-05,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,minmax,,,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.7754372809893839,False,True,1,squared_hinge,ovr,l1,3.9643594596766314e-05,,,,,,,,,,,,,,,,,,,,,,
-77,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.34936025988397124,None,0.0,2,13,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00018601309647406987,median,robust_scaler,,,0.8634309348843472,0.26976989465367607,extra_trees_preproc_for_classification,False,entropy,None,0.48025175288975036,None,0.0,6,17,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-78,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,0.011547155223520492,0.13665554140837183,auto,255,None,13,5,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.1824584492028449,median,minmax,,,,,feature_agglomeration,,,,,,,,,,,,,,,euclidean,average,261,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-79,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.933900673392165,None,0.0,7,4,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,minmax,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-80,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1661.2274747409913,,,0.025821208852387936,rbf,-1,False,0.0017011435104921824,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,none,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,306,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-81,none,adaboost,SAMME,0.011233995624432622,9,477,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.018370622484682127,mean,standardize,,,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6039710338898471,False,,,,,,,,,,,,,,,
-82,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,21.85207494001697,0.18872999589786676,4,1.1863646021868761,poly,-1,False,0.0021338371901294443,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0034080852834770217,mean,normalize,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.11326005109886934,fdr,f_classif
-83,none,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.563056219822946,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.32793677336996485,most_frequent,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-84,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,0.037849720944694,0.1268815770019783,auto,255,None,11,99,11,loss,1e-07,0.10654523627313527,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,
-85,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.593728171200453,None,0.0,7,20,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.02830941310359363,mean,normalize,,,,,extra_trees_preproc_for_classification,False,entropy,None,0.7389446586281172,None,0.0,20,11,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-86,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,1,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.019797877010737826,mean,normalize,,,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.8239678741128165,False,,,,,,,,,,,,,,,
-87,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.46057831591617715,False,True,hinge,0.04557857428827514,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00027457445401600137,median,standardize,,,,,extra_trees_preproc_for_classification,True,gini,None,0.48190346970486964,None,0.0,17,18,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-88,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,38,auto,,0.00019398431171606024,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010000000000000004,median,quantile_transformer,1440,normal,,,extra_trees_preproc_for_classification,True,gini,None,0.5126639956845847,None,0.0,16,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-89,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5722917944959481,None,0.0,1,14,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,
-90,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.26356754187780895,None,0.0,4,3,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010966178401758774,mean,robust_scaler,,,0.7817043861798886,0.24371720112407158,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
-91,none,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.563056219822946,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.32793677336996485,most_frequent,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-92,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,80.0028902468169,False,True,1,squared_hinge,ovr,l2,0.0024481999829574722,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.15047970487774687,mean,quantile_transformer,1130,normal,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,1.1912646635544712,False,True,1,squared_hinge,ovr,l1,0.00014314728486450857,,,,,,,,,,,,,,,,,,,,,,
-93,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.22879311080499845,None,0.0,8,4,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.014320022547615783,median,minmax,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.238630703440461,mutual_info,,,
-94,weighting,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,2,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,none,,,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6342574285315175,False,,,,,,,,,,,,,,,
-95,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.107967527639527e-05,True,True,hinge,0.00448294745544775,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,quantile_transformer,953,normal,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,35.00114345405331,chi2,,,
-96,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.14148638702297855,None,0.0,2,6,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.0038499650338378643,most_frequent,quantile_transformer,1759,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-97,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,1.1522414705935405e-10,0.6132800183073315,auto,255,None,1607,67,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010096911249562407,mean,robust_scaler,,,0.7663493326967942,0.136094217337434,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-98,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.35863733679484333,None,0.0,1,11,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.0009777923655842618,median,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-99,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.479405900046494,,,0.013340778632154543,rbf,-1,False,6.913586060890114e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,robust_scaler,,,0.9953232616818098,0.06450577886132274,fast_ica,,,,,,,,,,,deflation,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-100,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.825844582229201,None,0.0,12,9,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0008422508222149647,median,robust_scaler,,,0.8658803880857064,0.2528423686293341,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-101,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2574971638664341,True,True,hinge,1.8027411099296446e-05,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.00536396328125103,median,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,
-102,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.7033210835501171,None,0.0,3,19,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,robust_scaler,,,0.7699195245692886,0.29258311638476237,feature_agglomeration,,,,,,,,,,,,,,,euclidean,ward,143,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-103,none,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,128,manual,0.8574560771111307,0.010886139381599514,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.24115007944216788,most_frequent,normalize,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,62.26514170991452,mutual_info,,,
-104,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.6282663389679584,None,0.0,1,3,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,median,minmax,,,,,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-105,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,1.430083734915855e-09,0.08779621406981941,auto,255,None,17,20,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.29749837779115257,mean,robust_scaler,,,0.7236445277814435,0.25,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
-106,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.3294588572341214,None,0.0,1,3,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
-107,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,39.22980757485479,,,0.13597389869623427,rbf,-1,False,0.00011639060052687874,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.006288306814260224,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-108,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.7922837297504705,False,True,1,squared_hinge,ovr,l2,0.0005476597326342352,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.012674631437254894,most_frequent,none,,,,,extra_trees_preproc_for_classification,False,entropy,None,0.23145146971319638,None,0.0,7,20,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-109,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.18060139887649052,None,0.0,10,12,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.010000000000000004,most_frequent,robust_scaler,,,0.9298612094255864,0.1263597946722308,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
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-111,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6427543483503397,True,True,hinge,0.00045318775930916944,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.009338484332306904,median,quantile_transformer,174,normal,,,kernel_pca,,,,,,,,,,,,,,,,,,,,,,cosine,1848,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-112,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.044449043853721795,True,,,True,,optimal,log,l2,,0.0005323728333290294,no_encoding,no_coalescense,,most_frequent,quantile_transformer,588,normal,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,47.34146395379382,chi2,,,
-113,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,9.029473608568458e-09,0.15779990576964315,auto,255,None,37,71,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.018093177195644097,mean,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,False,,,,,,,,,,,,
-114,weighting,adaboost,SAMME.R,0.23766684501868082,10,97,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.004737885672001703,median,none,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50.0,chi2,,,
-115,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l2,2.9468189962573666e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.014870368357163507,most_frequent,quantile_transformer,1000,uniform,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,83.43414935094258,f_classif,,,
-116,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.4864284879550203,None,0.0,1,4,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.008665854799630503,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-117,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.8851946632713493,None,0.0,2,20,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.006095662608922393,median,quantile_transformer,1861,normal,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.10486328449423876,fdr,f_classif
-118,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.6922752267318475,True,True,squared_hinge,0.06299408576730353,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,quantile_transformer,1742,uniform,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,107,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-119,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.44727252219812963,None,0.0,2,10,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,standardize,,,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l1,8.17533189014765e-05,,,,,,,,,,,,,,,,,,,,,,
-120,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5479616732130552,None,0.0,16,4,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.0002189699211089695,mean,quantile_transformer,1290,normal,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.9999,False,,,,,,,,,,,,,,,
-121,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.4210978663047845,None,0.0,5,6,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.006365988550833196,most_frequent,robust_scaler,,,0.8348891074186082,0.00974961635730845,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0695919708890599,fpr,f_classif
-122,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.3341112040803983,False,True,hinge,0.003045682798589668,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.005225537488332702,median,quantile_transformer,1225,uniform,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.10310163232668391,fpr,chi2
-123,weighting,decision_tree,,,,,,,entropy,1.0435267397863155,1.0,None,0.0,15,3,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.36913659113454333,most_frequent,normalize,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4947786362821451,fpr,f_classif
-124,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1561.1002600303584,,,0.0024048551704194507,rbf,-1,False,0.0001936214667496851,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,minmax,,,,,fast_ica,,,,,,,,,,,parallel,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-125,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.087313242780732,0.0636709914987994,3,0.004481917125024585,poly,-1,False,0.01848880618941367,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.002136015035740733,mean,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-126,weighting,adaboost,SAMME,0.029932847627447023,8,483,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,quantile_transformer,1290,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-127,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.6466927589470762,None,0.0,11,2,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.009672465311659264,most_frequent,standardize,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,369,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+1,weighting,bernoulli_nb,,,,,3.80819075993021,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,quantile_transformer,1649,normal,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.06432376826473184,,mutual_info_classif
+3,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5707458022222313,None,0.0,3,12,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,minmax,,,,,extra_trees_preproc_for_classification,False,gini,None,0.5651056128131684,None,0.0,9,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+6,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.7283683762613216,None,0.0,12,20,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,median,standardize,,,,,fast_ica,,,,,,,,,,,deflation,exp,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+11,weighting,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,2,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.3865962574104293,mean,normalize,,,,,fast_ica,,,,,,,,,,,deflation,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+13,weighting,adaboost,SAMME,0.17983222024694556,4,357,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0008898781197407956,mean,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+15,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.21772092350860472,None,0.0,14,17,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.0020488212891693275,median,standardize,,,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,33.74257606530722,False,True,1,squared_hinge,ovr,l1,8.740452966205111e-05,,,,,,,,,,,,,,,,,,,,,,
+18,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,2.4243922131268786e-05,0.07570328162448307,auto,255,None,71,42,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,robust_scaler,,,0.8898729094898769,0.12138152991327475,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,94.96086286583851,chi2,,,
+20,none,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.9866058571747695,None,0.0,16,9,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.1680676743605393,most_frequent,robust_scaler,,,0.8840072797399463,0.08535534311393947,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,False,,,,,,,,,,,,
+23,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,0.00022373024203887573,0.027149343206135476,auto,255,None,49,13,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.00777614320416325,most_frequent,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,
+27,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.41025844265119926,None,0.0,18,15,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,median,quantile_transformer,1911,normal,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,False,,,,,,,,,,,,
+31,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,8.757412830636845e-06,0.10095376320667454,auto,255,None,3,2,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.015401766535365336,median,quantile_transformer,647,normal,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,
+32,none,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,174,manual,0.21501892897746921,2.0347110826262054e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.013754893527625522,most_frequent,normalize,,,,,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,-0.019221513778605592,3,0.10190948951339239,poly,2521,,,,,,,,,,,,,,,,,
+35,none,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9.608707084865884,False,True,1,squared_hinge,ovr,l2,0.008086910150491696,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.020901889243957545,median,minmax,,,,,kitchen_sinks,,,,,,,,,,,,,,,,,,,,,,,,0.5694399784333516,3230,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+39,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.6799215694343422,None,0.0,1,5,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.004724310659953132,most_frequent,quantile_transformer,1037,normal,,,fast_ica,,,,,,,,,,,parallel,exp,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+41,none,extra_trees,,,,,,,,,,,,,,,True,gini,None,0.5033866291997137,None,0.0,2,14,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,minmax,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+44,none,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l2,2.858526169098402e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0007827505640681123,median,standardize,,,,,extra_trees_preproc_for_classification,False,entropy,None,0.3443310459451119,None,0.0,3,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+49,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.049504283446131e-05,False,,0.0004438773852753879,True,2.4110816673432424e-07,constant,log,elasticnet,,0.01321814853186568,one_hot_encoding,minority_coalescer,0.01510416593631535,most_frequent,none,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,66.65285268958851,chi2,,,
+52,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,1.3755106326683952e-06,0.06364972357697396,auto,255,None,48,32,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.009206641394891614,median,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,
+55,none,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.9822179211645446,None,0.0,14,8,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.013966871874533052,most_frequent,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,
+58,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5214197490559657,None,0.0,1,2,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.00826182798588248,mean,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,
+63,none,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,130,manual,0.5446276346297756,0.00846307015171434,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.005736348762561667,median,quantile_transformer,973,uniform,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.08483615095863663,fdr,chi2
+65,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,1.2822815440468518e-10,0.12922700208645135,auto,255,None,31,44,13,loss,1e-07,0.08789566421608311,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.012230423571314038,mean,robust_scaler,,,0.71734690066056,0.20283418288016852,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,
+68,weighting,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,minmax,,,,,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+75,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13.284077389890939,-0.2621334760621076,5,0.9060908941898536,poly,-1,False,0.06323853917206591,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0011743021592446861,most_frequent,normalize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,
+82,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,3.79982070767762e-10,0.10307173798074214,auto,255,None,30,25,6,loss,1e-07,0.09705336338563596,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.004889186641013006,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+84,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.5507560981489436,None,0.0,7,4,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,
+91,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,0.0006322356877253852,0.02155479872417127,auto,255,None,6,15,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
+93,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,6.071314883067794e-10,0.04207928322543077,auto,255,None,29,2,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,none,,,,,feature_agglomeration,,,,,,,,,,,,,,,euclidean,average,46,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+97,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.4879444812369075,None,0.0,5,3,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.008704186680582812,mean,normalize,,,,,extra_trees_preproc_for_classification,False,entropy,None,0.6428064165338061,None,0.0,2,17,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+99,weighting,adaboost,SAMME,0.17983222024694556,4,357,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0008898781197407956,mean,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+105,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.268699692198443e-05,True,,4.1225573912733725e-06,True,,constant,log,l2,,4.729985534114764e-05,no_encoding,no_coalescense,,median,quantile_transformer,246,normal,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,20.73090458774975,chi2,,,
+106,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,2.047681354714616e-06,0.016073071519249763,auto,255,None,127,60,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.005262156195293648,median,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,
+110,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0021814768111094564,False,True,hinge,8.726644999887521e-05,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,none,,,,,kitchen_sinks,,,,,,,,,,,,,,,,,,,,,,,,0.0010909910768059797,970,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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+114,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,83,auto,,0.024875973599406742,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,normalize,,,,,kitchen_sinks,,,,,,,,,,,,,,,,,,,,,,,,0.041059688084044955,7269,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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+116,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,236,None,,0.08905291537442941,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,normalize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,
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+130,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,9.471664460969401e-09,0.05921990503832894,auto,255,None,1357,22,15,loss,1e-07,0.10690431328779236,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.08690713722954983,most_frequent,robust_scaler,,,0.9305626247167209,0.22989015341001506,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.33284956824843637,fwe,f_classif
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+139,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,10.855406135042964,0.9747671863774952,5,0.2387142708486043,poly,-1,False,0.015747719198070633,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.1749714579410719,median,minmax,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,53.581101959413125,mutual_info,,,
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+354,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.47037910442880493,None,0.0,10,3,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.17511596636550678,mean,none,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,87.25836994493127,mutual_info,,,
+362,none,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,147,None,,0.020167732354570436,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,minmax,,,,,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.39502590219018424,rbf,117,,,,,,,,,,,,,,,,,
+365,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.0479982468066316,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,quantile_transformer,1097,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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+374,none,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.7304811343030777,None,0.0,2,3,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,robust_scaler,,,0.7202654714984674,0.21555973677200435,feature_agglomeration,,,,,,,,,,,,,,,euclidean,average,149,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+379,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,1.2940289605295536e-09,0.15591343717459372,auto,255,None,7,59,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0005549095909238961,mean,quantile_transformer,297,uniform,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,58.10560700979111,chi2,,,
+385,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.8280243811834952,None,0.0,9,14,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.08913760502443781,median,none,,,,,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+388,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,53.67009896349757,,,0.03687753298495132,rbf,-1,False,0.028791286245157307,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.047859755696856916,mean,standardize,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,70,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+392,none,adaboost,SAMME,0.7387448781351068,8,450,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0473229636375385,median,standardize,,,,,feature_agglomeration,,,,,,,,,,,,,,,euclidean,ward,344,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+399,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.5864398404787432,None,0.0,1,14,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0070663750850608895,most_frequent,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
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+408,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.426289271149965,False,True,1,squared_hinge,ovr,l2,0.00012788535194515696,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,normalize,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,51.45463995444033,chi2,,,
+413,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,7.194851113809193e-10,0.013882834218943657,auto,255,None,18,2,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0005124238915083231,mean,quantile_transformer,948,normal,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,60.45885911036194,f_classif,,,
+415,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,5.215923689311285e-05,0.01709129738847392,auto,255,None,10,19,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0004854193510398933,most_frequent,quantile_transformer,709,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+422,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.36374112587320206,None,0.0,2,10,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010870084126959173,most_frequent,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,
+424,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,2.344883102482138e-07,0.0328548789988159,auto,255,None,42,24,16,loss,1e-07,0.13015466346727866,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,most_frequent,robust_scaler,,,0.7010898774332888,0.2534280821273264,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,
+428,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,3.738096432152635e-10,0.04468500715913894,auto,255,None,31,156,20,loss,1e-07,0.10279783687118692,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010000000000000004,most_frequent,quantile_transformer,1132,normal,,,feature_agglomeration,,,,,,,,,,,,,,,euclidean,ward,43,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+430,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5321814610661173,None,0.0,1,3,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,standardize,,,,,extra_trees_preproc_for_classification,True,gini,None,0.5,None,0.0,1,15,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+435,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.44903177399085925,None,0.0,2,17,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,mean,robust_scaler,,,0.7812408516672696,0.12791233577400205,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,
+438,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,6.090138036399552e-10,0.017305568433437588,auto,255,None,202,188,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,robust_scaler,,,0.7526196011313747,0.1563616005702636,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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+447,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.3431705351883592,None,0.0,4,16,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.09444471061261189,most_frequent,normalize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+450,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,7.837701348094735e-09,0.05315388879642303,auto,255,None,67,2,10,loss,1e-07,0.11655723047843726,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.001857226018763921,median,none,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.014648191862400293,fdr,f_classif
+458,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,5.581658271288433e-10,0.06171988234311011,auto,255,None,25,6,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.014859405637537319,most_frequent,robust_scaler,,,0.7983900845251328,0.2545046978458648,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,1.380085213170737,False,True,1,squared_hinge,ovr,l1,0.0061450732015354784,,,,,,,,,,,,,,,,,,,,,,
+462,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6,1,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.017471096998391875,median,none,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,67.7801145492284,f_classif,,,
diff --git a/autosklearn/metalearning/files/f1_weighted_multiclass.classification_dense/description.txt b/autosklearn/metalearning/files/f1_weighted_multiclass.classification_dense/description.txt
index 11ad5da820..72d787b6f3 100644
--- a/autosklearn/metalearning/files/f1_weighted_multiclass.classification_dense/description.txt
+++ b/autosklearn/metalearning/files/f1_weighted_multiclass.classification_dense/description.txt
@@ -1,8 +1,9 @@
 features_cutoff_time: 3600
 features_cutoff_memory: 3072
-number_of_feature_steps: 35
+number_of_feature_steps: 42
 feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances
 feature_step LogNumberOfInstances: LogNumberOfInstances
+feature_step NumberOfClasses: NumberOfClasses
 feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures
 feature_step LogNumberOfFeatures: LogNumberOfFeatures
 feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues
@@ -20,6 +21,11 @@ feature_step DatasetRatio: DatasetRatio, LogDatasetRatio
 feature_step LogDatasetRatio: LogDatasetRatio
 feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio
 feature_step LogInverseDatasetRatio: LogInverseDatasetRatio
+feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD
+feature_step ClassProbabilityMin: ClassProbabilityMin
+feature_step ClassProbabilityMax: ClassProbabilityMax
+feature_step ClassProbabilityMean: ClassProbabilityMean
+feature_step ClassProbabilitySTD: ClassProbabilitySTD
 feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum
 feature_step SymbolsMin: SymbolsMin
 feature_step SymbolsMax: SymbolsMax
@@ -36,11 +42,12 @@ feature_step SkewnessMin: SkewnessMin
 feature_step SkewnessMax: SkewnessMax
 feature_step SkewnessMean: SkewnessMean
 feature_step SkewnessSTD: SkewnessSTD
-features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
+feature_step ClassEntropy: ClassEntropy
+features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy
 features_stochastic: 
-default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
+default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy
 
-algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127
+algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235,236,237,238,239,240,241,242,243,244,245,246,247,248,249,250,251,252,253,254,255,256,257,258,259,260,261,262,263,264,265,266,267,268,269,270,271,272,273,274,275,276,277,278,279,280,281,282,283,284,285,286,287,288,289,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,312,313,314,315,316,317,318,319,320,321,322,323,324,325,326,327,328,329,330,331,332,333,334,335,336,337,338,339,340,341,342,343,344,345,346,347,348,349,350,351,352,353,354,355,356,357,358,359,360,361,362,363,364,365,366,367,368,369,370,371,372,373,374,375,376,377,378,379,380,381,382,383,384,385,386,387,388,389,390,391,392,393,394,395,396,397,398,399,400,401,402,403,404,405,406,407,408,409,410,411,412,413,414,415,416,417,418,419,420,421,422,423,424,425,426,427,428,429,430,431,432,433,434,435,436,437,438,439,440,441,442,443,444,445,446,447,448,449,450,451,452,453,454,455,456,457,458,459,460,461,462,463,464
 algorithms_stochastic: 
 performance_measures: f1_weighted
 performance_type: solution_quality
diff --git a/autosklearn/metalearning/files/f1_weighted_multiclass.classification_dense/feature_costs.arff b/autosklearn/metalearning/files/f1_weighted_multiclass.classification_dense/feature_costs.arff
index 0e17e20d22..8512fd9275 100644
--- a/autosklearn/metalearning/files/f1_weighted_multiclass.classification_dense/feature_costs.arff
+++ b/autosklearn/metalearning/files/f1_weighted_multiclass.classification_dense/feature_costs.arff
@@ -4,6 +4,7 @@
 @ATTRIBUTE repetition NUMERIC
 @ATTRIBUTE NumberOfInstances NUMERIC
 @ATTRIBUTE LogNumberOfInstances NUMERIC
+@ATTRIBUTE NumberOfClasses NUMERIC
 @ATTRIBUTE NumberOfFeatures NUMERIC
 @ATTRIBUTE LogNumberOfFeatures NUMERIC
 @ATTRIBUTE MissingValues NUMERIC
@@ -21,6 +22,11 @@
 @ATTRIBUTE LogDatasetRatio NUMERIC
 @ATTRIBUTE InverseDatasetRatio NUMERIC
 @ATTRIBUTE LogInverseDatasetRatio NUMERIC
+@ATTRIBUTE ClassOccurences NUMERIC
+@ATTRIBUTE ClassProbabilityMin NUMERIC
+@ATTRIBUTE ClassProbabilityMax NUMERIC
+@ATTRIBUTE ClassProbabilityMean NUMERIC
+@ATTRIBUTE ClassProbabilitySTD NUMERIC
 @ATTRIBUTE NumSymbols NUMERIC
 @ATTRIBUTE SymbolsMin NUMERIC
 @ATTRIBUTE SymbolsMax NUMERIC
@@ -37,162 +43,138 @@
 @ATTRIBUTE SkewnessMax NUMERIC
 @ATTRIBUTE SkewnessMean NUMERIC
 @ATTRIBUTE SkewnessSTD NUMERIC
+@ATTRIBUTE ClassEntropy NUMERIC
 
 @DATA
-75123,1.0,1e-05,1e-05,1e-05,1e-05,0.00253,0.00221,1e-05,0.00024,3e-05,0.00011,0.0,2e-05,2e-05,5e-05,8e-05,2e-05,1e-05,7e-05,1e-05,0.00072,1e-05,1e-05,0.00019,0.00041,0.00011,0.00061,0.00012,9e-05,0.00015,0.00025,0.00067,0.00014,0.00011,0.00016,0.00027
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-75217,1.0,1e-05,1e-05,2e-05,1e-05,0.00196,0.00158,1e-05,0.00026,2e-05,0.00016,1e-05,3e-05,3e-05,7e-05,0.0001,3e-05,1e-05,7e-05,2e-05,0.00089,1e-05,1e-05,0.00022,0.00053,0.00013,0.00074,9e-05,0.0001,0.00019,0.00036,0.00065,0.00013,0.0001,0.00011,0.00031
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+75249,1.0,1e-05,1e-05,0.00011,1e-05,1e-05,0.00192,0.00153,1e-05,0.00025,1e-05,0.00017,1e-05,2e-05,2e-05,5e-05,7e-05,2e-05,1e-05,5e-05,3e-05,0.00014,2e-05,1e-05,3e-05,8e-05,0.00029,1e-05,3e-05,6e-05,0.00012,7e-05,0.00041,6e-05,8e-05,0.0001,0.00017,0.00043,6e-05,7e-05,9e-05,0.00019,0.00092
+75250,1.0,1e-05,1e-05,0.00375,1e-05,1e-05,0.04994,0.047,1e-05,0.00247,1e-05,0.00049,1e-05,2e-05,2e-05,6e-05,7e-05,2e-05,1e-05,3e-05,1e-05,0.00022,4e-05,1e-05,4e-05,0.00012,0.00041,0.0,1e-05,0.00011,0.00023,6e-05,0.00076,0.00012,0.00012,0.00019,0.00032,0.00086,0.00012,0.00013,0.00018,0.00043,0.04178
diff --git a/autosklearn/metalearning/files/f1_weighted_multiclass.classification_dense/feature_runstatus.arff b/autosklearn/metalearning/files/f1_weighted_multiclass.classification_dense/feature_runstatus.arff
index 0034ad2bd6..5fff2dfed3 100644
--- a/autosklearn/metalearning/files/f1_weighted_multiclass.classification_dense/feature_runstatus.arff
+++ b/autosklearn/metalearning/files/f1_weighted_multiclass.classification_dense/feature_runstatus.arff
@@ -4,6 +4,7 @@
 @ATTRIBUTE repetition NUMERIC
 @ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other}
@@ -21,6 +22,11 @@
 @ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other}
@@ -37,162 +43,138 @@
 @ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other}
 
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diff --git a/autosklearn/metalearning/files/f1_weighted_multiclass.classification_dense/feature_values.arff b/autosklearn/metalearning/files/f1_weighted_multiclass.classification_dense/feature_values.arff
index a60400e3ce..736dc64321 100644
--- a/autosklearn/metalearning/files/f1_weighted_multiclass.classification_dense/feature_values.arff
+++ b/autosklearn/metalearning/files/f1_weighted_multiclass.classification_dense/feature_values.arff
@@ -13,7 +13,6 @@
 @ATTRIBUTE KurtosisMean NUMERIC
 @ATTRIBUTE KurtosisMin NUMERIC
 @ATTRIBUTE KurtosisSTD NUMERIC
-@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC
 @ATTRIBUTE LogDatasetRatio NUMERIC
 @ATTRIBUTE LogInverseDatasetRatio NUMERIC
 @ATTRIBUTE LogNumberOfFeatures NUMERIC
@@ -42,160 +41,135 @@
 @ATTRIBUTE SymbolsSum NUMERIC
 
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diff --git a/autosklearn/metalearning/files/f1_weighted_multiclass.classification_sparse/algorithm_runs.arff b/autosklearn/metalearning/files/f1_weighted_multiclass.classification_sparse/algorithm_runs.arff
index 8e10d2e6f6..40fbaf20fb 100644
--- a/autosklearn/metalearning/files/f1_weighted_multiclass.classification_sparse/algorithm_runs.arff
+++ b/autosklearn/metalearning/files/f1_weighted_multiclass.classification_sparse/algorithm_runs.arff
@@ -7,95 +7,133 @@
 @ATTRIBUTE runstatus {ok, timeout, memout, not_applicable, crash, other}
 
 @DATA
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diff --git a/autosklearn/metalearning/files/f1_weighted_multiclass.classification_sparse/configurations.csv b/autosklearn/metalearning/files/f1_weighted_multiclass.classification_sparse/configurations.csv
index 9e3b1a7294..7e1f113687 100644
--- a/autosklearn/metalearning/files/f1_weighted_multiclass.classification_sparse/configurations.csv
+++ b/autosklearn/metalearning/files/f1_weighted_multiclass.classification_sparse/configurations.csv
@@ -1,93 +1,131 @@
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-14,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.6039096182414764,None,0.0,2,20,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.1939609823704447,most_frequent,robust_scaler,,,0.7331327008874781,0.29747488162877694,extra_trees_preproc_for_classification,True,entropy,None,0.33401603690569853,None,0.0,4,4,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-15,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7865652301288171,None,0.0,1,4,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,median,robust_scaler,,,0.8623635641193496,0.13298297278730625,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-16,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.905401147837377,-0.32832122529048147,3,1.001896309040499,poly,-1,False,5.1523484258030785e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-17,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.21794354428393548,None,0.0,2,16,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0025451910134387575,median,quantile_transformer,1477,uniform,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,,
-18,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5010952533165973,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.005838375247601407,mean,normalize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,,
-19,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.7297122444166401,False,True,1,squared_hinge,ovr,l2,0.00023416745444541063,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.03941815152197094,most_frequent,normalize,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0973649546833947,fpr,chi2,
-20,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.4426097579931897,None,0.0,16,4,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,mean,robust_scaler,,,0.7407323456920554,0.0804756921037867,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,,
-21,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5607168729746175,None,0.0,2,3,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.009810589644392154,most_frequent,quantile_transformer,1132,normal,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.06211089008167909,fdr,chi2,
-22,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9333237919291875,None,0.0,8,3,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,,
-23,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.557321265077371,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-24,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,22194.242946902177,0.45210849669021247,,0.0005669713702171556,sigmoid,-1,False,0.03542142266081451,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,quantile_transformer,291,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-25,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.7772914874049878,None,0.0,10,15,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00015285482513309234,mean,robust_scaler,,,0.8373404000074934,0.14303151080827242,extra_trees_preproc_for_classification,True,entropy,None,0.5021713909357703,None,0.0,13,16,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-26,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7422641456052917,None,0.0,8,15,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.005561351266169067,most_frequent,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-27,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.887898394612674,None,0.0,10,19,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,robust_scaler,,,0.8350698288864961,0.2347163538066168,extra_trees_preproc_for_classification,False,entropy,None,0.49795108918064074,None,0.0,13,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-28,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7088164344533493,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,mean,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,,
-29,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,10,1,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0022606253047811435,median,quantile_transformer,1247,normal,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,25.327793585266683,chi2,,,,
-30,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,24.786439230569382,,,0.03411197494635044,rbf,-1,True,0.003003351499585181,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0007994838629325874,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-31,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2681.998807317996,-0.9558745731731211,,0.0011299948421773457,sigmoid,-1,False,1.5292507354478086e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-32,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.8095522998548572,None,0.0,17,10,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.031940595071539304,mean,quantile_transformer,1563,normal,,,extra_trees_preproc_for_classification,False,entropy,None,0.9330145598603196,None,0.0,18,10,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-33,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,26.502259985828186,,,9.475662388405154e-05,rbf,-1,False,0.00019690635968217866,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-34,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.339600708038189,False,True,1,squared_hinge,ovr,l2,9.572641158475304e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,quantile_transformer,1193,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-35,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5255991794296913,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-36,weighting,decision_tree,,,,,,,gini,1.7853447328017091,1.0,None,0.0,13,7,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0001265964225445088,median,normalize,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.26399878867042303,,mutual_info_classif,
-37,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.7610308078756552,None,0.0,1,2,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.010000000000000004,mean,robust_scaler,,,0.9205545465103644,0.20272761060743316,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-38,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,249.12499213127998,,,0.0005283374995918348,rbf,-1,False,0.0016932707039261864,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.48910068367909115,most_frequent,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-39,none,bernoulli_nb,,,,,0.6524090653939782,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.005164169126808402,most_frequent,quantile_transformer,16,normal,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.033082145443631535,False,True,1,squared_hinge,ovr,l1,0.002615020898325393,,,,,,,,,,,,,,,,,,,,,
-40,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.6453555269772124,None,0.0,1,4,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,normalize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-41,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.20911875507660596,None,0.0,12,8,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,median,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,,
-42,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.49918755377415064,None,0.0,1,2,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.36992315774620843,mean,robust_scaler,,,0.7410494147895569,0.29895918805929733,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-43,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,29.55731828912983,False,True,1,squared_hinge,ovr,l2,0.0011332486951954968,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,,
-44,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.37801720673725603,True,True,squared_hinge,0.0001255132393635067,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.025993178344125793,most_frequent,standardize,,,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,1.4421737618429475,False,True,1,squared_hinge,ovr,l1,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,
-45,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.10581257327556097,None,0.0,7,12,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.004219392676189224,median,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,,
-46,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.4188764793561401,None,0.0,5,5,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.010589444903605501,mean,robust_scaler,,,0.9796997860990453,0.27262832749694405,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-47,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0057657098053164255,False,True,squared_hinge,0.013468519636061941,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.484357187898904,mean,quantile_transformer,675,normal,,,kernel_pca,,,,,,,,,,,,,,cosine,1876,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-48,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.41019363453549706,None,0.0,15,2,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.0003507389224306018,median,none,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.20614098085343638,fdr,chi2,
-49,none,adaboost,SAMME,0.1280458968526125,7,125,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,robust_scaler,,,0.7156460334356771,0.2981902862926971,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,65.16171885308417,chi2,,,,
-50,none,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9931779675565967,None,0.0,18,17,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,robust_scaler,,,0.75,0.2945917010472818,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,,
-51,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.08646118576686276,None,0.0,4,9,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,most_frequent,quantile_transformer,959,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-52,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.6271213423927893,None,0.0,1,4,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.009403509495453675,mean,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,,
-53,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8.85547546146731,True,True,squared_hinge,4.0111304608748804e-05,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-54,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.34936025988397124,None,0.0,2,13,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00018601309647406987,median,robust_scaler,,,0.8634309348843472,0.26976989465367607,extra_trees_preproc_for_classification,False,entropy,None,0.48025175288975036,None,0.0,6,17,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-55,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.8793175987664956,None,0.0,10,5,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,robust_scaler,,,0.7645748130904642,0.2677156877155214,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,130.79089036625916,False,True,1,squared_hinge,ovr,l1,0.005538878421721295,,,,,,,,,,,,,,,,,,,,,
-56,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.014911374426433241,None,0.0,6,2,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,,
-57,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0016941428709087083,True,,,True,,optimal,squared_hinge,l1,,5.124265599423001e-05,no_encoding,no_coalescense,,most_frequent,quantile_transformer,461,uniform,,,extra_trees_preproc_for_classification,True,gini,None,0.31371431180257103,None,0.0,5,17,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-58,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.7591396576940418,-0.02447104417007273,2,0.7325180366342191,poly,-1,True,0.00045922794962860523,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,standardize,,,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,2.057731506094293,False,True,1,squared_hinge,ovr,l1,0.03575998247131604,,,,,,,,,,,,,,,,,,,,,
-59,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.593728171200453,None,0.0,7,20,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.02830941310359363,mean,normalize,,,,,extra_trees_preproc_for_classification,False,entropy,None,0.7389446586281172,None,0.0,20,11,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-60,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5542793596955801,None,0.0,1,3,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,most_frequent,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-61,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.46057831591617715,False,True,hinge,0.04557857428827514,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00027457445401600137,median,standardize,,,,,extra_trees_preproc_for_classification,True,gini,None,0.48190346970486964,None,0.0,17,18,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-62,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.46057831591617715,False,True,hinge,0.04557857428827514,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00027457445401600137,median,standardize,,,,,extra_trees_preproc_for_classification,True,gini,None,0.48190346970486964,None,0.0,17,18,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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+367,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.938372431886818e-05,True,True,hinge,0.0016143408148969647,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00024949978971758427,median,quantile_transformer,754,uniform,,,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,-0.22339839702350717,2,0.5718982387915436,poly,5260,,,,,,,,,,,,,,,,
+371,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4,1,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.01020427226149318,median,none,,,,,extra_trees_preproc_for_classification,True,entropy,None,0.3663460889081009,None,0.0,2,7,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
diff --git a/autosklearn/metalearning/files/f1_weighted_multiclass.classification_sparse/description.txt b/autosklearn/metalearning/files/f1_weighted_multiclass.classification_sparse/description.txt
index ab0061e897..8915e41e6b 100644
--- a/autosklearn/metalearning/files/f1_weighted_multiclass.classification_sparse/description.txt
+++ b/autosklearn/metalearning/files/f1_weighted_multiclass.classification_sparse/description.txt
@@ -1,8 +1,9 @@
 features_cutoff_time: 3600
 features_cutoff_memory: 3072
-number_of_feature_steps: 35
+number_of_feature_steps: 42
 feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances
 feature_step LogNumberOfInstances: LogNumberOfInstances
+feature_step NumberOfClasses: NumberOfClasses
 feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures
 feature_step LogNumberOfFeatures: LogNumberOfFeatures
 feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues
@@ -20,6 +21,11 @@ feature_step DatasetRatio: DatasetRatio, LogDatasetRatio
 feature_step LogDatasetRatio: LogDatasetRatio
 feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio
 feature_step LogInverseDatasetRatio: LogInverseDatasetRatio
+feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD
+feature_step ClassProbabilityMin: ClassProbabilityMin
+feature_step ClassProbabilityMax: ClassProbabilityMax
+feature_step ClassProbabilityMean: ClassProbabilityMean
+feature_step ClassProbabilitySTD: ClassProbabilitySTD
 feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum
 feature_step SymbolsMin: SymbolsMin
 feature_step SymbolsMax: SymbolsMax
@@ -36,11 +42,12 @@ feature_step SkewnessMin: SkewnessMin
 feature_step SkewnessMax: SkewnessMax
 feature_step SkewnessMean: SkewnessMean
 feature_step SkewnessSTD: SkewnessSTD
-features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
+feature_step ClassEntropy: ClassEntropy
+features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy
 features_stochastic: 
-default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
+default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy
 
-algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92
+algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235,236,237,238,239,240,241,242,243,244,245,246,247,248,249,250,251,252,253,254,255,256,257,258,259,260,261,262,263,264,265,266,267,268,269,270,271,272,273,274,275,276,277,278,279,280,281,282,283,284,285,286,287,288,289,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,312,313,314,315,316,317,318,319,320,321,322,323,324,325,326,327,328,329,330,331,332,333,334,335,336,337,338,339,340,341,342,343,344,345,346,347,348,349,350,351,352,353,354,355,356,357,358,359,360,361,362,363,364,365,366,367,368,369,370,371,372
 algorithms_stochastic: 
 performance_measures: f1_weighted
 performance_type: solution_quality
diff --git a/autosklearn/metalearning/files/f1_weighted_multiclass.classification_sparse/feature_costs.arff b/autosklearn/metalearning/files/f1_weighted_multiclass.classification_sparse/feature_costs.arff
index 0e17e20d22..8512fd9275 100644
--- a/autosklearn/metalearning/files/f1_weighted_multiclass.classification_sparse/feature_costs.arff
+++ b/autosklearn/metalearning/files/f1_weighted_multiclass.classification_sparse/feature_costs.arff
@@ -4,6 +4,7 @@
 @ATTRIBUTE repetition NUMERIC
 @ATTRIBUTE NumberOfInstances NUMERIC
 @ATTRIBUTE LogNumberOfInstances NUMERIC
+@ATTRIBUTE NumberOfClasses NUMERIC
 @ATTRIBUTE NumberOfFeatures NUMERIC
 @ATTRIBUTE LogNumberOfFeatures NUMERIC
 @ATTRIBUTE MissingValues NUMERIC
@@ -21,6 +22,11 @@
 @ATTRIBUTE LogDatasetRatio NUMERIC
 @ATTRIBUTE InverseDatasetRatio NUMERIC
 @ATTRIBUTE LogInverseDatasetRatio NUMERIC
+@ATTRIBUTE ClassOccurences NUMERIC
+@ATTRIBUTE ClassProbabilityMin NUMERIC
+@ATTRIBUTE ClassProbabilityMax NUMERIC
+@ATTRIBUTE ClassProbabilityMean NUMERIC
+@ATTRIBUTE ClassProbabilitySTD NUMERIC
 @ATTRIBUTE NumSymbols NUMERIC
 @ATTRIBUTE SymbolsMin NUMERIC
 @ATTRIBUTE SymbolsMax NUMERIC
@@ -37,162 +43,138 @@
 @ATTRIBUTE SkewnessMax NUMERIC
 @ATTRIBUTE SkewnessMean NUMERIC
 @ATTRIBUTE SkewnessSTD NUMERIC
+@ATTRIBUTE ClassEntropy NUMERIC
 
 @DATA
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diff --git a/autosklearn/metalearning/files/f1_weighted_multiclass.classification_sparse/feature_runstatus.arff b/autosklearn/metalearning/files/f1_weighted_multiclass.classification_sparse/feature_runstatus.arff
index 0034ad2bd6..5fff2dfed3 100644
--- a/autosklearn/metalearning/files/f1_weighted_multiclass.classification_sparse/feature_runstatus.arff
+++ b/autosklearn/metalearning/files/f1_weighted_multiclass.classification_sparse/feature_runstatus.arff
@@ -4,6 +4,7 @@
 @ATTRIBUTE repetition NUMERIC
 @ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other}
@@ -21,6 +22,11 @@
 @ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other}
@@ -37,162 +43,138 @@
 @ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other}
 
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diff --git a/autosklearn/metalearning/files/f1_weighted_multiclass.classification_sparse/feature_values.arff b/autosklearn/metalearning/files/f1_weighted_multiclass.classification_sparse/feature_values.arff
index a60400e3ce..736dc64321 100644
--- a/autosklearn/metalearning/files/f1_weighted_multiclass.classification_sparse/feature_values.arff
+++ b/autosklearn/metalearning/files/f1_weighted_multiclass.classification_sparse/feature_values.arff
@@ -13,7 +13,6 @@
 @ATTRIBUTE KurtosisMean NUMERIC
 @ATTRIBUTE KurtosisMin NUMERIC
 @ATTRIBUTE KurtosisSTD NUMERIC
-@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC
 @ATTRIBUTE LogDatasetRatio NUMERIC
 @ATTRIBUTE LogInverseDatasetRatio NUMERIC
 @ATTRIBUTE LogNumberOfFeatures NUMERIC
@@ -42,160 +41,135 @@
 @ATTRIBUTE SymbolsSum NUMERIC
 
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diff --git a/autosklearn/metalearning/files/log_loss_binary.classification_dense/algorithm_runs.arff b/autosklearn/metalearning/files/log_loss_binary.classification_dense/algorithm_runs.arff
index 0b3a037322..fe64ea9424 100644
--- a/autosklearn/metalearning/files/log_loss_binary.classification_dense/algorithm_runs.arff
+++ b/autosklearn/metalearning/files/log_loss_binary.classification_dense/algorithm_runs.arff
@@ -7,130 +7,135 @@
 @ATTRIBUTE runstatus {ok, timeout, memout, not_applicable, crash, other}
 
 @DATA
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diff --git a/autosklearn/metalearning/files/log_loss_binary.classification_dense/configurations.csv b/autosklearn/metalearning/files/log_loss_binary.classification_dense/configurations.csv
index b7e8632bbf..a8708d1d4f 100644
--- a/autosklearn/metalearning/files/log_loss_binary.classification_dense/configurations.csv
+++ b/autosklearn/metalearning/files/log_loss_binary.classification_dense/configurations.csv
@@ -1,128 +1,133 @@
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-1,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,154.65010711000676,-0.7535496516458353,2,4.566684304178357,poly,-1,False,1.895280127876398e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,minmax,,,,,extra_trees_preproc_for_classification,True,gini,None,0.698818742714691,None,0.0,20,12,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-2,none,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.9525269450493825,None,0.0,4,20,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,standardize,,,,,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-3,none,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,188,None,,0.09976330267940166,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,none,,,,,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,-0.05439674489937307,2,0.051865468486041774,poly,727,,,,,,,,,,,,,,,,,
-4,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,23916.78290516781,False,True,1,squared_hinge,ovr,l2,0.0006655607726658531,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.008808715242823137,median,none,,,,,kernel_pca,,,,,,,,,,,,,,,,,,,0.2633153190620261,2,0.004770332714291669,poly,1483,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-5,weighting,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,88,2,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.254263872179305,median,normalize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-6,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.933900673392165,None,0.0,7,4,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,minmax,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-7,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.8295237320761981,None,0.0,14,13,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.4912899096849949,most_frequent,robust_scaler,,,0.8602962469260254,0.1681311280324052,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l1,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,
-8,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5238814331135578,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,median,none,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,28,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-9,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1292.7448076998514,0.004489932344506231,2,1.3088036376373702,poly,-1,False,1.7943262139196537e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,minmax,,,,,feature_agglomeration,,,,,,,,,,,,,,,cosine,average,386,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-10,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.5244313211416313,None,0.0,1,2,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.454300236339658,most_frequent,robust_scaler,,,0.7858830918128734,0.2705174720960696,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-11,none,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.563056219822946,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.32793677336996485,most_frequent,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-12,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.541827838541001,True,True,squared_hinge,0.0001129851063838815,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,standardize,,,,,extra_trees_preproc_for_classification,True,entropy,None,0.5533459595240017,None,0.0,18,4,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-13,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.3652103526812861,None,0.0,6,8,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,quantile_transformer,79,uniform,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.17357047764133562,fwe,chi2
-14,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.4897130288457967,False,True,1,squared_hinge,ovr,l2,0.00023610948867176137,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.22014262985890154,median,quantile_transformer,1499,normal,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,331,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-15,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.643761610990255,None,0.0,20,6,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.039859381630488695,mean,normalize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,
-16,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.6931271368243409,None,0.0,11,10,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.362940004438799,most_frequent,robust_scaler,,,0.9177587796305063,0.17084038458597867,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,30.491514144623952,mutual_info,,,
-17,none,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.1769332447629957,mean,minmax,,,,,fast_ica,,,,,,,,,,,deflation,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-18,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,40,2,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,robust_scaler,,,0.8108128057144657,0.1573950644227126,fast_ica,,,,,,,,,,,parallel,exp,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-19,none,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,200,None,,0.0017457216340699586,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,none,,,,,kernel_pca,,,,,,,,,,,,,,,,,,,0.0,4,0.0007494648939847078,poly,1996,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-20,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.6712185710588089,None,0.0,1,16,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.42474816810717925,most_frequent,robust_scaler,,,0.7621746919286133,0.26457035780074745,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-21,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7865652301288171,None,0.0,1,4,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,median,robust_scaler,,,0.8623635641193496,0.13298297278730625,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-22,none,adaboost,SAMME,0.011233995624432622,9,477,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.018370622484682127,mean,standardize,,,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6039710338898471,False,,,,,,,,,,,,,,,
-23,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5413367884437563,None,0.0,3,3,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.010000000000000004,most_frequent,robust_scaler,,,0.7721364518819479,0.25,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50.95700838968518,chi2,,,
-24,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.8108388959518567,None,0.0,1,14,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.018555473651817696,median,quantile_transformer,181,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-25,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5010952533165973,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.005838375247601407,mean,normalize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,
-26,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.4679160469363683,None,0.0,2,20,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.07912956521752987,median,normalize,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1416161478257027,fpr,chi2
-27,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.9663114854040722,None,0.0,11,12,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.26932389682904634,median,quantile_transformer,23,uniform,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l1,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,
-28,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5607168729746175,None,0.0,2,3,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.009810589644392154,most_frequent,quantile_transformer,1132,normal,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.06211089008167909,fdr,chi2
-29,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9333237919291875,None,0.0,8,3,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,
-30,none,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.9728532763078892,None,0.0,13,9,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,none,,,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6651071190864879,False,,,,,,,,,,,,,,,
-31,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9561075301148197,None,0.0,6,2,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,
-32,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.557321265077371,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-33,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,1.0651134461859096e-10,0.015671246578050538,auto,255,None,45,157,3,loss,1e-07,0.09169649132824073,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,median,minmax,,,,,feature_agglomeration,,,,,,,,,,,,,,,euclidean,complete,52,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-34,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,22194.242946902177,0.45210849669021247,,0.0005669713702171556,sigmoid,-1,False,0.03542142266081451,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,quantile_transformer,291,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-35,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.47147939823444396,None,0.0,18,13,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,robust_scaler,,,0.9414880591550729,0.19219631546028484,fast_ica,,,,,,,,,,,parallel,logcosh,1978,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-36,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,3.6797768538445263e-09,0.05511004217375807,auto,255,None,72,62,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,robust_scaler,,,0.7241481576763391,0.0501033899901458,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,36,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-37,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.7521969592385672,None,0.0,9,5,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.020402264943872365,median,robust_scaler,,,0.737227213630259,0.20752747663727789,fast_ica,,,,,,,,,,,parallel,exp,312,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-38,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.887898394612674,None,0.0,10,19,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,robust_scaler,,,0.8350698288864961,0.2347163538066168,extra_trees_preproc_for_classification,False,entropy,None,0.49795108918064074,None,0.0,13,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-39,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,1.6343482806592892e-09,0.19537524572679268,auto,255,None,32,29,13,loss,1e-07,0.05061738224302791,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-40,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7088164344533493,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,mean,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,
-41,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,1.943186107984505e-07,0.08852455702036988,auto,255,None,5,72,19,loss,1e-07,0.039837460762271175,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.01591265419367627,median,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,
-42,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5341077774621746,None,0.0,1,5,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.006086995881116471,most_frequent,robust_scaler,,,0.75,0.24294324056114067,feature_agglomeration,,,,,,,,,,,,,,,cosine,complete,100,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-43,none,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0686374607394679,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,minmax,,,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.8490260132946401,False,,,,,,,,,,,,,,,
-44,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.6931271368243409,None,0.0,11,10,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.362940004438799,most_frequent,robust_scaler,,,0.9177587796305063,0.17084038458597867,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,30.491514144623952,mutual_info,,,
-45,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.9398869869697849,None,0.0,20,15,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,normalize,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,269,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-46,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5021793617292512,None,0.0,1,2,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.023334835668207096,mean,robust_scaler,,,0.7331073511267316,0.23282807499571243,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-47,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2681.998807317996,,,0.0003031656260149249,rbf,-1,True,1.681955497507309e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00023169496253863447,mean,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,
-48,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.8095522998548572,None,0.0,17,10,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.031940595071539304,mean,quantile_transformer,1563,normal,,,extra_trees_preproc_for_classification,False,entropy,None,0.9330145598603196,None,0.0,18,10,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-49,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,1,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,minmax,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-50,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,2.5360196477156387e-07,0.011018756408558218,auto,255,None,40,122,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.00011749164080669445,most_frequent,quantile_transformer,757,uniform,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,5.097834697156853,False,True,1,squared_hinge,ovr,l1,0.053877834919477424,,,,,,,,,,,,,,,,,,,,,,
-51,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5255991794296913,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-52,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.34697723213923637,None,0.0,15,18,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.1923594385988973,mean,normalize,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,25.77499302945827,chi2,,,
-53,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9504113840380227,None,0.0,9,14,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.006208327572483245,most_frequent,quantile_transformer,1625,uniform,,,fast_ica,,,,,,,,,,,parallel,logcosh,1190,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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-64,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,29.55731828912983,False,True,1,squared_hinge,ovr,l2,0.0011332486951954968,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,
-65,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,1.5321271364000576e-07,0.11792409216250074,auto,255,None,97,29,15,loss,1e-07,0.042222141934893126,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.012036588250849235,most_frequent,robust_scaler,,,0.7823054869103849,0.2910836607447845,fast_ica,,,,,,,,,,,parallel,logcosh,29,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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-68,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2632.609234569819,False,True,1,squared_hinge,ovr,l2,0.03509782347902799,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.05835083860531146,mean,minmax,,,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.9957442060679208,False,True,1,squared_hinge,ovr,l1,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,
-69,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7573078019937971,None,0.0,2,5,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,robust_scaler,,,0.7265312295801113,0.28332515882236425,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.09660564590146253,fpr,f_classif
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-76,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7.612992470072042,True,True,squared_hinge,1.2315231582137808e-05,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,minmax,,,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.7831190833947711,False,True,1,squared_hinge,ovr,l1,0.00015464720772409302,,,,,,,,,,,,,,,,,,,,,,
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-79,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.933900673392165,None,0.0,7,4,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,minmax,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-80,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,128.75020223284181,,,0.09047921703481414,rbf,-1,False,0.0028029846849427493,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,none,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,236,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-81,none,adaboost,SAMME,0.011233995624432622,9,477,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.018370622484682127,mean,standardize,,,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6039710338898471,False,,,,,,,,,,,,,,,
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-103,none,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,128,manual,0.8574560771111307,0.010886139381599514,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.24115007944216788,most_frequent,normalize,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,62.26514170991452,mutual_info,,,
-104,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.6282663389679584,None,0.0,1,3,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,median,minmax,,,,,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-105,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,1.430083734915855e-09,0.08779621406981941,auto,255,None,17,20,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.29749837779115257,mean,robust_scaler,,,0.7236445277814435,0.25,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
-106,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.3294588572341214,None,0.0,1,3,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
-107,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,39.22980757485479,,,0.13597389869623427,rbf,-1,False,0.00011639060052687874,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.006288306814260224,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-108,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.6728827282646637,None,0.0,18,8,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,robust_scaler,,,0.8512985751386942,0.14092824896182807,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,
-109,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.18060139887649052,None,0.0,10,12,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.010000000000000004,most_frequent,robust_scaler,,,0.9298612094255864,0.1263597946722308,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
-110,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.4111348710613201,None,0.0,16,8,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.01224348527546641,most_frequent,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,
-111,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.5614057563427897,True,True,squared_hinge,0.0007083715696258598,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,quantile_transformer,1134,normal,,,kernel_pca,,,,,,,,,,,,,,,,,,,,,,cosine,1990,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-112,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.044449043853721795,True,,,True,,optimal,log,l2,,0.0005323728333290294,no_encoding,no_coalescense,,most_frequent,quantile_transformer,588,normal,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,47.34146395379382,chi2,,,
-113,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,2.441161597349589e-08,0.1513055945853451,auto,255,None,31,59,19,loss,1e-07,0.07291045796502242,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.01851294290568789,median,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,
-114,weighting,adaboost,SAMME.R,0.10000000000000002,3,50,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,robust_scaler,,,0.7639429738274788,0.25,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,67.72501979095493,f_classif,,,
-115,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l2,2.9468189962573666e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.014870368357163507,most_frequent,quantile_transformer,1000,uniform,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,83.43414935094258,f_classif,,,
-116,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.4864284879550203,None,0.0,1,4,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.008665854799630503,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-117,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.8851946632713493,None,0.0,2,20,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.006095662608922393,median,quantile_transformer,1861,normal,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.10486328449423876,fdr,f_classif
-118,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.6922752267318475,True,True,squared_hinge,0.06299408576730353,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,quantile_transformer,1742,uniform,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,107,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-119,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.6052000756179459,None,0.0,1,3,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.0757697968626355,mean,minmax,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,58.17510598374729,chi2,,,
-120,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.46057831591617715,False,True,hinge,0.04557857428827514,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00027457445401600137,median,standardize,,,,,extra_trees_preproc_for_classification,True,gini,None,0.48190346970486964,None,0.0,17,18,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-121,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.4210978663047845,None,0.0,5,6,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.006365988550833196,most_frequent,robust_scaler,,,0.8348891074186082,0.00974961635730845,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0695919708890599,fpr,f_classif
-122,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.3341112040803983,False,True,hinge,0.003045682798589668,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.005225537488332702,median,quantile_transformer,1225,uniform,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.10310163232668391,fpr,chi2
-123,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.9293203182985876,False,True,1,squared_hinge,ovr,l2,0.00011039825617368385,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0009094644141741006,most_frequent,minmax,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.09592907718847755,fpr,chi2
-124,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,30407.26604365263,-0.1345005934312754,,0.0002902649409928334,sigmoid,-1,False,8.295933773161378e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.018532781671251185,mean,normalize,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.05411635987697527,fpr,f_classif
-125,weighting,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,quantile_transformer,1517,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-126,weighting,adaboost,SAMME.R,1.696588612003168,9,455,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,minmax,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,82.18726095926662,chi2,,,
-127,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.6466927589470762,None,0.0,11,2,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.009672465311659264,most_frequent,standardize,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,369,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+2,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.8739761373635383,None,0.0,5,9,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,quantile_transformer,1135,uniform,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.06130526963953546,fdr,f_classif
+9,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.685918004243461,True,True,squared_hinge,1.7784504877380735e-05,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010335606354842895,most_frequent,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,
+10,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.7396493120555564,None,0.0,1,18,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010000000000000004,median,robust_scaler,,,0.8908334215036855,0.08666907249785906,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,
+13,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.704884397561306,None,0.0,1,5,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.009223082116761061,median,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
+14,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,4.629265598705481e-10,0.03894546521791516,auto,255,None,5,22,18,loss,1e-07,0.0837137336215462,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.008397749525484112,mean,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
+15,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.21772092350860472,None,0.0,14,17,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.0020488212891693275,median,standardize,,,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,33.74257606530722,False,True,1,squared_hinge,ovr,l1,8.740452966205111e-05,,,,,,,,,,,,,,,,,,,,,,
+20,none,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9926064315489191,None,0.0,20,16,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010000000000000004,median,robust_scaler,,,0.9672394184167384,0.28729724413867797,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,
+27,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.4201478452716422,None,0.0,15,6,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.002848424451433087,most_frequent,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,
+29,weighting,bernoulli_nb,,,,,3.227246427900751,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,none,,,,,fast_ica,,,,,,,,,,,parallel,exp,788,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+34,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,0.06671977293714751,0.04456236025167353,auto,255,None,3,163,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.4333293305857194,mean,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+37,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0618160212153551,True,True,squared_hinge,1.2370453081062464e-05,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.01448339838697973,mean,standardize,,,,,kitchen_sinks,,,,,,,,,,,,,,,,,,,,,,,,0.018879929475513826,1637,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+39,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.4497379349749595,None,0.0,1,3,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.005133154680673187,most_frequent,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,
+41,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.5317964015704449,None,0.0,10,5,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.18839221681152665,mean,robust_scaler,,,0.7806260873936471,0.1164642202427843,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,9,None,17,2,1.0,51,,,,,
+43,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.9479098792950027,None,0.0,12,14,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,quantile_transformer,1758,normal,,,feature_agglomeration,,,,,,,,,,,,,,,cosine,complete,244,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+45,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7.32120401128449,-0.9668734475555864,3,6.237549077310441,poly,-1,True,0.0003314767142762954,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.007364519018575624,mean,none,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.19832252480387166,fwe,f_classif
+49,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00010123414317660679,False,,0.04011567850168966,True,,constant,log,l1,,0.00014179758438189507,one_hot_encoding,minority_coalescer,0.008920898694379564,mean,robust_scaler,,,0.7712963178784032,0.2702398922671455,kernel_pca,,,,,,,,,,,,,,,,,,,,,,cosine,1090,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+55,none,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.9822179211645446,None,0.0,14,8,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.013966871874533052,most_frequent,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,
+61,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,1.1372456480487086e-10,0.1751700205647847,auto,255,None,12,84,8,loss,1e-07,0.127765079614671,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,most_frequent,minmax,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.11517698763095738,fpr,chi2
+65,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,4.427693008576421e-07,0.1480512588831196,auto,255,None,5,46,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.07105836118280727,median,quantile_transformer,875,normal,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,False,,,,,,,,,,,,
+68,weighting,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,minmax,,,,,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+73,weighting,adaboost,SAMME.R,1.5192074582085118,10,368,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.01039662491396428,median,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
+77,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.2309302197434343e-05,False,,0.006503403719704897,True,,constant,log,l1,,1.531583361000749e-05,one_hot_encoding,no_coalescense,,most_frequent,robust_scaler,,,0.7530789965606406,0.2430090971909385,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,cosine,1693,,,,,,,,,,,,,,,,,
+82,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,3.79982070767762e-10,0.10307173798074214,auto,255,None,30,25,6,loss,1e-07,0.09705336338563596,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.004889186641013006,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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+420,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,666.9362018669741,,,0.000558595269034132,rbf,-1,True,0.040723467022917105,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010000000000000004,median,standardize,,,,,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+424,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,2.344883102482138e-07,0.0328548789988159,auto,255,None,42,24,16,loss,1e-07,0.13015466346727866,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,most_frequent,robust_scaler,,,0.7010898774332888,0.2534280821273264,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,
+428,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,3.854557911322108e-10,0.200240547199571,auto,255,None,3,149,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,quantile_transformer,1129,uniform,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+430,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7236748113015686,None,0.0,1,8,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.25238593745609994,most_frequent,robust_scaler,,,0.7549699159713029,0.2143583239849504,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,60.05843686145916,f_classif,,,
+434,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.0010490798809966694,None,0.0,2,2,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,median,quantile_transformer,1000,uniform,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,82.68945305987023,mutual_info,,,
+438,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,6.090138036399552e-10,0.017305568433437588,auto,255,None,202,188,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,robust_scaler,,,0.7526196011313747,0.1563616005702636,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+443,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.6269076084367177,None,0.0,1,7,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.12943250859943486,most_frequent,standardize,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,62.84526554323969,chi2,,,
+449,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,2.074711735072034e-07,0.01106799034691714,auto,255,None,13,24,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.020949205931423413,mean,standardize,,,,,extra_trees_preproc_for_classification,True,gini,None,0.43505400875624267,None,0.0,20,8,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+457,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.6615697881503035,None,0.0,6,18,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,none,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,16.06065091612414,chi2,,,
+462,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6,1,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.017471096998391875,median,none,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,67.7801145492284,f_classif,,,
diff --git a/autosklearn/metalearning/files/log_loss_binary.classification_dense/description.txt b/autosklearn/metalearning/files/log_loss_binary.classification_dense/description.txt
index 571eefb78c..4b195fe918 100644
--- a/autosklearn/metalearning/files/log_loss_binary.classification_dense/description.txt
+++ b/autosklearn/metalearning/files/log_loss_binary.classification_dense/description.txt
@@ -1,8 +1,9 @@
 features_cutoff_time: 3600
 features_cutoff_memory: 3072
-number_of_feature_steps: 35
+number_of_feature_steps: 42
 feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances
 feature_step LogNumberOfInstances: LogNumberOfInstances
+feature_step NumberOfClasses: NumberOfClasses
 feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures
 feature_step LogNumberOfFeatures: LogNumberOfFeatures
 feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues
@@ -20,6 +21,11 @@ feature_step DatasetRatio: DatasetRatio, LogDatasetRatio
 feature_step LogDatasetRatio: LogDatasetRatio
 feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio
 feature_step LogInverseDatasetRatio: LogInverseDatasetRatio
+feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD
+feature_step ClassProbabilityMin: ClassProbabilityMin
+feature_step ClassProbabilityMax: ClassProbabilityMax
+feature_step ClassProbabilityMean: ClassProbabilityMean
+feature_step ClassProbabilitySTD: ClassProbabilitySTD
 feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum
 feature_step SymbolsMin: SymbolsMin
 feature_step SymbolsMax: SymbolsMax
@@ -36,11 +42,12 @@ feature_step SkewnessMin: SkewnessMin
 feature_step SkewnessMax: SkewnessMax
 feature_step SkewnessMean: SkewnessMean
 feature_step SkewnessSTD: SkewnessSTD
-features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
+feature_step ClassEntropy: ClassEntropy
+features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy
 features_stochastic: 
-default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
+default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy
 
-algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127
+algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235,236,237,238,239,240,241,242,243,244,245,246,247,248,249,250,251,252,253,254,255,256,257,258,259,260,261,262,263,264,265,266,267,268,269,270,271,272,273,274,275,276,277,278,279,280,281,282,283,284,285,286,287,288,289,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,312,313,314,315,316,317,318,319,320,321,322,323,324,325,326,327,328,329,330,331,332,333,334,335,336,337,338,339,340,341,342,343,344,345,346,347,348,349,350,351,352,353,354,355,356,357,358,359,360,361,362,363,364,365,366,367,368,369,370,371,372,373,374,375,376,377,378,379,380,381,382,383,384,385,386,387,388,389,390,391,392,393,394,395,396,397,398,399,400,401,402,403,404,405,406,407,408,409,410,411,412,413,414,415,416,417,418,419,420,421,422,423,424,425,426,427,428,429,430,431,432,433,434,435,436,437,438,439,440,441,442,443,444,445,446,447,448,449,450,451,452,453,454,455,456,457,458,459,460,461,462,463,464
 algorithms_stochastic: 
 performance_measures: log_loss
 performance_type: solution_quality
diff --git a/autosklearn/metalearning/files/log_loss_binary.classification_dense/feature_costs.arff b/autosklearn/metalearning/files/log_loss_binary.classification_dense/feature_costs.arff
index 0e17e20d22..8512fd9275 100644
--- a/autosklearn/metalearning/files/log_loss_binary.classification_dense/feature_costs.arff
+++ b/autosklearn/metalearning/files/log_loss_binary.classification_dense/feature_costs.arff
@@ -4,6 +4,7 @@
 @ATTRIBUTE repetition NUMERIC
 @ATTRIBUTE NumberOfInstances NUMERIC
 @ATTRIBUTE LogNumberOfInstances NUMERIC
+@ATTRIBUTE NumberOfClasses NUMERIC
 @ATTRIBUTE NumberOfFeatures NUMERIC
 @ATTRIBUTE LogNumberOfFeatures NUMERIC
 @ATTRIBUTE MissingValues NUMERIC
@@ -21,6 +22,11 @@
 @ATTRIBUTE LogDatasetRatio NUMERIC
 @ATTRIBUTE InverseDatasetRatio NUMERIC
 @ATTRIBUTE LogInverseDatasetRatio NUMERIC
+@ATTRIBUTE ClassOccurences NUMERIC
+@ATTRIBUTE ClassProbabilityMin NUMERIC
+@ATTRIBUTE ClassProbabilityMax NUMERIC
+@ATTRIBUTE ClassProbabilityMean NUMERIC
+@ATTRIBUTE ClassProbabilitySTD NUMERIC
 @ATTRIBUTE NumSymbols NUMERIC
 @ATTRIBUTE SymbolsMin NUMERIC
 @ATTRIBUTE SymbolsMax NUMERIC
@@ -37,162 +43,138 @@
 @ATTRIBUTE SkewnessMax NUMERIC
 @ATTRIBUTE SkewnessMean NUMERIC
 @ATTRIBUTE SkewnessSTD NUMERIC
+@ATTRIBUTE ClassEntropy NUMERIC
 
 @DATA
-75123,1.0,1e-05,1e-05,1e-05,1e-05,0.00253,0.00221,1e-05,0.00024,3e-05,0.00011,0.0,2e-05,2e-05,5e-05,8e-05,2e-05,1e-05,7e-05,1e-05,0.00072,1e-05,1e-05,0.00019,0.00041,0.00011,0.00061,0.00012,9e-05,0.00015,0.00025,0.00067,0.00014,0.00011,0.00016,0.00027
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diff --git a/autosklearn/metalearning/files/log_loss_binary.classification_dense/feature_runstatus.arff b/autosklearn/metalearning/files/log_loss_binary.classification_dense/feature_runstatus.arff
index 0034ad2bd6..5fff2dfed3 100644
--- a/autosklearn/metalearning/files/log_loss_binary.classification_dense/feature_runstatus.arff
+++ b/autosklearn/metalearning/files/log_loss_binary.classification_dense/feature_runstatus.arff
@@ -4,6 +4,7 @@
 @ATTRIBUTE repetition NUMERIC
 @ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other}
@@ -21,6 +22,11 @@
 @ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other}
@@ -37,162 +43,138 @@
 @ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other}
 
 @DATA
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-266,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok
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-75188,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok
-75159,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok
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-262,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok
-233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok
-75177,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok
-261,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok
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-288,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok
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-251,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok
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-75150,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok
-2119,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok
-75146,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok
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-75096,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok
-75166,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok
-75169,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok
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-273,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok
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-75099,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok
-75223,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok
-75156,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok
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diff --git a/autosklearn/metalearning/files/log_loss_binary.classification_dense/feature_values.arff b/autosklearn/metalearning/files/log_loss_binary.classification_dense/feature_values.arff
index a60400e3ce..736dc64321 100644
--- a/autosklearn/metalearning/files/log_loss_binary.classification_dense/feature_values.arff
+++ b/autosklearn/metalearning/files/log_loss_binary.classification_dense/feature_values.arff
@@ -13,7 +13,6 @@
 @ATTRIBUTE KurtosisMean NUMERIC
 @ATTRIBUTE KurtosisMin NUMERIC
 @ATTRIBUTE KurtosisSTD NUMERIC
-@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC
 @ATTRIBUTE LogDatasetRatio NUMERIC
 @ATTRIBUTE LogInverseDatasetRatio NUMERIC
 @ATTRIBUTE LogNumberOfFeatures NUMERIC
@@ -42,160 +41,135 @@
 @ATTRIBUTE SymbolsSum NUMERIC
 
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diff --git a/autosklearn/metalearning/files/log_loss_binary.classification_sparse/algorithm_runs.arff b/autosklearn/metalearning/files/log_loss_binary.classification_sparse/algorithm_runs.arff
index 6ea05dd4ca..8ee7a0088f 100644
--- a/autosklearn/metalearning/files/log_loss_binary.classification_sparse/algorithm_runs.arff
+++ b/autosklearn/metalearning/files/log_loss_binary.classification_sparse/algorithm_runs.arff
@@ -7,95 +7,133 @@
 @ATTRIBUTE runstatus {ok, timeout, memout, not_applicable, crash, other}
 
 @DATA
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diff --git a/autosklearn/metalearning/files/log_loss_binary.classification_sparse/configurations.csv b/autosklearn/metalearning/files/log_loss_binary.classification_sparse/configurations.csv
index 3976ea738c..a26c8792a6 100644
--- a/autosklearn/metalearning/files/log_loss_binary.classification_sparse/configurations.csv
+++ b/autosklearn/metalearning/files/log_loss_binary.classification_sparse/configurations.csv
@@ -1,93 +1,131 @@
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+292,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.01199695484434634,None,0.0,1,3,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,,
+294,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.9171054533157257,None,0.0,3,6,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010685081661437827,most_frequent,standardize,,,,,extra_trees_preproc_for_classification,True,entropy,None,0.8058943227888568,None,0.0,1,10,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+297,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.0014200038703931547,most_frequent,robust_scaler,,,0.7373450748248416,0.26419938540085264,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+301,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.5,None,0.0,2,4,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.008039249201522535,most_frequent,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,,
+304,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.828038345740254,None,0.0,8,2,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,,
+307,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,33,None,,0.006687546229372618,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.06231343712708962,mean,none,,,,,kernel_pca,,,,,,,,,,,-0.7000777299811654,3,0.008780219231661734,poly,498,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+310,weighting,adaboost,SAMME.R,0.1491075093459469,1,152,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,quantile_transformer,1000,uniform,,,extra_trees_preproc_for_classification,True,entropy,None,0.9973265728983962,None,0.0,2,13,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+312,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,60.6761602925822,,,0.038224035513907226,rbf,-1,False,0.0018214343969776353,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.421025538999516,median,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+314,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.38997129721224555,True,True,hinge,0.0012504479998410043,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,quantile_transformer,1915,normal,,,extra_trees_preproc_for_classification,True,gini,None,0.7470878899161654,None,0.0,20,14,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+317,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.4572679693563934,None,0.0,1,11,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,mean,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,,
+318,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.843098530645432,None,0.0,1,4,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,mean,quantile_transformer,930,normal,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,,
+319,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5663133321395308,None,0.0,5,19,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0244551766287634,median,robust_scaler,,,0.75,0.21219212178051497,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,,
+321,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.5885417571664312,None,0.0,5,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.008176134400845601,median,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,,
+324,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.5240435604277579,None,0.0,1,3,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.05089089351168927,median,robust_scaler,,,0.8952718567345662,0.24647973407695942,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,,
+325,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.512626462010746,True,True,hinge,0.0003084047276694365,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.031263244219237256,mean,quantile_transformer,580,normal,,,kernel_pca,,,,,,,,,,,,,,cosine,875,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+328,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.7550129439547507,False,True,hinge,0.015586011226711433,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.009187407490525924,median,quantile_transformer,1340,uniform,,,kernel_pca,,,,,,,,,,,,,,cosine,1977,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+331,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.5,None,0.0,1,5,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.15244281596303316,mean,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+335,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.7273116108959005,None,0.0,20,6,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.0002372194230247208,median,robust_scaler,,,0.775690480416351,0.2540920245922966,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+338,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.37035966336010345,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.007136588202285817,mean,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+343,none,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9062834897256731,None,0.0,20,9,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.005187379382274718,mean,quantile_transformer,349,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+348,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7717211744793793,None,0.0,3,17,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,median,robust_scaler,,,0.7426192562776642,0.2500337305337367,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+350,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.295661967672618,None,0.0,1,9,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,mean,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+352,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5988524108452234,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,mean,robust_scaler,,,0.75,0.26961884796358077,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+353,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.6269076084367177,None,0.0,1,7,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.12943250859943486,most_frequent,standardize,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,62.84526554323969,chi2,,,,
+358,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.7463662441763204,None,0.0,20,18,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0004507212632016269,mean,none,,,,,extra_trees_preproc_for_classification,True,gini,None,0.4279327206778048,None,0.0,3,9,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+366,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.6615697881503035,None,0.0,6,18,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,none,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,16.06065091612414,chi2,,,,
+370,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7,1,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,none,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,53.44557420186805,chi2,,,,
diff --git a/autosklearn/metalearning/files/log_loss_binary.classification_sparse/description.txt b/autosklearn/metalearning/files/log_loss_binary.classification_sparse/description.txt
index 51bf90db62..2b0935a004 100644
--- a/autosklearn/metalearning/files/log_loss_binary.classification_sparse/description.txt
+++ b/autosklearn/metalearning/files/log_loss_binary.classification_sparse/description.txt
@@ -1,8 +1,9 @@
 features_cutoff_time: 3600
 features_cutoff_memory: 3072
-number_of_feature_steps: 35
+number_of_feature_steps: 42
 feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances
 feature_step LogNumberOfInstances: LogNumberOfInstances
+feature_step NumberOfClasses: NumberOfClasses
 feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures
 feature_step LogNumberOfFeatures: LogNumberOfFeatures
 feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues
@@ -20,6 +21,11 @@ feature_step DatasetRatio: DatasetRatio, LogDatasetRatio
 feature_step LogDatasetRatio: LogDatasetRatio
 feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio
 feature_step LogInverseDatasetRatio: LogInverseDatasetRatio
+feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD
+feature_step ClassProbabilityMin: ClassProbabilityMin
+feature_step ClassProbabilityMax: ClassProbabilityMax
+feature_step ClassProbabilityMean: ClassProbabilityMean
+feature_step ClassProbabilitySTD: ClassProbabilitySTD
 feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum
 feature_step SymbolsMin: SymbolsMin
 feature_step SymbolsMax: SymbolsMax
@@ -36,11 +42,12 @@ feature_step SkewnessMin: SkewnessMin
 feature_step SkewnessMax: SkewnessMax
 feature_step SkewnessMean: SkewnessMean
 feature_step SkewnessSTD: SkewnessSTD
-features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
+feature_step ClassEntropy: ClassEntropy
+features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy
 features_stochastic: 
-default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
+default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy
 
-algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92
+algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235,236,237,238,239,240,241,242,243,244,245,246,247,248,249,250,251,252,253,254,255,256,257,258,259,260,261,262,263,264,265,266,267,268,269,270,271,272,273,274,275,276,277,278,279,280,281,282,283,284,285,286,287,288,289,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,312,313,314,315,316,317,318,319,320,321,322,323,324,325,326,327,328,329,330,331,332,333,334,335,336,337,338,339,340,341,342,343,344,345,346,347,348,349,350,351,352,353,354,355,356,357,358,359,360,361,362,363,364,365,366,367,368,369,370,371,372
 algorithms_stochastic: 
 performance_measures: log_loss
 performance_type: solution_quality
diff --git a/autosklearn/metalearning/files/log_loss_binary.classification_sparse/feature_costs.arff b/autosklearn/metalearning/files/log_loss_binary.classification_sparse/feature_costs.arff
index 0e17e20d22..8512fd9275 100644
--- a/autosklearn/metalearning/files/log_loss_binary.classification_sparse/feature_costs.arff
+++ b/autosklearn/metalearning/files/log_loss_binary.classification_sparse/feature_costs.arff
@@ -4,6 +4,7 @@
 @ATTRIBUTE repetition NUMERIC
 @ATTRIBUTE NumberOfInstances NUMERIC
 @ATTRIBUTE LogNumberOfInstances NUMERIC
+@ATTRIBUTE NumberOfClasses NUMERIC
 @ATTRIBUTE NumberOfFeatures NUMERIC
 @ATTRIBUTE LogNumberOfFeatures NUMERIC
 @ATTRIBUTE MissingValues NUMERIC
@@ -21,6 +22,11 @@
 @ATTRIBUTE LogDatasetRatio NUMERIC
 @ATTRIBUTE InverseDatasetRatio NUMERIC
 @ATTRIBUTE LogInverseDatasetRatio NUMERIC
+@ATTRIBUTE ClassOccurences NUMERIC
+@ATTRIBUTE ClassProbabilityMin NUMERIC
+@ATTRIBUTE ClassProbabilityMax NUMERIC
+@ATTRIBUTE ClassProbabilityMean NUMERIC
+@ATTRIBUTE ClassProbabilitySTD NUMERIC
 @ATTRIBUTE NumSymbols NUMERIC
 @ATTRIBUTE SymbolsMin NUMERIC
 @ATTRIBUTE SymbolsMax NUMERIC
@@ -37,162 +43,138 @@
 @ATTRIBUTE SkewnessMax NUMERIC
 @ATTRIBUTE SkewnessMean NUMERIC
 @ATTRIBUTE SkewnessSTD NUMERIC
+@ATTRIBUTE ClassEntropy NUMERIC
 
 @DATA
-75123,1.0,1e-05,1e-05,1e-05,1e-05,0.00253,0.00221,1e-05,0.00024,3e-05,0.00011,0.0,2e-05,2e-05,5e-05,8e-05,2e-05,1e-05,7e-05,1e-05,0.00072,1e-05,1e-05,0.00019,0.00041,0.00011,0.00061,0.00012,9e-05,0.00015,0.00025,0.00067,0.00014,0.00011,0.00016,0.00027
-75090,1.0,1e-05,1e-05,2e-05,2e-05,0.00815,0.00368,7e-05,0.00228,9e-05,0.00235,0.0,5e-05,5e-05,0.0001,0.00013,0.00013,1e-05,0.00021,2e-05,0.00064,0.0,0.0,0.00014,0.00038,0.00011,0.00066,0.00011,0.00014,0.00014,0.00026,0.00066,0.00011,0.00013,0.00015,0.00027
-2123,1.0,1e-05,1e-05,1e-05,1e-05,0.00097,0.00079,1e-05,0.00013,1e-05,7e-05,0.0,2e-05,2e-05,5e-05,8e-05,2e-05,1e-05,5e-05,1e-05,0.0007,1e-05,0.0,0.00017,0.00042,0.0001,0.00052,6e-05,8e-05,0.00012,0.00026,0.00049,6e-05,0.00011,9e-05,0.00023
-248,1.0,1e-05,1e-05,1e-05,1e-05,0.00088,0.00077,1e-05,0.0001,1e-05,4e-05,0.0,1e-05,1e-05,4e-05,5e-05,2e-05,1e-05,4e-05,1e-05,0.00041,1e-05,0.0,9e-05,0.00022,9e-05,0.00046,6e-05,8e-05,9e-05,0.00023,0.00049,7e-05,0.0001,0.0001,0.00021
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+75237,1.0,2e-05,1e-05,0.00785,2e-05,1e-05,0.31031,0.27854,2e-05,0.01161,3e-05,0.02022,1e-05,4e-05,3e-05,9e-05,0.00024,6e-05,2e-05,9e-05,2e-05,0.00062,7e-05,3e-05,0.00017,0.00035,0.0008,1e-05,1e-05,0.00028,0.00043,8e-05,0.00043,8e-05,7e-05,9e-05,0.0002,0.00044,6e-05,8e-05,9e-05,0.00021,0.14028
+75239,1.0,1e-05,1e-05,7e-05,1e-05,1e-05,0.00105,0.00084,1e-05,0.00015,1e-05,8e-05,1e-05,2e-05,5e-05,4e-05,8e-05,2e-05,1e-05,3e-05,1e-05,0.00016,2e-05,1e-05,5e-05,9e-05,0.00049,0.0,0.0,0.00012,0.00026,0.00011,0.00046,0.00013,8e-05,8e-05,0.00018,0.00044,6e-05,9e-05,0.00011,0.00018,0.00065
+75240,1.0,2e-05,1e-05,0.00036,5e-05,4e-05,0.00845,0.00644,6e-05,0.00122,4e-05,0.00091,1e-05,4e-05,4e-05,9e-05,0.00013,5e-05,2e-05,9e-05,5e-05,0.00031,3e-05,1e-05,7e-05,0.0002,0.00073,3e-05,2e-05,0.00019,0.0003,0.00018,0.00055,9e-05,9e-05,0.00011,0.00025,0.0005,7e-05,8e-05,0.00015,0.0002,0.00365
+75243,1.0,1e-05,1e-05,0.00029,1e-05,1e-05,0.00461,0.00415,1e-05,0.00035,1e-05,0.00015,0.0,2e-05,2e-05,5e-05,7e-05,2e-05,1e-05,4e-05,1e-05,0.00018,2e-05,1e-05,4e-05,0.00011,0.00022,1e-05,1e-05,5e-05,0.00011,5e-05,0.0004,6e-05,6e-05,8e-05,0.0002,0.00039,6e-05,6e-05,8e-05,0.00019,0.00265
+75244,1.0,3e-05,2e-05,0.0004,3e-05,2e-05,0.0173,0.01298,4e-05,0.00267,4e-05,0.00174,1e-05,7e-05,9e-05,0.00024,0.00027,8e-05,2e-05,0.00013,3e-05,0.00045,7e-05,2e-05,0.00011,0.00025,0.00077,6e-05,2e-05,0.00021,0.00034,0.00013,0.00077,0.00014,0.00013,0.00026,0.00023,0.00044,7e-05,8e-05,0.0001,0.00019,0.00649
+75248,1.0,1e-05,1e-05,0.00017,1e-05,1e-05,0.00416,0.00305,1e-05,0.0006,2e-05,0.00054,1e-05,2e-05,2e-05,5e-05,7e-05,2e-05,1e-05,4e-05,1e-05,0.00017,2e-05,1e-05,4e-05,0.0001,0.00029,5e-05,1e-05,6e-05,0.00012,6e-05,0.00081,0.00011,0.00015,0.00016,0.00038,0.0005,7e-05,8e-05,0.00012,0.00024,0.00184
+75249,1.0,1e-05,1e-05,0.00011,1e-05,1e-05,0.00192,0.00153,1e-05,0.00025,1e-05,0.00017,1e-05,2e-05,2e-05,5e-05,7e-05,2e-05,1e-05,5e-05,3e-05,0.00014,2e-05,1e-05,3e-05,8e-05,0.00029,1e-05,3e-05,6e-05,0.00012,7e-05,0.00041,6e-05,8e-05,0.0001,0.00017,0.00043,6e-05,7e-05,9e-05,0.00019,0.00092
+75250,1.0,1e-05,1e-05,0.00375,1e-05,1e-05,0.04994,0.047,1e-05,0.00247,1e-05,0.00049,1e-05,2e-05,2e-05,6e-05,7e-05,2e-05,1e-05,3e-05,1e-05,0.00022,4e-05,1e-05,4e-05,0.00012,0.00041,0.0,1e-05,0.00011,0.00023,6e-05,0.00076,0.00012,0.00012,0.00019,0.00032,0.00086,0.00012,0.00013,0.00018,0.00043,0.04178
diff --git a/autosklearn/metalearning/files/log_loss_binary.classification_sparse/feature_runstatus.arff b/autosklearn/metalearning/files/log_loss_binary.classification_sparse/feature_runstatus.arff
index 0034ad2bd6..5fff2dfed3 100644
--- a/autosklearn/metalearning/files/log_loss_binary.classification_sparse/feature_runstatus.arff
+++ b/autosklearn/metalearning/files/log_loss_binary.classification_sparse/feature_runstatus.arff
@@ -4,6 +4,7 @@
 @ATTRIBUTE repetition NUMERIC
 @ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other}
@@ -21,6 +22,11 @@
 @ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other}
@@ -37,162 +43,138 @@
 @ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other}
 
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diff --git a/autosklearn/metalearning/files/log_loss_binary.classification_sparse/feature_values.arff b/autosklearn/metalearning/files/log_loss_binary.classification_sparse/feature_values.arff
index a60400e3ce..736dc64321 100644
--- a/autosklearn/metalearning/files/log_loss_binary.classification_sparse/feature_values.arff
+++ b/autosklearn/metalearning/files/log_loss_binary.classification_sparse/feature_values.arff
@@ -13,7 +13,6 @@
 @ATTRIBUTE KurtosisMean NUMERIC
 @ATTRIBUTE KurtosisMin NUMERIC
 @ATTRIBUTE KurtosisSTD NUMERIC
-@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC
 @ATTRIBUTE LogDatasetRatio NUMERIC
 @ATTRIBUTE LogInverseDatasetRatio NUMERIC
 @ATTRIBUTE LogNumberOfFeatures NUMERIC
@@ -42,160 +41,135 @@
 @ATTRIBUTE SymbolsSum NUMERIC
 
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diff --git a/autosklearn/metalearning/files/log_loss_multiclass.classification_dense/algorithm_runs.arff b/autosklearn/metalearning/files/log_loss_multiclass.classification_dense/algorithm_runs.arff
index 0b3a037322..fe64ea9424 100644
--- a/autosklearn/metalearning/files/log_loss_multiclass.classification_dense/algorithm_runs.arff
+++ b/autosklearn/metalearning/files/log_loss_multiclass.classification_dense/algorithm_runs.arff
@@ -7,130 +7,135 @@
 @ATTRIBUTE runstatus {ok, timeout, memout, not_applicable, crash, other}
 
 @DATA
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diff --git a/autosklearn/metalearning/files/log_loss_multiclass.classification_dense/configurations.csv b/autosklearn/metalearning/files/log_loss_multiclass.classification_dense/configurations.csv
index b7e8632bbf..a8708d1d4f 100644
--- a/autosklearn/metalearning/files/log_loss_multiclass.classification_dense/configurations.csv
+++ b/autosklearn/metalearning/files/log_loss_multiclass.classification_dense/configurations.csv
@@ -1,128 +1,133 @@
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-77,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.4557246694559742,None,0.0,2,17,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00044760203401312263,mean,robust_scaler,,,0.7111426078575654,0.25,extra_trees_preproc_for_classification,False,entropy,None,0.11395486180078526,None,0.0,1,7,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-78,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.4960989841248215,None,0.0,1,4,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.0002475266163561527,mean,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,False,,,,,,,,,,,,
-79,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.933900673392165,None,0.0,7,4,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,minmax,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-80,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,128.75020223284181,,,0.09047921703481414,rbf,-1,False,0.0028029846849427493,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,none,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,236,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-81,none,adaboost,SAMME,0.011233995624432622,9,477,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.018370622484682127,mean,standardize,,,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6039710338898471,False,,,,,,,,,,,,,,,
-82,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.6001807046726781,None,0.0,1,3,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.1086535878709322,mean,minmax,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-83,none,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.563056219822946,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.32793677336996485,most_frequent,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-84,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,0.037849720944694,0.1268815770019783,auto,255,None,11,99,11,loss,1e-07,0.10654523627313527,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,
-85,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.9499894634982698,None,0.0,20,15,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.01161399161402058,mean,normalize,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,42.76696665279698,f_classif,,,
-86,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5542793596955801,None,0.0,1,3,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,most_frequent,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-87,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.46057831591617715,False,True,hinge,0.04557857428827514,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00027457445401600137,median,standardize,,,,,extra_trees_preproc_for_classification,True,gini,None,0.48190346970486964,None,0.0,17,18,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-88,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.46057831591617715,False,True,hinge,0.04557857428827514,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00027457445401600137,median,standardize,,,,,extra_trees_preproc_for_classification,True,gini,None,0.48190346970486964,None,0.0,17,18,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-89,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5718455758074851,None,0.0,4,7,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,
-90,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.26356754187780895,None,0.0,4,3,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010966178401758774,mean,robust_scaler,,,0.7817043861798886,0.24371720112407158,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
-91,none,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.563056219822946,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.32793677336996485,most_frequent,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-92,weighting,bernoulli_nb,,,,,0.040311578519984834,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.23573161691606537,median,none,,,,,feature_agglomeration,,,,,,,,,,,,,,,euclidean,average,250,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-93,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.23342363767552785,None,0.0,8,3,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.015290584529531784,mean,quantile_transformer,1000,uniform,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.604846255570579,mutual_info,,,
-94,weighting,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,2,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0073667222552306465,median,robust_scaler,,,0.701595034693991,0.29514640404971515,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6066051105210388,False,,,,,,,,,,,,,,,
-95,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.012267521894249553,False,True,squared_hinge,0.008575628602115113,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00028561867572778396,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-96,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.14148638702297855,None,0.0,2,6,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.0038499650338378643,most_frequent,quantile_transformer,1759,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-97,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,1.1522414705935405e-10,0.6132800183073315,auto,255,None,1607,67,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010096911249562407,mean,robust_scaler,,,0.7663493326967942,0.136094217337434,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-98,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.35863733679484333,None,0.0,1,11,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.0009777923655842618,median,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-99,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.6038149793981155,None,0.0,1,11,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,robust_scaler,,,0.75,0.25,fast_ica,,,,,,,,,,,deflation,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-100,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.825844582229201,None,0.0,12,9,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0008422508222149647,median,robust_scaler,,,0.8658803880857064,0.2528423686293341,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-101,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2574971638664341,True,True,hinge,1.8027411099296446e-05,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.00536396328125103,median,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,
-102,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7908336536113714,None,0.0,9,3,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,minmax,,,,,extra_trees_preproc_for_classification,False,entropy,None,0.17440717696897978,None,0.0,5,18,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-103,none,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,128,manual,0.8574560771111307,0.010886139381599514,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.24115007944216788,most_frequent,normalize,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,62.26514170991452,mutual_info,,,
-104,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.6282663389679584,None,0.0,1,3,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,median,minmax,,,,,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-105,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,1.430083734915855e-09,0.08779621406981941,auto,255,None,17,20,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.29749837779115257,mean,robust_scaler,,,0.7236445277814435,0.25,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
-106,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.3294588572341214,None,0.0,1,3,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
-107,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,39.22980757485479,,,0.13597389869623427,rbf,-1,False,0.00011639060052687874,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.006288306814260224,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-108,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.6728827282646637,None,0.0,18,8,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,robust_scaler,,,0.8512985751386942,0.14092824896182807,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,
-109,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.18060139887649052,None,0.0,10,12,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.010000000000000004,most_frequent,robust_scaler,,,0.9298612094255864,0.1263597946722308,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
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-111,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.5614057563427897,True,True,squared_hinge,0.0007083715696258598,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,quantile_transformer,1134,normal,,,kernel_pca,,,,,,,,,,,,,,,,,,,,,,cosine,1990,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-112,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.044449043853721795,True,,,True,,optimal,log,l2,,0.0005323728333290294,no_encoding,no_coalescense,,most_frequent,quantile_transformer,588,normal,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,47.34146395379382,chi2,,,
-113,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,2.441161597349589e-08,0.1513055945853451,auto,255,None,31,59,19,loss,1e-07,0.07291045796502242,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.01851294290568789,median,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,
-114,weighting,adaboost,SAMME.R,0.10000000000000002,3,50,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,robust_scaler,,,0.7639429738274788,0.25,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,67.72501979095493,f_classif,,,
-115,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l2,2.9468189962573666e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.014870368357163507,most_frequent,quantile_transformer,1000,uniform,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,83.43414935094258,f_classif,,,
-116,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.4864284879550203,None,0.0,1,4,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.008665854799630503,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-117,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.8851946632713493,None,0.0,2,20,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.006095662608922393,median,quantile_transformer,1861,normal,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.10486328449423876,fdr,f_classif
-118,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.6922752267318475,True,True,squared_hinge,0.06299408576730353,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,quantile_transformer,1742,uniform,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,107,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-119,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.6052000756179459,None,0.0,1,3,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.0757697968626355,mean,minmax,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,58.17510598374729,chi2,,,
-120,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.46057831591617715,False,True,hinge,0.04557857428827514,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00027457445401600137,median,standardize,,,,,extra_trees_preproc_for_classification,True,gini,None,0.48190346970486964,None,0.0,17,18,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-121,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.4210978663047845,None,0.0,5,6,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.006365988550833196,most_frequent,robust_scaler,,,0.8348891074186082,0.00974961635730845,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0695919708890599,fpr,f_classif
-122,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.3341112040803983,False,True,hinge,0.003045682798589668,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.005225537488332702,median,quantile_transformer,1225,uniform,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.10310163232668391,fpr,chi2
-123,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.9293203182985876,False,True,1,squared_hinge,ovr,l2,0.00011039825617368385,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0009094644141741006,most_frequent,minmax,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.09592907718847755,fpr,chi2
-124,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,30407.26604365263,-0.1345005934312754,,0.0002902649409928334,sigmoid,-1,False,8.295933773161378e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.018532781671251185,mean,normalize,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.05411635987697527,fpr,f_classif
-125,weighting,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,quantile_transformer,1517,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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-127,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.6466927589470762,None,0.0,11,2,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.009672465311659264,most_frequent,standardize,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,369,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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+10,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.7396493120555564,None,0.0,1,18,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010000000000000004,median,robust_scaler,,,0.8908334215036855,0.08666907249785906,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,
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+339,none,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.4044504374398957,None,0.0,1,3,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.03023798880759052,median,quantile_transformer,740,uniform,,,fast_ica,,,,,,,,,,,parallel,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+342,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.9905534732113086,None,0.0,2,2,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,robust_scaler,,,0.767044470851414,0.2500106806001568,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.16487985974925493,,mutual_info_classif
+344,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,0.05520766117521455,0.020586747003747397,auto,255,None,338,78,13,loss,1e-07,0.1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,robust_scaler,,,0.7405447644693971,0.25116829111071487,fast_ica,,,,,,,,,,,deflation,exp,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+345,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,2.0223579847220305e-08,0.03656989532098845,auto,255,None,127,29,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.008330093535450863,median,robust_scaler,,,0.7406536709978547,0.17629993610224215,fast_ica,,,,,,,,,,,deflation,exp,98,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+346,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,0.15762740504817374,0.17692452709577644,auto,255,None,282,9,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.005207945885722629,most_frequent,robust_scaler,,,0.75,0.27100271805667314,feature_agglomeration,,,,,,,,,,,,,,,euclidean,complete,106,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+350,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.2318705830210126,True,True,hinge,0.0002994421772278671,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.13310408167721913,most_frequent,quantile_transformer,1036,uniform,,,kernel_pca,,,,,,,,,,,,,,,,,,,,,,cosine,703,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+355,none,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.7808023609615792,None,0.0,4,20,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0083781386734237,mean,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,
+360,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.5280692265743749,None,0.0,8,15,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.014553805743068082,mean,robust_scaler,,,0.914887306984024,0.03130238510641216,fast_ica,,,,,,,,,,,parallel,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+364,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,2.2734632786993861e-10,0.11885902820530701,auto,255,None,244,63,6,loss,1e-07,0.08525907407320439,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.017985293566311848,most_frequent,robust_scaler,,,0.7409095310781293,0.24411780871389036,fast_ica,,,,,,,,,,,parallel,cube,25,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+369,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.9347805195624181,None,0.0,2,19,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.1594813217854149,mean,normalize,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,52.65009549586377,f_classif,,,
+371,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,7.019760601230762e-10,0.012058662650533666,auto,255,None,1180,120,10,loss,1e-07,0.1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0003413902383295585,mean,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+374,none,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9780996507320936,None,0.0,2,10,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,robust_scaler,,,0.777827421232233,0.20819994127438712,feature_agglomeration,,,,,,,,,,,,,,,euclidean,ward,308,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+378,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,6.485533704735031e-07,0.03500257587365526,auto,255,None,4,7,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00026264639049847547,most_frequent,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+384,weighting,adaboost,SAMME.R,0.1491075093459469,1,152,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,quantile_transformer,1000,uniform,,,extra_trees_preproc_for_classification,True,entropy,None,0.9973265728983962,None,0.0,2,13,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+386,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.42001849975517414,None,0.0,1,5,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010569062733343642,most_frequent,none,,,,,fast_ica,,,,,,,,,,,parallel,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+390,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.9444382507251075,None,0.0,5,5,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,none,,,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.5047019469021539,False,,,,,,,,,,,,,,,
+391,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5803719095188344,None,0.0,1,2,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.03474093310240011,median,minmax,,,,,feature_agglomeration,,,,,,,,,,,,,,,euclidean,ward,41,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+395,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.4572679693563934,None,0.0,1,11,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,mean,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,
+397,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5663133321395308,None,0.0,5,19,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0244551766287634,median,robust_scaler,,,0.75,0.21219212178051497,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
+399,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.5885417571664312,None,0.0,5,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.008176134400845601,median,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,
+404,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.512626462010746,True,True,hinge,0.0003084047276694365,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.031263244219237256,mean,quantile_transformer,580,normal,,,kernel_pca,,,,,,,,,,,,,,,,,,,,,,cosine,875,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+407,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.7550129439547507,False,True,hinge,0.015586011226711433,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.009187407490525924,median,quantile_transformer,1340,uniform,,,kernel_pca,,,,,,,,,,,,,,,,,,,,,,cosine,1977,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+411,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,4.19193023861634e-10,0.038642881408467146,auto,255,None,51,15,20,loss,1e-07,0.07276467040106233,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.008649201455212148,mean,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
+415,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,0.00037966471646911845,0.016422945536911454,auto,255,None,10,56,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.002419439132687246,median,quantile_transformer,828,uniform,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+420,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,666.9362018669741,,,0.000558595269034132,rbf,-1,True,0.040723467022917105,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010000000000000004,median,standardize,,,,,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+424,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,2.344883102482138e-07,0.0328548789988159,auto,255,None,42,24,16,loss,1e-07,0.13015466346727866,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,most_frequent,robust_scaler,,,0.7010898774332888,0.2534280821273264,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,
+428,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,3.854557911322108e-10,0.200240547199571,auto,255,None,3,149,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,quantile_transformer,1129,uniform,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+430,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7236748113015686,None,0.0,1,8,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.25238593745609994,most_frequent,robust_scaler,,,0.7549699159713029,0.2143583239849504,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,60.05843686145916,f_classif,,,
+434,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.0010490798809966694,None,0.0,2,2,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,median,quantile_transformer,1000,uniform,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,82.68945305987023,mutual_info,,,
+438,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,6.090138036399552e-10,0.017305568433437588,auto,255,None,202,188,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,robust_scaler,,,0.7526196011313747,0.1563616005702636,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+443,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.6269076084367177,None,0.0,1,7,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.12943250859943486,most_frequent,standardize,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,62.84526554323969,chi2,,,
+449,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,2.074711735072034e-07,0.01106799034691714,auto,255,None,13,24,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.020949205931423413,mean,standardize,,,,,extra_trees_preproc_for_classification,True,gini,None,0.43505400875624267,None,0.0,20,8,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+457,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.6615697881503035,None,0.0,6,18,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,none,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,16.06065091612414,chi2,,,
+462,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6,1,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.017471096998391875,median,none,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,67.7801145492284,f_classif,,,
diff --git a/autosklearn/metalearning/files/log_loss_multiclass.classification_dense/description.txt b/autosklearn/metalearning/files/log_loss_multiclass.classification_dense/description.txt
index 571eefb78c..4b195fe918 100644
--- a/autosklearn/metalearning/files/log_loss_multiclass.classification_dense/description.txt
+++ b/autosklearn/metalearning/files/log_loss_multiclass.classification_dense/description.txt
@@ -1,8 +1,9 @@
 features_cutoff_time: 3600
 features_cutoff_memory: 3072
-number_of_feature_steps: 35
+number_of_feature_steps: 42
 feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances
 feature_step LogNumberOfInstances: LogNumberOfInstances
+feature_step NumberOfClasses: NumberOfClasses
 feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures
 feature_step LogNumberOfFeatures: LogNumberOfFeatures
 feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues
@@ -20,6 +21,11 @@ feature_step DatasetRatio: DatasetRatio, LogDatasetRatio
 feature_step LogDatasetRatio: LogDatasetRatio
 feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio
 feature_step LogInverseDatasetRatio: LogInverseDatasetRatio
+feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD
+feature_step ClassProbabilityMin: ClassProbabilityMin
+feature_step ClassProbabilityMax: ClassProbabilityMax
+feature_step ClassProbabilityMean: ClassProbabilityMean
+feature_step ClassProbabilitySTD: ClassProbabilitySTD
 feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum
 feature_step SymbolsMin: SymbolsMin
 feature_step SymbolsMax: SymbolsMax
@@ -36,11 +42,12 @@ feature_step SkewnessMin: SkewnessMin
 feature_step SkewnessMax: SkewnessMax
 feature_step SkewnessMean: SkewnessMean
 feature_step SkewnessSTD: SkewnessSTD
-features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
+feature_step ClassEntropy: ClassEntropy
+features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy
 features_stochastic: 
-default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
+default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy
 
-algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127
+algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235,236,237,238,239,240,241,242,243,244,245,246,247,248,249,250,251,252,253,254,255,256,257,258,259,260,261,262,263,264,265,266,267,268,269,270,271,272,273,274,275,276,277,278,279,280,281,282,283,284,285,286,287,288,289,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,312,313,314,315,316,317,318,319,320,321,322,323,324,325,326,327,328,329,330,331,332,333,334,335,336,337,338,339,340,341,342,343,344,345,346,347,348,349,350,351,352,353,354,355,356,357,358,359,360,361,362,363,364,365,366,367,368,369,370,371,372,373,374,375,376,377,378,379,380,381,382,383,384,385,386,387,388,389,390,391,392,393,394,395,396,397,398,399,400,401,402,403,404,405,406,407,408,409,410,411,412,413,414,415,416,417,418,419,420,421,422,423,424,425,426,427,428,429,430,431,432,433,434,435,436,437,438,439,440,441,442,443,444,445,446,447,448,449,450,451,452,453,454,455,456,457,458,459,460,461,462,463,464
 algorithms_stochastic: 
 performance_measures: log_loss
 performance_type: solution_quality
diff --git a/autosklearn/metalearning/files/log_loss_multiclass.classification_dense/feature_costs.arff b/autosklearn/metalearning/files/log_loss_multiclass.classification_dense/feature_costs.arff
index 0e17e20d22..8512fd9275 100644
--- a/autosklearn/metalearning/files/log_loss_multiclass.classification_dense/feature_costs.arff
+++ b/autosklearn/metalearning/files/log_loss_multiclass.classification_dense/feature_costs.arff
@@ -4,6 +4,7 @@
 @ATTRIBUTE repetition NUMERIC
 @ATTRIBUTE NumberOfInstances NUMERIC
 @ATTRIBUTE LogNumberOfInstances NUMERIC
+@ATTRIBUTE NumberOfClasses NUMERIC
 @ATTRIBUTE NumberOfFeatures NUMERIC
 @ATTRIBUTE LogNumberOfFeatures NUMERIC
 @ATTRIBUTE MissingValues NUMERIC
@@ -21,6 +22,11 @@
 @ATTRIBUTE LogDatasetRatio NUMERIC
 @ATTRIBUTE InverseDatasetRatio NUMERIC
 @ATTRIBUTE LogInverseDatasetRatio NUMERIC
+@ATTRIBUTE ClassOccurences NUMERIC
+@ATTRIBUTE ClassProbabilityMin NUMERIC
+@ATTRIBUTE ClassProbabilityMax NUMERIC
+@ATTRIBUTE ClassProbabilityMean NUMERIC
+@ATTRIBUTE ClassProbabilitySTD NUMERIC
 @ATTRIBUTE NumSymbols NUMERIC
 @ATTRIBUTE SymbolsMin NUMERIC
 @ATTRIBUTE SymbolsMax NUMERIC
@@ -37,162 +43,138 @@
 @ATTRIBUTE SkewnessMax NUMERIC
 @ATTRIBUTE SkewnessMean NUMERIC
 @ATTRIBUTE SkewnessSTD NUMERIC
+@ATTRIBUTE ClassEntropy NUMERIC
 
 @DATA
-75123,1.0,1e-05,1e-05,1e-05,1e-05,0.00253,0.00221,1e-05,0.00024,3e-05,0.00011,0.0,2e-05,2e-05,5e-05,8e-05,2e-05,1e-05,7e-05,1e-05,0.00072,1e-05,1e-05,0.00019,0.00041,0.00011,0.00061,0.00012,9e-05,0.00015,0.00025,0.00067,0.00014,0.00011,0.00016,0.00027
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-2123,1.0,1e-05,1e-05,1e-05,1e-05,0.00097,0.00079,1e-05,0.00013,1e-05,7e-05,0.0,2e-05,2e-05,5e-05,8e-05,2e-05,1e-05,5e-05,1e-05,0.0007,1e-05,0.0,0.00017,0.00042,0.0001,0.00052,6e-05,8e-05,0.00012,0.00026,0.00049,6e-05,0.00011,9e-05,0.00023
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-75217,1.0,1e-05,1e-05,2e-05,1e-05,0.00196,0.00158,1e-05,0.00026,2e-05,0.00016,1e-05,3e-05,3e-05,7e-05,0.0001,3e-05,1e-05,7e-05,2e-05,0.00089,1e-05,1e-05,0.00022,0.00053,0.00013,0.00074,9e-05,0.0001,0.00019,0.00036,0.00065,0.00013,0.0001,0.00011,0.00031
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+75230,1.0,1e-05,0.0,9e-05,1e-05,1e-05,0.00087,0.0006,1e-05,0.00018,1e-05,0.00011,0.0,2e-05,2e-05,4e-05,6e-05,2e-05,1e-05,3e-05,1e-05,0.00015,2e-05,1e-05,4e-05,8e-05,0.00038,0.0,1e-05,0.0001,0.00021,5e-05,0.00037,5e-05,6e-05,8e-05,0.00018,0.00033,5e-05,6e-05,7e-05,0.00015,0.00048
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+75234,1.0,1e-05,1e-05,0.00015,1e-05,1e-05,0.0058,0.00534,1e-05,0.00033,1e-05,0.00016,0.0,2e-05,2e-05,7e-05,0.00204,3e-05,1e-05,5e-05,1e-05,0.00024,3e-05,2e-05,0.0001,9e-05,0.00043,0.0,0.0,0.00012,0.00024,6e-05,0.00156,6e-05,7e-05,0.00121,0.00021,0.00054,6e-05,7e-05,9e-05,0.00032,0.00501
+75235,1.0,1e-05,1e-05,0.0003,2e-05,1e-05,0.00461,0.00385,2e-05,0.00055,2e-05,0.00026,1e-05,3e-05,3e-05,0.0001,0.00011,3e-05,1e-05,5e-05,2e-05,0.00026,3e-05,1e-05,8e-05,0.00014,0.00075,1e-05,1e-05,0.0002,0.00043,0.00011,0.00127,0.00017,0.0002,0.00029,0.00061,0.0008,0.00015,0.00012,0.00016,0.00038,0.00242
+75236,1.0,1e-05,1e-05,0.0001,1e-05,1e-05,0.00218,0.00118,2e-05,0.00065,3e-05,0.00041,1e-05,3e-05,3e-05,9e-05,9e-05,4e-05,1e-05,7e-05,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.0006,1e-05,1e-05,0.00021,0.00032,6e-05,0.00043,7e-05,7e-05,9e-05,0.00019,0.00062,7e-05,8e-05,0.0001,0.00037,0.00048
+75237,1.0,2e-05,1e-05,0.00785,2e-05,1e-05,0.31031,0.27854,2e-05,0.01161,3e-05,0.02022,1e-05,4e-05,3e-05,9e-05,0.00024,6e-05,2e-05,9e-05,2e-05,0.00062,7e-05,3e-05,0.00017,0.00035,0.0008,1e-05,1e-05,0.00028,0.00043,8e-05,0.00043,8e-05,7e-05,9e-05,0.0002,0.00044,6e-05,8e-05,9e-05,0.00021,0.14028
+75239,1.0,1e-05,1e-05,7e-05,1e-05,1e-05,0.00105,0.00084,1e-05,0.00015,1e-05,8e-05,1e-05,2e-05,5e-05,4e-05,8e-05,2e-05,1e-05,3e-05,1e-05,0.00016,2e-05,1e-05,5e-05,9e-05,0.00049,0.0,0.0,0.00012,0.00026,0.00011,0.00046,0.00013,8e-05,8e-05,0.00018,0.00044,6e-05,9e-05,0.00011,0.00018,0.00065
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+75243,1.0,1e-05,1e-05,0.00029,1e-05,1e-05,0.00461,0.00415,1e-05,0.00035,1e-05,0.00015,0.0,2e-05,2e-05,5e-05,7e-05,2e-05,1e-05,4e-05,1e-05,0.00018,2e-05,1e-05,4e-05,0.00011,0.00022,1e-05,1e-05,5e-05,0.00011,5e-05,0.0004,6e-05,6e-05,8e-05,0.0002,0.00039,6e-05,6e-05,8e-05,0.00019,0.00265
+75244,1.0,3e-05,2e-05,0.0004,3e-05,2e-05,0.0173,0.01298,4e-05,0.00267,4e-05,0.00174,1e-05,7e-05,9e-05,0.00024,0.00027,8e-05,2e-05,0.00013,3e-05,0.00045,7e-05,2e-05,0.00011,0.00025,0.00077,6e-05,2e-05,0.00021,0.00034,0.00013,0.00077,0.00014,0.00013,0.00026,0.00023,0.00044,7e-05,8e-05,0.0001,0.00019,0.00649
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+75249,1.0,1e-05,1e-05,0.00011,1e-05,1e-05,0.00192,0.00153,1e-05,0.00025,1e-05,0.00017,1e-05,2e-05,2e-05,5e-05,7e-05,2e-05,1e-05,5e-05,3e-05,0.00014,2e-05,1e-05,3e-05,8e-05,0.00029,1e-05,3e-05,6e-05,0.00012,7e-05,0.00041,6e-05,8e-05,0.0001,0.00017,0.00043,6e-05,7e-05,9e-05,0.00019,0.00092
+75250,1.0,1e-05,1e-05,0.00375,1e-05,1e-05,0.04994,0.047,1e-05,0.00247,1e-05,0.00049,1e-05,2e-05,2e-05,6e-05,7e-05,2e-05,1e-05,3e-05,1e-05,0.00022,4e-05,1e-05,4e-05,0.00012,0.00041,0.0,1e-05,0.00011,0.00023,6e-05,0.00076,0.00012,0.00012,0.00019,0.00032,0.00086,0.00012,0.00013,0.00018,0.00043,0.04178
diff --git a/autosklearn/metalearning/files/log_loss_multiclass.classification_dense/feature_runstatus.arff b/autosklearn/metalearning/files/log_loss_multiclass.classification_dense/feature_runstatus.arff
index 0034ad2bd6..5fff2dfed3 100644
--- a/autosklearn/metalearning/files/log_loss_multiclass.classification_dense/feature_runstatus.arff
+++ b/autosklearn/metalearning/files/log_loss_multiclass.classification_dense/feature_runstatus.arff
@@ -4,6 +4,7 @@
 @ATTRIBUTE repetition NUMERIC
 @ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other}
@@ -21,6 +22,11 @@
 @ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other}
@@ -37,162 +43,138 @@
 @ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other}
 
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diff --git a/autosklearn/metalearning/files/log_loss_multiclass.classification_dense/feature_values.arff b/autosklearn/metalearning/files/log_loss_multiclass.classification_dense/feature_values.arff
index a60400e3ce..736dc64321 100644
--- a/autosklearn/metalearning/files/log_loss_multiclass.classification_dense/feature_values.arff
+++ b/autosklearn/metalearning/files/log_loss_multiclass.classification_dense/feature_values.arff
@@ -13,7 +13,6 @@
 @ATTRIBUTE KurtosisMean NUMERIC
 @ATTRIBUTE KurtosisMin NUMERIC
 @ATTRIBUTE KurtosisSTD NUMERIC
-@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC
 @ATTRIBUTE LogDatasetRatio NUMERIC
 @ATTRIBUTE LogInverseDatasetRatio NUMERIC
 @ATTRIBUTE LogNumberOfFeatures NUMERIC
@@ -42,160 +41,135 @@
 @ATTRIBUTE SymbolsSum NUMERIC
 
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diff --git a/autosklearn/metalearning/files/log_loss_multiclass.classification_sparse/algorithm_runs.arff b/autosklearn/metalearning/files/log_loss_multiclass.classification_sparse/algorithm_runs.arff
index 6ea05dd4ca..8ee7a0088f 100644
--- a/autosklearn/metalearning/files/log_loss_multiclass.classification_sparse/algorithm_runs.arff
+++ b/autosklearn/metalearning/files/log_loss_multiclass.classification_sparse/algorithm_runs.arff
@@ -7,95 +7,133 @@
 @ATTRIBUTE runstatus {ok, timeout, memout, not_applicable, crash, other}
 
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diff --git a/autosklearn/metalearning/files/log_loss_multiclass.classification_sparse/configurations.csv b/autosklearn/metalearning/files/log_loss_multiclass.classification_sparse/configurations.csv
index 3976ea738c..a26c8792a6 100644
--- a/autosklearn/metalearning/files/log_loss_multiclass.classification_sparse/configurations.csv
+++ b/autosklearn/metalearning/files/log_loss_multiclass.classification_sparse/configurations.csv
@@ -1,93 +1,131 @@
 idx,balancing:strategy,classifier:__choice__,classifier:adaboost:algorithm,classifier:adaboost:learning_rate,classifier:adaboost:max_depth,classifier:adaboost:n_estimators,classifier:bernoulli_nb:alpha,classifier:bernoulli_nb:fit_prior,classifier:decision_tree:criterion,classifier:decision_tree:max_depth_factor,classifier:decision_tree:max_features,classifier:decision_tree:max_leaf_nodes,classifier:decision_tree:min_impurity_decrease,classifier:decision_tree:min_samples_leaf,classifier:decision_tree:min_samples_split,classifier:decision_tree:min_weight_fraction_leaf,classifier:extra_trees:bootstrap,classifier:extra_trees:criterion,classifier:extra_trees:max_depth,classifier:extra_trees:max_features,classifier:extra_trees:max_leaf_nodes,classifier:extra_trees:min_impurity_decrease,classifier:extra_trees:min_samples_leaf,classifier:extra_trees:min_samples_split,classifier:extra_trees:min_weight_fraction_leaf,classifier:gradient_boosting:early_stop,classifier:gradient_boosting:l2_regularization,classifier:gradient_boosting:learning_rate,classifier:gradient_boosting:loss,classifier:gradient_boosting:max_bins,classifier:gradient_boosting:max_depth,classifier:gradient_boosting:max_leaf_nodes,classifier:gradient_boosting:min_samples_leaf,classifier:gradient_boosting:n_iter_no_change,classifier:gradient_boosting:scoring,classifier:gradient_boosting:tol,classifier:gradient_boosting:validation_fraction,classifier:k_nearest_neighbors:n_neighbors,classifier:k_nearest_neighbors:p,classifier:k_nearest_neighbors:weights,classifier:lda:n_components,classifier:lda:shrinkage,classifier:lda:shrinkage_factor,classifier:lda:tol,classifier:liblinear_svc:C,classifier:liblinear_svc:dual,classifier:liblinear_svc:fit_intercept,classifier:liblinear_svc:intercept_scaling,classifier:liblinear_svc:loss,classifier:liblinear_svc:multi_class,classifier:liblinear_svc:penalty,classifier:liblinear_svc:tol,classifier:libsvm_svc:C,classifier:libsvm_svc:coef0,classifier:libsvm_svc:degree,classifier:libsvm_svc:gamma,classifier:libsvm_svc:kernel,classifier:libsvm_svc:max_iter,classifier:libsvm_svc:shrinking,classifier:libsvm_svc:tol,classifier:multinomial_nb:alpha,classifier:multinomial_nb:fit_prior,classifier:passive_aggressive:C,classifier:passive_aggressive:average,classifier:passive_aggressive:fit_intercept,classifier:passive_aggressive:loss,classifier:passive_aggressive:tol,classifier:qda:reg_param,classifier:random_forest:bootstrap,classifier:random_forest:criterion,classifier:random_forest:max_depth,classifier:random_forest:max_features,classifier:random_forest:max_leaf_nodes,classifier:random_forest:min_impurity_decrease,classifier:random_forest:min_samples_leaf,classifier:random_forest:min_samples_split,classifier:random_forest:min_weight_fraction_leaf,classifier:sgd:alpha,classifier:sgd:average,classifier:sgd:epsilon,classifier:sgd:eta0,classifier:sgd:fit_intercept,classifier:sgd:l1_ratio,classifier:sgd:learning_rate,classifier:sgd:loss,classifier:sgd:penalty,classifier:sgd:power_t,classifier:sgd:tol,data_preprocessing:categorical_transformer:categorical_encoding:__choice__,data_preprocessing:categorical_transformer:category_coalescence:__choice__,data_preprocessing:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction,data_preprocessing:numerical_transformer:imputation:strategy,data_preprocessing:numerical_transformer:rescaling:__choice__,data_preprocessing:numerical_transformer:rescaling:quantile_transformer:n_quantiles,data_preprocessing:numerical_transformer:rescaling:quantile_transformer:output_distribution,data_preprocessing:numerical_transformer:rescaling:robust_scaler:q_max,data_preprocessing:numerical_transformer:rescaling:robust_scaler:q_min,feature_preprocessor:__choice__,feature_preprocessor:extra_trees_preproc_for_classification:bootstrap,feature_preprocessor:extra_trees_preproc_for_classification:criterion,feature_preprocessor:extra_trees_preproc_for_classification:max_depth,feature_preprocessor:extra_trees_preproc_for_classification:max_features,feature_preprocessor:extra_trees_preproc_for_classification:max_leaf_nodes,feature_preprocessor:extra_trees_preproc_for_classification:min_impurity_decrease,feature_preprocessor:extra_trees_preproc_for_classification:min_samples_leaf,feature_preprocessor:extra_trees_preproc_for_classification:min_samples_split,feature_preprocessor:extra_trees_preproc_for_classification:min_weight_fraction_leaf,feature_preprocessor:extra_trees_preproc_for_classification:n_estimators,feature_preprocessor:kernel_pca:coef0,feature_preprocessor:kernel_pca:degree,feature_preprocessor:kernel_pca:gamma,feature_preprocessor:kernel_pca:kernel,feature_preprocessor:kernel_pca:n_components,feature_preprocessor:kitchen_sinks:gamma,feature_preprocessor:kitchen_sinks:n_components,feature_preprocessor:liblinear_svc_preprocessor:C,feature_preprocessor:liblinear_svc_preprocessor:dual,feature_preprocessor:liblinear_svc_preprocessor:fit_intercept,feature_preprocessor:liblinear_svc_preprocessor:intercept_scaling,feature_preprocessor:liblinear_svc_preprocessor:loss,feature_preprocessor:liblinear_svc_preprocessor:multi_class,feature_preprocessor:liblinear_svc_preprocessor:penalty,feature_preprocessor:liblinear_svc_preprocessor:tol,feature_preprocessor:nystroem_sampler:coef0,feature_preprocessor:nystroem_sampler:degree,feature_preprocessor:nystroem_sampler:gamma,feature_preprocessor:nystroem_sampler:kernel,feature_preprocessor:nystroem_sampler:n_components,feature_preprocessor:polynomial:degree,feature_preprocessor:polynomial:include_bias,feature_preprocessor:polynomial:interaction_only,feature_preprocessor:random_trees_embedding:bootstrap,feature_preprocessor:random_trees_embedding:max_depth,feature_preprocessor:random_trees_embedding:max_leaf_nodes,feature_preprocessor:random_trees_embedding:min_samples_leaf,feature_preprocessor:random_trees_embedding:min_samples_split,feature_preprocessor:random_trees_embedding:min_weight_fraction_leaf,feature_preprocessor:random_trees_embedding:n_estimators,feature_preprocessor:select_percentile_classification:percentile,feature_preprocessor:select_percentile_classification:score_func,feature_preprocessor:select_rates_classification:alpha,feature_preprocessor:select_rates_classification:mode,feature_preprocessor:select_rates_classification:score_func,feature_preprocessor:truncatedSVD:target_dim
-1,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.3163020577549344,None,0.0,15,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.01322981552715978,most_frequent,robust_scaler,,,0.9746667009556722,0.1974876581478311,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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-11,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.06263739023781334,None,0.0,18,8,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.02206379295100344,most_frequent,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,,
-12,none,decision_tree,,,,,,,entropy,0.9175027108465073,1.0,None,0.0,18,13,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0005144268441601888,most_frequent,none,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.36269138268682594,fdr,chi2,
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diff --git a/autosklearn/metalearning/files/log_loss_multiclass.classification_sparse/description.txt b/autosklearn/metalearning/files/log_loss_multiclass.classification_sparse/description.txt
index 51bf90db62..2b0935a004 100644
--- a/autosklearn/metalearning/files/log_loss_multiclass.classification_sparse/description.txt
+++ b/autosklearn/metalearning/files/log_loss_multiclass.classification_sparse/description.txt
@@ -1,8 +1,9 @@
 features_cutoff_time: 3600
 features_cutoff_memory: 3072
-number_of_feature_steps: 35
+number_of_feature_steps: 42
 feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances
 feature_step LogNumberOfInstances: LogNumberOfInstances
+feature_step NumberOfClasses: NumberOfClasses
 feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures
 feature_step LogNumberOfFeatures: LogNumberOfFeatures
 feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues
@@ -20,6 +21,11 @@ feature_step DatasetRatio: DatasetRatio, LogDatasetRatio
 feature_step LogDatasetRatio: LogDatasetRatio
 feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio
 feature_step LogInverseDatasetRatio: LogInverseDatasetRatio
+feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD
+feature_step ClassProbabilityMin: ClassProbabilityMin
+feature_step ClassProbabilityMax: ClassProbabilityMax
+feature_step ClassProbabilityMean: ClassProbabilityMean
+feature_step ClassProbabilitySTD: ClassProbabilitySTD
 feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum
 feature_step SymbolsMin: SymbolsMin
 feature_step SymbolsMax: SymbolsMax
@@ -36,11 +42,12 @@ feature_step SkewnessMin: SkewnessMin
 feature_step SkewnessMax: SkewnessMax
 feature_step SkewnessMean: SkewnessMean
 feature_step SkewnessSTD: SkewnessSTD
-features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
+feature_step ClassEntropy: ClassEntropy
+features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy
 features_stochastic: 
-default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
+default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy
 
-algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92
+algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235,236,237,238,239,240,241,242,243,244,245,246,247,248,249,250,251,252,253,254,255,256,257,258,259,260,261,262,263,264,265,266,267,268,269,270,271,272,273,274,275,276,277,278,279,280,281,282,283,284,285,286,287,288,289,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,312,313,314,315,316,317,318,319,320,321,322,323,324,325,326,327,328,329,330,331,332,333,334,335,336,337,338,339,340,341,342,343,344,345,346,347,348,349,350,351,352,353,354,355,356,357,358,359,360,361,362,363,364,365,366,367,368,369,370,371,372
 algorithms_stochastic: 
 performance_measures: log_loss
 performance_type: solution_quality
diff --git a/autosklearn/metalearning/files/log_loss_multiclass.classification_sparse/feature_costs.arff b/autosklearn/metalearning/files/log_loss_multiclass.classification_sparse/feature_costs.arff
index 0e17e20d22..8512fd9275 100644
--- a/autosklearn/metalearning/files/log_loss_multiclass.classification_sparse/feature_costs.arff
+++ b/autosklearn/metalearning/files/log_loss_multiclass.classification_sparse/feature_costs.arff
@@ -4,6 +4,7 @@
 @ATTRIBUTE repetition NUMERIC
 @ATTRIBUTE NumberOfInstances NUMERIC
 @ATTRIBUTE LogNumberOfInstances NUMERIC
+@ATTRIBUTE NumberOfClasses NUMERIC
 @ATTRIBUTE NumberOfFeatures NUMERIC
 @ATTRIBUTE LogNumberOfFeatures NUMERIC
 @ATTRIBUTE MissingValues NUMERIC
@@ -21,6 +22,11 @@
 @ATTRIBUTE LogDatasetRatio NUMERIC
 @ATTRIBUTE InverseDatasetRatio NUMERIC
 @ATTRIBUTE LogInverseDatasetRatio NUMERIC
+@ATTRIBUTE ClassOccurences NUMERIC
+@ATTRIBUTE ClassProbabilityMin NUMERIC
+@ATTRIBUTE ClassProbabilityMax NUMERIC
+@ATTRIBUTE ClassProbabilityMean NUMERIC
+@ATTRIBUTE ClassProbabilitySTD NUMERIC
 @ATTRIBUTE NumSymbols NUMERIC
 @ATTRIBUTE SymbolsMin NUMERIC
 @ATTRIBUTE SymbolsMax NUMERIC
@@ -37,162 +43,138 @@
 @ATTRIBUTE SkewnessMax NUMERIC
 @ATTRIBUTE SkewnessMean NUMERIC
 @ATTRIBUTE SkewnessSTD NUMERIC
+@ATTRIBUTE ClassEntropy NUMERIC
 
 @DATA
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+75234,1.0,1e-05,1e-05,0.00015,1e-05,1e-05,0.0058,0.00534,1e-05,0.00033,1e-05,0.00016,0.0,2e-05,2e-05,7e-05,0.00204,3e-05,1e-05,5e-05,1e-05,0.00024,3e-05,2e-05,0.0001,9e-05,0.00043,0.0,0.0,0.00012,0.00024,6e-05,0.00156,6e-05,7e-05,0.00121,0.00021,0.00054,6e-05,7e-05,9e-05,0.00032,0.00501
+75235,1.0,1e-05,1e-05,0.0003,2e-05,1e-05,0.00461,0.00385,2e-05,0.00055,2e-05,0.00026,1e-05,3e-05,3e-05,0.0001,0.00011,3e-05,1e-05,5e-05,2e-05,0.00026,3e-05,1e-05,8e-05,0.00014,0.00075,1e-05,1e-05,0.0002,0.00043,0.00011,0.00127,0.00017,0.0002,0.00029,0.00061,0.0008,0.00015,0.00012,0.00016,0.00038,0.00242
+75236,1.0,1e-05,1e-05,0.0001,1e-05,1e-05,0.00218,0.00118,2e-05,0.00065,3e-05,0.00041,1e-05,3e-05,3e-05,9e-05,9e-05,4e-05,1e-05,7e-05,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.0006,1e-05,1e-05,0.00021,0.00032,6e-05,0.00043,7e-05,7e-05,9e-05,0.00019,0.00062,7e-05,8e-05,0.0001,0.00037,0.00048
+75237,1.0,2e-05,1e-05,0.00785,2e-05,1e-05,0.31031,0.27854,2e-05,0.01161,3e-05,0.02022,1e-05,4e-05,3e-05,9e-05,0.00024,6e-05,2e-05,9e-05,2e-05,0.00062,7e-05,3e-05,0.00017,0.00035,0.0008,1e-05,1e-05,0.00028,0.00043,8e-05,0.00043,8e-05,7e-05,9e-05,0.0002,0.00044,6e-05,8e-05,9e-05,0.00021,0.14028
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+75243,1.0,1e-05,1e-05,0.00029,1e-05,1e-05,0.00461,0.00415,1e-05,0.00035,1e-05,0.00015,0.0,2e-05,2e-05,5e-05,7e-05,2e-05,1e-05,4e-05,1e-05,0.00018,2e-05,1e-05,4e-05,0.00011,0.00022,1e-05,1e-05,5e-05,0.00011,5e-05,0.0004,6e-05,6e-05,8e-05,0.0002,0.00039,6e-05,6e-05,8e-05,0.00019,0.00265
+75244,1.0,3e-05,2e-05,0.0004,3e-05,2e-05,0.0173,0.01298,4e-05,0.00267,4e-05,0.00174,1e-05,7e-05,9e-05,0.00024,0.00027,8e-05,2e-05,0.00013,3e-05,0.00045,7e-05,2e-05,0.00011,0.00025,0.00077,6e-05,2e-05,0.00021,0.00034,0.00013,0.00077,0.00014,0.00013,0.00026,0.00023,0.00044,7e-05,8e-05,0.0001,0.00019,0.00649
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+75249,1.0,1e-05,1e-05,0.00011,1e-05,1e-05,0.00192,0.00153,1e-05,0.00025,1e-05,0.00017,1e-05,2e-05,2e-05,5e-05,7e-05,2e-05,1e-05,5e-05,3e-05,0.00014,2e-05,1e-05,3e-05,8e-05,0.00029,1e-05,3e-05,6e-05,0.00012,7e-05,0.00041,6e-05,8e-05,0.0001,0.00017,0.00043,6e-05,7e-05,9e-05,0.00019,0.00092
+75250,1.0,1e-05,1e-05,0.00375,1e-05,1e-05,0.04994,0.047,1e-05,0.00247,1e-05,0.00049,1e-05,2e-05,2e-05,6e-05,7e-05,2e-05,1e-05,3e-05,1e-05,0.00022,4e-05,1e-05,4e-05,0.00012,0.00041,0.0,1e-05,0.00011,0.00023,6e-05,0.00076,0.00012,0.00012,0.00019,0.00032,0.00086,0.00012,0.00013,0.00018,0.00043,0.04178
diff --git a/autosklearn/metalearning/files/log_loss_multiclass.classification_sparse/feature_runstatus.arff b/autosklearn/metalearning/files/log_loss_multiclass.classification_sparse/feature_runstatus.arff
index 0034ad2bd6..5fff2dfed3 100644
--- a/autosklearn/metalearning/files/log_loss_multiclass.classification_sparse/feature_runstatus.arff
+++ b/autosklearn/metalearning/files/log_loss_multiclass.classification_sparse/feature_runstatus.arff
@@ -4,6 +4,7 @@
 @ATTRIBUTE repetition NUMERIC
 @ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other}
@@ -21,6 +22,11 @@
 @ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other}
@@ -37,162 +43,138 @@
 @ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other}
 
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diff --git a/autosklearn/metalearning/files/log_loss_multiclass.classification_sparse/feature_values.arff b/autosklearn/metalearning/files/log_loss_multiclass.classification_sparse/feature_values.arff
index a60400e3ce..736dc64321 100644
--- a/autosklearn/metalearning/files/log_loss_multiclass.classification_sparse/feature_values.arff
+++ b/autosklearn/metalearning/files/log_loss_multiclass.classification_sparse/feature_values.arff
@@ -13,7 +13,6 @@
 @ATTRIBUTE KurtosisMean NUMERIC
 @ATTRIBUTE KurtosisMin NUMERIC
 @ATTRIBUTE KurtosisSTD NUMERIC
-@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC
 @ATTRIBUTE LogDatasetRatio NUMERIC
 @ATTRIBUTE LogInverseDatasetRatio NUMERIC
 @ATTRIBUTE LogNumberOfFeatures NUMERIC
@@ -42,160 +41,135 @@
 @ATTRIBUTE SymbolsSum NUMERIC
 
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diff --git a/autosklearn/metalearning/files/mean_absolute_error_regression_dense/algorithm_runs.arff b/autosklearn/metalearning/files/mean_absolute_error_regression_dense/algorithm_runs.arff
index 8f9393b4e9..199f9f16df 100644
--- a/autosklearn/metalearning/files/mean_absolute_error_regression_dense/algorithm_runs.arff
+++ b/autosklearn/metalearning/files/mean_absolute_error_regression_dense/algorithm_runs.arff
@@ -7,27 +7,28 @@
 @ATTRIBUTE runstatus {ok, timeout, memout, not_applicable, crash, other}
 
 @DATA
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diff --git a/autosklearn/metalearning/files/mean_absolute_error_regression_dense/configurations.csv b/autosklearn/metalearning/files/mean_absolute_error_regression_dense/configurations.csv
index aef11a2a95..1d06eb0cd9 100644
--- a/autosklearn/metalearning/files/mean_absolute_error_regression_dense/configurations.csv
+++ b/autosklearn/metalearning/files/mean_absolute_error_regression_dense/configurations.csv
@@ -1,25 +1,26 @@
 idx,data_preprocessing:categorical_transformer:categorical_encoding:__choice__,data_preprocessing:categorical_transformer:category_coalescence:__choice__,data_preprocessing:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction,data_preprocessing:numerical_transformer:imputation:strategy,data_preprocessing:numerical_transformer:rescaling:__choice__,data_preprocessing:numerical_transformer:rescaling:quantile_transformer:n_quantiles,data_preprocessing:numerical_transformer:rescaling:quantile_transformer:output_distribution,data_preprocessing:numerical_transformer:rescaling:robust_scaler:q_max,data_preprocessing:numerical_transformer:rescaling:robust_scaler:q_min,feature_preprocessor:__choice__,feature_preprocessor:extra_trees_preproc_for_regression:bootstrap,feature_preprocessor:extra_trees_preproc_for_regression:criterion,feature_preprocessor:extra_trees_preproc_for_regression:max_depth,feature_preprocessor:extra_trees_preproc_for_regression:max_features,feature_preprocessor:extra_trees_preproc_for_regression:max_leaf_nodes,feature_preprocessor:extra_trees_preproc_for_regression:min_samples_leaf,feature_preprocessor:extra_trees_preproc_for_regression:min_samples_split,feature_preprocessor:extra_trees_preproc_for_regression:min_weight_fraction_leaf,feature_preprocessor:extra_trees_preproc_for_regression:n_estimators,feature_preprocessor:fast_ica:algorithm,feature_preprocessor:fast_ica:fun,feature_preprocessor:fast_ica:n_components,feature_preprocessor:fast_ica:whiten,feature_preprocessor:feature_agglomeration:affinity,feature_preprocessor:feature_agglomeration:linkage,feature_preprocessor:feature_agglomeration:n_clusters,feature_preprocessor:feature_agglomeration:pooling_func,feature_preprocessor:kernel_pca:coef0,feature_preprocessor:kernel_pca:degree,feature_preprocessor:kernel_pca:gamma,feature_preprocessor:kernel_pca:kernel,feature_preprocessor:kernel_pca:n_components,feature_preprocessor:kitchen_sinks:gamma,feature_preprocessor:kitchen_sinks:n_components,feature_preprocessor:nystroem_sampler:coef0,feature_preprocessor:nystroem_sampler:degree,feature_preprocessor:nystroem_sampler:gamma,feature_preprocessor:nystroem_sampler:kernel,feature_preprocessor:nystroem_sampler:n_components,feature_preprocessor:pca:keep_variance,feature_preprocessor:pca:whiten,feature_preprocessor:polynomial:degree,feature_preprocessor:polynomial:include_bias,feature_preprocessor:polynomial:interaction_only,feature_preprocessor:random_trees_embedding:bootstrap,feature_preprocessor:random_trees_embedding:max_depth,feature_preprocessor:random_trees_embedding:max_leaf_nodes,feature_preprocessor:random_trees_embedding:min_samples_leaf,feature_preprocessor:random_trees_embedding:min_samples_split,feature_preprocessor:random_trees_embedding:min_weight_fraction_leaf,feature_preprocessor:random_trees_embedding:n_estimators,feature_preprocessor:select_percentile_regression:percentile,feature_preprocessor:select_percentile_regression:score_func,feature_preprocessor:select_rates_regression:alpha,feature_preprocessor:select_rates_regression:mode,feature_preprocessor:select_rates_regression:score_func,regressor:__choice__,regressor:adaboost:learning_rate,regressor:adaboost:loss,regressor:adaboost:max_depth,regressor:adaboost:n_estimators,regressor:ard_regression:alpha_1,regressor:ard_regression:alpha_2,regressor:ard_regression:fit_intercept,regressor:ard_regression:lambda_1,regressor:ard_regression:lambda_2,regressor:ard_regression:n_iter,regressor:ard_regression:threshold_lambda,regressor:ard_regression:tol,regressor:decision_tree:criterion,regressor:decision_tree:max_depth_factor,regressor:decision_tree:max_features,regressor:decision_tree:max_leaf_nodes,regressor:decision_tree:min_impurity_decrease,regressor:decision_tree:min_samples_leaf,regressor:decision_tree:min_samples_split,regressor:decision_tree:min_weight_fraction_leaf,regressor:extra_trees:bootstrap,regressor:extra_trees:criterion,regressor:extra_trees:max_depth,regressor:extra_trees:max_features,regressor:extra_trees:max_leaf_nodes,regressor:extra_trees:min_impurity_decrease,regressor:extra_trees:min_samples_leaf,regressor:extra_trees:min_samples_split,regressor:extra_trees:min_weight_fraction_leaf,regressor:gaussian_process:alpha,regressor:gaussian_process:thetaL,regressor:gaussian_process:thetaU,regressor:gradient_boosting:early_stop,regressor:gradient_boosting:l2_regularization,regressor:gradient_boosting:learning_rate,regressor:gradient_boosting:loss,regressor:gradient_boosting:max_bins,regressor:gradient_boosting:max_depth,regressor:gradient_boosting:max_leaf_nodes,regressor:gradient_boosting:min_samples_leaf,regressor:gradient_boosting:n_iter_no_change,regressor:gradient_boosting:scoring,regressor:gradient_boosting:tol,regressor:gradient_boosting:validation_fraction,regressor:k_nearest_neighbors:n_neighbors,regressor:k_nearest_neighbors:p,regressor:k_nearest_neighbors:weights,regressor:liblinear_svr:C,regressor:liblinear_svr:dual,regressor:liblinear_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+24,no_encoding,no_coalescense,,median,quantile_transformer,1150,uniform,,,kitchen_sinks,,,,,,,,,,,,,,,,,,,,,,,1.1605111479672479,5499,,,,,,,,,,,,,,,,,,,,,,,libsvm_svr,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,272.83550277553246,0.1471721071594263,2,0.18104912091393513,0.09926116813777154,poly,-1,True,0.06084368714049917,,,,,,,,,,,,,,,,,,,,
+28,one_hot_encoding,no_coalescense,,mean,robust_scaler,,,0.9087144668719139,0.028588482644776998,feature_agglomeration,,,,,,,,,,,,,,euclidean,average,301,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,mae,None,0.5097264302298797,None,0.0,14,19,0.0,,,,,,,,,,,
+34,no_encoding,no_coalescense,,most_frequent,robust_scaler,,,0.7049491520337817,0.23602880130851764,fast_ica,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,off,4.696295762197504e-10,0.03269357604116943,least_squares,255,None,32,14,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+36,one_hot_encoding,no_coalescense,,most_frequent,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,mse,None,0.1686847993740384,None,0.0,1,2,0.0,,,,,,,,,,,
+41,no_encoding,minority_coalescer,0.010000000000000004,median,none,,,,,select_percentile_regression,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,42.409671463494774,mutual_info,,,,libsvm_svr,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3400.978964974929,,3,0.07276129539751543,0.04818277174554651,rbf,-1,True,0.028807473423121257,,,,,,,,,,,,,,,,,,,,
+46,no_encoding,minority_coalescer,0.016414029151950702,most_frequent,robust_scaler,,,0.7433110819250283,0.24401838405994994,feature_agglomeration,,,,,,,,,,,,,,euclidean,complete,11,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,libsvm_svr,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4786.70957992255,,5,0.0010856317305813427,0.03211446445672321,rbf,-1,False,5.044549911761054e-05,,,,,,,,,,,,,,,,,,,,
+48,one_hot_encoding,no_coalescense,,mean,quantile_transformer,148,uniform,,,select_rates_regression,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.10789818795901182,fwe,f_regression,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,off,0.0002841741346637058,0.024253735132716756,least_squares,255,None,36,1,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+51,one_hot_encoding,no_coalescense,,median,minmax,,,,,select_rates_regression,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.32764403627543237,fdr,f_regression,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,off,0.000153437102327148,0.01698754273672824,least_squares,255,None,47,76,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+54,no_encoding,minority_coalescer,0.01313719797407571,median,normalize,,,,,extra_trees_preproc_for_regression,True,friedman_mse,None,0.16064185697909528,None,10,18,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,extra_trees,,,,,,,,,,,,,,,,,,,,,False,mae,None,0.6372173660778738,None,0.0,11,8,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+55,one_hot_encoding,minority_coalescer,0.013396456809645676,median,minmax,,,,,select_rates_regression,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.43509268545733737,fwe,f_regression,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,off,6.064350782602736e-07,0.06889221218528321,least_squares,255,None,26,1,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+60,no_encoding,minority_coalescer,0.04227296653657646,mean,standardize,,,,,select_rates_regression,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.40891411817637474,fpr,f_regression,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,mae,None,0.16018711332683905,None,0.0,14,14,0.0,,,,,,,,,,,
+63,no_encoding,no_coalescense,,most_frequent,robust_scaler,,,0.8188160371310065,0.01729210740064783,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,3,None,11,9,1.0,12,,,,,,decision_tree,,,,,,,,,,,,,mae,0.7686005175457582,1.0,None,0.0,20,17,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+66,no_encoding,minority_coalescer,0.006136459298464629,mean,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,extra_trees,,,,,,,,,,,,,,,,,,,,,False,friedman_mse,None,0.9490464657381125,None,0.0,1,6,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+68,one_hot_encoding,minority_coalescer,0.00924813449669181,mean,quantile_transformer,987,uniform,,,select_rates_regression,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fwe,f_regression,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,off,2.5833231171101403e-10,0.10682575320034993,least_squares,255,None,31,12,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+70,no_encoding,no_coalescense,,median,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,mse,None,0.9234161475923298,None,0.0,6,13,0.0,,,,,,,,,,,
+73,no_encoding,minority_coalescer,0.00011785495524909325,most_frequent,quantile_transformer,1167,normal,,,kernel_pca,,,,,,,,,,,,,,,,,,,,0.0006351555622197741,rbf,1241,,,,,,,,,,,,,,,,,,,,,,,,,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9.475806401216432e-07,True,0.030139777633001096,,True,0.002755758655854434,optimal,huber,elasticnet,,6.928717804354516e-05
diff --git a/autosklearn/metalearning/files/mean_absolute_error_regression_dense/description.txt b/autosklearn/metalearning/files/mean_absolute_error_regression_dense/description.txt
index ce6d87131f..bc49c5363b 100644
--- a/autosklearn/metalearning/files/mean_absolute_error_regression_dense/description.txt
+++ b/autosklearn/metalearning/files/mean_absolute_error_regression_dense/description.txt
@@ -40,7 +40,7 @@ features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfFeature
 features_stochastic: 
 default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
 
-algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24
+algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73
 algorithms_stochastic: 
 performance_measures: mean_absolute_error
 performance_type: solution_quality
diff --git a/autosklearn/metalearning/files/mean_absolute_error_regression_dense/feature_costs.arff b/autosklearn/metalearning/files/mean_absolute_error_regression_dense/feature_costs.arff
index 0e17e20d22..2c1c95a875 100644
--- a/autosklearn/metalearning/files/mean_absolute_error_regression_dense/feature_costs.arff
+++ b/autosklearn/metalearning/files/mean_absolute_error_regression_dense/feature_costs.arff
@@ -39,160 +39,28 @@
 @ATTRIBUTE SkewnessSTD NUMERIC
 
 @DATA
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-75148,1.0,1e-05,1e-05,2e-05,2e-05,0.00129,0.00115,1e-05,0.00012,1e-05,4e-05,0.0,2e-05,2e-05,4e-05,5e-05,2e-05,1e-05,4e-05,1e-05,0.0004,1e-05,0.0,9e-05,0.00021,9e-05,0.00036,7e-05,7e-05,8e-05,0.00015,0.00041,6e-05,8e-05,8e-05,0.00019
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-75157,1.0,1e-05,1e-05,1e-05,1e-05,0.00125,0.00106,1e-05,0.00015,2e-05,7e-05,0.0,2e-05,2e-05,5e-05,6e-05,2e-05,1e-05,4e-05,1e-05,0.00048,2e-05,0.0,0.0001,0.00026,0.0001,0.00039,6e-05,7e-05,8e-05,0.00019,0.00043,6e-05,9e-05,8e-05,0.0002
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diff --git a/autosklearn/metalearning/files/mean_absolute_error_regression_dense/feature_runstatus.arff b/autosklearn/metalearning/files/mean_absolute_error_regression_dense/feature_runstatus.arff
index 0034ad2bd6..0166848554 100644
--- a/autosklearn/metalearning/files/mean_absolute_error_regression_dense/feature_runstatus.arff
+++ b/autosklearn/metalearning/files/mean_absolute_error_regression_dense/feature_runstatus.arff
@@ -39,160 +39,28 @@
 @ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other}
 
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diff --git a/autosklearn/metalearning/files/mean_absolute_error_regression_dense/feature_values.arff b/autosklearn/metalearning/files/mean_absolute_error_regression_dense/feature_values.arff
index a60400e3ce..ee955516b5 100644
--- a/autosklearn/metalearning/files/mean_absolute_error_regression_dense/feature_values.arff
+++ b/autosklearn/metalearning/files/mean_absolute_error_regression_dense/feature_values.arff
@@ -2,24 +2,17 @@
 
 @ATTRIBUTE instance_id STRING
 @ATTRIBUTE repetition NUMERIC
-@ATTRIBUTE ClassEntropy NUMERIC
-@ATTRIBUTE ClassProbabilityMax NUMERIC
-@ATTRIBUTE ClassProbabilityMean NUMERIC
-@ATTRIBUTE ClassProbabilityMin NUMERIC
-@ATTRIBUTE ClassProbabilitySTD NUMERIC
 @ATTRIBUTE DatasetRatio NUMERIC
 @ATTRIBUTE InverseDatasetRatio NUMERIC
 @ATTRIBUTE KurtosisMax NUMERIC
 @ATTRIBUTE KurtosisMean NUMERIC
 @ATTRIBUTE KurtosisMin NUMERIC
 @ATTRIBUTE KurtosisSTD NUMERIC
-@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC
 @ATTRIBUTE LogDatasetRatio NUMERIC
 @ATTRIBUTE LogInverseDatasetRatio NUMERIC
 @ATTRIBUTE LogNumberOfFeatures NUMERIC
 @ATTRIBUTE LogNumberOfInstances NUMERIC
 @ATTRIBUTE NumberOfCategoricalFeatures NUMERIC
-@ATTRIBUTE NumberOfClasses NUMERIC
 @ATTRIBUTE NumberOfFeatures NUMERIC
 @ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC
 @ATTRIBUTE NumberOfInstances NUMERIC
@@ -42,160 +35,28 @@
 @ATTRIBUTE SymbolsSum NUMERIC
 
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diff --git a/autosklearn/metalearning/files/mean_absolute_error_regression_sparse/algorithm_runs.arff b/autosklearn/metalearning/files/mean_absolute_error_regression_sparse/algorithm_runs.arff
index 8f9393b4e9..199f9f16df 100644
--- a/autosklearn/metalearning/files/mean_absolute_error_regression_sparse/algorithm_runs.arff
+++ b/autosklearn/metalearning/files/mean_absolute_error_regression_sparse/algorithm_runs.arff
@@ -7,27 +7,28 @@
 @ATTRIBUTE runstatus {ok, timeout, memout, not_applicable, crash, other}
 
 @DATA
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diff --git a/autosklearn/metalearning/files/mean_absolute_error_regression_sparse/configurations.csv b/autosklearn/metalearning/files/mean_absolute_error_regression_sparse/configurations.csv
index aef11a2a95..1d06eb0cd9 100644
--- a/autosklearn/metalearning/files/mean_absolute_error_regression_sparse/configurations.csv
+++ b/autosklearn/metalearning/files/mean_absolute_error_regression_sparse/configurations.csv
@@ -1,25 +1,26 @@
 idx,data_preprocessing:categorical_transformer:categorical_encoding:__choice__,data_preprocessing:categorical_transformer:category_coalescence:__choice__,data_preprocessing:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction,data_preprocessing:numerical_transformer:imputation:strategy,data_preprocessing:numerical_transformer:rescaling:__choice__,data_preprocessing:numerical_transformer:rescaling:quantile_transformer:n_quantiles,data_preprocessing:numerical_transformer:rescaling:quantile_transformer:output_distribution,data_preprocessing:numerical_transformer:rescaling:robust_scaler:q_max,data_preprocessing:numerical_transformer:rescaling:robust_scaler:q_min,feature_preprocessor:__choice__,feature_preprocessor:extra_trees_preproc_for_regression:bootstrap,feature_preprocessor:extra_trees_preproc_for_regression:criterion,feature_preprocessor:extra_trees_preproc_for_regression:max_depth,feature_preprocessor:extra_trees_preproc_for_regression:max_features,feature_preprocessor:extra_trees_preproc_for_regression:max_leaf_nodes,feature_preprocessor:extra_trees_preproc_for_regression:min_samples_leaf,feature_preprocessor:extra_trees_preproc_for_regression:min_samples_split,feature_preprocessor:extra_trees_preproc_for_regression:min_weight_fraction_leaf,feature_preprocessor:extra_trees_preproc_for_regression:n_estimators,feature_preprocessor:fast_ica:algorithm,feature_preprocessor:fast_ica:fun,feature_preprocessor:fast_ica:n_components,feature_preprocessor:fast_ica:whiten,feature_preprocessor:feature_agglomeration:affinity,feature_preprocessor:feature_agglomeration:linkage,feature_preprocessor:feature_agglomeration:n_clusters,feature_preprocessor:feature_agglomeration:pooling_func,feature_preprocessor:kernel_pca:coef0,feature_preprocessor:kernel_pca:degree,feature_preprocessor:kernel_pca:gamma,feature_preprocessor:kernel_pca:kernel,feature_preprocessor:kernel_pca:n_components,feature_preprocessor:kitchen_sinks:gamma,feature_preprocessor:kitchen_sinks:n_components,feature_preprocessor:nystroem_sampler:coef0,feature_preprocessor:nystroem_sampler:degree,feature_preprocessor:nystroem_sampler:gamma,feature_preprocessor:nystroem_sampler:kernel,feature_preprocessor:nystroem_sampler:n_components,feature_preprocessor:pca:keep_variance,feature_preprocessor:pca:whiten,feature_preprocessor:polynomial:degree,feature_preprocessor:polynomial:include_bias,feature_preprocessor:polynomial:interaction_only,feature_preprocessor:random_trees_embedding:bootstrap,feature_preprocessor:random_trees_embedding:max_depth,feature_preprocessor:random_trees_embedding:max_leaf_nodes,feature_preprocessor:random_trees_embedding:min_samples_leaf,feature_preprocessor:random_trees_embedding:min_samples_split,feature_preprocessor:random_trees_embedding:min_weight_fraction_leaf,feature_preprocessor:random_trees_embedding:n_estimators,feature_preprocessor:select_percentile_regression:percentile,feature_preprocessor:select_percentile_regression:score_func,feature_preprocessor:select_rates_regression:alpha,feature_preprocessor:select_rates_regression:mode,feature_preprocessor:select_rates_regression:score_func,regressor:__choice__,regressor:adaboost:learning_rate,regressor:adaboost:loss,regressor:adaboost:max_depth,regressor:adaboost:n_estimators,regressor:ard_regression:alpha_1,regressor:ard_regression:alpha_2,regressor:ard_regression:fit_intercept,regressor:ard_regression:lambda_1,regressor:ard_regression:lambda_2,regressor:ard_regression:n_iter,regressor:ard_regression:threshold_lambda,regressor:ard_regression:tol,regressor:decision_tree:criterion,regressor:decision_tree:max_depth_factor,regressor:decision_tree:max_features,regressor:decision_tree:max_leaf_nodes,regressor:decision_tree:min_impurity_decrease,regressor:decision_tree:min_samples_leaf,regressor:decision_tree:min_samples_split,regressor:decision_tree:min_weight_fraction_leaf,regressor:extra_trees:bootstrap,regressor:extra_trees:criterion,regressor:extra_trees:max_depth,regressor:extra_trees:max_features,regressor:extra_trees:max_leaf_nodes,regressor:extra_trees:min_impurity_decrease,regressor:extra_trees:min_samples_leaf,regressor:extra_trees:min_samples_split,regressor:extra_trees:min_weight_fraction_leaf,regressor:gaussian_process:alpha,regressor:gaussian_process:thetaL,regressor:gaussian_process:thetaU,regressor:gradient_boosting:early_stop,regressor:gradient_boosting:l2_regularization,regressor:gradient_boosting:learning_rate,regressor:gradient_boosting:loss,regressor:gradient_boosting:max_bins,regressor:gradient_boosting:max_depth,regressor:gradient_boosting:max_leaf_nodes,regressor:gradient_boosting:min_samples_leaf,regressor:gradient_boosting:n_iter_no_change,regressor:gradient_boosting:scoring,regressor:gradient_boosting:tol,regressor:gradient_boosting:validation_fraction,regressor:k_nearest_neighbors:n_neighbors,regressor:k_nearest_neighbors:p,regressor:k_nearest_neighbors:weights,regressor:liblinear_svr:C,regressor:liblinear_svr:dual,regressor:liblinear_svr:epsilon,regressor:liblinear_svr:fit_intercept,regressor:liblinear_svr:intercept_scaling,regressor:liblinear_svr:loss,regressor:liblinear_svr:tol,regressor:libsvm_svr:C,regressor:libsvm_svr:coef0,regressor:libsvm_svr:degree,regressor:libsvm_svr:epsilon,regressor:libsvm_svr:gamma,regressor:libsvm_svr:kernel,regressor:libsvm_svr:max_iter,regressor:libsvm_svr:shrinking,regressor:libsvm_svr:tol,regressor:random_forest:bootstrap,regressor:random_forest:criterion,regressor:random_forest:max_depth,regressor:random_forest:max_features,regressor:random_forest:max_leaf_nodes,regressor:random_forest:min_impurity_decrease,regressor:random_forest:min_samples_leaf,regressor:random_forest:min_samples_split,regressor:random_forest:min_weight_fraction_leaf,regressor:sgd:alpha,regressor:sgd:average,regressor:sgd:epsilon,regressor:sgd:eta0,regressor:sgd:fit_intercept,regressor:sgd:l1_ratio,regressor:sgd:learning_rate,regressor:sgd:loss,regressor:sgd:penalty,regressor:sgd:power_t,regressor:sgd:tol
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-19,no_encoding,minority_coalescer,0.07072534022332494,mean,minmax,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,mse,None,0.9209937718223583,None,0.0,3,12,0.0,,,,,,,,,,,
-20,no_encoding,minority_coalescer,0.0026150904111835473,mean,normalize,,,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.5410109609735668,True,,,,,,,,,,,,,,,,decision_tree,,,,,,,,,,,,,friedman_mse,1.240365240027156,1.0,None,0.0,19,11,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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-22,one_hot_encoding,minority_coalescer,0.010000000000000004,median,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,mse,None,1.0,None,0.0,1,3,0.0,,,,,,,,,,,
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+1,one_hot_encoding,minority_coalescer,0.00011362210866200227,median,robust_scaler,,,0.7164327623696907,0.2120143177624721,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,friedman_mse,None,0.9174503965721693,None,0.0,1,2,0.0,,,,,,,,,,,
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+9,no_encoding,minority_coalescer,0.019377929484205567,median,quantile_transformer,59,uniform,,,select_percentile_regression,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,72.39505885805667,f_regression,,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,off,0.015327517814673708,0.011227079271135762,least_squares,255,None,164,1,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+13,no_encoding,minority_coalescer,0.010000000000000004,median,quantile_transformer,780,uniform,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,,libsvm_svr,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,10.974102872028766,,4,0.09412187025168416,0.04653835917168057,rbf,-1,False,0.0010357973041304064,,,,,,,,,,,,,,,,,,,,
+15,no_encoding,no_coalescense,,most_frequent,none,,,,,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00025844019785412086,rbf,305,,,,,,,,,,,,,,,,,,liblinear_svr,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,18080.177016227895,False,0.0017155220175954669,True,1,squared_epsilon_insensitive,7.035440846539455e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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+36,one_hot_encoding,no_coalescense,,most_frequent,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,mse,None,0.1686847993740384,None,0.0,1,2,0.0,,,,,,,,,,,
+41,no_encoding,minority_coalescer,0.010000000000000004,median,none,,,,,select_percentile_regression,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,42.409671463494774,mutual_info,,,,libsvm_svr,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3400.978964974929,,3,0.07276129539751543,0.04818277174554651,rbf,-1,True,0.028807473423121257,,,,,,,,,,,,,,,,,,,,
+46,no_encoding,minority_coalescer,0.016414029151950702,most_frequent,robust_scaler,,,0.7433110819250283,0.24401838405994994,feature_agglomeration,,,,,,,,,,,,,,euclidean,complete,11,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,libsvm_svr,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4786.70957992255,,5,0.0010856317305813427,0.03211446445672321,rbf,-1,False,5.044549911761054e-05,,,,,,,,,,,,,,,,,,,,
+48,one_hot_encoding,no_coalescense,,mean,quantile_transformer,148,uniform,,,select_rates_regression,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.10789818795901182,fwe,f_regression,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,off,0.0002841741346637058,0.024253735132716756,least_squares,255,None,36,1,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+51,one_hot_encoding,no_coalescense,,median,minmax,,,,,select_rates_regression,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.32764403627543237,fdr,f_regression,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,off,0.000153437102327148,0.01698754273672824,least_squares,255,None,47,76,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+54,no_encoding,minority_coalescer,0.01313719797407571,median,normalize,,,,,extra_trees_preproc_for_regression,True,friedman_mse,None,0.16064185697909528,None,10,18,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,extra_trees,,,,,,,,,,,,,,,,,,,,,False,mae,None,0.6372173660778738,None,0.0,11,8,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+55,one_hot_encoding,minority_coalescer,0.013396456809645676,median,minmax,,,,,select_rates_regression,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.43509268545733737,fwe,f_regression,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,off,6.064350782602736e-07,0.06889221218528321,least_squares,255,None,26,1,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+60,no_encoding,minority_coalescer,0.04227296653657646,mean,standardize,,,,,select_rates_regression,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.40891411817637474,fpr,f_regression,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,mae,None,0.16018711332683905,None,0.0,14,14,0.0,,,,,,,,,,,
+63,no_encoding,no_coalescense,,most_frequent,robust_scaler,,,0.8188160371310065,0.01729210740064783,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,3,None,11,9,1.0,12,,,,,,decision_tree,,,,,,,,,,,,,mae,0.7686005175457582,1.0,None,0.0,20,17,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+66,no_encoding,minority_coalescer,0.006136459298464629,mean,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,extra_trees,,,,,,,,,,,,,,,,,,,,,False,friedman_mse,None,0.9490464657381125,None,0.0,1,6,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+68,one_hot_encoding,minority_coalescer,0.00924813449669181,mean,quantile_transformer,987,uniform,,,select_rates_regression,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fwe,f_regression,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,off,2.5833231171101403e-10,0.10682575320034993,least_squares,255,None,31,12,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+70,no_encoding,no_coalescense,,median,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,mse,None,0.9234161475923298,None,0.0,6,13,0.0,,,,,,,,,,,
+73,no_encoding,minority_coalescer,0.00011785495524909325,most_frequent,quantile_transformer,1167,normal,,,kernel_pca,,,,,,,,,,,,,,,,,,,,0.0006351555622197741,rbf,1241,,,,,,,,,,,,,,,,,,,,,,,,,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9.475806401216432e-07,True,0.030139777633001096,,True,0.002755758655854434,optimal,huber,elasticnet,,6.928717804354516e-05
diff --git a/autosklearn/metalearning/files/mean_absolute_error_regression_sparse/description.txt b/autosklearn/metalearning/files/mean_absolute_error_regression_sparse/description.txt
index ce6d87131f..bc49c5363b 100644
--- a/autosklearn/metalearning/files/mean_absolute_error_regression_sparse/description.txt
+++ b/autosklearn/metalearning/files/mean_absolute_error_regression_sparse/description.txt
@@ -40,7 +40,7 @@ features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfFeature
 features_stochastic: 
 default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
 
-algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24
+algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73
 algorithms_stochastic: 
 performance_measures: mean_absolute_error
 performance_type: solution_quality
diff --git a/autosklearn/metalearning/files/mean_absolute_error_regression_sparse/feature_costs.arff b/autosklearn/metalearning/files/mean_absolute_error_regression_sparse/feature_costs.arff
index 0e17e20d22..2c1c95a875 100644
--- a/autosklearn/metalearning/files/mean_absolute_error_regression_sparse/feature_costs.arff
+++ b/autosklearn/metalearning/files/mean_absolute_error_regression_sparse/feature_costs.arff
@@ -39,160 +39,28 @@
 @ATTRIBUTE SkewnessSTD NUMERIC
 
 @DATA
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-75148,1.0,1e-05,1e-05,2e-05,2e-05,0.00129,0.00115,1e-05,0.00012,1e-05,4e-05,0.0,2e-05,2e-05,4e-05,5e-05,2e-05,1e-05,4e-05,1e-05,0.0004,1e-05,0.0,9e-05,0.00021,9e-05,0.00036,7e-05,7e-05,8e-05,0.00015,0.00041,6e-05,8e-05,8e-05,0.00019
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-75157,1.0,1e-05,1e-05,1e-05,1e-05,0.00125,0.00106,1e-05,0.00015,2e-05,7e-05,0.0,2e-05,2e-05,5e-05,6e-05,2e-05,1e-05,4e-05,1e-05,0.00048,2e-05,0.0,0.0001,0.00026,0.0001,0.00039,6e-05,7e-05,8e-05,0.00019,0.00043,6e-05,9e-05,8e-05,0.0002
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diff --git a/autosklearn/metalearning/files/mean_absolute_error_regression_sparse/feature_runstatus.arff b/autosklearn/metalearning/files/mean_absolute_error_regression_sparse/feature_runstatus.arff
index 0034ad2bd6..0166848554 100644
--- a/autosklearn/metalearning/files/mean_absolute_error_regression_sparse/feature_runstatus.arff
+++ b/autosklearn/metalearning/files/mean_absolute_error_regression_sparse/feature_runstatus.arff
@@ -39,160 +39,28 @@
 @ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other}
 
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diff --git a/autosklearn/metalearning/files/mean_absolute_error_regression_sparse/feature_values.arff b/autosklearn/metalearning/files/mean_absolute_error_regression_sparse/feature_values.arff
index a60400e3ce..ee955516b5 100644
--- a/autosklearn/metalearning/files/mean_absolute_error_regression_sparse/feature_values.arff
+++ b/autosklearn/metalearning/files/mean_absolute_error_regression_sparse/feature_values.arff
@@ -2,24 +2,17 @@
 
 @ATTRIBUTE instance_id STRING
 @ATTRIBUTE repetition NUMERIC
-@ATTRIBUTE ClassEntropy NUMERIC
-@ATTRIBUTE ClassProbabilityMax NUMERIC
-@ATTRIBUTE ClassProbabilityMean NUMERIC
-@ATTRIBUTE ClassProbabilityMin NUMERIC
-@ATTRIBUTE ClassProbabilitySTD NUMERIC
 @ATTRIBUTE DatasetRatio NUMERIC
 @ATTRIBUTE InverseDatasetRatio NUMERIC
 @ATTRIBUTE KurtosisMax NUMERIC
 @ATTRIBUTE KurtosisMean NUMERIC
 @ATTRIBUTE KurtosisMin NUMERIC
 @ATTRIBUTE KurtosisSTD NUMERIC
-@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC
 @ATTRIBUTE LogDatasetRatio NUMERIC
 @ATTRIBUTE LogInverseDatasetRatio NUMERIC
 @ATTRIBUTE LogNumberOfFeatures NUMERIC
 @ATTRIBUTE LogNumberOfInstances NUMERIC
 @ATTRIBUTE NumberOfCategoricalFeatures NUMERIC
-@ATTRIBUTE NumberOfClasses NUMERIC
 @ATTRIBUTE NumberOfFeatures NUMERIC
 @ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC
 @ATTRIBUTE NumberOfInstances NUMERIC
@@ -42,160 +35,28 @@
 @ATTRIBUTE SymbolsSum NUMERIC
 
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diff --git a/autosklearn/metalearning/files/mean_squared_error_regression_dense/algorithm_runs.arff b/autosklearn/metalearning/files/mean_squared_error_regression_dense/algorithm_runs.arff
index 857e039141..9673d3cc66 100644
--- a/autosklearn/metalearning/files/mean_squared_error_regression_dense/algorithm_runs.arff
+++ b/autosklearn/metalearning/files/mean_squared_error_regression_dense/algorithm_runs.arff
@@ -7,27 +7,28 @@
 @ATTRIBUTE runstatus {ok, timeout, memout, not_applicable, crash, other}
 
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diff --git a/autosklearn/metalearning/files/mean_squared_error_regression_dense/configurations.csv b/autosklearn/metalearning/files/mean_squared_error_regression_dense/configurations.csv
index 0dc194dd38..c300d7f3da 100644
--- a/autosklearn/metalearning/files/mean_squared_error_regression_dense/configurations.csv
+++ b/autosklearn/metalearning/files/mean_squared_error_regression_dense/configurations.csv
@@ -1,25 +1,26 @@
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svr:epsilon,regressor:liblinear_svr:fit_intercept,regressor:liblinear_svr:intercept_scaling,regressor:liblinear_svr:loss,regressor:liblinear_svr:tol,regressor:libsvm_svr:C,regressor:libsvm_svr:coef0,regressor:libsvm_svr:degree,regressor:libsvm_svr:epsilon,regressor:libsvm_svr:gamma,regressor:libsvm_svr:kernel,regressor:libsvm_svr:max_iter,regressor:libsvm_svr:shrinking,regressor:libsvm_svr:tol,regressor:random_forest:bootstrap,regressor:random_forest:criterion,regressor:random_forest:max_depth,regressor:random_forest:max_features,regressor:random_forest:max_leaf_nodes,regressor:random_forest:min_impurity_decrease,regressor:random_forest:min_samples_leaf,regressor:random_forest:min_samples_split,regressor:random_forest:min_weight_fraction_leaf,regressor:sgd:alpha,regressor:sgd:average,regressor:sgd:epsilon,regressor:sgd:eta0,regressor:sgd:fit_intercept,regressor:sgd:l1_ratio,regressor:sgd:learning_rate,regressor:sgd:loss,regressor:sgd:penalty,regressor:sgd:power_t,regressor:sgd:tol
-1,one_hot_encoding,minority_coalescer,0.00022127044391040656,mean,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,mse,None,0.5956294688991997,None,0.0,3,3,0.0,,,,,,,,,,,
-2,one_hot_encoding,no_coalescense,,median,quantile_transformer,85,normal,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,valid,0.00018237876768886392,0.09686188418297718,least_squares,255,None,55,2,15,loss,1e-07,0.2847485487624638,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-3,one_hot_encoding,minority_coalescer,0.003623240287266379,median,robust_scaler,,,0.8134431529581335,0.2230614629084614,select_rates_regression,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.09849735185327581,fpr,f_regression,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,off,5.5749535239648375e-06,0.033744850983537196,least_squares,255,None,27,116,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-4,no_encoding,minority_coalescer,0.17967809313159305,mean,quantile_transformer,764,uniform,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,off,0.09913219372742708,0.034528129708702164,least_squares,255,None,14,77,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-5,one_hot_encoding,no_coalescense,,mean,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,valid,0.0004581673561271838,0.32962137015342907,least_squares,255,None,3,84,20,loss,1e-07,0.1407503228863241,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-6,no_encoding,minority_coalescer,0.04925167304520271,median,standardize,,,,,feature_agglomeration,,,,,,,,,,,,,,manhattan,average,27,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,off,3.0777178597597097e-10,0.11742891344336259,least_squares,255,None,14,12,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-7,one_hot_encoding,minority_coalescer,0.012113085213710002,median,minmax,,,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.923739124770368,False,,,,,,,,,,,,,,,,libsvm_svr,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.9115865125692801,,5,0.10643586278948972,2.184494531191916,rbf,-1,True,0.002318776631832561,,,,,,,,,,,,,,,,,,,,
-8,no_encoding,minority_coalescer,0.007693066549798328,most_frequent,none,,,,,feature_agglomeration,,,,,,,,,,,,,,manhattan,average,320,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,extra_trees,,,,,,,,,,,,,,,,,,,,,True,friedman_mse,None,0.9274157646998513,None,0.0,4,9,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-9,no_encoding,minority_coalescer,0.06968801120619206,mean,minmax,,,,,kitchen_sinks,,,,,,,,,,,,,,,,,,,,,,,2.4233915766526617,359,,,,,,,,,,,,,,,,,,,,,,,libsvm_svr,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,93.53649669563134,,3,0.0074352080325016025,0.9629552978655305,rbf,-1,False,0.0001374930573423754,,,,,,,,,,,,,,,,,,,,
-10,no_encoding,no_coalescense,,median,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,friedman_mse,None,0.3378464988163372,None,0.0,1,19,0.0,,,,,,,,,,,
-11,no_encoding,minority_coalescer,0.0075899693709838454,mean,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,mse,None,1.0,None,0.0,1,3,0.0,,,,,,,,,,,
-12,no_encoding,no_coalescense,,median,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,friedman_mse,None,0.3378464988163372,None,0.0,1,19,0.0,,,,,,,,,,,
-13,no_encoding,minority_coalescer,0.010000000000000004,most_frequent,none,,,,,extra_trees_preproc_for_regression,False,mae,None,0.16371799057722908,None,19,14,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,libsvm_svr,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,152.23753997019216,,2,0.4080599256803034,0.09254299447226481,rbf,-1,False,0.00039021643225095956,,,,,,,,,,,,,,,,,,,,
-14,no_encoding,minority_coalescer,0.005788594071307701,median,minmax,,,,,select_rates_regression,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.41365987272753285,fdr,f_regression,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,friedman_mse,None,0.22442857329791677,None,0.0,18,16,0.0,,,,,,,,,,,
-15,one_hot_encoding,minority_coalescer,0.0037423052714792336,mean,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,mse,None,0.4339166051934824,None,0.0,4,10,0.0,,,,,,,,,,,
-16,one_hot_encoding,minority_coalescer,0.010000000000000004,median,normalize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7.395586042195198e-06,True,0.039363539389536205,,True,,optimal,epsilon_insensitive,l2,,0.00032230798144272904
-17,one_hot_encoding,minority_coalescer,0.009171724071325382,most_frequent,standardize,,,,,feature_agglomeration,,,,,,,,,,,,,,manhattan,average,258,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,friedman_mse,None,0.7429459921040217,None,0.0,1,4,0.0,,,,,,,,,,,
-18,one_hot_encoding,minority_coalescer,0.07864422900237864,median,standardize,,,,,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,0.2993150897295005,,1.2747696325201687,sigmoid,977,,,,,,,,,,,,,,,,,,ard_regression,,,,,0.00029800225431691,5.551214723404076e-08,True,4.61049163663029e-07,5.874293238908804e-10,300,85889.14023423038,0.00027499470605731914,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-19,no_encoding,minority_coalescer,0.07072534022332494,mean,minmax,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,mse,None,0.9209937718223583,None,0.0,3,12,0.0,,,,,,,,,,,
-20,one_hot_encoding,no_coalescense,,mean,robust_scaler,,,0.8821344085141198,0.20696951294776308,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,10,None,3,15,1.0,33,,,,,,decision_tree,,,,,,,,,,,,,friedman_mse,0.8666369072341655,1.0,None,0.0,19,7,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-21,one_hot_encoding,minority_coalescer,0.004474714505413804,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,extra_trees,,,,,,,,,,,,,,,,,,,,,False,mse,None,0.8442614504957053,None,0.0,1,5,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-22,one_hot_encoding,minority_coalescer,0.010000000000000004,median,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,mse,None,1.0,None,0.0,1,3,0.0,,,,,,,,,,,
-23,no_encoding,minority_coalescer,0.007150902883091326,mean,robust_scaler,,,0.8663777312763623,0.02160040060766837,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,mse,None,0.9994655923368939,None,0.0,4,6,0.0,,,,,,,,,,,
-24,one_hot_encoding,no_coalescense,,most_frequent,robust_scaler,,,0.7484939216574421,0.20183301399810935,select_rates_regression,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.18375456889543734,fdr,f_regression,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,off,4.81881052684467e-05,0.10285955822720894,least_squares,255,None,8,1,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+1,one_hot_encoding,no_coalescense,,mean,standardize,,,,,select_rates_regression,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.40612979303062824,fdr,f_regression,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,off,2.2742572602302616e-08,0.02539518548794612,least_squares,255,None,57,10,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+2,one_hot_encoding,minority_coalescer,0.010000000000000004,most_frequent,quantile_transformer,1818,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,mse,None,0.9744859278597818,None,0.0,11,2,0.0,,,,,,,,,,,
+5,no_encoding,minority_coalescer,0.06912558240513342,median,quantile_transformer,119,uniform,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,off,3.79271698524212e-09,0.025159826241956527,least_squares,255,None,42,29,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+9,no_encoding,minority_coalescer,0.014087234899150132,most_frequent,minmax,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,libsvm_svr,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,243.80018215429655,,5,0.0010313680328976054,0.12602136380125653,rbf,-1,True,0.0003015355281210108,,,,,,,,,,,,,,,,,,,,
+13,no_encoding,minority_coalescer,0.010000000000000004,median,quantile_transformer,780,uniform,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,,libsvm_svr,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,10.974102872028766,,4,0.09412187025168416,0.04653835917168057,rbf,-1,False,0.0010357973041304064,,,,,,,,,,,,,,,,,,,,
+15,no_encoding,no_coalescense,,most_frequent,none,,,,,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00025844019785412086,rbf,305,,,,,,,,,,,,,,,,,,liblinear_svr,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,18080.177016227895,False,0.0017155220175954669,True,1,squared_epsilon_insensitive,7.035440846539455e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+17,no_encoding,no_coalescense,,most_frequent,robust_scaler,,,0.8280417820125114,0.0781634653874277,kitchen_sinks,,,,,,,,,,,,,,,,,,,,,,,8.438432240830361e-05,2984,,,,,,,,,,,,,,,,,,,,,,,ard_regression,,,,,0.00044036509169446026,4.039147500668822e-10,True,8.922721154590444e-05,3.0105431227198885e-05,300,1899.168836704701,0.011611373742389547,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+19,one_hot_encoding,no_coalescense,,mean,quantile_transformer,148,uniform,,,select_rates_regression,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.10789818795901182,fwe,f_regression,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,off,0.0002841741346637058,0.024253735132716756,least_squares,255,None,36,1,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+22,one_hot_encoding,no_coalescense,,most_frequent,standardize,,,,,fast_ica,,,,,,,,,,parallel,logcosh,215,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,off,4.242786820644205e-10,0.0953131401479951,least_squares,255,None,16,17,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+24,no_encoding,no_coalescense,,median,quantile_transformer,1150,uniform,,,kitchen_sinks,,,,,,,,,,,,,,,,,,,,,,,1.1605111479672479,5499,,,,,,,,,,,,,,,,,,,,,,,libsvm_svr,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,272.83550277553246,0.1471721071594263,2,0.18104912091393513,0.09926116813777154,poly,-1,True,0.06084368714049917,,,,,,,,,,,,,,,,,,,,
+30,no_encoding,no_coalescense,,most_frequent,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,friedman_mse,None,0.2961138557020546,None,0.0,14,12,0.0,,,,,,,,,,,
+34,no_encoding,no_coalescense,,most_frequent,robust_scaler,,,0.7049491520337817,0.23602880130851764,fast_ica,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,off,4.696295762197504e-10,0.03269357604116943,least_squares,255,None,32,14,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+36,one_hot_encoding,no_coalescense,,most_frequent,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,mse,None,0.1686847993740384,None,0.0,1,2,0.0,,,,,,,,,,,
+38,no_encoding,no_coalescense,,median,none,,,,,select_rates_regression,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.13294870400704156,fwe,f_regression,adaboost,0.47982456668237294,square,10,143,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+42,no_encoding,minority_coalescer,0.0074923422486941615,mean,quantile_transformer,919,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,mse,None,0.7102443468613595,None,0.0,20,4,0.0,,,,,,,,,,,
+45,no_encoding,minority_coalescer,0.00010476041523839592,median,standardize,,,,,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,-0.8964384900187328,,0.015021411517772181,sigmoid,7415,,,,,,,,,,,,,,,,,,ard_regression,,,,,4.544163312418526e-05,0.0003398091634662838,True,6.154987146709e-06,1.2041910604531783e-05,300,21462.056617690938,0.07078995384093524,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+48,one_hot_encoding,no_coalescense,,mean,quantile_transformer,148,uniform,,,select_rates_regression,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.10789818795901182,fwe,f_regression,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,off,0.0002841741346637058,0.024253735132716756,least_squares,255,None,36,1,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+51,one_hot_encoding,no_coalescense,,median,minmax,,,,,select_rates_regression,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.32764403627543237,fdr,f_regression,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,off,0.000153437102327148,0.01698754273672824,least_squares,255,None,47,76,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+52,no_encoding,no_coalescense,,most_frequent,quantile_transformer,1425,uniform,,,feature_agglomeration,,,,,,,,,,,,,,cosine,complete,276,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,off,0.00018459550741867383,0.04757728173371449,least_squares,255,None,16,6,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+57,one_hot_encoding,minority_coalescer,0.00924813449669181,mean,quantile_transformer,987,uniform,,,select_rates_regression,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fwe,f_regression,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,off,2.5833231171101403e-10,0.10682575320034993,least_squares,255,None,31,12,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+59,no_encoding,minority_coalescer,0.010000000000000004,most_frequent,robust_scaler,,,0.7414290235319753,0.2631696868735969,extra_trees_preproc_for_regression,True,mse,None,0.8529069417035176,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,extra_trees,,,,,,,,,,,,,,,,,,,,,True,mse,None,0.9698708516736436,None,0.0,4,2,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+62,one_hot_encoding,minority_coalescer,0.027537836751886195,median,robust_scaler,,,0.9003944797470034,0.2566610073208164,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,5,None,1,2,1.0,10,,,,,,decision_tree,,,,,,,,,,,,,friedman_mse,0.23884067765544847,1.0,None,0.0,11,13,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+66,no_encoding,minority_coalescer,0.006136459298464629,mean,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,extra_trees,,,,,,,,,,,,,,,,,,,,,False,friedman_mse,None,0.9490464657381125,None,0.0,1,6,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+68,one_hot_encoding,minority_coalescer,0.00924813449669181,mean,quantile_transformer,987,uniform,,,select_rates_regression,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fwe,f_regression,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,off,2.5833231171101403e-10,0.10682575320034993,least_squares,255,None,31,12,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+70,no_encoding,no_coalescense,,median,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,mse,None,0.9234161475923298,None,0.0,6,13,0.0,,,,,,,,,,,
diff --git a/autosklearn/metalearning/files/mean_squared_error_regression_dense/description.txt b/autosklearn/metalearning/files/mean_squared_error_regression_dense/description.txt
index 8856512c05..cf01e24b4b 100644
--- a/autosklearn/metalearning/files/mean_squared_error_regression_dense/description.txt
+++ b/autosklearn/metalearning/files/mean_squared_error_regression_dense/description.txt
@@ -40,7 +40,7 @@ features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfFeature
 features_stochastic: 
 default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
 
-algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24
+algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73
 algorithms_stochastic: 
 performance_measures: mean_squared_error
 performance_type: solution_quality
diff --git a/autosklearn/metalearning/files/mean_squared_error_regression_dense/feature_costs.arff b/autosklearn/metalearning/files/mean_squared_error_regression_dense/feature_costs.arff
index 0e17e20d22..2c1c95a875 100644
--- a/autosklearn/metalearning/files/mean_squared_error_regression_dense/feature_costs.arff
+++ b/autosklearn/metalearning/files/mean_squared_error_regression_dense/feature_costs.arff
@@ -39,160 +39,28 @@
 @ATTRIBUTE SkewnessSTD NUMERIC
 
 @DATA
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-75232,1.0,1e-05,1e-05,1e-05,1e-05,0.00108,0.00076,1e-05,0.00021,2e-05,0.00015,1e-05,2e-05,2e-05,5e-05,8e-05,3e-05,1e-05,6e-05,1e-05,0.00055,1e-05,1e-05,0.00013,0.00028,0.00012,0.00039,7e-05,7e-05,8e-05,0.00016,0.00046,6e-05,8e-05,0.0001,0.00022
-75235,1.0,1e-05,1e-05,2e-05,1e-05,0.00408,0.0035,1e-05,0.00039,1e-05,0.00022,1e-05,2e-05,2e-05,5e-05,0.0001,3e-05,1e-05,7e-05,2e-05,0.00067,1e-05,1e-05,0.00013,0.00034,0.00018,0.00039,6e-05,9e-05,8e-05,0.00017,0.00051,6e-05,9e-05,0.00011,0.00024
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diff --git a/autosklearn/metalearning/files/mean_squared_error_regression_dense/feature_runstatus.arff b/autosklearn/metalearning/files/mean_squared_error_regression_dense/feature_runstatus.arff
index 0034ad2bd6..0166848554 100644
--- a/autosklearn/metalearning/files/mean_squared_error_regression_dense/feature_runstatus.arff
+++ b/autosklearn/metalearning/files/mean_squared_error_regression_dense/feature_runstatus.arff
@@ -39,160 +39,28 @@
 @ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other}
 
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diff --git a/autosklearn/metalearning/files/mean_squared_error_regression_dense/feature_values.arff b/autosklearn/metalearning/files/mean_squared_error_regression_dense/feature_values.arff
index a60400e3ce..ee955516b5 100644
--- a/autosklearn/metalearning/files/mean_squared_error_regression_dense/feature_values.arff
+++ b/autosklearn/metalearning/files/mean_squared_error_regression_dense/feature_values.arff
@@ -2,24 +2,17 @@
 
 @ATTRIBUTE instance_id STRING
 @ATTRIBUTE repetition NUMERIC
-@ATTRIBUTE ClassEntropy NUMERIC
-@ATTRIBUTE ClassProbabilityMax NUMERIC
-@ATTRIBUTE ClassProbabilityMean NUMERIC
-@ATTRIBUTE ClassProbabilityMin NUMERIC
-@ATTRIBUTE ClassProbabilitySTD NUMERIC
 @ATTRIBUTE DatasetRatio NUMERIC
 @ATTRIBUTE InverseDatasetRatio NUMERIC
 @ATTRIBUTE KurtosisMax NUMERIC
 @ATTRIBUTE KurtosisMean NUMERIC
 @ATTRIBUTE KurtosisMin NUMERIC
 @ATTRIBUTE KurtosisSTD NUMERIC
-@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC
 @ATTRIBUTE LogDatasetRatio NUMERIC
 @ATTRIBUTE LogInverseDatasetRatio NUMERIC
 @ATTRIBUTE LogNumberOfFeatures NUMERIC
 @ATTRIBUTE LogNumberOfInstances NUMERIC
 @ATTRIBUTE NumberOfCategoricalFeatures NUMERIC
-@ATTRIBUTE NumberOfClasses NUMERIC
 @ATTRIBUTE NumberOfFeatures NUMERIC
 @ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC
 @ATTRIBUTE NumberOfInstances NUMERIC
@@ -42,160 +35,28 @@
 @ATTRIBUTE SymbolsSum NUMERIC
 
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diff --git a/autosklearn/metalearning/files/mean_squared_error_regression_sparse/algorithm_runs.arff b/autosklearn/metalearning/files/mean_squared_error_regression_sparse/algorithm_runs.arff
index 857e039141..9673d3cc66 100644
--- a/autosklearn/metalearning/files/mean_squared_error_regression_sparse/algorithm_runs.arff
+++ b/autosklearn/metalearning/files/mean_squared_error_regression_sparse/algorithm_runs.arff
@@ -7,27 +7,28 @@
 @ATTRIBUTE runstatus {ok, timeout, memout, not_applicable, crash, other}
 
 @DATA
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diff --git a/autosklearn/metalearning/files/mean_squared_error_regression_sparse/configurations.csv b/autosklearn/metalearning/files/mean_squared_error_regression_sparse/configurations.csv
index 0dc194dd38..c300d7f3da 100644
--- a/autosklearn/metalearning/files/mean_squared_error_regression_sparse/configurations.csv
+++ b/autosklearn/metalearning/files/mean_squared_error_regression_sparse/configurations.csv
@@ -1,25 +1,26 @@
 idx,data_preprocessing:categorical_transformer:categorical_encoding:__choice__,data_preprocessing:categorical_transformer:category_coalescence:__choice__,data_preprocessing:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction,data_preprocessing:numerical_transformer:imputation:strategy,data_preprocessing:numerical_transformer:rescaling:__choice__,data_preprocessing:numerical_transformer:rescaling:quantile_transformer:n_quantiles,data_preprocessing:numerical_transformer:rescaling:quantile_transformer:output_distribution,data_preprocessing:numerical_transformer:rescaling:robust_scaler:q_max,data_preprocessing:numerical_transformer:rescaling:robust_scaler:q_min,feature_preprocessor:__choice__,feature_preprocessor:extra_trees_preproc_for_regression:bootstrap,feature_preprocessor:extra_trees_preproc_for_regression:criterion,feature_preprocessor:extra_trees_preproc_for_regression:max_depth,feature_preprocessor:extra_trees_preproc_for_regression:max_features,feature_preprocessor:extra_trees_preproc_for_regression:max_leaf_nodes,feature_preprocessor:extra_trees_preproc_for_regression:min_samples_leaf,feature_preprocessor:extra_trees_preproc_for_regression:min_samples_split,feature_preprocessor:extra_trees_preproc_for_regression:min_weight_fraction_leaf,feature_preprocessor:extra_trees_preproc_for_regression:n_estimators,feature_preprocessor:fast_ica:algorithm,feature_preprocessor:fast_ica:fun,feature_preprocessor:fast_ica:n_components,feature_preprocessor:fast_ica:whiten,feature_preprocessor:feature_agglomeration:affinity,feature_preprocessor:feature_agglomeration:linkage,feature_preprocessor:feature_agglomeration:n_clusters,feature_preprocessor:feature_agglomeration:pooling_func,feature_preprocessor:kernel_pca:coef0,feature_preprocessor:kernel_pca:degree,feature_preprocessor:kernel_pca:gamma,feature_preprocessor:kernel_pca:kernel,feature_preprocessor:kernel_pca:n_components,feature_preprocessor:kitchen_sinks:gamma,feature_preprocessor:kitchen_sinks:n_components,feature_preprocessor:nystroem_sampler:coef0,feature_preprocessor:nystroem_sampler:degree,feature_preprocessor:nystroem_sampler:gamma,feature_preprocessor:nystroem_sampler:kernel,feature_preprocessor:nystroem_sampler:n_components,feature_preprocessor:pca:keep_variance,feature_preprocessor:pca:whiten,feature_preprocessor:polynomial:degree,feature_preprocessor:polynomial:include_bias,feature_preprocessor:polynomial:interaction_only,feature_preprocessor:random_trees_embedding:bootstrap,feature_preprocessor:random_trees_embedding:max_depth,feature_preprocessor:random_trees_embedding:max_leaf_nodes,feature_preprocessor:random_trees_embedding:min_samples_leaf,feature_preprocessor:random_trees_embedding:min_samples_split,feature_preprocessor:random_trees_embedding:min_weight_fraction_leaf,feature_preprocessor:random_trees_embedding:n_estimators,feature_preprocessor:select_percentile_regression:percentile,feature_preprocessor:select_percentile_regression:score_func,feature_preprocessor:select_rates_regression:alpha,feature_preprocessor:select_rates_regression:mode,feature_preprocessor:select_rates_regression:score_func,regressor:__choice__,regressor:adaboost:learning_rate,regressor:adaboost:loss,regressor:adaboost:max_depth,regressor:adaboost:n_estimators,regressor:ard_regression:alpha_1,regressor:ard_regression:alpha_2,regressor:ard_regression:fit_intercept,regressor:ard_regression:lambda_1,regressor:ard_regression:lambda_2,regressor:ard_regression:n_iter,regressor:ard_regression:threshold_lambda,regressor:ard_regression:tol,regressor:decision_tree:criterion,regressor:decision_tree:max_depth_factor,regressor:decision_tree:max_features,regressor:decision_tree:max_leaf_nodes,regressor:decision_tree:min_impurity_decrease,regressor:decision_tree:min_samples_leaf,regressor:decision_tree:min_samples_split,regressor:decision_tree:min_weight_fraction_leaf,regressor:extra_trees:bootstrap,regressor:extra_trees:criterion,regressor:extra_trees:max_depth,regressor:extra_trees:max_features,regressor:extra_trees:max_leaf_nodes,regressor:extra_trees:min_impurity_decrease,regressor:extra_trees:min_samples_leaf,regressor:extra_trees:min_samples_split,regressor:extra_trees:min_weight_fraction_leaf,regressor:gaussian_process:alpha,regressor:gaussian_process:thetaL,regressor:gaussian_process:thetaU,regressor:gradient_boosting:early_stop,regressor:gradient_boosting:l2_regularization,regressor:gradient_boosting:learning_rate,regressor:gradient_boosting:loss,regressor:gradient_boosting:max_bins,regressor:gradient_boosting:max_depth,regressor:gradient_boosting:max_leaf_nodes,regressor:gradient_boosting:min_samples_leaf,regressor:gradient_boosting:n_iter_no_change,regressor:gradient_boosting:scoring,regressor:gradient_boosting:tol,regressor:gradient_boosting:validation_fraction,regressor:k_nearest_neighbors:n_neighbors,regressor:k_nearest_neighbors:p,regressor:k_nearest_neighbors:weights,regressor:liblinear_svr:C,regressor:liblinear_svr:dual,regressor:liblinear_svr:epsilon,regressor:liblinear_svr:fit_intercept,regressor:liblinear_svr:intercept_scaling,regressor:liblinear_svr:loss,regressor:liblinear_svr:tol,regressor:libsvm_svr:C,regressor:libsvm_svr:coef0,regressor:libsvm_svr:degree,regressor:libsvm_svr:epsilon,regressor:libsvm_svr:gamma,regressor:libsvm_svr:kernel,regressor:libsvm_svr:max_iter,regressor:libsvm_svr:shrinking,regressor:libsvm_svr:tol,regressor:random_forest:bootstrap,regressor:random_forest:criterion,regressor:random_forest:max_depth,regressor:random_forest:max_features,regressor:random_forest:max_leaf_nodes,regressor:random_forest:min_impurity_decrease,regressor:random_forest:min_samples_leaf,regressor:random_forest:min_samples_split,regressor:random_forest:min_weight_fraction_leaf,regressor:sgd:alpha,regressor:sgd:average,regressor:sgd:epsilon,regressor:sgd:eta0,regressor:sgd:fit_intercept,regressor:sgd:l1_ratio,regressor:sgd:learning_rate,regressor:sgd:loss,regressor:sgd:penalty,regressor:sgd:power_t,regressor:sgd:tol
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-3,one_hot_encoding,minority_coalescer,0.003623240287266379,median,robust_scaler,,,0.8134431529581335,0.2230614629084614,select_rates_regression,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.09849735185327581,fpr,f_regression,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,off,5.5749535239648375e-06,0.033744850983537196,least_squares,255,None,27,116,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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-5,one_hot_encoding,no_coalescense,,mean,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,valid,0.0004581673561271838,0.32962137015342907,least_squares,255,None,3,84,20,loss,1e-07,0.1407503228863241,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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-7,one_hot_encoding,minority_coalescer,0.012113085213710002,median,minmax,,,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.923739124770368,False,,,,,,,,,,,,,,,,libsvm_svr,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.9115865125692801,,5,0.10643586278948972,2.184494531191916,rbf,-1,True,0.002318776631832561,,,,,,,,,,,,,,,,,,,,
-8,no_encoding,minority_coalescer,0.007693066549798328,most_frequent,none,,,,,feature_agglomeration,,,,,,,,,,,,,,manhattan,average,320,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,extra_trees,,,,,,,,,,,,,,,,,,,,,True,friedman_mse,None,0.9274157646998513,None,0.0,4,9,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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-15,one_hot_encoding,minority_coalescer,0.0037423052714792336,mean,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,mse,None,0.4339166051934824,None,0.0,4,10,0.0,,,,,,,,,,,
-16,one_hot_encoding,minority_coalescer,0.010000000000000004,median,normalize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7.395586042195198e-06,True,0.039363539389536205,,True,,optimal,epsilon_insensitive,l2,,0.00032230798144272904
-17,one_hot_encoding,minority_coalescer,0.009171724071325382,most_frequent,standardize,,,,,feature_agglomeration,,,,,,,,,,,,,,manhattan,average,258,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,friedman_mse,None,0.7429459921040217,None,0.0,1,4,0.0,,,,,,,,,,,
-18,one_hot_encoding,minority_coalescer,0.07864422900237864,median,standardize,,,,,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,0.2993150897295005,,1.2747696325201687,sigmoid,977,,,,,,,,,,,,,,,,,,ard_regression,,,,,0.00029800225431691,5.551214723404076e-08,True,4.61049163663029e-07,5.874293238908804e-10,300,85889.14023423038,0.00027499470605731914,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-19,no_encoding,minority_coalescer,0.07072534022332494,mean,minmax,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,mse,None,0.9209937718223583,None,0.0,3,12,0.0,,,,,,,,,,,
-20,one_hot_encoding,no_coalescense,,mean,robust_scaler,,,0.8821344085141198,0.20696951294776308,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,10,None,3,15,1.0,33,,,,,,decision_tree,,,,,,,,,,,,,friedman_mse,0.8666369072341655,1.0,None,0.0,19,7,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-21,one_hot_encoding,minority_coalescer,0.004474714505413804,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,extra_trees,,,,,,,,,,,,,,,,,,,,,False,mse,None,0.8442614504957053,None,0.0,1,5,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-22,one_hot_encoding,minority_coalescer,0.010000000000000004,median,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,mse,None,1.0,None,0.0,1,3,0.0,,,,,,,,,,,
-23,no_encoding,minority_coalescer,0.007150902883091326,mean,robust_scaler,,,0.8663777312763623,0.02160040060766837,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,mse,None,0.9994655923368939,None,0.0,4,6,0.0,,,,,,,,,,,
-24,one_hot_encoding,no_coalescense,,most_frequent,robust_scaler,,,0.7484939216574421,0.20183301399810935,select_rates_regression,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.18375456889543734,fdr,f_regression,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,off,4.81881052684467e-05,0.10285955822720894,least_squares,255,None,8,1,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+1,one_hot_encoding,no_coalescense,,mean,standardize,,,,,select_rates_regression,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.40612979303062824,fdr,f_regression,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,off,2.2742572602302616e-08,0.02539518548794612,least_squares,255,None,57,10,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+2,one_hot_encoding,minority_coalescer,0.010000000000000004,most_frequent,quantile_transformer,1818,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,mse,None,0.9744859278597818,None,0.0,11,2,0.0,,,,,,,,,,,
+5,no_encoding,minority_coalescer,0.06912558240513342,median,quantile_transformer,119,uniform,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,off,3.79271698524212e-09,0.025159826241956527,least_squares,255,None,42,29,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+9,no_encoding,minority_coalescer,0.014087234899150132,most_frequent,minmax,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,libsvm_svr,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,243.80018215429655,,5,0.0010313680328976054,0.12602136380125653,rbf,-1,True,0.0003015355281210108,,,,,,,,,,,,,,,,,,,,
+13,no_encoding,minority_coalescer,0.010000000000000004,median,quantile_transformer,780,uniform,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,,libsvm_svr,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,10.974102872028766,,4,0.09412187025168416,0.04653835917168057,rbf,-1,False,0.0010357973041304064,,,,,,,,,,,,,,,,,,,,
+15,no_encoding,no_coalescense,,most_frequent,none,,,,,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00025844019785412086,rbf,305,,,,,,,,,,,,,,,,,,liblinear_svr,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,18080.177016227895,False,0.0017155220175954669,True,1,squared_epsilon_insensitive,7.035440846539455e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+17,no_encoding,no_coalescense,,most_frequent,robust_scaler,,,0.8280417820125114,0.0781634653874277,kitchen_sinks,,,,,,,,,,,,,,,,,,,,,,,8.438432240830361e-05,2984,,,,,,,,,,,,,,,,,,,,,,,ard_regression,,,,,0.00044036509169446026,4.039147500668822e-10,True,8.922721154590444e-05,3.0105431227198885e-05,300,1899.168836704701,0.011611373742389547,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+19,one_hot_encoding,no_coalescense,,mean,quantile_transformer,148,uniform,,,select_rates_regression,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.10789818795901182,fwe,f_regression,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,off,0.0002841741346637058,0.024253735132716756,least_squares,255,None,36,1,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+22,one_hot_encoding,no_coalescense,,most_frequent,standardize,,,,,fast_ica,,,,,,,,,,parallel,logcosh,215,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,off,4.242786820644205e-10,0.0953131401479951,least_squares,255,None,16,17,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+24,no_encoding,no_coalescense,,median,quantile_transformer,1150,uniform,,,kitchen_sinks,,,,,,,,,,,,,,,,,,,,,,,1.1605111479672479,5499,,,,,,,,,,,,,,,,,,,,,,,libsvm_svr,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,272.83550277553246,0.1471721071594263,2,0.18104912091393513,0.09926116813777154,poly,-1,True,0.06084368714049917,,,,,,,,,,,,,,,,,,,,
+30,no_encoding,no_coalescense,,most_frequent,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,friedman_mse,None,0.2961138557020546,None,0.0,14,12,0.0,,,,,,,,,,,
+34,no_encoding,no_coalescense,,most_frequent,robust_scaler,,,0.7049491520337817,0.23602880130851764,fast_ica,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,off,4.696295762197504e-10,0.03269357604116943,least_squares,255,None,32,14,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+36,one_hot_encoding,no_coalescense,,most_frequent,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,mse,None,0.1686847993740384,None,0.0,1,2,0.0,,,,,,,,,,,
+38,no_encoding,no_coalescense,,median,none,,,,,select_rates_regression,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.13294870400704156,fwe,f_regression,adaboost,0.47982456668237294,square,10,143,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+42,no_encoding,minority_coalescer,0.0074923422486941615,mean,quantile_transformer,919,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,mse,None,0.7102443468613595,None,0.0,20,4,0.0,,,,,,,,,,,
+45,no_encoding,minority_coalescer,0.00010476041523839592,median,standardize,,,,,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,-0.8964384900187328,,0.015021411517772181,sigmoid,7415,,,,,,,,,,,,,,,,,,ard_regression,,,,,4.544163312418526e-05,0.0003398091634662838,True,6.154987146709e-06,1.2041910604531783e-05,300,21462.056617690938,0.07078995384093524,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+48,one_hot_encoding,no_coalescense,,mean,quantile_transformer,148,uniform,,,select_rates_regression,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.10789818795901182,fwe,f_regression,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,off,0.0002841741346637058,0.024253735132716756,least_squares,255,None,36,1,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+51,one_hot_encoding,no_coalescense,,median,minmax,,,,,select_rates_regression,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.32764403627543237,fdr,f_regression,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,off,0.000153437102327148,0.01698754273672824,least_squares,255,None,47,76,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+52,no_encoding,no_coalescense,,most_frequent,quantile_transformer,1425,uniform,,,feature_agglomeration,,,,,,,,,,,,,,cosine,complete,276,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,off,0.00018459550741867383,0.04757728173371449,least_squares,255,None,16,6,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+57,one_hot_encoding,minority_coalescer,0.00924813449669181,mean,quantile_transformer,987,uniform,,,select_rates_regression,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fwe,f_regression,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,off,2.5833231171101403e-10,0.10682575320034993,least_squares,255,None,31,12,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+59,no_encoding,minority_coalescer,0.010000000000000004,most_frequent,robust_scaler,,,0.7414290235319753,0.2631696868735969,extra_trees_preproc_for_regression,True,mse,None,0.8529069417035176,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,extra_trees,,,,,,,,,,,,,,,,,,,,,True,mse,None,0.9698708516736436,None,0.0,4,2,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+62,one_hot_encoding,minority_coalescer,0.027537836751886195,median,robust_scaler,,,0.9003944797470034,0.2566610073208164,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,5,None,1,2,1.0,10,,,,,,decision_tree,,,,,,,,,,,,,friedman_mse,0.23884067765544847,1.0,None,0.0,11,13,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+66,no_encoding,minority_coalescer,0.006136459298464629,mean,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,extra_trees,,,,,,,,,,,,,,,,,,,,,False,friedman_mse,None,0.9490464657381125,None,0.0,1,6,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+68,one_hot_encoding,minority_coalescer,0.00924813449669181,mean,quantile_transformer,987,uniform,,,select_rates_regression,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fwe,f_regression,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,off,2.5833231171101403e-10,0.10682575320034993,least_squares,255,None,31,12,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+70,no_encoding,no_coalescense,,median,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,mse,None,0.9234161475923298,None,0.0,6,13,0.0,,,,,,,,,,,
diff --git a/autosklearn/metalearning/files/mean_squared_error_regression_sparse/description.txt b/autosklearn/metalearning/files/mean_squared_error_regression_sparse/description.txt
index 8856512c05..cf01e24b4b 100644
--- a/autosklearn/metalearning/files/mean_squared_error_regression_sparse/description.txt
+++ b/autosklearn/metalearning/files/mean_squared_error_regression_sparse/description.txt
@@ -40,7 +40,7 @@ features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfFeature
 features_stochastic: 
 default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
 
-algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24
+algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73
 algorithms_stochastic: 
 performance_measures: mean_squared_error
 performance_type: solution_quality
diff --git a/autosklearn/metalearning/files/mean_squared_error_regression_sparse/feature_costs.arff b/autosklearn/metalearning/files/mean_squared_error_regression_sparse/feature_costs.arff
index 0e17e20d22..2c1c95a875 100644
--- a/autosklearn/metalearning/files/mean_squared_error_regression_sparse/feature_costs.arff
+++ b/autosklearn/metalearning/files/mean_squared_error_regression_sparse/feature_costs.arff
@@ -39,160 +39,28 @@
 @ATTRIBUTE SkewnessSTD NUMERIC
 
 @DATA
-75123,1.0,1e-05,1e-05,1e-05,1e-05,0.00253,0.00221,1e-05,0.00024,3e-05,0.00011,0.0,2e-05,2e-05,5e-05,8e-05,2e-05,1e-05,7e-05,1e-05,0.00072,1e-05,1e-05,0.00019,0.00041,0.00011,0.00061,0.00012,9e-05,0.00015,0.00025,0.00067,0.00014,0.00011,0.00016,0.00027
-75090,1.0,1e-05,1e-05,2e-05,2e-05,0.00815,0.00368,7e-05,0.00228,9e-05,0.00235,0.0,5e-05,5e-05,0.0001,0.00013,0.00013,1e-05,0.00021,2e-05,0.00064,0.0,0.0,0.00014,0.00038,0.00011,0.00066,0.00011,0.00014,0.00014,0.00026,0.00066,0.00011,0.00013,0.00015,0.00027
-2123,1.0,1e-05,1e-05,1e-05,1e-05,0.00097,0.00079,1e-05,0.00013,1e-05,7e-05,0.0,2e-05,2e-05,5e-05,8e-05,2e-05,1e-05,5e-05,1e-05,0.0007,1e-05,0.0,0.00017,0.00042,0.0001,0.00052,6e-05,8e-05,0.00012,0.00026,0.00049,6e-05,0.00011,9e-05,0.00023
-248,1.0,1e-05,1e-05,1e-05,1e-05,0.00088,0.00077,1e-05,0.0001,1e-05,4e-05,0.0,1e-05,1e-05,4e-05,5e-05,2e-05,1e-05,4e-05,1e-05,0.00041,1e-05,0.0,9e-05,0.00022,9e-05,0.00046,6e-05,8e-05,9e-05,0.00023,0.00049,7e-05,0.0001,0.0001,0.00021
-75101,1.0,1e-05,1e-05,1e-05,1e-05,0.0418,0.03546,1e-05,0.00389,1e-05,0.00248,0.0,2e-05,2e-05,4e-05,6e-05,2e-05,1e-05,5e-05,2e-05,0.00045,1e-05,0.0,0.00011,0.00023,0.0001,0.00041,6e-05,8e-05,0.0001,0.00018,0.00047,6e-05,0.0001,8e-05,0.00023
-75191,1.0,1e-05,1e-05,3e-05,1e-05,0.53436,0.40777,2e-05,0.09655,3e-05,0.0301,1e-05,2e-05,3e-05,5e-05,0.00011,7e-05,1e-05,0.00011,3e-05,0.00084,5e-05,7e-05,0.00017,0.00033,0.00023,0.00088,0.00012,0.00017,0.00032,0.00026,0.00068,9e-05,0.00012,0.00013,0.00035
-75187,1.0,1e-05,1e-05,2e-05,1e-05,0.00548,0.00473,1e-05,0.0005,2e-05,0.00029,1e-05,2e-05,2e-05,7e-05,9e-05,3e-05,1e-05,6e-05,1e-05,0.00062,1e-05,1e-05,0.00014,0.00033,0.00013,0.00038,7e-05,7e-05,8e-05,0.00016,0.00047,6e-05,9e-05,9e-05,0.00023
-266,1.0,1e-05,1e-05,1e-05,1e-05,0.0018,0.00146,1e-05,0.00023,2e-05,0.00015,2e-05,2e-05,2e-05,6e-05,8e-05,3e-05,1e-05,6e-05,1e-05,0.00059,1e-05,1e-05,0.00014,0.00031,0.00013,0.0004,5e-05,7e-05,8e-05,0.0002,0.00044,8e-05,8e-05,8e-05,0.0002
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diff --git a/autosklearn/metalearning/files/mean_squared_error_regression_sparse/feature_runstatus.arff b/autosklearn/metalearning/files/mean_squared_error_regression_sparse/feature_runstatus.arff
index 0034ad2bd6..0166848554 100644
--- a/autosklearn/metalearning/files/mean_squared_error_regression_sparse/feature_runstatus.arff
+++ b/autosklearn/metalearning/files/mean_squared_error_regression_sparse/feature_runstatus.arff
@@ -39,160 +39,28 @@
 @ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other}
 
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diff --git a/autosklearn/metalearning/files/mean_squared_error_regression_sparse/feature_values.arff b/autosklearn/metalearning/files/mean_squared_error_regression_sparse/feature_values.arff
index a60400e3ce..ee955516b5 100644
--- a/autosklearn/metalearning/files/mean_squared_error_regression_sparse/feature_values.arff
+++ b/autosklearn/metalearning/files/mean_squared_error_regression_sparse/feature_values.arff
@@ -2,24 +2,17 @@
 
 @ATTRIBUTE instance_id STRING
 @ATTRIBUTE repetition NUMERIC
-@ATTRIBUTE ClassEntropy NUMERIC
-@ATTRIBUTE ClassProbabilityMax NUMERIC
-@ATTRIBUTE ClassProbabilityMean NUMERIC
-@ATTRIBUTE ClassProbabilityMin NUMERIC
-@ATTRIBUTE ClassProbabilitySTD NUMERIC
 @ATTRIBUTE DatasetRatio NUMERIC
 @ATTRIBUTE InverseDatasetRatio NUMERIC
 @ATTRIBUTE KurtosisMax NUMERIC
 @ATTRIBUTE KurtosisMean NUMERIC
 @ATTRIBUTE KurtosisMin NUMERIC
 @ATTRIBUTE KurtosisSTD NUMERIC
-@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC
 @ATTRIBUTE LogDatasetRatio NUMERIC
 @ATTRIBUTE LogInverseDatasetRatio NUMERIC
 @ATTRIBUTE LogNumberOfFeatures NUMERIC
 @ATTRIBUTE LogNumberOfInstances NUMERIC
 @ATTRIBUTE NumberOfCategoricalFeatures NUMERIC
-@ATTRIBUTE NumberOfClasses NUMERIC
 @ATTRIBUTE NumberOfFeatures NUMERIC
 @ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC
 @ATTRIBUTE NumberOfInstances NUMERIC
@@ -42,160 +35,28 @@
 @ATTRIBUTE SymbolsSum NUMERIC
 
 @DATA
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diff --git a/autosklearn/metalearning/files/mean_squared_log_error_regression_dense/algorithm_runs.arff b/autosklearn/metalearning/files/mean_squared_log_error_regression_dense/algorithm_runs.arff
new file mode 100644
index 0000000000..a2fefc45f5
--- /dev/null
+++ b/autosklearn/metalearning/files/mean_squared_log_error_regression_dense/algorithm_runs.arff
@@ -0,0 +1,34 @@
+@RELATION auto-sklearn_ALGORITHM_RUNS
+
+@ATTRIBUTE instance_id STRING
+@ATTRIBUTE repetition NUMERIC
+@ATTRIBUTE algorithm STRING
+@ATTRIBUTE mean_squared_log_error NUMERIC
+@ATTRIBUTE runstatus {ok, timeout, memout, not_applicable, crash, other}
+
+@DATA
+2292,1.0,47,0.2503821083688043,ok
+4796,1.0,2,0.00018089013171282441,ok
+4893,1.0,6,0.41601335699608416,ok
+4769,1.0,8,?,not_applicable
+4885,1.0,13,0.006643222641643715,ok
+5024,1.0,17,0.11806590621422001,ok
+2306,1.0,15,?,not_applicable
+4892,1.0,67,0.0014897419437288832,ok
+4883,1.0,40,0.0016677789758697756,ok
+2307,1.0,22,3.7005532258042856e-06,ok
+2280,1.0,24,0.0020620960561170028,ok
+2309,1.0,27,0.42169161203022204,ok
+2289,1.0,28,?,not_applicable
+4835,1.0,32,?,not_applicable
+2315,1.0,35,0.001597049943240552,ok
+4768,1.0,36,0.007346666433819575,ok
+4779,1.0,38,0.005914639997710185,ok
+2313,1.0,41,?,not_applicable
+5022,1.0,45,0.017833754039762714,ok
+4881,1.0,53,0.004807782582693809,ok
+2288,1.0,55,0.002446305766281673,ok
+4790,1.0,59,0.00017269205377503513,ok
+2300,1.0,61,0.0006264188056380751,ok
+4774,1.0,64,?,not_applicable
+4772,1.0,69,?,not_applicable
diff --git a/autosklearn/metalearning/files/mean_squared_log_error_regression_dense/configurations.csv b/autosklearn/metalearning/files/mean_squared_log_error_regression_dense/configurations.csv
new file mode 100644
index 0000000000..dd946eee42
--- /dev/null
+++ b/autosklearn/metalearning/files/mean_squared_log_error_regression_dense/configurations.csv
@@ -0,0 +1,26 @@
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svr:epsilon,regressor:liblinear_svr:fit_intercept,regressor:liblinear_svr:intercept_scaling,regressor:liblinear_svr:loss,regressor:liblinear_svr:tol,regressor:libsvm_svr:C,regressor:libsvm_svr:coef0,regressor:libsvm_svr:degree,regressor:libsvm_svr:epsilon,regressor:libsvm_svr:gamma,regressor:libsvm_svr:kernel,regressor:libsvm_svr:max_iter,regressor:libsvm_svr:shrinking,regressor:libsvm_svr:tol,regressor:random_forest:bootstrap,regressor:random_forest:criterion,regressor:random_forest:max_depth,regressor:random_forest:max_features,regressor:random_forest:max_leaf_nodes,regressor:random_forest:min_impurity_decrease,regressor:random_forest:min_samples_leaf,regressor:random_forest:min_samples_split,regressor:random_forest:min_weight_fraction_leaf,regressor:sgd:alpha,regressor:sgd:average,regressor:sgd:epsilon,regressor:sgd:eta0,regressor:sgd:fit_intercept,regressor:sgd:l1_ratio,regressor:sgd:learning_rate,regressor:sgd:loss,regressor:sgd:penalty,regressor:sgd:power_t,regressor:sgd:tol
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diff --git a/autosklearn/metalearning/files/mean_squared_log_error_regression_dense/description.txt b/autosklearn/metalearning/files/mean_squared_log_error_regression_dense/description.txt
new file mode 100644
index 0000000000..977af9f6b1
--- /dev/null
+++ b/autosklearn/metalearning/files/mean_squared_log_error_regression_dense/description.txt
@@ -0,0 +1,51 @@
+features_cutoff_time: 3600
+features_cutoff_memory: 3072
+number_of_feature_steps: 35
+feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances
+feature_step LogNumberOfInstances: LogNumberOfInstances
+feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures
+feature_step LogNumberOfFeatures: LogNumberOfFeatures
+feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues
+feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues
+feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues
+feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues
+feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues
+feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues
+feature_step PercentageOfMissingValues: PercentageOfMissingValues
+feature_step NumberOfNumericFeatures: NumberOfNumericFeatures
+feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures
+feature_step RatioNumericalToNominal: RatioNumericalToNominal
+feature_step RatioNominalToNumerical: RatioNominalToNumerical
+feature_step DatasetRatio: DatasetRatio, LogDatasetRatio
+feature_step LogDatasetRatio: LogDatasetRatio
+feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio
+feature_step LogInverseDatasetRatio: LogInverseDatasetRatio
+feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum
+feature_step SymbolsMin: SymbolsMin
+feature_step SymbolsMax: SymbolsMax
+feature_step SymbolsMean: SymbolsMean
+feature_step SymbolsSTD: SymbolsSTD
+feature_step SymbolsSum: SymbolsSum
+feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD
+feature_step KurtosisMin: KurtosisMin
+feature_step KurtosisMax: KurtosisMax
+feature_step KurtosisMean: KurtosisMean
+feature_step KurtosisSTD: KurtosisSTD
+feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
+feature_step SkewnessMin: SkewnessMin
+feature_step SkewnessMax: SkewnessMax
+feature_step SkewnessMean: SkewnessMean
+feature_step SkewnessSTD: SkewnessSTD
+features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
+features_stochastic: 
+default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
+
+algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73
+algorithms_stochastic: 
+performance_measures: mean_squared_log_error
+performance_type: solution_quality
+
+scenario_id: auto-sklearn
+maximize: false
+algorithm_cutoff_time: 1800
+algorithm_cutoff_memory: 3072
diff --git a/autosklearn/metalearning/files/mean_squared_log_error_regression_dense/feature_costs.arff b/autosklearn/metalearning/files/mean_squared_log_error_regression_dense/feature_costs.arff
new file mode 100644
index 0000000000..2c1c95a875
--- /dev/null
+++ b/autosklearn/metalearning/files/mean_squared_log_error_regression_dense/feature_costs.arff
@@ -0,0 +1,66 @@
+@RELATION auto-sklearn_FEATURE_COSTS
+
+@ATTRIBUTE instance_id STRING
+@ATTRIBUTE repetition NUMERIC
+@ATTRIBUTE NumberOfInstances NUMERIC
+@ATTRIBUTE LogNumberOfInstances NUMERIC
+@ATTRIBUTE NumberOfFeatures NUMERIC
+@ATTRIBUTE LogNumberOfFeatures NUMERIC
+@ATTRIBUTE MissingValues NUMERIC
+@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC
+@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC
+@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC
+@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC
+@ATTRIBUTE NumberOfMissingValues NUMERIC
+@ATTRIBUTE PercentageOfMissingValues NUMERIC
+@ATTRIBUTE NumberOfNumericFeatures NUMERIC
+@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC
+@ATTRIBUTE RatioNumericalToNominal NUMERIC
+@ATTRIBUTE RatioNominalToNumerical NUMERIC
+@ATTRIBUTE DatasetRatio NUMERIC
+@ATTRIBUTE LogDatasetRatio NUMERIC
+@ATTRIBUTE InverseDatasetRatio NUMERIC
+@ATTRIBUTE LogInverseDatasetRatio NUMERIC
+@ATTRIBUTE NumSymbols NUMERIC
+@ATTRIBUTE SymbolsMin NUMERIC
+@ATTRIBUTE SymbolsMax NUMERIC
+@ATTRIBUTE SymbolsMean NUMERIC
+@ATTRIBUTE SymbolsSTD NUMERIC
+@ATTRIBUTE SymbolsSum NUMERIC
+@ATTRIBUTE Kurtosisses NUMERIC
+@ATTRIBUTE KurtosisMin NUMERIC
+@ATTRIBUTE KurtosisMax NUMERIC
+@ATTRIBUTE KurtosisMean NUMERIC
+@ATTRIBUTE KurtosisSTD NUMERIC
+@ATTRIBUTE Skewnesses NUMERIC
+@ATTRIBUTE SkewnessMin NUMERIC
+@ATTRIBUTE SkewnessMax NUMERIC
+@ATTRIBUTE SkewnessMean NUMERIC
+@ATTRIBUTE SkewnessSTD NUMERIC
+
+@DATA
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+4796,1.0,2e-05,1e-05,2e-05,1e-05,0.03581,0.0194,4e-05,0.00921,5e-05,0.00729,1e-05,8e-05,0.0001,0.00013,0.00016,8e-05,2e-05,0.00012,3e-05,0.00083,1e-05,1e-05,0.00023,0.00045,0.00015,0.00103,0.00015,0.00019,0.0003,0.00039,0.0009,0.00016,0.00015,0.00019,0.0004
+4835,1.0,2e-05,2e-05,3e-05,2e-05,0.00597,0.00541,3e-05,0.0005,3e-05,0.00014,1e-05,5e-05,5e-05,0.00014,0.00022,7e-05,2e-05,0.0001,3e-05,0.0031,1e-05,1e-05,0.00113,0.00174,0.0002,0.00113,0.00022,0.00019,0.00023,0.00049,0.00086,0.00013,0.00015,0.00026,0.00033
+4881,1.0,2e-05,1e-05,3e-05,1e-05,0.01016,0.00811,2e-05,0.00138,2e-05,0.00073,1e-05,4e-05,4e-05,8e-05,0.00011,4e-05,2e-05,6e-05,3e-05,0.00083,1e-05,1e-05,0.00022,0.00043,0.00017,0.00079,0.00011,0.00012,0.00016,0.00039,0.00104,0.00012,0.00013,0.00022,0.00058
+4883,1.0,1e-05,1e-05,3e-05,3e-05,0.00448,0.00395,1e-05,0.0004,1e-05,0.00017,1e-05,2e-05,2e-05,7e-05,8e-05,3e-05,1e-05,4e-05,2e-05,0.00042,0.0,0.0,0.00011,0.00023,7e-05,0.00179,0.00025,0.00035,0.00044,0.00075,0.00072,0.00011,0.00012,0.00016,0.00033
+4885,1.0,2e-05,1e-05,3e-05,1e-05,0.04525,0.04018,2e-05,0.00396,2e-05,0.00116,1e-05,5e-05,7e-05,9e-05,0.00015,6e-05,1e-05,0.00013,7e-05,0.00092,1e-05,1e-05,0.00032,0.00045,0.00014,0.00082,0.00012,0.00013,0.00017,0.0004,0.00076,0.00011,0.00012,0.00016,0.00036
+4892,1.0,1e-05,1e-05,2e-05,1e-05,0.00802,0.00665,2e-05,0.00093,2e-05,0.00048,1e-05,3e-05,3e-05,7e-05,0.0001,6e-05,1e-05,5e-05,2e-05,0.00112,0.0,1e-05,0.00034,0.00066,0.00011,0.00041,8e-05,7e-05,8e-05,0.00018,0.00046,6e-05,7e-05,9e-05,0.00025
+4893,1.0,3e-05,2e-05,4e-05,2e-05,0.02994,0.02648,3e-05,0.00238,4e-05,0.00116,1e-05,4e-05,5e-05,8e-05,0.00011,5e-05,2e-05,0.0001,4e-05,0.00089,1e-05,1e-05,0.00026,0.00049,0.00012,0.00077,0.00013,0.00012,0.00016,0.00036,0.00092,0.00019,0.00013,0.00017,0.00042
+5022,1.0,3e-05,2e-05,4e-05,3e-05,0.00228,0.00193,3e-05,0.00033,3e-05,0.0001,1e-05,7e-05,6e-05,0.00015,0.00018,7e-05,2e-05,9e-05,3e-05,0.00126,1e-05,1e-05,0.00037,0.00068,0.0002,0.00076,0.00011,0.00013,0.00018,0.00034,0.00094,0.00017,0.0002,0.00019,0.00039
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diff --git a/autosklearn/metalearning/files/mean_squared_log_error_regression_dense/feature_runstatus.arff b/autosklearn/metalearning/files/mean_squared_log_error_regression_dense/feature_runstatus.arff
new file mode 100644
index 0000000000..0166848554
--- /dev/null
+++ b/autosklearn/metalearning/files/mean_squared_log_error_regression_dense/feature_runstatus.arff
@@ -0,0 +1,66 @@
+@RELATION auto-sklearn_FEATURE_RUNSTATUS
+
+@ATTRIBUTE instance_id STRING
+@ATTRIBUTE repetition NUMERIC
+@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other}
+
+@DATA
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diff --git a/autosklearn/metalearning/files/mean_squared_log_error_regression_dense/feature_values.arff b/autosklearn/metalearning/files/mean_squared_log_error_regression_dense/feature_values.arff
new file mode 100644
index 0000000000..ee955516b5
--- /dev/null
+++ b/autosklearn/metalearning/files/mean_squared_log_error_regression_dense/feature_values.arff
@@ -0,0 +1,62 @@
+@RELATION auto-sklearn_FEATURE_VALUES
+
+@ATTRIBUTE instance_id STRING
+@ATTRIBUTE repetition NUMERIC
+@ATTRIBUTE DatasetRatio NUMERIC
+@ATTRIBUTE InverseDatasetRatio NUMERIC
+@ATTRIBUTE KurtosisMax NUMERIC
+@ATTRIBUTE KurtosisMean NUMERIC
+@ATTRIBUTE KurtosisMin NUMERIC
+@ATTRIBUTE KurtosisSTD NUMERIC
+@ATTRIBUTE LogDatasetRatio NUMERIC
+@ATTRIBUTE LogInverseDatasetRatio NUMERIC
+@ATTRIBUTE LogNumberOfFeatures NUMERIC
+@ATTRIBUTE LogNumberOfInstances NUMERIC
+@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC
+@ATTRIBUTE NumberOfFeatures NUMERIC
+@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC
+@ATTRIBUTE NumberOfInstances NUMERIC
+@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC
+@ATTRIBUTE NumberOfMissingValues NUMERIC
+@ATTRIBUTE NumberOfNumericFeatures NUMERIC
+@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC
+@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC
+@ATTRIBUTE PercentageOfMissingValues NUMERIC
+@ATTRIBUTE RatioNominalToNumerical NUMERIC
+@ATTRIBUTE RatioNumericalToNominal NUMERIC
+@ATTRIBUTE SkewnessMax NUMERIC
+@ATTRIBUTE SkewnessMean NUMERIC
+@ATTRIBUTE SkewnessMin NUMERIC
+@ATTRIBUTE SkewnessSTD NUMERIC
+@ATTRIBUTE SymbolsMax NUMERIC
+@ATTRIBUTE SymbolsMean NUMERIC
+@ATTRIBUTE SymbolsMin NUMERIC
+@ATTRIBUTE SymbolsSTD NUMERIC
+@ATTRIBUTE SymbolsSum NUMERIC
+
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diff --git a/test/test_ensemble_builder/data/.auto-sklearn/done/0_1 b/autosklearn/metalearning/files/mean_squared_log_error_regression_dense/readme.txt
similarity index 100%
rename from test/test_ensemble_builder/data/.auto-sklearn/done/0_1
rename to autosklearn/metalearning/files/mean_squared_log_error_regression_dense/readme.txt
diff --git a/autosklearn/metalearning/files/mean_squared_log_error_regression_sparse/algorithm_runs.arff b/autosklearn/metalearning/files/mean_squared_log_error_regression_sparse/algorithm_runs.arff
new file mode 100644
index 0000000000..a2fefc45f5
--- /dev/null
+++ b/autosklearn/metalearning/files/mean_squared_log_error_regression_sparse/algorithm_runs.arff
@@ -0,0 +1,34 @@
+@RELATION auto-sklearn_ALGORITHM_RUNS
+
+@ATTRIBUTE instance_id STRING
+@ATTRIBUTE repetition NUMERIC
+@ATTRIBUTE algorithm STRING
+@ATTRIBUTE mean_squared_log_error NUMERIC
+@ATTRIBUTE runstatus {ok, timeout, memout, not_applicable, crash, other}
+
+@DATA
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diff --git a/autosklearn/metalearning/files/mean_squared_log_error_regression_sparse/configurations.csv b/autosklearn/metalearning/files/mean_squared_log_error_regression_sparse/configurations.csv
new file mode 100644
index 0000000000..dd946eee42
--- /dev/null
+++ b/autosklearn/metalearning/files/mean_squared_log_error_regression_sparse/configurations.csv
@@ -0,0 +1,26 @@
+idx,data_preprocessing:categorical_transformer:categorical_encoding:__choice__,data_preprocessing:categorical_transformer:category_coalescence:__choice__,data_preprocessing:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction,data_preprocessing:numerical_transformer:imputation:strategy,data_preprocessing:numerical_transformer:rescaling:__choice__,data_preprocessing:numerical_transformer:rescaling:quantile_transformer:n_quantiles,data_preprocessing:numerical_transformer:rescaling:quantile_transformer:output_distribution,data_preprocessing:numerical_transformer:rescaling:robust_scaler:q_max,data_preprocessing:numerical_transformer:rescaling:robust_scaler:q_min,feature_preprocessor:__choice__,feature_preprocessor:extra_trees_preproc_for_regression:bootstrap,feature_preprocessor:extra_trees_preproc_for_regression:criterion,feature_preprocessor:extra_trees_preproc_for_regression:max_depth,feature_preprocessor:extra_trees_preproc_for_regression:max_features,feature_preprocessor:extra_trees_preproc_for_regression:max_leaf_nodes,feature_preprocessor:extra_trees_preproc_for_regression:min_samples_leaf,feature_preprocessor:extra_trees_preproc_for_regression:min_samples_split,feature_preprocessor:extra_trees_preproc_for_regression:min_weight_fraction_leaf,feature_preprocessor:extra_trees_preproc_for_regression:n_estimators,feature_preprocessor:fast_ica:algorithm,feature_preprocessor:fast_ica:fun,feature_preprocessor:fast_ica:n_components,feature_preprocessor:fast_ica:whiten,feature_preprocessor:feature_agglomeration:affinity,feature_preprocessor:feature_agglomeration:linkage,feature_preprocessor:feature_agglomeration:n_clusters,feature_preprocessor:feature_agglomeration:pooling_func,feature_preprocessor:kernel_pca:coef0,feature_preprocessor:kernel_pca:degree,feature_preprocessor:kernel_pca:gamma,feature_preprocessor:kernel_pca:kernel,feature_preprocessor:kernel_pca:n_components,feature_preprocessor:kitchen_sinks:gamma,feature_preprocessor:kitchen_sinks:n_components,feature_preprocessor:nystroem_sampler:coef0,feature_preprocessor:nystroem_sampler:degree,feature_preprocessor:nystroem_sampler:gamma,feature_preprocessor:nystroem_sampler:kernel,feature_preprocessor:nystroem_sampler:n_components,feature_preprocessor:pca:keep_variance,feature_preprocessor:pca:whiten,feature_preprocessor:polynomial:degree,feature_preprocessor:polynomial:include_bias,feature_preprocessor:polynomial:interaction_only,feature_preprocessor:random_trees_embedding:bootstrap,feature_preprocessor:random_trees_embedding:max_depth,feature_preprocessor:random_trees_embedding:max_leaf_nodes,feature_preprocessor:random_trees_embedding:min_samples_leaf,feature_preprocessor:random_trees_embedding:min_samples_split,feature_preprocessor:random_trees_embedding:min_weight_fraction_leaf,feature_preprocessor:random_trees_embedding:n_estimators,feature_preprocessor:select_percentile_regression:percentile,feature_preprocessor:select_percentile_regression:score_func,feature_preprocessor:select_rates_regression:alpha,feature_preprocessor:select_rates_regression:mode,feature_preprocessor:select_rates_regression:score_func,regressor:__choice__,regressor:adaboost:learning_rate,regressor:adaboost:loss,regressor:adaboost:max_depth,regressor:adaboost:n_estimators,regressor:ard_regression:alpha_1,regressor:ard_regression:alpha_2,regressor:ard_regression:fit_intercept,regressor:ard_regression:lambda_1,regressor:ard_regression:lambda_2,regressor:ard_regression:n_iter,regressor:ard_regression:threshold_lambda,regressor:ard_regression:tol,regressor:decision_tree:criterion,regressor:decision_tree:max_depth_factor,regressor:decision_tree:max_features,regressor:decision_tree:max_leaf_nodes,regressor:decision_tree:min_impurity_decrease,regressor:decision_tree:min_samples_leaf,regressor:decision_tree:min_samples_split,regressor:decision_tree:min_weight_fraction_leaf,regressor:extra_trees:bootstrap,regressor:extra_trees:criterion,regressor:extra_trees:max_depth,regressor:extra_trees:max_features,regressor:extra_trees:max_leaf_nodes,regressor:extra_trees:min_impurity_decrease,regressor:extra_trees:min_samples_leaf,regressor:extra_trees:min_samples_split,regressor:extra_trees:min_weight_fraction_leaf,regressor:gaussian_process:alpha,regressor:gaussian_process:thetaL,regressor:gaussian_process:thetaU,regressor:gradient_boosting:early_stop,regressor:gradient_boosting:l2_regularization,regressor:gradient_boosting:learning_rate,regressor:gradient_boosting:loss,regressor:gradient_boosting:max_bins,regressor:gradient_boosting:max_depth,regressor:gradient_boosting:max_leaf_nodes,regressor:gradient_boosting:min_samples_leaf,regressor:gradient_boosting:n_iter_no_change,regressor:gradient_boosting:scoring,regressor:gradient_boosting:tol,regressor:gradient_boosting:validation_fraction,regressor:k_nearest_neighbors:n_neighbors,regressor:k_nearest_neighbors:p,regressor:k_nearest_neighbors:weights,regressor:liblinear_svr:C,regressor:liblinear_svr:dual,regressor:liblinear_svr:epsilon,regressor:liblinear_svr:fit_intercept,regressor:liblinear_svr:intercept_scaling,regressor:liblinear_svr:loss,regressor:liblinear_svr:tol,regressor:libsvm_svr:C,regressor:libsvm_svr:coef0,regressor:libsvm_svr:degree,regressor:libsvm_svr:epsilon,regressor:libsvm_svr:gamma,regressor:libsvm_svr:kernel,regressor:libsvm_svr:max_iter,regressor:libsvm_svr:shrinking,regressor:libsvm_svr:tol,regressor:random_forest:bootstrap,regressor:random_forest:criterion,regressor:random_forest:max_depth,regressor:random_forest:max_features,regressor:random_forest:max_leaf_nodes,regressor:random_forest:min_impurity_decrease,regressor:random_forest:min_samples_leaf,regressor:random_forest:min_samples_split,regressor:random_forest:min_weight_fraction_leaf,regressor:sgd:alpha,regressor:sgd:average,regressor:sgd:epsilon,regressor:sgd:eta0,regressor:sgd:fit_intercept,regressor:sgd:l1_ratio,regressor:sgd:learning_rate,regressor:sgd:loss,regressor:sgd:penalty,regressor:sgd:power_t,regressor:sgd:tol
+2,one_hot_encoding,minority_coalescer,0.010000000000000004,most_frequent,quantile_transformer,1818,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,mse,None,0.9744859278597818,None,0.0,11,2,0.0,,,,,,,,,,,
+6,no_encoding,no_coalescense,,mean,none,,,,,feature_agglomeration,,,,,,,,,,,,,,manhattan,average,259,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,off,0.19437910312263157,0.07323693968035645,least_squares,255,None,289,1,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+8,no_encoding,minority_coalescer,0.17411569485628128,median,standardize,,,,,select_rates_regression,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.14914671666646578,fdr,f_regression,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,mae,None,0.5100485349756239,None,0.0,1,7,0.0,,,,,,,,,,,
+13,no_encoding,minority_coalescer,0.010000000000000004,median,quantile_transformer,780,uniform,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,,libsvm_svr,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,10.974102872028766,,4,0.09412187025168416,0.04653835917168057,rbf,-1,False,0.0010357973041304064,,,,,,,,,,,,,,,,,,,,
+15,no_encoding,no_coalescense,,most_frequent,none,,,,,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,-0.6005053595097949,3,0.0007972870768406728,poly,217,,,,,,,,,,,,,,,,,,liblinear_svr,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3464.3505521686156,False,0.0013186332807499494,True,1,squared_epsilon_insensitive,0.0001208398274412593,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+17,no_encoding,no_coalescense,,most_frequent,robust_scaler,,,0.8280417820125114,0.0781634653874277,kitchen_sinks,,,,,,,,,,,,,,,,,,,,,,,8.438432240830361e-05,2984,,,,,,,,,,,,,,,,,,,,,,,ard_regression,,,,,0.00044036509169446026,4.039147500668822e-10,True,8.922721154590444e-05,3.0105431227198885e-05,300,1899.168836704701,0.011611373742389547,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+22,one_hot_encoding,no_coalescense,,most_frequent,standardize,,,,,fast_ica,,,,,,,,,,parallel,logcosh,215,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,off,4.242786820644205e-10,0.0953131401479951,least_squares,255,None,16,17,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+24,no_encoding,no_coalescense,,median,quantile_transformer,1150,uniform,,,kitchen_sinks,,,,,,,,,,,,,,,,,,,,,,,1.1605111479672479,5499,,,,,,,,,,,,,,,,,,,,,,,libsvm_svr,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,272.83550277553246,0.1471721071594263,2,0.18104912091393513,0.09926116813777154,poly,-1,True,0.06084368714049917,,,,,,,,,,,,,,,,,,,,
+27,one_hot_encoding,minority_coalescer,0.03729785572869407,most_frequent,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,,extra_trees,,,,,,,,,,,,,,,,,,,,,False,mse,None,0.9313381000630087,None,0.0,2,7,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+28,no_encoding,minority_coalescer,0.009110244546491056,most_frequent,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,mse,None,0.2623298824715591,None,0.0,15,14,0.0,,,,,,,,,,,
+32,no_encoding,no_coalescense,,mean,standardize,,,,,kernel_pca,,,,,,,,,,,,,,,,,,,,0.2693086920713033,rbf,313,,,,,,,,,,,,,,,,,,,,,,,,,ard_regression,,,,,0.00014101370586668783,4.5920279428358985e-05,True,2.1239495683504874e-07,1.554352516840477e-09,300,72661.47966222059,0.015890250125901458,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+35,no_encoding,no_coalescense,,mean,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,False,,,,,,,,,,,,,adaboost,0.950333009718573,exponential,10,445,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+36,one_hot_encoding,no_coalescense,,most_frequent,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,mse,None,0.1686847993740384,None,0.0,1,2,0.0,,,,,,,,,,,
+38,no_encoding,no_coalescense,,median,none,,,,,select_rates_regression,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.13294870400704156,fwe,f_regression,adaboost,0.47982456668237294,square,10,143,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+40,no_encoding,minority_coalescer,0.272028565183623,mean,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,friedman_mse,None,0.3173934512781902,None,0.0,6,12,0.0,,,,,,,,,,,
+41,one_hot_encoding,minority_coalescer,0.012966946253956882,median,none,,,,,select_rates_regression,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.09707818933172224,fpr,f_regression,libsvm_svr,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3420.2627079271324,,5,0.0034042816647235144,0.03195984622202108,rbf,-1,False,0.0001822785255282969,,,,,,,,,,,,,,,,,,,,
+45,no_encoding,minority_coalescer,0.00010476041523839592,median,standardize,,,,,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,-0.8964384900187328,,0.015021411517772181,sigmoid,7415,,,,,,,,,,,,,,,,,,ard_regression,,,,,4.544163312418526e-05,0.0003398091634662838,True,6.154987146709e-06,1.2041910604531783e-05,300,21462.056617690938,0.07078995384093524,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+47,no_encoding,minority_coalescer,0.01721530778946639,mean,robust_scaler,,,0.7276385022589964,0.11526510416512617,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,mse,None,1.0,None,0.0,1,2,0.0,,,,,,,,,,,
+53,no_encoding,minority_coalescer,0.0478249942439368,median,normalize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,,extra_trees,,,,,,,,,,,,,,,,,,,,,True,friedman_mse,None,0.7016426261898782,None,0.0,4,20,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+55,one_hot_encoding,minority_coalescer,0.011311144975325095,median,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,friedman_mse,None,0.5767479298331539,None,0.0,3,4,0.0,,,,,,,,,,,
+59,no_encoding,minority_coalescer,0.010000000000000004,most_frequent,robust_scaler,,,0.7414290235319753,0.2631696868735969,extra_trees_preproc_for_regression,True,mse,None,0.8529069417035176,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,extra_trees,,,,,,,,,,,,,,,,,,,,,True,mse,None,0.9698708516736436,None,0.0,4,2,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+61,no_encoding,minority_coalescer,0.0017563083910912887,mean,standardize,,,,,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,7,None,16,8,1.0,80,,,,,,decision_tree,,,,,,,,,,,,,friedman_mse,0.43466555280407243,1.0,None,0.0,17,12,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+64,no_encoding,no_coalescense,,mean,robust_scaler,,,0.9145357534382171,0.25630346118699227,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,extra_trees,,,,,,,,,,,,,,,,,,,,,False,mse,None,0.9784004031942577,None,0.0,2,2,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+67,one_hot_encoding,no_coalescense,,mean,robust_scaler,,,0.75,0.24328975064303876,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,,extra_trees,,,,,,,,,,,,,,,,,,,,,False,friedman_mse,None,0.9963592694153689,None,0.0,1,13,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+69,no_encoding,minority_coalescer,0.25213930464867856,mean,quantile_transformer,896,normal,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,mse,None,0.8973902991846858,None,0.0,1,17,0.0,,,,,,,,,,,
diff --git a/autosklearn/metalearning/files/mean_squared_log_error_regression_sparse/description.txt b/autosklearn/metalearning/files/mean_squared_log_error_regression_sparse/description.txt
new file mode 100644
index 0000000000..977af9f6b1
--- /dev/null
+++ b/autosklearn/metalearning/files/mean_squared_log_error_regression_sparse/description.txt
@@ -0,0 +1,51 @@
+features_cutoff_time: 3600
+features_cutoff_memory: 3072
+number_of_feature_steps: 35
+feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances
+feature_step LogNumberOfInstances: LogNumberOfInstances
+feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures
+feature_step LogNumberOfFeatures: LogNumberOfFeatures
+feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues
+feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues
+feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues
+feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues
+feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues
+feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues
+feature_step PercentageOfMissingValues: PercentageOfMissingValues
+feature_step NumberOfNumericFeatures: NumberOfNumericFeatures
+feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures
+feature_step RatioNumericalToNominal: RatioNumericalToNominal
+feature_step RatioNominalToNumerical: RatioNominalToNumerical
+feature_step DatasetRatio: DatasetRatio, LogDatasetRatio
+feature_step LogDatasetRatio: LogDatasetRatio
+feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio
+feature_step LogInverseDatasetRatio: LogInverseDatasetRatio
+feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum
+feature_step SymbolsMin: SymbolsMin
+feature_step SymbolsMax: SymbolsMax
+feature_step SymbolsMean: SymbolsMean
+feature_step SymbolsSTD: SymbolsSTD
+feature_step SymbolsSum: SymbolsSum
+feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD
+feature_step KurtosisMin: KurtosisMin
+feature_step KurtosisMax: KurtosisMax
+feature_step KurtosisMean: KurtosisMean
+feature_step KurtosisSTD: KurtosisSTD
+feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
+feature_step SkewnessMin: SkewnessMin
+feature_step SkewnessMax: SkewnessMax
+feature_step SkewnessMean: SkewnessMean
+feature_step SkewnessSTD: SkewnessSTD
+features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
+features_stochastic: 
+default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
+
+algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73
+algorithms_stochastic: 
+performance_measures: mean_squared_log_error
+performance_type: solution_quality
+
+scenario_id: auto-sklearn
+maximize: false
+algorithm_cutoff_time: 1800
+algorithm_cutoff_memory: 3072
diff --git a/autosklearn/metalearning/files/mean_squared_log_error_regression_sparse/feature_costs.arff b/autosklearn/metalearning/files/mean_squared_log_error_regression_sparse/feature_costs.arff
new file mode 100644
index 0000000000..2c1c95a875
--- /dev/null
+++ b/autosklearn/metalearning/files/mean_squared_log_error_regression_sparse/feature_costs.arff
@@ -0,0 +1,66 @@
+@RELATION auto-sklearn_FEATURE_COSTS
+
+@ATTRIBUTE instance_id STRING
+@ATTRIBUTE repetition NUMERIC
+@ATTRIBUTE NumberOfInstances NUMERIC
+@ATTRIBUTE LogNumberOfInstances NUMERIC
+@ATTRIBUTE NumberOfFeatures NUMERIC
+@ATTRIBUTE LogNumberOfFeatures NUMERIC
+@ATTRIBUTE MissingValues NUMERIC
+@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC
+@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC
+@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC
+@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC
+@ATTRIBUTE NumberOfMissingValues NUMERIC
+@ATTRIBUTE PercentageOfMissingValues NUMERIC
+@ATTRIBUTE NumberOfNumericFeatures NUMERIC
+@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC
+@ATTRIBUTE RatioNumericalToNominal NUMERIC
+@ATTRIBUTE RatioNominalToNumerical NUMERIC
+@ATTRIBUTE DatasetRatio NUMERIC
+@ATTRIBUTE LogDatasetRatio NUMERIC
+@ATTRIBUTE InverseDatasetRatio NUMERIC
+@ATTRIBUTE LogInverseDatasetRatio NUMERIC
+@ATTRIBUTE NumSymbols NUMERIC
+@ATTRIBUTE SymbolsMin NUMERIC
+@ATTRIBUTE SymbolsMax NUMERIC
+@ATTRIBUTE SymbolsMean NUMERIC
+@ATTRIBUTE SymbolsSTD NUMERIC
+@ATTRIBUTE SymbolsSum NUMERIC
+@ATTRIBUTE Kurtosisses NUMERIC
+@ATTRIBUTE KurtosisMin NUMERIC
+@ATTRIBUTE KurtosisMax NUMERIC
+@ATTRIBUTE KurtosisMean NUMERIC
+@ATTRIBUTE KurtosisSTD NUMERIC
+@ATTRIBUTE Skewnesses NUMERIC
+@ATTRIBUTE SkewnessMin NUMERIC
+@ATTRIBUTE SkewnessMax NUMERIC
+@ATTRIBUTE SkewnessMean NUMERIC
+@ATTRIBUTE SkewnessSTD NUMERIC
+
+@DATA
+2280,1.0,2e-05,2e-05,3e-05,2e-05,0.01525,0.01381,3e-05,0.0012,3e-05,0.00031,1e-05,5e-05,5e-05,0.00013,0.00016,6e-05,2e-05,8e-05,3e-05,0.00118,1e-05,1e-05,0.00034,0.00064,0.00018,0.00052,7e-05,8e-05,0.0001,0.00027,0.00052,6e-05,0.0001,0.00012,0.00025
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diff --git a/autosklearn/metalearning/files/mean_squared_log_error_regression_sparse/feature_runstatus.arff b/autosklearn/metalearning/files/mean_squared_log_error_regression_sparse/feature_runstatus.arff
new file mode 100644
index 0000000000..0166848554
--- /dev/null
+++ b/autosklearn/metalearning/files/mean_squared_log_error_regression_sparse/feature_runstatus.arff
@@ -0,0 +1,66 @@
+@RELATION auto-sklearn_FEATURE_RUNSTATUS
+
+@ATTRIBUTE instance_id STRING
+@ATTRIBUTE repetition NUMERIC
+@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other}
+
+@DATA
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+4885,1.0,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,other,ok,ok,ok,ok
+4892,1.0,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,other,ok,ok,ok,ok
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+5024,1.0,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,other,ok,ok,ok,ok
diff --git a/autosklearn/metalearning/files/mean_squared_log_error_regression_sparse/feature_values.arff b/autosklearn/metalearning/files/mean_squared_log_error_regression_sparse/feature_values.arff
new file mode 100644
index 0000000000..ee955516b5
--- /dev/null
+++ b/autosklearn/metalearning/files/mean_squared_log_error_regression_sparse/feature_values.arff
@@ -0,0 +1,62 @@
+@RELATION auto-sklearn_FEATURE_VALUES
+
+@ATTRIBUTE instance_id STRING
+@ATTRIBUTE repetition NUMERIC
+@ATTRIBUTE DatasetRatio NUMERIC
+@ATTRIBUTE InverseDatasetRatio NUMERIC
+@ATTRIBUTE KurtosisMax NUMERIC
+@ATTRIBUTE KurtosisMean NUMERIC
+@ATTRIBUTE KurtosisMin NUMERIC
+@ATTRIBUTE KurtosisSTD NUMERIC
+@ATTRIBUTE LogDatasetRatio NUMERIC
+@ATTRIBUTE LogInverseDatasetRatio NUMERIC
+@ATTRIBUTE LogNumberOfFeatures NUMERIC
+@ATTRIBUTE LogNumberOfInstances NUMERIC
+@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC
+@ATTRIBUTE NumberOfFeatures NUMERIC
+@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC
+@ATTRIBUTE NumberOfInstances NUMERIC
+@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC
+@ATTRIBUTE NumberOfMissingValues NUMERIC
+@ATTRIBUTE NumberOfNumericFeatures NUMERIC
+@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC
+@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC
+@ATTRIBUTE PercentageOfMissingValues NUMERIC
+@ATTRIBUTE RatioNominalToNumerical NUMERIC
+@ATTRIBUTE RatioNumericalToNominal NUMERIC
+@ATTRIBUTE SkewnessMax NUMERIC
+@ATTRIBUTE SkewnessMean NUMERIC
+@ATTRIBUTE SkewnessMin NUMERIC
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diff --git a/test/test_ensemble_builder/data/.auto-sklearn/done/0_2 b/autosklearn/metalearning/files/mean_squared_log_error_regression_sparse/readme.txt
similarity index 100%
rename from test/test_ensemble_builder/data/.auto-sklearn/done/0_2
rename to autosklearn/metalearning/files/mean_squared_log_error_regression_sparse/readme.txt
diff --git a/autosklearn/metalearning/files/median_absolute_error_regression_dense/algorithm_runs.arff b/autosklearn/metalearning/files/median_absolute_error_regression_dense/algorithm_runs.arff
index 5b4672cd8a..0e24b64a9a 100644
--- a/autosklearn/metalearning/files/median_absolute_error_regression_dense/algorithm_runs.arff
+++ b/autosklearn/metalearning/files/median_absolute_error_regression_dense/algorithm_runs.arff
@@ -7,27 +7,28 @@
 @ATTRIBUTE runstatus {ok, timeout, memout, not_applicable, crash, other}
 
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diff --git a/autosklearn/metalearning/files/median_absolute_error_regression_dense/configurations.csv b/autosklearn/metalearning/files/median_absolute_error_regression_dense/configurations.csv
index a650b738e9..c5fde87b4d 100644
--- a/autosklearn/metalearning/files/median_absolute_error_regression_dense/configurations.csv
+++ b/autosklearn/metalearning/files/median_absolute_error_regression_dense/configurations.csv
@@ -1,25 +1,26 @@
 idx,data_preprocessing:categorical_transformer:categorical_encoding:__choice__,data_preprocessing:categorical_transformer:category_coalescence:__choice__,data_preprocessing:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction,data_preprocessing:numerical_transformer:imputation:strategy,data_preprocessing:numerical_transformer:rescaling:__choice__,data_preprocessing:numerical_transformer:rescaling:quantile_transformer:n_quantiles,data_preprocessing:numerical_transformer:rescaling:quantile_transformer:output_distribution,data_preprocessing:numerical_transformer:rescaling:robust_scaler:q_max,data_preprocessing:numerical_transformer:rescaling:robust_scaler:q_min,feature_preprocessor:__choice__,feature_preprocessor:extra_trees_preproc_for_regression:bootstrap,feature_preprocessor:extra_trees_preproc_for_regression:criterion,feature_preprocessor:extra_trees_preproc_for_regression:max_depth,feature_preprocessor:extra_trees_preproc_for_regression:max_features,feature_preprocessor:extra_trees_preproc_for_regression:max_leaf_nodes,feature_preprocessor:extra_trees_preproc_for_regression:min_samples_leaf,feature_preprocessor:extra_trees_preproc_for_regression:min_samples_split,feature_preprocessor:extra_trees_preproc_for_regression:min_weight_fraction_leaf,feature_preprocessor:extra_trees_preproc_for_regression:n_estimators,feature_preprocessor:fast_ica:algorithm,feature_preprocessor:fast_ica:fun,feature_preprocessor:fast_ica:n_components,feature_preprocessor:fast_ica:whiten,feature_preprocessor:feature_agglomeration:affinity,feature_preprocessor:feature_agglomeration:linkage,feature_preprocessor:feature_agglomeration:n_clusters,feature_preprocessor:feature_agglomeration:pooling_func,feature_preprocessor:kernel_pca:coef0,feature_preprocessor:kernel_pca:degree,feature_preprocessor:kernel_pca:gamma,feature_preprocessor:kernel_pca:kernel,feature_preprocessor:kernel_pca:n_components,feature_preprocessor:kitchen_sinks:gamma,feature_preprocessor:kitchen_sinks:n_components,feature_preprocessor:nystroem_sampler:coef0,feature_preprocessor:nystroem_sampler:degree,feature_preprocessor:nystroem_sampler:gamma,feature_preprocessor:nystroem_sampler:kernel,feature_preprocessor:nystroem_sampler:n_components,feature_preprocessor:pca:keep_variance,feature_preprocessor:pca:whiten,feature_preprocessor:polynomial:degree,feature_preprocessor:polynomial:include_bias,feature_preprocessor:polynomial:interaction_only,feature_preprocessor:random_trees_embedding:bootstrap,feature_preprocessor:random_trees_embedding:max_depth,feature_preprocessor:random_trees_embedding:max_leaf_nodes,feature_preprocessor:random_trees_embedding:min_samples_leaf,feature_preprocessor:random_trees_embedding:min_samples_split,feature_preprocessor:random_trees_embedding:min_weight_fraction_leaf,feature_preprocessor:random_trees_embedding:n_estimators,feature_preprocessor:select_percentile_regression:percentile,feature_preprocessor:select_percentile_regression:score_func,feature_preprocessor:select_rates_regression:alpha,feature_preprocessor:select_rates_regression:mode,feature_preprocessor:select_rates_regression:score_func,regressor:__choice__,regressor:adaboost:learning_rate,regressor:adaboost:loss,regressor:adaboost:max_depth,regressor:adaboost:n_estimators,regressor:ard_regression:alpha_1,regressor:ard_regression:alpha_2,regressor:ard_regression:fit_intercept,regressor:ard_regression:lambda_1,regressor:ard_regression:lambda_2,regressor:ard_regression:n_iter,regressor:ard_regression:threshold_lambda,regressor:ard_regression:tol,regressor:decision_tree:criterion,regressor:decision_tree:max_depth_factor,regressor:decision_tree:max_features,regressor:decision_tree:max_leaf_nodes,regressor:decision_tree:min_impurity_decrease,regressor:decision_tree:min_samples_leaf,regressor:decision_tree:min_samples_split,regressor:decision_tree:min_weight_fraction_leaf,regressor:extra_trees:bootstrap,regressor:extra_trees:criterion,regressor:extra_trees:max_depth,regressor:extra_trees:max_features,regressor:extra_trees:max_leaf_nodes,regressor:extra_trees:min_impurity_decrease,regressor:extra_trees:min_samples_leaf,regressor:extra_trees:min_samples_split,regressor:extra_trees:min_weight_fraction_leaf,regressor:gaussian_process:alpha,regressor:gaussian_process:thetaL,regressor:gaussian_process:thetaU,regressor:gradient_boosting:early_stop,regressor:gradient_boosting:l2_regularization,regressor:gradient_boosting:learning_rate,regressor:gradient_boosting:loss,regressor:gradient_boosting:max_bins,regressor:gradient_boosting:max_depth,regressor:gradient_boosting:max_leaf_nodes,regressor:gradient_boosting:min_samples_leaf,regressor:gradient_boosting:n_iter_no_change,regressor:gradient_boosting:scoring,regressor:gradient_boosting:tol,regressor:gradient_boosting:validation_fraction,regressor:k_nearest_neighbors:n_neighbors,regressor:k_nearest_neighbors:p,regressor:k_nearest_neighbors:weights,regressor:liblinear_svr:C,regressor:liblinear_svr:dual,regressor:liblinear_svr:epsilon,regressor:liblinear_svr:fit_intercept,regressor:liblinear_svr:intercept_scaling,regressor:liblinear_svr:loss,regressor:liblinear_svr:tol,regressor:libsvm_svr:C,regressor:libsvm_svr:coef0,regressor:libsvm_svr:degree,regressor:libsvm_svr:epsilon,regressor:libsvm_svr:gamma,regressor:libsvm_svr:kernel,regressor:libsvm_svr:max_iter,regressor:libsvm_svr:shrinking,regressor:libsvm_svr:tol,regressor:random_forest:bootstrap,regressor:random_forest:criterion,regressor:random_forest:max_depth,regressor:random_forest:max_features,regressor:random_forest:max_leaf_nodes,regressor:random_forest:min_impurity_decrease,regressor:random_forest:min_samples_leaf,regressor:random_forest:min_samples_split,regressor:random_forest:min_weight_fraction_leaf,regressor:sgd:alpha,regressor:sgd:average,regressor:sgd:epsilon,regressor:sgd:eta0,regressor:sgd:fit_intercept,regressor:sgd:l1_ratio,regressor:sgd:learning_rate,regressor:sgd:loss,regressor:sgd:penalty,regressor:sgd:power_t,regressor:sgd:tol
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+8,no_encoding,minority_coalescer,0.15131192011148917,median,standardize,,,,,select_rates_regression,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.17559888557371564,fwe,f_regression,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,mae,None,0.5424163937340171,None,0.0,1,20,0.0,,,,,,,,,,,
+13,no_encoding,minority_coalescer,0.011552765521322953,most_frequent,quantile_transformer,792,uniform,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,,libsvm_svr,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7.270623544331069,,4,0.001277400357934126,0.053991396619662395,rbf,-1,False,0.0004473294003696904,,,,,,,,,,,,,,,,,,,,
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+42,no_encoding,minority_coalescer,0.004404360944958882,median,none,,,,,select_percentile_regression,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,17.526294746539527,mutual_info,,,,libsvm_svr,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,169.95256699617016,,2,0.011220628161011253,0.10326172623550924,rbf,-1,True,0.0001569923346580558,,,,,,,,,,,,,,,,,,,,
+46,no_encoding,minority_coalescer,0.016414029151950702,most_frequent,robust_scaler,,,0.7433110819250283,0.24401838405994994,feature_agglomeration,,,,,,,,,,,,,,euclidean,complete,11,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,libsvm_svr,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4786.70957992255,,5,0.0010856317305813427,0.03211446445672321,rbf,-1,False,5.044549911761054e-05,,,,,,,,,,,,,,,,,,,,
+48,one_hot_encoding,no_coalescense,,mean,quantile_transformer,148,uniform,,,select_rates_regression,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.10789818795901182,fwe,f_regression,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,off,0.0002841741346637058,0.024253735132716756,least_squares,255,None,36,1,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+54,no_encoding,minority_coalescer,0.01313719797407571,median,normalize,,,,,extra_trees_preproc_for_regression,True,friedman_mse,None,0.16064185697909528,None,10,18,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,extra_trees,,,,,,,,,,,,,,,,,,,,,False,mae,None,0.6372173660778738,None,0.0,11,8,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+55,one_hot_encoding,minority_coalescer,0.013396456809645676,median,minmax,,,,,select_rates_regression,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.43509268545733737,fwe,f_regression,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,off,6.064350782602736e-07,0.06889221218528321,least_squares,255,None,26,1,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+58,one_hot_encoding,minority_coalescer,0.002557406319492487,median,standardize,,,,,select_rates_regression,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.34150402255098683,fwe,f_regression,decision_tree,,,,,,,,,,,,,mae,1.1562052073346882,1.0,None,0.0,11,18,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+60,no_encoding,minority_coalescer,0.04227296653657646,mean,standardize,,,,,select_rates_regression,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.40891411817637474,fpr,f_regression,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,mae,None,0.16018711332683905,None,0.0,14,14,0.0,,,,,,,,,,,
+63,no_encoding,no_coalescense,,most_frequent,robust_scaler,,,0.8188160371310065,0.01729210740064783,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,3,None,11,9,1.0,12,,,,,,decision_tree,,,,,,,,,,,,,mae,0.7686005175457582,1.0,None,0.0,20,17,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+66,no_encoding,minority_coalescer,0.00614294035751333,most_frequent,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,extra_trees,,,,,,,,,,,,,,,,,,,,,False,friedman_mse,None,0.935915230502498,None,0.0,2,9,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+68,no_encoding,minority_coalescer,0.009119013213341071,most_frequent,robust_scaler,,,0.9844157177007067,0.050992524778151874,select_rates_regression,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.48040209447571275,fwe,f_regression,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,off,0.26133195496239414,0.0795466073599418,least_squares,255,None,1429,13,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+70,no_encoding,minority_coalescer,0.026458669430594654,median,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,mse,None,0.9950063906520379,None,0.0,3,13,0.0,,,,,,,,,,,
+73,no_encoding,minority_coalescer,0.00011785495524909325,most_frequent,quantile_transformer,1167,normal,,,kernel_pca,,,,,,,,,,,,,,,,,,,,0.0006351555622197741,rbf,1241,,,,,,,,,,,,,,,,,,,,,,,,,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9.475806401216432e-07,True,0.030139777633001096,,True,0.002755758655854434,optimal,huber,elasticnet,,6.928717804354516e-05
diff --git a/autosklearn/metalearning/files/median_absolute_error_regression_dense/description.txt b/autosklearn/metalearning/files/median_absolute_error_regression_dense/description.txt
index 3529b773bc..680c6028c6 100644
--- a/autosklearn/metalearning/files/median_absolute_error_regression_dense/description.txt
+++ b/autosklearn/metalearning/files/median_absolute_error_regression_dense/description.txt
@@ -40,7 +40,7 @@ features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfFeature
 features_stochastic: 
 default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
 
-algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24
+algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73
 algorithms_stochastic: 
 performance_measures: median_absolute_error
 performance_type: solution_quality
diff --git a/autosklearn/metalearning/files/median_absolute_error_regression_dense/feature_costs.arff b/autosklearn/metalearning/files/median_absolute_error_regression_dense/feature_costs.arff
index 0e17e20d22..2c1c95a875 100644
--- a/autosklearn/metalearning/files/median_absolute_error_regression_dense/feature_costs.arff
+++ b/autosklearn/metalearning/files/median_absolute_error_regression_dense/feature_costs.arff
@@ -39,160 +39,28 @@
 @ATTRIBUTE SkewnessSTD NUMERIC
 
 @DATA
-75123,1.0,1e-05,1e-05,1e-05,1e-05,0.00253,0.00221,1e-05,0.00024,3e-05,0.00011,0.0,2e-05,2e-05,5e-05,8e-05,2e-05,1e-05,7e-05,1e-05,0.00072,1e-05,1e-05,0.00019,0.00041,0.00011,0.00061,0.00012,9e-05,0.00015,0.00025,0.00067,0.00014,0.00011,0.00016,0.00027
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-75187,1.0,1e-05,1e-05,2e-05,1e-05,0.00548,0.00473,1e-05,0.0005,2e-05,0.00029,1e-05,2e-05,2e-05,7e-05,9e-05,3e-05,1e-05,6e-05,1e-05,0.00062,1e-05,1e-05,0.00014,0.00033,0.00013,0.00038,7e-05,7e-05,8e-05,0.00016,0.00047,6e-05,9e-05,9e-05,0.00023
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-75217,1.0,1e-05,1e-05,2e-05,1e-05,0.00196,0.00158,1e-05,0.00026,2e-05,0.00016,1e-05,3e-05,3e-05,7e-05,0.0001,3e-05,1e-05,7e-05,2e-05,0.00089,1e-05,1e-05,0.00022,0.00053,0.00013,0.00074,9e-05,0.0001,0.00019,0.00036,0.00065,0.00013,0.0001,0.00011,0.00031
-75176,1.0,1e-05,1e-05,2e-05,1e-05,0.01406,0.01285,1e-05,0.00094,2e-05,0.00031,1e-05,3e-05,3e-05,6e-05,9e-05,3e-05,1e-05,7e-05,2e-05,0.00069,1e-05,1e-05,0.00017,0.00035,0.00015,0.00085,0.00018,0.00011,0.0002,0.00036,0.0011,0.00022,0.00023,0.00023,0.00042
-2117,1.0,1e-05,1e-05,1e-05,1e-05,0.01735,0.0154,1e-05,0.00141,1e-05,0.00057,0.0,1e-05,1e-05,3e-05,6e-05,2e-05,1e-05,5e-05,1e-05,0.0004,1e-05,0.0,8e-05,0.00021,0.0001,0.00065,0.00012,0.00011,0.00015,0.00026,0.00055,7e-05,0.00014,9e-05,0.00025
-75148,1.0,1e-05,1e-05,2e-05,2e-05,0.00129,0.00115,1e-05,0.00012,1e-05,4e-05,0.0,2e-05,2e-05,4e-05,5e-05,2e-05,1e-05,4e-05,1e-05,0.0004,1e-05,0.0,9e-05,0.00021,9e-05,0.00036,7e-05,7e-05,8e-05,0.00015,0.00041,6e-05,8e-05,8e-05,0.00019
-75103,1.0,1e-05,1e-05,1e-05,1e-05,0.01302,0.00819,3e-05,0.00234,3e-05,0.00255,0.0,2e-05,2e-05,6e-05,0.00012,5e-05,1e-05,0.00011,1e-05,0.0005,1e-05,0.0,9e-05,0.00029,0.00011,0.00054,8e-05,0.00011,0.00011,0.00024,0.00066,7e-05,0.00015,0.00011,0.00032
-75168,1.0,1e-05,0.0,1e-05,1e-05,0.02353,0.00601,0.00016,0.01754,0.00018,0.00031,0.0,0.00013,0.00013,0.00027,0.0003,0.00032,1e-05,0.00042,1e-05,0.0004,1e-05,0.0,8e-05,0.00021,0.0001,0.0011,0.00022,0.00025,0.00026,0.00037,0.00124,0.00022,0.00025,0.00028,0.00049
-75157,1.0,1e-05,1e-05,1e-05,1e-05,0.00125,0.00106,1e-05,0.00015,2e-05,7e-05,0.0,2e-05,2e-05,5e-05,6e-05,2e-05,1e-05,4e-05,1e-05,0.00048,2e-05,0.0,0.0001,0.00026,0.0001,0.00039,6e-05,7e-05,8e-05,0.00019,0.00043,6e-05,9e-05,8e-05,0.0002
-75092,1.0,1e-05,1e-05,1e-05,1e-05,0.00099,0.00072,1e-05,0.00018,1e-05,0.00011,0.0,2e-05,2e-05,4e-05,6e-05,2e-05,1e-05,5e-05,1e-05,0.00058,1e-05,0.0,0.00014,0.00034,9e-05,0.00041,6e-05,8e-05,9e-05,0.00017,0.00045,7e-05,0.0001,9e-05,0.00019
-75213,1.0,1e-05,1e-05,1e-05,1e-05,0.00096,0.00079,1e-05,0.00013,1e-05,7e-05,1e-05,2e-05,2e-05,5e-05,7e-05,2e-05,1e-05,6e-05,1e-05,0.00056,1e-05,1e-05,0.00013,0.00029,0.00012,0.00056,6e-05,8e-05,9e-05,0.00033,0.00063,8e-05,0.00014,0.00013,0.00029
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-75159,1.0,2e-05,2e-05,2e-05,2e-05,0.00136,0.00099,7e-05,0.00033,8e-05,0.00021,2e-05,0.0001,0.0001,0.00014,0.00016,0.00016,7e-05,0.00019,8e-05,0.00092,1e-05,9e-05,0.0002,0.00039,0.00023,0.00049,7e-05,8e-05,0.00012,0.00023,0.00043,6e-05,8e-05,8e-05,0.0002
-75192,1.0,1e-05,1e-05,2e-05,1e-05,0.00188,0.00165,1e-05,0.00016,1e-05,9e-05,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,1e-05,4e-05,1e-05,0.00039,1e-05,0.0,9e-05,0.00021,9e-05,0.00048,5e-05,8e-05,0.0001,0.00025,0.00045,6e-05,0.0001,0.0001,0.0002
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diff --git a/autosklearn/metalearning/files/median_absolute_error_regression_dense/feature_runstatus.arff b/autosklearn/metalearning/files/median_absolute_error_regression_dense/feature_runstatus.arff
index 0034ad2bd6..0166848554 100644
--- a/autosklearn/metalearning/files/median_absolute_error_regression_dense/feature_runstatus.arff
+++ b/autosklearn/metalearning/files/median_absolute_error_regression_dense/feature_runstatus.arff
@@ -39,160 +39,28 @@
 @ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other}
 
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diff --git a/autosklearn/metalearning/files/median_absolute_error_regression_dense/feature_values.arff b/autosklearn/metalearning/files/median_absolute_error_regression_dense/feature_values.arff
index a60400e3ce..ee955516b5 100644
--- a/autosklearn/metalearning/files/median_absolute_error_regression_dense/feature_values.arff
+++ b/autosklearn/metalearning/files/median_absolute_error_regression_dense/feature_values.arff
@@ -2,24 +2,17 @@
 
 @ATTRIBUTE instance_id STRING
 @ATTRIBUTE repetition NUMERIC
-@ATTRIBUTE ClassEntropy NUMERIC
-@ATTRIBUTE ClassProbabilityMax NUMERIC
-@ATTRIBUTE ClassProbabilityMean NUMERIC
-@ATTRIBUTE ClassProbabilityMin NUMERIC
-@ATTRIBUTE ClassProbabilitySTD NUMERIC
 @ATTRIBUTE DatasetRatio NUMERIC
 @ATTRIBUTE InverseDatasetRatio NUMERIC
 @ATTRIBUTE KurtosisMax NUMERIC
 @ATTRIBUTE KurtosisMean NUMERIC
 @ATTRIBUTE KurtosisMin NUMERIC
 @ATTRIBUTE KurtosisSTD NUMERIC
-@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC
 @ATTRIBUTE LogDatasetRatio NUMERIC
 @ATTRIBUTE LogInverseDatasetRatio NUMERIC
 @ATTRIBUTE LogNumberOfFeatures NUMERIC
 @ATTRIBUTE LogNumberOfInstances NUMERIC
 @ATTRIBUTE NumberOfCategoricalFeatures NUMERIC
-@ATTRIBUTE NumberOfClasses NUMERIC
 @ATTRIBUTE NumberOfFeatures NUMERIC
 @ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC
 @ATTRIBUTE NumberOfInstances NUMERIC
@@ -42,160 +35,28 @@
 @ATTRIBUTE SymbolsSum NUMERIC
 
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diff --git a/autosklearn/metalearning/files/median_absolute_error_regression_sparse/algorithm_runs.arff b/autosklearn/metalearning/files/median_absolute_error_regression_sparse/algorithm_runs.arff
index 5b4672cd8a..0e24b64a9a 100644
--- a/autosklearn/metalearning/files/median_absolute_error_regression_sparse/algorithm_runs.arff
+++ b/autosklearn/metalearning/files/median_absolute_error_regression_sparse/algorithm_runs.arff
@@ -7,27 +7,28 @@
 @ATTRIBUTE runstatus {ok, timeout, memout, not_applicable, crash, other}
 
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diff --git a/autosklearn/metalearning/files/median_absolute_error_regression_sparse/configurations.csv b/autosklearn/metalearning/files/median_absolute_error_regression_sparse/configurations.csv
index a650b738e9..c5fde87b4d 100644
--- a/autosklearn/metalearning/files/median_absolute_error_regression_sparse/configurations.csv
+++ b/autosklearn/metalearning/files/median_absolute_error_regression_sparse/configurations.csv
@@ -1,25 +1,26 @@
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-1,one_hot_encoding,minority_coalescer,0.00022127044391040656,mean,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,mse,None,0.5956294688991997,None,0.0,3,3,0.0,,,,,,,,,,,
-2,one_hot_encoding,no_coalescense,,most_frequent,standardize,,,,,feature_agglomeration,,,,,,,,,,,,,,cosine,complete,337,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,off,0.5764826313877036,0.13094704614038463,least_squares,255,None,55,10,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-3,no_encoding,no_coalescense,,median,quantile_transformer,1744,uniform,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,off,5.403227360209131e-06,0.12157133651416789,least_squares,255,None,6,22,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-4,one_hot_encoding,minority_coalescer,0.43594380166568214,most_frequent,quantile_transformer,1278,uniform,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,off,0.46254920942130184,0.017627752319596398,least_squares,255,None,21,109,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-5,one_hot_encoding,minority_coalescer,0.00868781370381196,median,none,,,,,feature_agglomeration,,,,,,,,,,,,,,manhattan,average,198,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,valid,0.9944613954414814,0.41504722433335367,least_squares,255,None,3,7,13,loss,1e-07,0.34272133911153263,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-6,no_encoding,no_coalescense,,most_frequent,standardize,,,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.9996948860926494,False,,,,,,,,,,,,,,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,off,2.8245520690444007e-10,0.10345412822170329,least_squares,255,None,37,8,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-7,one_hot_encoding,minority_coalescer,0.01335639882137022,median,none,,,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.9007320777101533,False,,,,,,,,,,,,,,,,libsvm_svr,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.7946544567476077,,3,0.10835064199172931,0.3219727973317554,rbf,-1,False,0.002309685915029067,,,,,,,,,,,,,,,,,,,,
-8,no_encoding,no_coalescense,,most_frequent,quantile_transformer,1000,normal,,,feature_agglomeration,,,,,,,,,,,,,,manhattan,average,400,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,valid,1.1749311532144497e-10,0.10192476775649895,least_squares,255,None,34,20,10,loss,1e-07,0.06904805033619427,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-9,no_encoding,minority_coalescer,0.06968801120619206,mean,minmax,,,,,kitchen_sinks,,,,,,,,,,,,,,,,,,,,,,,2.4233915766526617,359,,,,,,,,,,,,,,,,,,,,,,,libsvm_svr,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,93.53649669563134,,3,0.0074352080325016025,0.9629552978655305,rbf,-1,False,0.0001374930573423754,,,,,,,,,,,,,,,,,,,,
-10,no_encoding,no_coalescense,,median,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,friedman_mse,None,0.3378464988163372,None,0.0,1,19,0.0,,,,,,,,,,,
-11,no_encoding,minority_coalescer,0.0075899693709838454,mean,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,mse,None,1.0,None,0.0,1,3,0.0,,,,,,,,,,,
-12,no_encoding,no_coalescense,,median,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,friedman_mse,None,0.3378464988163372,None,0.0,1,19,0.0,,,,,,,,,,,
-13,one_hot_encoding,minority_coalescer,0.008596845586715194,median,none,,,,,extra_trees_preproc_for_regression,False,mae,None,0.5182056852692017,None,18,15,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,libsvm_svr,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,512.45217334863,,5,0.012572706963865274,0.30386262073302794,rbf,-1,False,0.00012371722469580823,,,,,,,,,,,,,,,,,,,,
-14,no_encoding,minority_coalescer,0.005788594071307701,median,minmax,,,,,select_rates_regression,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.41365987272753285,fdr,f_regression,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,friedman_mse,None,0.22442857329791677,None,0.0,18,16,0.0,,,,,,,,,,,
-15,one_hot_encoding,minority_coalescer,0.0037423052714792336,mean,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,mse,None,0.4339166051934824,None,0.0,4,10,0.0,,,,,,,,,,,
-16,no_encoding,minority_coalescer,0.008514018627304457,most_frequent,normalize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,mse,None,0.3848774435493376,None,0.0,9,4,0.0,,,,,,,,,,,
-17,one_hot_encoding,minority_coalescer,0.009171724071325382,most_frequent,standardize,,,,,feature_agglomeration,,,,,,,,,,,,,,manhattan,average,258,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,friedman_mse,None,0.7429459921040217,None,0.0,1,4,0.0,,,,,,,,,,,
-18,no_encoding,no_coalescense,,mean,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,mse,None,0.35006559494724565,None,0.0,5,5,0.0,,,,,,,,,,,
-19,one_hot_encoding,minority_coalescer,0.028491382625620388,mean,robust_scaler,,,0.7912317192638986,0.08487413285214757,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,mse,None,0.16353088233338742,None,0.0,1,3,0.0,,,,,,,,,,,
-20,no_encoding,minority_coalescer,0.0026150904111835473,mean,normalize,,,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.5410109609735668,True,,,,,,,,,,,,,,,,decision_tree,,,,,,,,,,,,,friedman_mse,1.240365240027156,1.0,None,0.0,19,11,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-21,one_hot_encoding,minority_coalescer,0.004474714505413804,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,extra_trees,,,,,,,,,,,,,,,,,,,,,False,mse,None,0.8442614504957053,None,0.0,1,5,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-22,one_hot_encoding,minority_coalescer,0.010000000000000004,median,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,mse,None,1.0,None,0.0,1,3,0.0,,,,,,,,,,,
-23,no_encoding,minority_coalescer,0.007150902883091326,mean,robust_scaler,,,0.8663777312763623,0.02160040060766837,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,mse,None,0.9994655923368939,None,0.0,4,6,0.0,,,,,,,,,,,
-24,one_hot_encoding,no_coalescense,,most_frequent,robust_scaler,,,0.7484939216574421,0.20183301399810935,select_rates_regression,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.18375456889543734,fdr,f_regression,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,off,4.81881052684467e-05,0.10285955822720894,least_squares,255,None,8,1,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+1,one_hot_encoding,minority_coalescer,0.00018093770594127258,mean,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,mse,None,0.9656059771129771,None,0.0,1,2,0.0,,,,,,,,,,,
+4,no_encoding,no_coalescense,,most_frequent,none,,,,,feature_agglomeration,,,,,,,,,,,,,,manhattan,complete,105,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,libsvm_svr,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,55.717535514923476,,4,0.005567083136973741,3.7535220040570636e-05,rbf,-1,False,0.034310339387666075,,,,,,,,,,,,,,,,,,,,
+6,no_encoding,no_coalescense,,mean,none,,,,,feature_agglomeration,,,,,,,,,,,,,,manhattan,average,259,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,off,0.19437910312263157,0.07323693968035645,least_squares,255,None,289,1,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+8,no_encoding,minority_coalescer,0.15131192011148917,median,standardize,,,,,select_rates_regression,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.17559888557371564,fwe,f_regression,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,mae,None,0.5424163937340171,None,0.0,1,20,0.0,,,,,,,,,,,
+13,no_encoding,minority_coalescer,0.011552765521322953,most_frequent,quantile_transformer,792,uniform,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,,libsvm_svr,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7.270623544331069,,4,0.001277400357934126,0.053991396619662395,rbf,-1,False,0.0004473294003696904,,,,,,,,,,,,,,,,,,,,
+19,one_hot_encoding,no_coalescense,,mean,quantile_transformer,148,uniform,,,select_rates_regression,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.10789818795901182,fwe,f_regression,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,off,0.0002841741346637058,0.024253735132716756,least_squares,255,None,36,1,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+22,one_hot_encoding,minority_coalescer,0.00010233218183804107,most_frequent,standardize,,,,,fast_ica,,,,,,,,,,parallel,logcosh,177,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,off,1.913840786763519e-10,0.10000000000000002,least_squares,255,None,16,22,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+24,no_encoding,no_coalescense,,median,quantile_transformer,1150,uniform,,,kitchen_sinks,,,,,,,,,,,,,,,,,,,,,,,1.1605111479672479,5499,,,,,,,,,,,,,,,,,,,,,,,libsvm_svr,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,272.83550277553246,0.1471721071594263,2,0.18104912091393513,0.09926116813777154,poly,-1,True,0.06084368714049917,,,,,,,,,,,,,,,,,,,,
+26,one_hot_encoding,minority_coalescer,0.010000000000000004,median,quantile_transformer,1739,uniform,,,select_rates_regression,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.13203168505812135,fdr,f_regression,libsvm_svr,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,615.8308516697139,,4,0.00722529519103612,0.02177878280633833,rbf,-1,True,0.00030436051321079026,,,,,,,,,,,,,,,,,,,,
+27,one_hot_encoding,minority_coalescer,0.08325697355876861,mean,robust_scaler,,,0.7402147208741261,0.25119719661825834,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,mse,None,0.866263852905554,None,0.0,2,8,0.0,,,,,,,,,,,
+29,no_encoding,no_coalescense,,mean,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,valid,6.086270145315262e-07,0.09279816631205667,least_squares,255,None,10,29,16,loss,1e-07,0.27813587365927,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+34,no_encoding,no_coalescense,,most_frequent,robust_scaler,,,0.7049491520337817,0.23602880130851764,fast_ica,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,off,4.696295762197504e-10,0.03269357604116943,least_squares,255,None,32,14,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+36,one_hot_encoding,no_coalescense,,most_frequent,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,mse,None,0.1686847993740384,None,0.0,1,2,0.0,,,,,,,,,,,
+42,no_encoding,minority_coalescer,0.004404360944958882,median,none,,,,,select_percentile_regression,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,17.526294746539527,mutual_info,,,,libsvm_svr,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,169.95256699617016,,2,0.011220628161011253,0.10326172623550924,rbf,-1,True,0.0001569923346580558,,,,,,,,,,,,,,,,,,,,
+46,no_encoding,minority_coalescer,0.016414029151950702,most_frequent,robust_scaler,,,0.7433110819250283,0.24401838405994994,feature_agglomeration,,,,,,,,,,,,,,euclidean,complete,11,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,libsvm_svr,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4786.70957992255,,5,0.0010856317305813427,0.03211446445672321,rbf,-1,False,5.044549911761054e-05,,,,,,,,,,,,,,,,,,,,
+48,one_hot_encoding,no_coalescense,,mean,quantile_transformer,148,uniform,,,select_rates_regression,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.10789818795901182,fwe,f_regression,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,off,0.0002841741346637058,0.024253735132716756,least_squares,255,None,36,1,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+54,no_encoding,minority_coalescer,0.01313719797407571,median,normalize,,,,,extra_trees_preproc_for_regression,True,friedman_mse,None,0.16064185697909528,None,10,18,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,extra_trees,,,,,,,,,,,,,,,,,,,,,False,mae,None,0.6372173660778738,None,0.0,11,8,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+55,one_hot_encoding,minority_coalescer,0.013396456809645676,median,minmax,,,,,select_rates_regression,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.43509268545733737,fwe,f_regression,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,off,6.064350782602736e-07,0.06889221218528321,least_squares,255,None,26,1,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+58,one_hot_encoding,minority_coalescer,0.002557406319492487,median,standardize,,,,,select_rates_regression,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.34150402255098683,fwe,f_regression,decision_tree,,,,,,,,,,,,,mae,1.1562052073346882,1.0,None,0.0,11,18,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+60,no_encoding,minority_coalescer,0.04227296653657646,mean,standardize,,,,,select_rates_regression,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.40891411817637474,fpr,f_regression,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,mae,None,0.16018711332683905,None,0.0,14,14,0.0,,,,,,,,,,,
+63,no_encoding,no_coalescense,,most_frequent,robust_scaler,,,0.8188160371310065,0.01729210740064783,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,3,None,11,9,1.0,12,,,,,,decision_tree,,,,,,,,,,,,,mae,0.7686005175457582,1.0,None,0.0,20,17,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+66,no_encoding,minority_coalescer,0.00614294035751333,most_frequent,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,extra_trees,,,,,,,,,,,,,,,,,,,,,False,friedman_mse,None,0.935915230502498,None,0.0,2,9,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+68,no_encoding,minority_coalescer,0.009119013213341071,most_frequent,robust_scaler,,,0.9844157177007067,0.050992524778151874,select_rates_regression,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.48040209447571275,fwe,f_regression,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,off,0.26133195496239414,0.0795466073599418,least_squares,255,None,1429,13,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+70,no_encoding,minority_coalescer,0.026458669430594654,median,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,mse,None,0.9950063906520379,None,0.0,3,13,0.0,,,,,,,,,,,
+73,no_encoding,minority_coalescer,0.00011785495524909325,most_frequent,quantile_transformer,1167,normal,,,kernel_pca,,,,,,,,,,,,,,,,,,,,0.0006351555622197741,rbf,1241,,,,,,,,,,,,,,,,,,,,,,,,,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9.475806401216432e-07,True,0.030139777633001096,,True,0.002755758655854434,optimal,huber,elasticnet,,6.928717804354516e-05
diff --git a/autosklearn/metalearning/files/median_absolute_error_regression_sparse/description.txt b/autosklearn/metalearning/files/median_absolute_error_regression_sparse/description.txt
index 3529b773bc..680c6028c6 100644
--- a/autosklearn/metalearning/files/median_absolute_error_regression_sparse/description.txt
+++ b/autosklearn/metalearning/files/median_absolute_error_regression_sparse/description.txt
@@ -40,7 +40,7 @@ features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfFeature
 features_stochastic: 
 default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
 
-algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24
+algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73
 algorithms_stochastic: 
 performance_measures: median_absolute_error
 performance_type: solution_quality
diff --git a/autosklearn/metalearning/files/median_absolute_error_regression_sparse/feature_costs.arff b/autosklearn/metalearning/files/median_absolute_error_regression_sparse/feature_costs.arff
index 0e17e20d22..2c1c95a875 100644
--- a/autosklearn/metalearning/files/median_absolute_error_regression_sparse/feature_costs.arff
+++ b/autosklearn/metalearning/files/median_absolute_error_regression_sparse/feature_costs.arff
@@ -39,160 +39,28 @@
 @ATTRIBUTE SkewnessSTD NUMERIC
 
 @DATA
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diff --git a/autosklearn/metalearning/files/median_absolute_error_regression_sparse/feature_runstatus.arff b/autosklearn/metalearning/files/median_absolute_error_regression_sparse/feature_runstatus.arff
index 0034ad2bd6..0166848554 100644
--- a/autosklearn/metalearning/files/median_absolute_error_regression_sparse/feature_runstatus.arff
+++ b/autosklearn/metalearning/files/median_absolute_error_regression_sparse/feature_runstatus.arff
@@ -39,160 +39,28 @@
 @ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other}
 
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diff --git a/autosklearn/metalearning/files/median_absolute_error_regression_sparse/feature_values.arff b/autosklearn/metalearning/files/median_absolute_error_regression_sparse/feature_values.arff
index a60400e3ce..ee955516b5 100644
--- a/autosklearn/metalearning/files/median_absolute_error_regression_sparse/feature_values.arff
+++ b/autosklearn/metalearning/files/median_absolute_error_regression_sparse/feature_values.arff
@@ -2,24 +2,17 @@
 
 @ATTRIBUTE instance_id STRING
 @ATTRIBUTE repetition NUMERIC
-@ATTRIBUTE ClassEntropy NUMERIC
-@ATTRIBUTE ClassProbabilityMax NUMERIC
-@ATTRIBUTE ClassProbabilityMean NUMERIC
-@ATTRIBUTE ClassProbabilityMin NUMERIC
-@ATTRIBUTE ClassProbabilitySTD NUMERIC
 @ATTRIBUTE DatasetRatio NUMERIC
 @ATTRIBUTE InverseDatasetRatio NUMERIC
 @ATTRIBUTE KurtosisMax NUMERIC
 @ATTRIBUTE KurtosisMean NUMERIC
 @ATTRIBUTE KurtosisMin NUMERIC
 @ATTRIBUTE KurtosisSTD NUMERIC
-@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC
 @ATTRIBUTE LogDatasetRatio NUMERIC
 @ATTRIBUTE LogInverseDatasetRatio NUMERIC
 @ATTRIBUTE LogNumberOfFeatures NUMERIC
 @ATTRIBUTE LogNumberOfInstances NUMERIC
 @ATTRIBUTE NumberOfCategoricalFeatures NUMERIC
-@ATTRIBUTE NumberOfClasses NUMERIC
 @ATTRIBUTE NumberOfFeatures NUMERIC
 @ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC
 @ATTRIBUTE NumberOfInstances NUMERIC
@@ -42,160 +35,28 @@
 @ATTRIBUTE SymbolsSum NUMERIC
 
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+4883,1.0,0.0016277807921866521,614.3333333333334,323.7693923069449,79.67254878426856,0.9639408588409424,105.42530803556463,-6.420537669001714,6.420537669001714,2.4849066497880004,8.905444318789714,0.0,12.0,0.0,7372.0,0.0,0.0,12.0,0.0,0.0,0.0,0.0,0.0,14.269277572631836,5.179579322536786,-0.7831974029541016,4.424204515765703,0.0,0.0,0.0,0.0,0.0
+4885,1.0,0.00027254640102477445,3669.1,-1.188598230219335,-1.1964564993963673,-1.2026724815368652,0.003664290259953897,-8.2077016793668,8.2077016793668,2.302585092994046,10.510286772360844,0.0,10.0,0.0,36691.0,0.0,0.0,10.0,0.0,0.0,0.0,0.0,0.0,0.018026534467935562,0.0004419666947796941,-0.011050965636968613,0.007399082491716184,0.0,0.0,0.0,0.0,0.0
+4892,1.0,0.0028486163863266412,351.04761904761904,576.329345703125,81.51044419461924,0.9639408588409424,139.3485299654942,-5.860921881066291,5.860921881066291,3.044522437723423,8.905444318789714,0.0,21.0,0.0,7372.0,0.0,0.0,21.0,0.0,0.0,0.0,0.0,0.0,20.96137046813965,5.398353062924885,-0.7831974029541016,4.943832847158382,0.0,0.0,0.0,0.0,0.0
+4893,1.0,0.0007802974884174591,1281.5625,6719.606795643044,437.862979925658,-1.0920182685895878,1622.03517764269,-7.155835315586378,7.155835315586378,2.772588722239781,9.92842403782616,0.0,16.0,0.0,20505.0,0.0,0.0,16.0,0.0,0.0,0.0,0.0,0.0,70.76852416992188,5.9781048079021275,-5.867455959320068,16.97724081800652,0.0,0.0,0.0,0.0,0.0
+5022,1.0,0.011111111111111112,90.0,-1.0762021359915683,-1.3324955659833357,-1.9520033311864657,0.2528537963602448,-4.499809670330265,4.499809670330265,2.302585092994046,6.802394763324311,0.0,10.0,0.0,900.0,0.0,0.0,10.0,0.0,0.0,0.0,0.0,0.0,0.33015167713165283,0.019484431575983764,-0.31836414337158203,0.19549899975606772,0.0,0.0,0.0,0.0,0.0
+5024,1.0,0.0044444444444444444,225.0,-0.866234540939331,-1.0928374249016086,-1.3201078176498413,0.16201579691665904,-5.41610040220442,5.41610040220442,1.3862943611198906,6.802394763324311,0.0,4.0,0.0,900.0,0.0,0.0,4.0,0.0,0.0,0.0,0.0,0.0,0.28722548484802246,0.05653903912752867,-0.35667797923088074,0.256064936877358,0.0,0.0,0.0,0.0,0.0
diff --git a/autosklearn/metalearning/files/precision_binary.classification_dense/algorithm_runs.arff b/autosklearn/metalearning/files/precision_binary.classification_dense/algorithm_runs.arff
index f1d428d668..9a2e1a5829 100644
--- a/autosklearn/metalearning/files/precision_binary.classification_dense/algorithm_runs.arff
+++ b/autosklearn/metalearning/files/precision_binary.classification_dense/algorithm_runs.arff
@@ -7,130 +7,135 @@
 @ATTRIBUTE runstatus {ok, timeout, memout, not_applicable, crash, other}
 
 @DATA
-75119,1.0,1,0.004576659038901587,ok
-75212,1.0,2,0.2361809045226131,ok
-252,1.0,3,?,not_applicable
-246,1.0,4,?,not_applicable
-75178,1.0,5,?,not_applicable
-75239,1.0,6,0.0,ok
-75171,1.0,7,0.1652892561983471,ok
-75233,1.0,8,0.039279869067103124,ok
-248,1.0,9,?,not_applicable
-75231,1.0,10,?,not_applicable
-2123,1.0,11,?,not_applicable
-75196,1.0,12,0.01904761904761909,ok
-75188,1.0,13,?,not_applicable
-75092,1.0,14,0.5901639344262295,ok
-75248,1.0,15,0.752823086574655,ok
-75126,1.0,16,0.01385681293302543,ok
-75234,1.0,17,0.027709861450692763,ok
-75150,1.0,18,0.27717391304347827,ok
-258,1.0,19,?,not_applicable
-75168,1.0,20,?,not_applicable
-75235,1.0,21,?,not_applicable
-75159,1.0,22,0.7415730337078652,ok
-244,1.0,23,?,not_applicable
-75221,1.0,24,?,not_applicable
-75219,1.0,25,0.02992633517495391,ok
-75202,1.0,26,?,not_applicable
-3043,1.0,27,0.19318181818181823,ok
-75205,1.0,28,?,not_applicable
-75174,1.0,29,0.22988992379339546,ok
-288,1.0,30,?,not_applicable
-75250,1.0,31,?,not_applicable
-275,1.0,32,?,not_applicable
-75142,1.0,33,0.07092198581560283,ok
-75213,1.0,34,0.14457831325301207,ok
-75099,1.0,35,0.5972222222222222,ok
-75243,1.0,36,?,not_applicable
-75184,1.0,37,0.09474412171507607,ok
-75222,1.0,38,0.5211267605633803,ok
-75175,1.0,39,0.10366766467065869,ok
-233,1.0,40,0.004073319755600768,ok
-75161,1.0,41,0.05748148148148147,ok
-75176,1.0,42,0.014147236049253387,ok
-75090,1.0,43,?,not_applicable
-75114,1.0,44,0.01749999999999996,ok
-260,1.0,45,?,not_applicable
-236,1.0,46,?,not_applicable
-75141,1.0,47,0.07511312217194566,ok
-75107,1.0,48,0.29939393939393943,ok
-262,1.0,49,?,not_applicable
-75146,1.0,50,0.0912992586812329,ok
-75189,1.0,51,0.013769684304798857,ok
-2350,1.0,52,0.5842329404773695,ok
-253,1.0,53,?,not_applicable
-2122,1.0,54,?,not_applicable
-75110,1.0,55,?,not_applicable
-75249,1.0,56,0.02020202020202022,ok
-75108,1.0,57,0.0,ok
-242,1.0,58,?,not_applicable
-75117,1.0,59,0.005494505494505475,ok
-75191,1.0,60,0.08133086876155271,ok
-75226,1.0,61,0.0007194244604316058,ok
-261,1.0,62,0.4301075268817204,ok
-75236,1.0,63,?,not_applicable
-75095,1.0,64,0.24038461538461542,ok
-75148,1.0,65,0.12572533849129597,ok
-75093,1.0,66,0.5923076923076923,ok
-75223,1.0,67,?,not_applicable
-75244,1.0,68,0.7897862232779097,ok
-75109,1.0,69,?,not_applicable
-75197,1.0,70,?,not_applicable
-75127,1.0,71,0.33647102251753414,ok
-75143,1.0,72,0.008919722497522264,ok
-75153,1.0,73,0.0755685986793837,ok
-75173,1.0,74,0.10812539987204095,ok
-75215,1.0,75,0.02931283037001442,ok
-75195,1.0,76,0.0,ok
-75207,1.0,77,?,not_applicable
-266,1.0,78,?,not_applicable
-75225,1.0,79,0.6031746031746033,ok
-75166,1.0,80,0.09728506787330315,ok
-75100,1.0,81,0.5,ok
-75169,1.0,82,?,not_applicable
-75132,1.0,83,0.918880122973981,ok
-273,1.0,84,0.047863247863247915,ok
-75121,1.0,85,0.0,ok
-75098,1.0,86,?,not_applicable
-75115,1.0,87,0.01698513800424628,ok
-75116,1.0,88,0.0023866348448687846,ok
-75185,1.0,89,0.11976047904191611,ok
-2119,1.0,90,?,not_applicable
-75157,1.0,91,0.44099378881987583,ok
-75113,1.0,92,0.08463949843260188,ok
-75134,1.0,93,?,not_applicable
-75096,1.0,94,?,not_applicable
-75203,1.0,95,?,not_applicable
-75182,1.0,96,0.17280995691718526,ok
-251,1.0,97,?,not_applicable
-75123,1.0,98,?,not_applicable
-75237,1.0,99,0.0,ok
-75125,1.0,100,0.02211302211302213,ok
-75232,1.0,101,0.16666666666666663,ok
-75103,1.0,102,0.06109324758842438,ok
-75192,1.0,103,0.4867109634551495,ok
-75230,1.0,104,?,not_applicable
-75139,1.0,105,0.014101778050275904,ok
-75227,1.0,106,0.15169660678642716,ok
-2120,1.0,107,?,not_applicable
-75124,1.0,108,0.6305732484076434,ok
-75240,1.0,109,0.05513513513513513,ok
-75129,1.0,110,0.6944444444444444,ok
-75198,1.0,111,?,not_applicable
-75201,1.0,112,?,not_applicable
-75112,1.0,113,0.11776753712237586,ok
-75133,1.0,114,0.6388888888888888,ok
-75105,1.0,115,0.9518353490345852,ok
-75154,1.0,116,?,not_applicable
-75177,1.0,117,0.17647058823529416,ok
-2117,1.0,118,0.05458937198067637,ok
-75156,1.0,119,0.19851851851851854,ok
-75097,1.0,120,0.021656050955414008,ok
-75101,1.0,121,0.2653600140836805,ok
-75172,1.0,122,?,not_applicable
-75106,1.0,123,0.8779436152570481,ok
-75179,1.0,124,0.35317460317460314,ok
-75187,1.0,125,0.01266891891891897,ok
-75120,1.0,126,0.01541850220264318,ok
-75193,1.0,127,?,not_applicable
+75192,1.0,1,0.41666666666666663,ok
+75119,1.0,4,0.0022883295194507935,ok
+75139,1.0,321,0.01167793484941615,ok
+75212,1.0,7,0.227979274611399,ok
+246,1.0,8,?,not_applicable
+252,1.0,9,?,not_applicable
+75178,1.0,10,?,not_applicable
+75177,1.0,425,0.026666666666666616,ok
+75092,1.0,52,0.34883720930232553,ok
+75239,1.0,15,0.0,ok
+75173,1.0,264,0.10553410553410558,ok
+75215,1.0,269,0.02467343976777936,ok
+75171,1.0,20,0.1593323216995448,ok
+75112,1.0,42,0.11276127612761278,ok
+75227,1.0,190,0.15127701375245584,ok
+75233,1.0,128,0.03593145384190155,ok
+75182,1.0,138,0.14901165737455646,ok
+253,1.0,27,?,not_applicable
+75157,1.0,331,0.4285714285714286,ok
+75187,1.0,267,0.011824324324324342,ok
+75124,1.0,31,0.3571428571428571,ok
+75090,1.0,32,?,not_applicable
+75222,1.0,33,0.10344827586206895,ok
+75231,1.0,34,?,not_applicable
+75185,1.0,325,0.11234817813765186,ok
+2123,1.0,39,?,not_applicable
+75150,1.0,72,0.297752808988764,ok
+75143,1.0,340,0.007960199004975133,ok
+75196,1.0,44,0.028301886792452824,ok
+75188,1.0,49,?,not_applicable
+75248,1.0,55,0.44999999999999996,ok
+75249,1.0,204,0.01041666666666663,ok
+75113,1.0,335,0.02464788732394363,ok
+75126,1.0,62,0.013698630136986356,ok
+288,1.0,64,?,not_applicable
+251,1.0,65,?,not_applicable
+75184,1.0,156,0.03386960203217615,ok
+75234,1.0,70,0.027687296416938123,ok
+258,1.0,73,?,not_applicable
+75166,1.0,281,0.08320251177394034,ok
+75168,1.0,77,?,not_applicable
+75148,1.0,329,0.1372180451127819,ok
+75235,1.0,81,?,not_applicable
+75159,1.0,84,0.0,ok
+75146,1.0,178,0.0851393188854489,ok
+244,1.0,89,?,not_applicable
+75141,1.0,90,0.051406401551891356,ok
+75221,1.0,92,?,not_applicable
+75219,1.0,101,0.013513513513513487,ok
+75202,1.0,96,?,not_applicable
+3043,1.0,99,0.013698630136986356,ok
+75205,1.0,103,?,not_applicable
+75174,1.0,108,0.16874400767018216,ok
+75250,1.0,111,?,not_applicable
+75179,1.0,114,0.25528700906344415,ok
+275,1.0,115,?,not_applicable
+242,1.0,116,?,not_applicable
+75207,1.0,117,?,not_applicable
+75142,1.0,118,0.06557867944303042,ok
+75099,1.0,124,0.0,ok
+75243,1.0,126,?,not_applicable
+75175,1.0,259,0.10056497175141244,ok
+233,1.0,136,0.002044989775051076,ok
+75161,1.0,139,0.05600709324663811,ok
+75176,1.0,328,0.013371788148925035,ok
+262,1.0,146,?,not_applicable
+75129,1.0,339,0.0,ok
+261,1.0,179,0.2678571428571429,ok
+75114,1.0,155,0.012626262626262652,ok
+75093,1.0,157,0.3197278911564626,ok
+260,1.0,158,?,not_applicable
+236,1.0,160,?,not_applicable
+254,1.0,166,0.0,ok
+75107,1.0,169,0.12364425162689807,ok
+75181,1.0,175,?,not_applicable
+75189,1.0,187,0.009679150534749104,ok
+75163,1.0,189,0.04433962264150948,ok
+2350,1.0,192,0.381578947368421,ok
+2122,1.0,195,?,not_applicable
+75110,1.0,198,?,not_applicable
+75213,1.0,424,0.09756097560975607,ok
+75095,1.0,284,0.02941176470588236,ok
+75108,1.0,207,0.0,ok
+75117,1.0,212,0.027837259100642386,ok
+75191,1.0,217,0.0786632744275596,ok
+75226,1.0,220,0.0010857763300760048,ok
+75244,1.0,372,0.15384615384615385,ok
+75236,1.0,223,?,not_applicable
+75169,1.0,227,?,not_applicable
+75116,1.0,320,0.0023474178403756207,ok
+75223,1.0,235,?,not_applicable
+75109,1.0,242,?,not_applicable
+75197,1.0,245,?,not_applicable
+75237,1.0,364,4.677268475206109e-05,ok
+248,1.0,250,?,not_applicable
+2119,1.0,254,?,not_applicable
+75127,1.0,255,0.30221535291087065,ok
+75153,1.0,263,0.07380073800738007,ok
+75195,1.0,271,0.0,ok
+266,1.0,277,?,not_applicable
+75225,1.0,297,0.2857142857142857,ok
+75100,1.0,286,0.5,ok
+75132,1.0,293,0.32793522267206476,ok
+75210,1.0,298,0.0,ok
+273,1.0,300,0.045060658578856105,ok
+75133,1.0,413,0.19999999999999996,ok
+75121,1.0,304,0.0,ok
+75098,1.0,310,?,not_applicable
+75115,1.0,315,0.010638297872340385,ok
+75217,1.0,337,?,not_applicable
+75134,1.0,341,?,not_applicable
+75096,1.0,346,?,not_applicable
+75203,1.0,350,?,not_applicable
+75123,1.0,360,?,not_applicable
+75125,1.0,369,0.026066350710900466,ok
+2120,1.0,371,?,not_applicable
+75232,1.0,374,0.13888888888888884,ok
+75103,1.0,379,0.04870129870129869,ok
+75230,1.0,386,?,not_applicable
+75240,1.0,400,0.050605060506050625,ok
+75198,1.0,404,?,not_applicable
+75201,1.0,407,?,not_applicable
+75105,1.0,415,0.0,ok
+75154,1.0,420,?,not_applicable
+2117,1.0,429,0.04640735343571345,ok
+75156,1.0,433,0.19444444444444442,ok
+75097,1.0,437,0.021655490821149814,ok
+75101,1.0,439,0.2614270449958447,ok
+75172,1.0,443,?,not_applicable
+75106,1.0,449,0.19047619047619047,ok
+75120,1.0,459,0.009009009009009028,ok
+75193,1.0,461,?,not_applicable
diff --git a/autosklearn/metalearning/files/precision_binary.classification_dense/configurations.csv b/autosklearn/metalearning/files/precision_binary.classification_dense/configurations.csv
index 374eda8a54..05968940e2 100644
--- a/autosklearn/metalearning/files/precision_binary.classification_dense/configurations.csv
+++ b/autosklearn/metalearning/files/precision_binary.classification_dense/configurations.csv
@@ -1,128 +1,133 @@
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-21,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,2.5882079595129472e-05,0.36125906161027654,auto,255,None,341,119,16,loss,1e-07,0.2924550184445239,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.02338478303505337,most_frequent,robust_scaler,,,0.7866731278723753,0.29543475006822734,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,68,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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-27,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.5,None,0.0,10,15,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,minmax,,,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l1,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,
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-29,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9333237919291875,None,0.0,8,3,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,
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-37,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.8680237807500206,None,0.0,6,4,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.021223427667606046,median,standardize,,,,,fast_ica,,,,,,,,,,,deflation,logcosh,364,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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-39,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,0.0029579681987136263,0.03811885694667444,auto,255,None,86,60,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.2946153662568939,most_frequent,quantile_transformer,1646,normal,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,
-40,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7088164344533493,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,mean,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,
-41,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,1.943186107984505e-07,0.08852455702036988,auto,255,None,5,72,19,loss,1e-07,0.039837460762271175,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.01591265419367627,median,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,
-42,weighting,adaboost,SAMME,0.5210651042094924,10,401,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,standardize,,,,,feature_agglomeration,,,,,,,,,,,,,,,euclidean,complete,89,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-43,none,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0686374607394679,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,minmax,,,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.8490260132946401,False,,,,,,,,,,,,,,,
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-46,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,92.66144306787051,,,0.04654639302091653,rbf,-1,True,0.0022231544280610005,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.005194838694455963,mean,none,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,15,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-47,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,2,8,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,median,robust_scaler,,,0.75,0.25,feature_agglomeration,,,,,,,,,,,,,,,euclidean,complete,12,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-48,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.8095522998548572,None,0.0,17,10,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.031940595071539304,mean,quantile_transformer,1563,normal,,,extra_trees_preproc_for_classification,False,entropy,None,0.9330145598603196,None,0.0,18,10,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-49,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,26.502259985828186,,,9.475662388405154e-05,rbf,-1,False,0.00019690635968217866,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-50,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.339600708038189,False,True,1,squared_hinge,ovr,l2,9.572641158475304e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,quantile_transformer,1193,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-51,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5255991794296913,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-52,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.34697723213923637,None,0.0,15,18,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.1923594385988973,mean,normalize,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,25.77499302945827,chi2,,,
-53,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1252.7997374154156,0.20811431041897355,5,0.0018140593074227453,poly,-1,False,0.03149782358621565,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.001930822730474197,most_frequent,quantile_transformer,233,normal,,,feature_agglomeration,,,,,,,,,,,,,,,euclidean,ward,29,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-54,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,1.5661823810486637e-10,0.08996863554015262,auto,255,None,156,89,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.009331521286159971,median,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,
-55,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.6109468076475573,0.8748383232154038,4,0.41333070422336576,poly,-1,False,3.591268778393482e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.011804925819789203,median,minmax,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-56,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.7610308078756552,None,0.0,1,2,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.010000000000000004,mean,robust_scaler,,,0.9205545465103644,0.20272761060743316,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-57,weighting,adaboost,SAMME.R,0.03127273017917347,10,496,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,standardize,,,,,fast_ica,,,,,,,,,,,deflation,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-58,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,151,None,,0.0003283777607180646,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.2510527431514227,mean,normalize,,,,,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,-0.96093182232312,,0.05762510964607661,sigmoid,2632,,,,,,,,,,,,,,,,,
-59,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.014684111714732086,True,,0.05499824464552364,True,,constant,log,l2,,0.008675315723129805,no_encoding,no_coalescense,,mean,quantile_transformer,51,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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-61,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,940.9437776449834,,,1.5839338084625942,rbf,-1,False,0.023072781592565438,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,none,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,77.25763685070707,mutual_info,,,
-62,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,220,auto,,0.0013824567992319003,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00021328932075284388,mean,minmax,,,,,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.039972474835314076,,0.01017810882899714,sigmoid,295,,,,,,,,,,,,,,,,,
-63,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.34534888916828577,0.02513634776687268,2,1.0733091880109409,poly,-1,True,0.0006974263047918307,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,quantile_transformer,1000,normal,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,77.99934218022861,mutual_info,,,
-64,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,29.55731828912983,False,True,1,squared_hinge,ovr,l2,0.0011332486951954968,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,
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-68,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,925.3053743248586,False,True,1,squared_hinge,ovr,l2,0.02764481198395717,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.3850718018366796,most_frequent,quantile_transformer,786,uniform,,,feature_agglomeration,,,,,,,,,,,,,,,cosine,average,156,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-69,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.8278163313806293,None,0.0,3,5,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,robust_scaler,,,0.725835793765774,0.2571431961341018,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.07784020380291123,fdr,f_classif
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-71,none,adaboost,SAMME,0.011233995624432622,9,477,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.018370622484682127,mean,standardize,,,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6039710338898471,False,,,,,,,,,,,,,,,
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-75,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,3.044019499065853e-08,0.030966538899129755,auto,255,None,64,50,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.00860619823050038,median,minmax,,,,,feature_agglomeration,,,,,,,,,,,,,,,cosine,average,36,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-76,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7.491039171224502,True,True,squared_hinge,2.8058138048327884e-05,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,minmax,,,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.7754372809893839,False,True,1,squared_hinge,ovr,l1,3.9643594596766314e-05,,,,,,,,,,,,,,,,,,,,,,
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-78,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,2.925599188129995e-05,0.12560285030651847,auto,255,None,300,193,17,loss,1e-07,0.039442997915546514,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00299400754997757,mean,robust_scaler,,,0.7093292916146515,0.009366983030047793,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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-80,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,128.75020223284181,,,0.09047921703481414,rbf,-1,False,0.0028029846849427493,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,none,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,236,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-81,none,adaboost,SAMME,0.011233995624432622,9,477,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.018370622484682127,mean,standardize,,,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6039710338898471,False,,,,,,,,,,,,,,,
-82,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,21.85207494001697,0.18872999589786676,4,1.1863646021868761,poly,-1,False,0.0021338371901294443,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0034080852834770217,mean,normalize,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.11326005109886934,fdr,f_classif
-83,weighting,adaboost,SAMME,0.38341570193537416,9,250,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,minmax,,,,,feature_agglomeration,,,,,,,,,,,,,,,euclidean,average,13,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-84,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,0.037849720944694,0.1268815770019783,auto,255,None,11,99,11,loss,1e-07,0.10654523627313527,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,
-85,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.593728171200453,None,0.0,7,20,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.02830941310359363,mean,normalize,,,,,extra_trees_preproc_for_classification,False,entropy,None,0.7389446586281172,None,0.0,20,11,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-86,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,1,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.019797877010737826,mean,normalize,,,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.8239678741128165,False,,,,,,,,,,,,,,,
-87,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.46057831591617715,False,True,hinge,0.04557857428827514,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00027457445401600137,median,standardize,,,,,extra_trees_preproc_for_classification,True,gini,None,0.48190346970486964,None,0.0,17,18,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-88,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.46057831591617715,False,True,hinge,0.04557857428827514,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00027457445401600137,median,standardize,,,,,extra_trees_preproc_for_classification,True,gini,None,0.48190346970486964,None,0.0,17,18,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-89,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,64,auto,,0.00021332620906594865,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.026032882800756493,median,standardize,,,,,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,-0.9513223792709335,,0.00029881582960753004,sigmoid,543,,,,,,,,,,,,,,,,,
-90,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.26356754187780895,None,0.0,4,3,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010966178401758774,mean,robust_scaler,,,0.7817043861798886,0.24371720112407158,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
-91,weighting,adaboost,SAMME.R,0.13547552520095074,4,62,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.011851011202809002,most_frequent,standardize,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4888776875314595,fpr,chi2
-92,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,80.0028902468169,False,True,1,squared_hinge,ovr,l2,0.0024481999829574722,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.15047970487774687,mean,quantile_transformer,1130,normal,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,1.1912646635544712,False,True,1,squared_hinge,ovr,l1,0.00014314728486450857,,,,,,,,,,,,,,,,,,,,,,
-93,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.22879311080499845,None,0.0,8,4,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.014320022547615783,median,minmax,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.238630703440461,mutual_info,,,
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-96,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.14148638702297855,None,0.0,2,6,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.0038499650338378643,most_frequent,quantile_transformer,1759,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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-98,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1228.3897711257332,0.6862670093933454,5,1.113863025113022,poly,-1,True,1.9271116749907707e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.018161834200128672,mean,normalize,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.06278549919408385,fpr,chi2
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-100,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.9815182201168626,None,0.0,15,19,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,robust_scaler,,,0.8878662977383482,0.25666364929970126,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,44.15748219826322,mutual_info,,,
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-102,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7908336536113714,None,0.0,9,3,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,minmax,,,,,extra_trees_preproc_for_classification,False,entropy,None,0.17440717696897978,None,0.0,5,18,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-103,none,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,128,manual,0.8574560771111307,0.010886139381599514,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.24115007944216788,most_frequent,normalize,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,62.26514170991452,mutual_info,,,
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-105,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,8.14394583618027e-09,0.1314274861878701,auto,255,None,27,15,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.3598067419611515,mean,robust_scaler,,,0.7494615433236279,0.260649229829574,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
-106,none,adaboost,SAMME.R,0.9261146078592859,9,476,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.012453052108117785,median,robust_scaler,,,0.7492076537583867,0.25853088636132765,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,6,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-107,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,11.046854203258679,,,0.07809571976769347,rbf,-1,False,0.00036193159909053734,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.006445557635410205,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-108,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.7922837297504705,False,True,1,squared_hinge,ovr,l2,0.0005476597326342352,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.012674631437254894,most_frequent,none,,,,,extra_trees_preproc_for_classification,False,entropy,None,0.23145146971319638,None,0.0,7,20,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-109,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.18060139887649052,None,0.0,10,12,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.010000000000000004,most_frequent,robust_scaler,,,0.9298612094255864,0.1263597946722308,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
-110,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.4111348710613201,None,0.0,16,8,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.01224348527546641,most_frequent,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,
-111,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6427543483503397,True,True,hinge,0.00045318775930916944,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.009338484332306904,median,quantile_transformer,174,normal,,,kernel_pca,,,,,,,,,,,,,,,,,,,,,,cosine,1848,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-112,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.02021502857757827,True,,0.055848937053280076,True,,constant,log,l2,,0.00016356162584832537,no_encoding,no_coalescense,,median,quantile_transformer,27,normal,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.11873149622606233,fwe,chi2
-113,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,9.029473608568458e-09,0.15779990576964315,auto,255,None,37,71,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.018093177195644097,mean,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,False,,,,,,,,,,,,
-114,weighting,adaboost,SAMME.R,0.23766684501868082,10,97,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.004737885672001703,median,none,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50.0,chi2,,,
-115,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.129143822880219,True,True,hinge,0.00012612093569084375,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0016222546082911887,most_frequent,quantile_transformer,1309,uniform,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8.816417526015055,f_classif,,,
-116,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.4864284879550203,None,0.0,1,4,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.008665854799630503,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-117,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.43699642289829416,None,0.0,6,20,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,quantile_transformer,1604,uniform,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-118,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.536709062860521e-07,True,,,True,,optimal,squared_hinge,l1,,1.6934515188687678e-05,one_hot_encoding,no_coalescense,,most_frequent,normalize,,,,,feature_agglomeration,,,,,,,,,,,,,,,cosine,complete,306,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-119,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5508478903876926,None,0.0,5,3,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0020120736976747405,most_frequent,minmax,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-120,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.5603377230962405,False,True,hinge,0.04986267764654238,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00014501962937745386,median,standardize,,,,,extra_trees_preproc_for_classification,True,gini,None,0.44197026095225617,None,0.0,20,4,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-121,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5836006490230695,None,0.0,1,19,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.02127792104027424,most_frequent,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,
-122,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.922871100218626,False,True,squared_hinge,0.09957180737451943,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0016563186856411274,median,quantile_transformer,850,uniform,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.09123695282772847,fpr,chi2
-123,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.20612848067724507,False,True,1,squared_hinge,ovr,l2,0.00017832980955957004,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0007502816173508168,median,quantile_transformer,1083,uniform,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.02820386332530596,fwe,f_classif
-124,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1561.1002600303584,,,0.0024048551704194507,rbf,-1,False,0.0001936214667496851,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,minmax,,,,,fast_ica,,,,,,,,,,,parallel,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-125,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.087313242780732,0.0636709914987994,3,0.004481917125024585,poly,-1,False,0.01848880618941367,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.002136015035740733,mean,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-126,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.03204653439894958,True,True,squared_hinge,0.00013006393401112688,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.013506575694382031,most_frequent,normalize,,,,,feature_agglomeration,,,,,,,,,,,,,,,euclidean,ward,356,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-127,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.6466927589470762,None,0.0,11,2,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.009672465311659264,most_frequent,standardize,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,369,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+1,none,decision_tree,,,,,,,gini,0.7229490531192699,1.0,None,0.0,1,18,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.00013586681143040562,median,quantile_transformer,647,uniform,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+4,none,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,quantile_transformer,111,uniform,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4748887602127273,fdr,f_classif
+7,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.36028513793687317,None,0.0,19,8,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,standardize,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,48.144408204553606,chi2,,,
+8,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,114,manual,0.11778631707605758,0.0029112721339713465,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.015582195257240751,median,normalize,,,,,kernel_pca,,,,,,,,,,,,,,,,,,,0.42909623253327767,3,0.25407612000343777,poly,1689,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+9,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.685918004243461,True,True,squared_hinge,1.7784504877380735e-05,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010335606354842895,most_frequent,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,
+10,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.7396493120555564,None,0.0,1,18,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010000000000000004,median,robust_scaler,,,0.8908334215036855,0.08666907249785906,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,
+15,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.21772092350860472,None,0.0,14,17,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.0020488212891693275,median,standardize,,,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,33.74257606530722,False,True,1,squared_hinge,ovr,l1,8.740452966205111e-05,,,,,,,,,,,,,,,,,,,,,,
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+371,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,4.198700105243736e-09,0.011070860038925674,auto,255,None,9,116,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010247554402833578,median,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,
+372,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,0.09219798181799597,0.010664673522048937,auto,255,None,8,100,18,loss,1e-07,0.06150512744434074,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,quantile_transformer,967,uniform,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+374,none,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.7304811343030777,None,0.0,2,3,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,robust_scaler,,,0.7202654714984674,0.21555973677200435,feature_agglomeration,,,,,,,,,,,,,,,euclidean,average,149,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+379,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,1.2940289605295536e-09,0.15591343717459372,auto,255,None,7,59,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0005549095909238961,mean,quantile_transformer,297,uniform,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,58.10560700979111,chi2,,,
+386,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9358939105347388,None,0.0,1,5,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,none,,,,,fast_ica,,,,,,,,,,,parallel,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+400,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,0.0014622110897382257,0.031971624780486804,auto,255,None,195,15,2,loss,1e-07,0.07459887038090841,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.013029979892169268,mean,standardize,,,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.08573874028833707,False,True,1,squared_hinge,ovr,l1,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,
+404,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8.380057363340162,False,True,hinge,0.0020653831067503525,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.05020677662128942,median,quantile_transformer,560,normal,,,kernel_pca,,,,,,,,,,,,,,,,,,,,,,cosine,891,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+407,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.7292011996224884,True,True,hinge,8.162763086383311e-05,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010337761588362885,median,quantile_transformer,877,uniform,,,kernel_pca,,,,,,,,,,,,,,,,,,,,,,cosine,1317,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+413,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,3.562032726117879e-09,0.07935074296383826,auto,255,None,25,20,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0035033827976084825,mean,minmax,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+415,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,0.00037966471646911845,0.016422945536911454,auto,255,None,10,56,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.002419439132687246,median,quantile_transformer,828,uniform,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+420,weighting,adaboost,SAMME.R,0.05920795019389743,8,313,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,standardize,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,104,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+424,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,2.344883102482138e-07,0.0328548789988159,auto,255,None,42,24,16,loss,1e-07,0.13015466346727866,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,most_frequent,robust_scaler,,,0.7010898774332888,0.2534280821273264,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,
+425,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,1.1007644378635384e-06,0.1638498547151709,auto,255,None,601,3,16,loss,1e-07,0.024706703809421183,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.11489180733211171,most_frequent,quantile_transformer,285,uniform,,,feature_agglomeration,,,,,,,,,,,,,,,euclidean,average,332,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+429,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.03329934974307803,False,True,squared_hinge,0.0009304676085290937,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,minmax,,,,,kitchen_sinks,,,,,,,,,,,,,,,,,,,,,,,,0.11746032349918363,139,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+433,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5323411241263409,None,0.0,2,18,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,median,standardize,,,,,extra_trees_preproc_for_classification,True,entropy,None,0.6132742708509392,None,0.0,6,16,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+437,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.758794895040923,None,0.0,18,19,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,quantile_transformer,1000,uniform,,,extra_trees_preproc_for_classification,False,gini,None,0.9944228208678969,None,0.0,20,6,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+439,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.5983462947802015,None,0.0,2,16,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00020635833184954756,most_frequent,minmax,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.3423975961749763,fdr,f_classif
+443,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.6269076084367177,None,0.0,1,7,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.12943250859943486,most_frequent,standardize,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,62.84526554323969,chi2,,,
+449,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,2.074711735072034e-07,0.01106799034691714,auto,255,None,13,24,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.020949205931423413,mean,standardize,,,,,extra_trees_preproc_for_classification,True,gini,None,0.43505400875624267,None,0.0,20,8,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+459,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.745748188733041e-05,True,True,hinge,2.181659959258924e-05,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.13921206193066876,most_frequent,quantile_transformer,60,normal,,,extra_trees_preproc_for_classification,False,entropy,None,0.7619669647366906,None,0.0,3,4,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+461,none,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.9796199105973851,None,0.0,2,13,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.00845950033601661,mean,robust_scaler,,,0.8275228560550734,0.23814615163106348,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
diff --git a/autosklearn/metalearning/files/precision_binary.classification_dense/description.txt b/autosklearn/metalearning/files/precision_binary.classification_dense/description.txt
index cecf1a1e4d..b9c894dc95 100644
--- a/autosklearn/metalearning/files/precision_binary.classification_dense/description.txt
+++ b/autosklearn/metalearning/files/precision_binary.classification_dense/description.txt
@@ -1,8 +1,9 @@
 features_cutoff_time: 3600
 features_cutoff_memory: 3072
-number_of_feature_steps: 35
+number_of_feature_steps: 42
 feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances
 feature_step LogNumberOfInstances: LogNumberOfInstances
+feature_step NumberOfClasses: NumberOfClasses
 feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures
 feature_step LogNumberOfFeatures: LogNumberOfFeatures
 feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues
@@ -20,6 +21,11 @@ feature_step DatasetRatio: DatasetRatio, LogDatasetRatio
 feature_step LogDatasetRatio: LogDatasetRatio
 feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio
 feature_step LogInverseDatasetRatio: LogInverseDatasetRatio
+feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD
+feature_step ClassProbabilityMin: ClassProbabilityMin
+feature_step ClassProbabilityMax: ClassProbabilityMax
+feature_step ClassProbabilityMean: ClassProbabilityMean
+feature_step ClassProbabilitySTD: ClassProbabilitySTD
 feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum
 feature_step SymbolsMin: SymbolsMin
 feature_step SymbolsMax: SymbolsMax
@@ -36,11 +42,12 @@ feature_step SkewnessMin: SkewnessMin
 feature_step SkewnessMax: SkewnessMax
 feature_step SkewnessMean: SkewnessMean
 feature_step SkewnessSTD: SkewnessSTD
-features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
+feature_step ClassEntropy: ClassEntropy
+features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy
 features_stochastic: 
-default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
+default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy
 
-algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127
+algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235,236,237,238,239,240,241,242,243,244,245,246,247,248,249,250,251,252,253,254,255,256,257,258,259,260,261,262,263,264,265,266,267,268,269,270,271,272,273,274,275,276,277,278,279,280,281,282,283,284,285,286,287,288,289,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,312,313,314,315,316,317,318,319,320,321,322,323,324,325,326,327,328,329,330,331,332,333,334,335,336,337,338,339,340,341,342,343,344,345,346,347,348,349,350,351,352,353,354,355,356,357,358,359,360,361,362,363,364,365,366,367,368,369,370,371,372,373,374,375,376,377,378,379,380,381,382,383,384,385,386,387,388,389,390,391,392,393,394,395,396,397,398,399,400,401,402,403,404,405,406,407,408,409,410,411,412,413,414,415,416,417,418,419,420,421,422,423,424,425,426,427,428,429,430,431,432,433,434,435,436,437,438,439,440,441,442,443,444,445,446,447,448,449,450,451,452,453,454,455,456,457,458,459,460,461,462,463,464
 algorithms_stochastic: 
 performance_measures: precision
 performance_type: solution_quality
diff --git a/autosklearn/metalearning/files/precision_binary.classification_dense/feature_costs.arff b/autosklearn/metalearning/files/precision_binary.classification_dense/feature_costs.arff
index 0e17e20d22..8512fd9275 100644
--- a/autosklearn/metalearning/files/precision_binary.classification_dense/feature_costs.arff
+++ b/autosklearn/metalearning/files/precision_binary.classification_dense/feature_costs.arff
@@ -4,6 +4,7 @@
 @ATTRIBUTE repetition NUMERIC
 @ATTRIBUTE NumberOfInstances NUMERIC
 @ATTRIBUTE LogNumberOfInstances NUMERIC
+@ATTRIBUTE NumberOfClasses NUMERIC
 @ATTRIBUTE NumberOfFeatures NUMERIC
 @ATTRIBUTE LogNumberOfFeatures NUMERIC
 @ATTRIBUTE MissingValues NUMERIC
@@ -21,6 +22,11 @@
 @ATTRIBUTE LogDatasetRatio NUMERIC
 @ATTRIBUTE InverseDatasetRatio NUMERIC
 @ATTRIBUTE LogInverseDatasetRatio NUMERIC
+@ATTRIBUTE ClassOccurences NUMERIC
+@ATTRIBUTE ClassProbabilityMin NUMERIC
+@ATTRIBUTE ClassProbabilityMax NUMERIC
+@ATTRIBUTE ClassProbabilityMean NUMERIC
+@ATTRIBUTE ClassProbabilitySTD NUMERIC
 @ATTRIBUTE NumSymbols NUMERIC
 @ATTRIBUTE SymbolsMin NUMERIC
 @ATTRIBUTE SymbolsMax NUMERIC
@@ -37,162 +43,138 @@
 @ATTRIBUTE SkewnessMax NUMERIC
 @ATTRIBUTE SkewnessMean NUMERIC
 @ATTRIBUTE SkewnessSTD NUMERIC
+@ATTRIBUTE ClassEntropy NUMERIC
 
 @DATA
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-75101,1.0,1e-05,1e-05,1e-05,1e-05,0.0418,0.03546,1e-05,0.00389,1e-05,0.00248,0.0,2e-05,2e-05,4e-05,6e-05,2e-05,1e-05,5e-05,2e-05,0.00045,1e-05,0.0,0.00011,0.00023,0.0001,0.00041,6e-05,8e-05,0.0001,0.00018,0.00047,6e-05,0.0001,8e-05,0.00023
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+75239,1.0,1e-05,1e-05,7e-05,1e-05,1e-05,0.00105,0.00084,1e-05,0.00015,1e-05,8e-05,1e-05,2e-05,5e-05,4e-05,8e-05,2e-05,1e-05,3e-05,1e-05,0.00016,2e-05,1e-05,5e-05,9e-05,0.00049,0.0,0.0,0.00012,0.00026,0.00011,0.00046,0.00013,8e-05,8e-05,0.00018,0.00044,6e-05,9e-05,0.00011,0.00018,0.00065
+75240,1.0,2e-05,1e-05,0.00036,5e-05,4e-05,0.00845,0.00644,6e-05,0.00122,4e-05,0.00091,1e-05,4e-05,4e-05,9e-05,0.00013,5e-05,2e-05,9e-05,5e-05,0.00031,3e-05,1e-05,7e-05,0.0002,0.00073,3e-05,2e-05,0.00019,0.0003,0.00018,0.00055,9e-05,9e-05,0.00011,0.00025,0.0005,7e-05,8e-05,0.00015,0.0002,0.00365
+75243,1.0,1e-05,1e-05,0.00029,1e-05,1e-05,0.00461,0.00415,1e-05,0.00035,1e-05,0.00015,0.0,2e-05,2e-05,5e-05,7e-05,2e-05,1e-05,4e-05,1e-05,0.00018,2e-05,1e-05,4e-05,0.00011,0.00022,1e-05,1e-05,5e-05,0.00011,5e-05,0.0004,6e-05,6e-05,8e-05,0.0002,0.00039,6e-05,6e-05,8e-05,0.00019,0.00265
+75244,1.0,3e-05,2e-05,0.0004,3e-05,2e-05,0.0173,0.01298,4e-05,0.00267,4e-05,0.00174,1e-05,7e-05,9e-05,0.00024,0.00027,8e-05,2e-05,0.00013,3e-05,0.00045,7e-05,2e-05,0.00011,0.00025,0.00077,6e-05,2e-05,0.00021,0.00034,0.00013,0.00077,0.00014,0.00013,0.00026,0.00023,0.00044,7e-05,8e-05,0.0001,0.00019,0.00649
+75248,1.0,1e-05,1e-05,0.00017,1e-05,1e-05,0.00416,0.00305,1e-05,0.0006,2e-05,0.00054,1e-05,2e-05,2e-05,5e-05,7e-05,2e-05,1e-05,4e-05,1e-05,0.00017,2e-05,1e-05,4e-05,0.0001,0.00029,5e-05,1e-05,6e-05,0.00012,6e-05,0.00081,0.00011,0.00015,0.00016,0.00038,0.0005,7e-05,8e-05,0.00012,0.00024,0.00184
+75249,1.0,1e-05,1e-05,0.00011,1e-05,1e-05,0.00192,0.00153,1e-05,0.00025,1e-05,0.00017,1e-05,2e-05,2e-05,5e-05,7e-05,2e-05,1e-05,5e-05,3e-05,0.00014,2e-05,1e-05,3e-05,8e-05,0.00029,1e-05,3e-05,6e-05,0.00012,7e-05,0.00041,6e-05,8e-05,0.0001,0.00017,0.00043,6e-05,7e-05,9e-05,0.00019,0.00092
+75250,1.0,1e-05,1e-05,0.00375,1e-05,1e-05,0.04994,0.047,1e-05,0.00247,1e-05,0.00049,1e-05,2e-05,2e-05,6e-05,7e-05,2e-05,1e-05,3e-05,1e-05,0.00022,4e-05,1e-05,4e-05,0.00012,0.00041,0.0,1e-05,0.00011,0.00023,6e-05,0.00076,0.00012,0.00012,0.00019,0.00032,0.00086,0.00012,0.00013,0.00018,0.00043,0.04178
diff --git a/autosklearn/metalearning/files/precision_binary.classification_dense/feature_runstatus.arff b/autosklearn/metalearning/files/precision_binary.classification_dense/feature_runstatus.arff
index 0034ad2bd6..5fff2dfed3 100644
--- a/autosklearn/metalearning/files/precision_binary.classification_dense/feature_runstatus.arff
+++ b/autosklearn/metalearning/files/precision_binary.classification_dense/feature_runstatus.arff
@@ -4,6 +4,7 @@
 @ATTRIBUTE repetition NUMERIC
 @ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other}
@@ -21,6 +22,11 @@
 @ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other}
@@ -37,162 +43,138 @@
 @ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other}
 
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diff --git a/autosklearn/metalearning/files/precision_binary.classification_dense/feature_values.arff b/autosklearn/metalearning/files/precision_binary.classification_dense/feature_values.arff
index a60400e3ce..736dc64321 100644
--- a/autosklearn/metalearning/files/precision_binary.classification_dense/feature_values.arff
+++ b/autosklearn/metalearning/files/precision_binary.classification_dense/feature_values.arff
@@ -13,7 +13,6 @@
 @ATTRIBUTE KurtosisMean NUMERIC
 @ATTRIBUTE KurtosisMin NUMERIC
 @ATTRIBUTE KurtosisSTD NUMERIC
-@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC
 @ATTRIBUTE LogDatasetRatio NUMERIC
 @ATTRIBUTE LogInverseDatasetRatio NUMERIC
 @ATTRIBUTE LogNumberOfFeatures NUMERIC
@@ -42,160 +41,135 @@
 @ATTRIBUTE SymbolsSum NUMERIC
 
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+75233,1.0,0.8824680010722367,0.6990344325013663,0.5,0.3009655674986336,0.19903443250136635,0.0038258334851521223,261.3809523809524,619.9618265719739,76.9042121753925,1.1050496101379395,139.01256776590665,-5.565978930826466,5.565978930826466,3.044522437723423,8.610501368549889,0.0,2.0,21.0,0.0,5489.0,0.0,0.0,21.0,0.0,0.0,0.0,0.0,0.0,21.77604866027832,5.3457508484522505,-0.7721167802810669,4.832918145056104,0.0,0.0,0.0,0.0,0.0
+75234,1.0,0.9998393017810485,0.5074626865671642,0.5,0.4925373134328358,0.007462686567164201,0.004033884630899556,247.9,1.9309582710266113,1.5332300662994385,1.3253521919250488,0.13647900129618118,-5.513025439041145,5.513025439041145,2.995732273553991,8.508757712595136,0.0,2.0,20.0,0.0,4958.0,0.0,0.0,20.0,0.0,0.0,0.0,0.0,0.0,-0.1076231524348259,-0.23315090499818325,-0.33683285117149353,0.052567253463639066,0.0,0.0,0.0,0.0,0.0
+75235,1.0,1.7067919449664923,0.4105579868708972,0.25,0.05579868708971553,0.1492903085996621,0.006564551422319475,152.33333333333334,13.76956558227539,2.346758103856697,-1.6145207422400454,4.524616106763882,-5.026071102226095,5.026071102226095,3.1780538303479458,8.204124932574041,0.0,4.0,24.0,0.0,3656.0,0.0,0.0,24.0,0.0,0.0,0.0,0.0,0.0,3.779214382171631,1.515954251983203,0.004355928860604763,1.0657977665170062,0.0,0.0,0.0,0.0,0.0
+75236,1.0,3.3208751934243548,0.10486891385767791,0.1,0.09363295880149813,0.003810297743328803,0.2397003745318352,4.171875,22.422517776489258,-0.9225467168815645,-1.9997756481170654,1.9352856443240884,-1.4283655750405777,1.4283655750405777,5.545177444479562,6.97354301952014,0.0,10.0,256.0,0.0,1068.0,0.0,0.0,256.0,0.0,0.0,0.0,0.0,0.0,4.94191312789917,0.8186543448100565,-0.6194983720779419,0.6381679017395948,0.0,0.0,0.0,0.0,0.0
+75237,1.0,0.7378736147990802,0.7918800894091566,0.5,0.20811991059084348,0.29188008940915655,1.8271625991997027e-05,54729.666666666664,-0.5975548261535093,-0.7926032911766772,-0.9140236377716064,0.13929334047064063,-10.910161193614513,10.910161193614513,1.0986122886681098,12.008773482282624,0.0,2.0,3.0,0.0,164189.0,0.0,0.0,3.0,0.0,0.0,0.0,0.0,0.0,-0.039879728108644485,-0.3348021370669206,-0.6223916411399841,0.2378660138627058,0.0,0.0,0.0,0.0,0.0
+75239,1.0,0.9443547030267275,0.6379707916986933,0.5,0.3620292083013067,0.1379707916986933,0.02536510376633359,39.42424242424242,30.96304702758789,5.229958094222913,-1.8830041885375977,9.159290860687747,-3.674380917046025,3.674380917046025,3.4965075614664802,7.170888478512505,0.0,2.0,33.0,0.0,1301.0,0.0,0.0,33.0,0.0,0.0,0.0,0.0,0.0,5.591684341430664,1.5151810998266393,-0.8763390183448792,1.68213778251707,0.0,0.0,0.0,0.0,0.0
+75240,1.0,0.9473321982231081,0.6342759940463534,0.5,0.3657240059536466,0.13427599404635338,0.01275781416117372,78.38333333333334,1911.103482572295,38.512800597597035,-3.0,145.76440092342054,-4.361611319722948,4.361611319722948,4.0943445622221,8.455955881945048,27.0,2.0,60.0,17.0,4703.0,4703.0,29447.0,33.0,0.2833333333333333,1.0,0.10435537600113402,0.8181818181818182,1.2222222222222223,43.144344696960424,3.913912678196276,-22.79380338813253,4.943976022970944,173.0,22.59259259259259,1.0,43.77997111758117,610.0
+75243,1.0,1.7118844784475225,0.33529140750979036,0.25,0.024648698456576825,0.13045500689907696,0.0009214466712739,1085.25,0.37898291200086787,-0.9656219973073414,-1.9999312246360998,0.7132002663612025,-6.989565654179703,6.989565654179703,2.0794415416798357,9.06900719585954,8.0,4.0,8.0,0.0,8682.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.5423951867147632,0.9342552225820131,-0.008293091335592482,0.40192683634075954,5.0,3.375,2.0,0.8569568250501305,27.0
+75244,1.0,0.33901826511567296,0.9370570367870401,0.5,0.06294296321295983,0.43705703678704017,0.009449881876476544,105.82142857142857,1180.200844451951,40.51648623113333,-3.0,132.43484378998235,-4.661753037398361,4.661753037398361,4.02535169073515,8.68710472813351,41.0,2.0,56.0,14.0,5926.0,5926.0,23694.0,15.0,0.25,1.0,0.0713984378766694,2.7333333333333334,0.36585365853658536,34.38314768097812,3.7769009011075547,-8.659484722748674,5.307279668054912,16.0,5.975609756097561,1.0,3.692363938923924,245.0
+75248,1.0,0.4971128374263667,0.8909272183449651,0.5,0.1090727816550349,0.39092721834496513,0.010967098703888335,91.18181818181819,5010.000199441565,104.23108191482348,-3.0,468.735807382562,-4.512855515163565,4.512855515163565,4.007333185232471,8.520188700396035,39.0,2.0,55.0,12.0,5015.0,4938.0,21515.0,16.0,0.21818181818181817,0.9846460618145564,0.07800235656666364,2.4375,0.41025641025641024,70.79548149028699,4.06998328231248,-50.04498375424548,9.467860234489164,14.0,5.769230769230769,1.0,3.407845687351005,225.0
+75249,1.0,0.3891675492147647,0.9236550632911392,0.5,0.07634493670886076,0.4236550632911392,0.011471518987341773,87.17241379310344,416.3357123975682,46.57141260912883,-3.0,84.69777944086748,-4.467887925280274,4.467887925280274,3.367295829986474,7.8351837552667485,22.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,7.0,0.2413793103448276,1.0,0.056048123090353556,3.142857142857143,0.3181818181818182,20.453256767506936,0.8884855226401125,-20.453256767506936,6.7466910388456185,5.0,2.0454545454545454,1.0,0.7056443043754556,45.0
+75250,1.0,3.992354626842552,0.146342438507591,0.04545454545454545,0.005796927628356971,0.03949259311161838,3.997881123004808e-05,25013.25,2.1476065760596077,1.4719570787187288,0.3045189380645752,0.6973345081454226,-10.127160963449944,10.127160963449944,1.3862943611198906,11.513455324569835,0.0,22.0,4.0,0.0,100053.0,0.0,0.0,4.0,0.0,0.0,0.0,0.0,0.0,1.2135857343673706,0.3996680751442909,-0.3896799087524414,0.7616029272290122,0.0,0.0,0.0,0.0,0.0
diff --git a/autosklearn/metalearning/files/precision_binary.classification_sparse/algorithm_runs.arff b/autosklearn/metalearning/files/precision_binary.classification_sparse/algorithm_runs.arff
index 1266b4164b..bdf3ec4584 100644
--- a/autosklearn/metalearning/files/precision_binary.classification_sparse/algorithm_runs.arff
+++ b/autosklearn/metalearning/files/precision_binary.classification_sparse/algorithm_runs.arff
@@ -7,95 +7,133 @@
 @ATTRIBUTE runstatus {ok, timeout, memout, not_applicable, crash, other}
 
 @DATA
-75212,1.0,1,0.245,ok
-246,1.0,2,?,not_applicable
-75178,1.0,3,?,not_applicable
-75171,1.0,4,0.16604057099924874,ok
-248,1.0,5,?,not_applicable
-75231,1.0,6,?,not_applicable
-75196,1.0,7,0.01904761904761909,ok
-75188,1.0,8,?,not_applicable
-75248,1.0,9,0.752823086574655,ok
-75126,1.0,10,0.01834862385321101,ok
-75234,1.0,11,0.03255425709515858,ok
-75150,1.0,12,0.27717391304347827,ok
-258,1.0,13,?,not_applicable
-75168,1.0,14,?,not_applicable
-75235,1.0,15,?,not_applicable
-244,1.0,16,?,not_applicable
-75221,1.0,17,?,not_applicable
-75219,1.0,18,0.02992633517495391,ok
-75202,1.0,19,?,not_applicable
-3043,1.0,20,0.19565217391304346,ok
-75205,1.0,21,?,not_applicable
-75174,1.0,22,0.22988992379339546,ok
-275,1.0,23,?,not_applicable
-75213,1.0,24,0.14457831325301207,ok
-75099,1.0,25,0.6680851063829787,ok
-75184,1.0,26,0.19008782936010038,ok
-75222,1.0,27,0.5211267605633803,ok
-233,1.0,28,0.004073319755600768,ok
-75114,1.0,29,0.027295285359801524,ok
-236,1.0,30,?,not_applicable
-75141,1.0,31,0.08148804251550046,ok
-75107,1.0,32,0.29939393939393943,ok
-262,1.0,33,?,not_applicable
-75146,1.0,34,0.0912992586812329,ok
-75189,1.0,35,0.013769684304798857,ok
-2350,1.0,36,0.5842329404773695,ok
-75249,1.0,37,0.02020202020202022,ok
-242,1.0,38,?,not_applicable
-75117,1.0,39,0.005494505494505475,ok
-75191,1.0,40,0.08133086876155271,ok
-261,1.0,41,0.46341463414634143,ok
-75236,1.0,42,?,not_applicable
-75095,1.0,43,0.24038461538461542,ok
-75093,1.0,44,0.6726495726495727,ok
-75223,1.0,45,?,not_applicable
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-75197,1.0,47,?,not_applicable
-75127,1.0,48,0.3729369997582297,ok
-75143,1.0,49,0.008919722497522264,ok
-75153,1.0,50,0.0755685986793837,ok
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-75215,1.0,52,0.029750479846449185,ok
-75195,1.0,53,0.00037565740045075735,ok
-75207,1.0,54,?,not_applicable
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-75166,1.0,56,0.14041892940263767,ok
-75100,1.0,57,0.980544747081712,ok
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-75121,1.0,59,0.0,ok
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-75115,1.0,61,0.01698513800424628,ok
-75116,1.0,62,0.0023866348448687846,ok
-75185,1.0,63,0.14503816793893132,ok
-2119,1.0,64,?,not_applicable
-75157,1.0,65,0.44099378881987583,ok
-75113,1.0,66,0.08463949843260188,ok
-75203,1.0,67,?,not_applicable
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-75125,1.0,71,0.028436018957345932,ok
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-75103,1.0,73,0.1308139534883721,ok
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-75230,1.0,75,?,not_applicable
-75139,1.0,76,0.018404907975460127,ok
-75227,1.0,77,0.15936254980079678,ok
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-75124,1.0,79,0.6305732484076434,ok
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-75187,1.0,91,0.01266891891891897,ok
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+75177,1.0,11,0.0273972602739726,ok
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+75142,1.0,94,0.07191176470588234,ok
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+75129,1.0,324,0.5714285714285714,ok
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+254,1.0,132,0.0,ok
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+75127,1.0,207,0.30221535291087065,ok
+75153,1.0,215,0.07380073800738007,ok
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+75115,1.0,255,0.013015184381778733,ok
+266,1.0,258,?,not_applicable
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+75237,1.0,291,4.677268475206109e-05,ok
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+2120,1.0,297,?,not_applicable
+75232,1.0,300,0.15841584158415845,ok
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+75240,1.0,321,0.05411255411255411,ok
+75198,1.0,325,?,not_applicable
+75201,1.0,328,?,not_applicable
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+75097,1.0,351,0.021655490821149814,ok
+75101,1.0,352,0.2686696268957961,ok
+75172,1.0,353,?,not_applicable
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+75120,1.0,368,0.009009009009009028,ok
+75193,1.0,369,?,not_applicable
diff --git a/autosklearn/metalearning/files/precision_binary.classification_sparse/configurations.csv b/autosklearn/metalearning/files/precision_binary.classification_sparse/configurations.csv
index 20dc59b32a..a868976598 100644
--- a/autosklearn/metalearning/files/precision_binary.classification_sparse/configurations.csv
+++ b/autosklearn/metalearning/files/precision_binary.classification_sparse/configurations.csv
@@ -1,93 +1,131 @@
 idx,balancing:strategy,classifier:__choice__,classifier:adaboost:algorithm,classifier:adaboost:learning_rate,classifier:adaboost:max_depth,classifier:adaboost:n_estimators,classifier:bernoulli_nb:alpha,classifier:bernoulli_nb:fit_prior,classifier:decision_tree:criterion,classifier:decision_tree:max_depth_factor,classifier:decision_tree:max_features,classifier:decision_tree:max_leaf_nodes,classifier:decision_tree:min_impurity_decrease,classifier:decision_tree:min_samples_leaf,classifier:decision_tree:min_samples_split,classifier:decision_tree:min_weight_fraction_leaf,classifier:extra_trees:bootstrap,classifier:extra_trees:criterion,classifier:extra_trees:max_depth,classifier:extra_trees:max_features,classifier:extra_trees:max_leaf_nodes,classifier:extra_trees:min_impurity_decrease,classifier:extra_trees:min_samples_leaf,classifier:extra_trees:min_samples_split,classifier:extra_trees:min_weight_fraction_leaf,classifier:gradient_boosting:early_stop,classifier:gradient_boosting:l2_regularization,classifier:gradient_boosting:learning_rate,classifier:gradient_boosting:loss,classifier:gradient_boosting:max_bins,classifier:gradient_boosting:max_depth,classifier:gradient_boosting:max_leaf_nodes,classifier:gradient_boosting:min_samples_leaf,classifier:gradient_boosting:n_iter_no_change,classifier:gradient_boosting:scoring,classifier:gradient_boosting:tol,classifier:gradient_boosting:validation_fraction,classifier:k_nearest_neighbors:n_neighbors,classifier:k_nearest_neighbors:p,classifier:k_nearest_neighbors:weights,classifier:lda:n_components,classifier:lda:shrinkage,classifier:lda:shrinkage_factor,classifier:lda:tol,classifier:liblinear_svc:C,classifier:liblinear_svc:dual,classifier:liblinear_svc:fit_intercept,classifier:liblinear_svc:intercept_scaling,classifier:liblinear_svc:loss,classifier:liblinear_svc:multi_class,classifier:liblinear_svc:penalty,classifier:liblinear_svc:tol,classifier:libsvm_svc:C,classifier:libsvm_svc:coef0,classifier:libsvm_svc:degree,classifier:libsvm_svc:gamma,classifier:libsvm_svc:kernel,classifier:libsvm_svc:max_iter,classifier:libsvm_svc:shrinking,classifier:libsvm_svc:tol,classifier:multinomial_nb:alpha,classifier:multinomial_nb:fit_prior,classifier:passive_aggressive:C,classifier:passive_aggressive:average,classifier:passive_aggressive:fit_intercept,classifier:passive_aggressive:loss,classifier:passive_aggressive:tol,classifier:qda:reg_param,classifier:random_forest:bootstrap,classifier:random_forest:criterion,classifier:random_forest:max_depth,classifier:random_forest:max_features,classifier:random_forest:max_leaf_nodes,classifier:random_forest:min_impurity_decrease,classifier:random_forest:min_samples_leaf,classifier:random_forest:min_samples_split,classifier:random_forest:min_weight_fraction_leaf,classifier:sgd:alpha,classifier:sgd:average,classifier:sgd:epsilon,classifier:sgd:eta0,classifier:sgd:fit_intercept,classifier:sgd:l1_ratio,classifier:sgd:learning_rate,classifier:sgd:loss,classifier:sgd:penalty,classifier:sgd:power_t,classifier:sgd:tol,data_preprocessing:categorical_transformer:categorical_encoding:__choice__,data_preprocessing:categorical_transformer:category_coalescence:__choice__,data_preprocessing:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction,data_preprocessing:numerical_transformer:imputation:strategy,data_preprocessing:numerical_transformer:rescaling:__choice__,data_preprocessing:numerical_transformer:rescaling:quantile_transformer:n_quantiles,data_preprocessing:numerical_transformer:rescaling:quantile_transformer:output_distribution,data_preprocessing:numerical_transformer:rescaling:robust_scaler:q_max,data_preprocessing:numerical_transformer:rescaling:robust_scaler:q_min,feature_preprocessor:__choice__,feature_preprocessor:extra_trees_preproc_for_classification:bootstrap,feature_preprocessor:extra_trees_preproc_for_classification:criterion,feature_preprocessor:extra_trees_preproc_for_classification:max_depth,feature_preprocessor:extra_trees_preproc_for_classification:max_features,feature_preprocessor:extra_trees_preproc_for_classification:max_leaf_nodes,feature_preprocessor:extra_trees_preproc_for_classification:min_impurity_decrease,feature_preprocessor:extra_trees_preproc_for_classification:min_samples_leaf,feature_preprocessor:extra_trees_preproc_for_classification:min_samples_split,feature_preprocessor:extra_trees_preproc_for_classification:min_weight_fraction_leaf,feature_preprocessor:extra_trees_preproc_for_classification:n_estimators,feature_preprocessor:kernel_pca:coef0,feature_preprocessor:kernel_pca:degree,feature_preprocessor:kernel_pca:gamma,feature_preprocessor:kernel_pca:kernel,feature_preprocessor:kernel_pca:n_components,feature_preprocessor:kitchen_sinks:gamma,feature_preprocessor:kitchen_sinks:n_components,feature_preprocessor:liblinear_svc_preprocessor:C,feature_preprocessor:liblinear_svc_preprocessor:dual,feature_preprocessor:liblinear_svc_preprocessor:fit_intercept,feature_preprocessor:liblinear_svc_preprocess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-89,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.922871100218626,False,True,squared_hinge,0.09957180737451943,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0016563186856411274,median,quantile_transformer,850,uniform,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.09123695282772847,fpr,chi2,
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+273,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.7302408485410412,None,0.0,8,12,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.027592968439566132,median,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+274,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.513560345508016,None,0.0,3,2,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,median,quantile_transformer,1044,uniform,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.07550342883878675,,mutual_info_classif,
+277,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,57,2,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+280,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.2318705830210126,True,True,hinge,0.0002994421772278671,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.13310408167721913,most_frequent,quantile_transformer,1036,uniform,,,kernel_pca,,,,,,,,,,,,,,cosine,703,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+284,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5609113236355627,None,0.0,2,2,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.47917908217902894,mean,quantile_transformer,673,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+287,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.3850609070926397,None,0.0,1,2,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.013992309764275707,median,normalize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,,
+288,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.5719833791553314,None,0.0,16,3,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.13804007452389966,mean,normalize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+291,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.11789257899273609,None,0.0,2,3,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.4082847014868829,median,robust_scaler,,,0.9073691418507908,0.1954896244813517,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,,
+296,weighting,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,1,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.10025594399125329,median,none,,,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.22421266641512966,False,True,1,squared_hinge,ovr,l1,0.0036601423446597503,,,,,,,,,,,,,,,,,,,,,
+297,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.0014200038703931547,most_frequent,robust_scaler,,,0.7373450748248416,0.26419938540085264,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+300,none,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.7010137052424128,None,0.0,7,10,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.2761813291805052,most_frequent,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,,
+305,none,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.318789969510999e-05,True,0.00020676665332856436,,True,0.3135652769555971,optimal,modified_huber,elasticnet,,0.00017449676135991563,one_hot_encoding,minority_coalescer,0.002260448647328741,mean,quantile_transformer,1262,uniform,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,,
+307,none,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,249,None,,0.0005516162390755682,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010000000000000004,mean,quantile_transformer,1350,uniform,,,kernel_pca,,,,,,,,,,,0.162692683181769,3,1.0,poly,487,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+312,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,60.6761602925822,,,0.038224035513907226,rbf,-1,False,0.0018214343969776353,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.421025538999516,median,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+313,weighting,adaboost,SAMME,1.2423888874490505,5,284,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.01582563952987033,most_frequent,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+317,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.4572679693563934,None,0.0,1,11,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,mean,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,,
+321,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.5864398404787432,None,0.0,1,14,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0070663750850608895,most_frequent,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,,
+324,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.5240435604277579,None,0.0,1,3,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.05089089351168927,median,robust_scaler,,,0.8952718567345662,0.24647973407695942,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,,
+325,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8.380057363340162,False,True,hinge,0.0020653831067503525,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.05020677662128942,median,quantile_transformer,560,normal,,,kernel_pca,,,,,,,,,,,,,,cosine,891,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+328,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.7292011996224884,True,True,hinge,8.162763086383311e-05,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010337761588362885,median,quantile_transformer,877,uniform,,,kernel_pca,,,,,,,,,,,,,,cosine,1317,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+331,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.5,None,0.0,1,5,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.15244281596303316,mean,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+336,weighting,adaboost,SAMME,0.1313487280585511,3,189,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,robust_scaler,,,0.8008090310131288,0.2935856792824304,extra_trees_preproc_for_classification,False,entropy,None,0.7722400064297721,None,0.0,5,4,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+338,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.37035966336010345,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.007136588202285817,mean,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+344,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.9039555800017817e-05,False,True,squared_hinge,0.0011536981768375681,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,quantile_transformer,1122,uniform,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,,
+348,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5323411241263409,None,0.0,2,18,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,median,standardize,,,,,extra_trees_preproc_for_classification,True,entropy,None,0.6132742708509392,None,0.0,6,16,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+351,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.758794895040923,None,0.0,18,19,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,quantile_transformer,1000,uniform,,,extra_trees_preproc_for_classification,False,gini,None,0.9944228208678969,None,0.0,20,6,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+352,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5988524108452234,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,mean,robust_scaler,,,0.75,0.26961884796358077,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+353,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.6269076084367177,None,0.0,1,7,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.12943250859943486,most_frequent,standardize,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,62.84526554323969,chi2,,,,
+359,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.9282407375709691,None,0.0,16,3,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.015995805068365877,mean,standardize,,,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.10042232633120375,False,True,1,squared_hinge,ovr,l1,0.004451009526629745,,,,,,,,,,,,,,,,,,,,,
+368,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.745748188733041e-05,True,True,hinge,2.181659959258924e-05,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.13921206193066876,most_frequent,quantile_transformer,60,normal,,,extra_trees_preproc_for_classification,False,entropy,None,0.7619669647366906,None,0.0,3,4,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+369,none,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.9796199105973851,None,0.0,2,13,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.00845950033601661,mean,robust_scaler,,,0.8275228560550734,0.23814615163106348,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
diff --git a/autosklearn/metalearning/files/precision_binary.classification_sparse/description.txt b/autosklearn/metalearning/files/precision_binary.classification_sparse/description.txt
index 66cde4ec09..45ba5041ea 100644
--- a/autosklearn/metalearning/files/precision_binary.classification_sparse/description.txt
+++ b/autosklearn/metalearning/files/precision_binary.classification_sparse/description.txt
@@ -1,8 +1,9 @@
 features_cutoff_time: 3600
 features_cutoff_memory: 3072
-number_of_feature_steps: 35
+number_of_feature_steps: 42
 feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances
 feature_step LogNumberOfInstances: LogNumberOfInstances
+feature_step NumberOfClasses: NumberOfClasses
 feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures
 feature_step LogNumberOfFeatures: LogNumberOfFeatures
 feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues
@@ -20,6 +21,11 @@ feature_step DatasetRatio: DatasetRatio, LogDatasetRatio
 feature_step LogDatasetRatio: LogDatasetRatio
 feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio
 feature_step LogInverseDatasetRatio: LogInverseDatasetRatio
+feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD
+feature_step ClassProbabilityMin: ClassProbabilityMin
+feature_step ClassProbabilityMax: ClassProbabilityMax
+feature_step ClassProbabilityMean: ClassProbabilityMean
+feature_step ClassProbabilitySTD: ClassProbabilitySTD
 feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum
 feature_step SymbolsMin: SymbolsMin
 feature_step SymbolsMax: SymbolsMax
@@ -36,11 +42,12 @@ feature_step SkewnessMin: SkewnessMin
 feature_step SkewnessMax: SkewnessMax
 feature_step SkewnessMean: SkewnessMean
 feature_step SkewnessSTD: SkewnessSTD
-features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
+feature_step ClassEntropy: ClassEntropy
+features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy
 features_stochastic: 
-default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
+default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy
 
-algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92
+algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235,236,237,238,239,240,241,242,243,244,245,246,247,248,249,250,251,252,253,254,255,256,257,258,259,260,261,262,263,264,265,266,267,268,269,270,271,272,273,274,275,276,277,278,279,280,281,282,283,284,285,286,287,288,289,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,312,313,314,315,316,317,318,319,320,321,322,323,324,325,326,327,328,329,330,331,332,333,334,335,336,337,338,339,340,341,342,343,344,345,346,347,348,349,350,351,352,353,354,355,356,357,358,359,360,361,362,363,364,365,366,367,368,369,370,371,372
 algorithms_stochastic: 
 performance_measures: precision
 performance_type: solution_quality
diff --git a/autosklearn/metalearning/files/precision_binary.classification_sparse/feature_costs.arff b/autosklearn/metalearning/files/precision_binary.classification_sparse/feature_costs.arff
index 0e17e20d22..8512fd9275 100644
--- a/autosklearn/metalearning/files/precision_binary.classification_sparse/feature_costs.arff
+++ b/autosklearn/metalearning/files/precision_binary.classification_sparse/feature_costs.arff
@@ -4,6 +4,7 @@
 @ATTRIBUTE repetition NUMERIC
 @ATTRIBUTE NumberOfInstances NUMERIC
 @ATTRIBUTE LogNumberOfInstances NUMERIC
+@ATTRIBUTE NumberOfClasses NUMERIC
 @ATTRIBUTE NumberOfFeatures NUMERIC
 @ATTRIBUTE LogNumberOfFeatures NUMERIC
 @ATTRIBUTE MissingValues NUMERIC
@@ -21,6 +22,11 @@
 @ATTRIBUTE LogDatasetRatio NUMERIC
 @ATTRIBUTE InverseDatasetRatio NUMERIC
 @ATTRIBUTE LogInverseDatasetRatio NUMERIC
+@ATTRIBUTE ClassOccurences NUMERIC
+@ATTRIBUTE ClassProbabilityMin NUMERIC
+@ATTRIBUTE ClassProbabilityMax NUMERIC
+@ATTRIBUTE ClassProbabilityMean NUMERIC
+@ATTRIBUTE ClassProbabilitySTD NUMERIC
 @ATTRIBUTE NumSymbols NUMERIC
 @ATTRIBUTE SymbolsMin NUMERIC
 @ATTRIBUTE SymbolsMax NUMERIC
@@ -37,162 +43,138 @@
 @ATTRIBUTE SkewnessMax NUMERIC
 @ATTRIBUTE SkewnessMean NUMERIC
 @ATTRIBUTE SkewnessSTD NUMERIC
+@ATTRIBUTE ClassEntropy NUMERIC
 
 @DATA
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+75234,1.0,1e-05,1e-05,0.00015,1e-05,1e-05,0.0058,0.00534,1e-05,0.00033,1e-05,0.00016,0.0,2e-05,2e-05,7e-05,0.00204,3e-05,1e-05,5e-05,1e-05,0.00024,3e-05,2e-05,0.0001,9e-05,0.00043,0.0,0.0,0.00012,0.00024,6e-05,0.00156,6e-05,7e-05,0.00121,0.00021,0.00054,6e-05,7e-05,9e-05,0.00032,0.00501
+75235,1.0,1e-05,1e-05,0.0003,2e-05,1e-05,0.00461,0.00385,2e-05,0.00055,2e-05,0.00026,1e-05,3e-05,3e-05,0.0001,0.00011,3e-05,1e-05,5e-05,2e-05,0.00026,3e-05,1e-05,8e-05,0.00014,0.00075,1e-05,1e-05,0.0002,0.00043,0.00011,0.00127,0.00017,0.0002,0.00029,0.00061,0.0008,0.00015,0.00012,0.00016,0.00038,0.00242
+75236,1.0,1e-05,1e-05,0.0001,1e-05,1e-05,0.00218,0.00118,2e-05,0.00065,3e-05,0.00041,1e-05,3e-05,3e-05,9e-05,9e-05,4e-05,1e-05,7e-05,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.0006,1e-05,1e-05,0.00021,0.00032,6e-05,0.00043,7e-05,7e-05,9e-05,0.00019,0.00062,7e-05,8e-05,0.0001,0.00037,0.00048
+75237,1.0,2e-05,1e-05,0.00785,2e-05,1e-05,0.31031,0.27854,2e-05,0.01161,3e-05,0.02022,1e-05,4e-05,3e-05,9e-05,0.00024,6e-05,2e-05,9e-05,2e-05,0.00062,7e-05,3e-05,0.00017,0.00035,0.0008,1e-05,1e-05,0.00028,0.00043,8e-05,0.00043,8e-05,7e-05,9e-05,0.0002,0.00044,6e-05,8e-05,9e-05,0.00021,0.14028
+75239,1.0,1e-05,1e-05,7e-05,1e-05,1e-05,0.00105,0.00084,1e-05,0.00015,1e-05,8e-05,1e-05,2e-05,5e-05,4e-05,8e-05,2e-05,1e-05,3e-05,1e-05,0.00016,2e-05,1e-05,5e-05,9e-05,0.00049,0.0,0.0,0.00012,0.00026,0.00011,0.00046,0.00013,8e-05,8e-05,0.00018,0.00044,6e-05,9e-05,0.00011,0.00018,0.00065
+75240,1.0,2e-05,1e-05,0.00036,5e-05,4e-05,0.00845,0.00644,6e-05,0.00122,4e-05,0.00091,1e-05,4e-05,4e-05,9e-05,0.00013,5e-05,2e-05,9e-05,5e-05,0.00031,3e-05,1e-05,7e-05,0.0002,0.00073,3e-05,2e-05,0.00019,0.0003,0.00018,0.00055,9e-05,9e-05,0.00011,0.00025,0.0005,7e-05,8e-05,0.00015,0.0002,0.00365
+75243,1.0,1e-05,1e-05,0.00029,1e-05,1e-05,0.00461,0.00415,1e-05,0.00035,1e-05,0.00015,0.0,2e-05,2e-05,5e-05,7e-05,2e-05,1e-05,4e-05,1e-05,0.00018,2e-05,1e-05,4e-05,0.00011,0.00022,1e-05,1e-05,5e-05,0.00011,5e-05,0.0004,6e-05,6e-05,8e-05,0.0002,0.00039,6e-05,6e-05,8e-05,0.00019,0.00265
+75244,1.0,3e-05,2e-05,0.0004,3e-05,2e-05,0.0173,0.01298,4e-05,0.00267,4e-05,0.00174,1e-05,7e-05,9e-05,0.00024,0.00027,8e-05,2e-05,0.00013,3e-05,0.00045,7e-05,2e-05,0.00011,0.00025,0.00077,6e-05,2e-05,0.00021,0.00034,0.00013,0.00077,0.00014,0.00013,0.00026,0.00023,0.00044,7e-05,8e-05,0.0001,0.00019,0.00649
+75248,1.0,1e-05,1e-05,0.00017,1e-05,1e-05,0.00416,0.00305,1e-05,0.0006,2e-05,0.00054,1e-05,2e-05,2e-05,5e-05,7e-05,2e-05,1e-05,4e-05,1e-05,0.00017,2e-05,1e-05,4e-05,0.0001,0.00029,5e-05,1e-05,6e-05,0.00012,6e-05,0.00081,0.00011,0.00015,0.00016,0.00038,0.0005,7e-05,8e-05,0.00012,0.00024,0.00184
+75249,1.0,1e-05,1e-05,0.00011,1e-05,1e-05,0.00192,0.00153,1e-05,0.00025,1e-05,0.00017,1e-05,2e-05,2e-05,5e-05,7e-05,2e-05,1e-05,5e-05,3e-05,0.00014,2e-05,1e-05,3e-05,8e-05,0.00029,1e-05,3e-05,6e-05,0.00012,7e-05,0.00041,6e-05,8e-05,0.0001,0.00017,0.00043,6e-05,7e-05,9e-05,0.00019,0.00092
+75250,1.0,1e-05,1e-05,0.00375,1e-05,1e-05,0.04994,0.047,1e-05,0.00247,1e-05,0.00049,1e-05,2e-05,2e-05,6e-05,7e-05,2e-05,1e-05,3e-05,1e-05,0.00022,4e-05,1e-05,4e-05,0.00012,0.00041,0.0,1e-05,0.00011,0.00023,6e-05,0.00076,0.00012,0.00012,0.00019,0.00032,0.00086,0.00012,0.00013,0.00018,0.00043,0.04178
diff --git a/autosklearn/metalearning/files/precision_binary.classification_sparse/feature_runstatus.arff b/autosklearn/metalearning/files/precision_binary.classification_sparse/feature_runstatus.arff
index 0034ad2bd6..5fff2dfed3 100644
--- a/autosklearn/metalearning/files/precision_binary.classification_sparse/feature_runstatus.arff
+++ b/autosklearn/metalearning/files/precision_binary.classification_sparse/feature_runstatus.arff
@@ -4,6 +4,7 @@
 @ATTRIBUTE repetition NUMERIC
 @ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other}
@@ -21,6 +22,11 @@
 @ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other}
@@ -37,162 +43,138 @@
 @ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other}
 
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diff --git a/autosklearn/metalearning/files/precision_binary.classification_sparse/feature_values.arff b/autosklearn/metalearning/files/precision_binary.classification_sparse/feature_values.arff
index a60400e3ce..736dc64321 100644
--- a/autosklearn/metalearning/files/precision_binary.classification_sparse/feature_values.arff
+++ b/autosklearn/metalearning/files/precision_binary.classification_sparse/feature_values.arff
@@ -13,7 +13,6 @@
 @ATTRIBUTE KurtosisMean NUMERIC
 @ATTRIBUTE KurtosisMin NUMERIC
 @ATTRIBUTE KurtosisSTD NUMERIC
-@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC
 @ATTRIBUTE LogDatasetRatio NUMERIC
 @ATTRIBUTE LogInverseDatasetRatio NUMERIC
 @ATTRIBUTE LogNumberOfFeatures NUMERIC
@@ -42,160 +41,135 @@
 @ATTRIBUTE SymbolsSum NUMERIC
 
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diff --git a/autosklearn/metalearning/files/precision_macro_binary.classification_dense/algorithm_runs.arff b/autosklearn/metalearning/files/precision_macro_binary.classification_dense/algorithm_runs.arff
index 1741657493..f565c325e1 100644
--- a/autosklearn/metalearning/files/precision_macro_binary.classification_dense/algorithm_runs.arff
+++ b/autosklearn/metalearning/files/precision_macro_binary.classification_dense/algorithm_runs.arff
@@ -7,130 +7,135 @@
 @ATTRIBUTE runstatus {ok, timeout, memout, not_applicable, crash, other}
 
 @DATA
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diff --git a/autosklearn/metalearning/files/precision_macro_binary.classification_dense/configurations.csv b/autosklearn/metalearning/files/precision_macro_binary.classification_dense/configurations.csv
index 6509d0ad02..2f0e2cf69e 100644
--- a/autosklearn/metalearning/files/precision_macro_binary.classification_dense/configurations.csv
+++ b/autosklearn/metalearning/files/precision_macro_binary.classification_dense/configurations.csv
@@ -1,128 +1,133 @@
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-23,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7734.521233796252,,,2.6048661611376747,rbf,-1,True,4.68717607435371e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,none,,,,,feature_agglomeration,,,,,,,,,,,,,,,euclidean,average,25,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-24,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.8367655423352977,None,0.0,1,5,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.01866043949164283,median,standardize,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.3824115704370156,fpr,f_classif
-25,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5010952533165973,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.005838375247601407,mean,normalize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,
-26,none,decision_tree,,,,,,,gini,1.195211653203929,1.0,None,0.0,1,3,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.011971971469963474,median,robust_scaler,,,0.7659403182030059,0.25,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,223.9949500136512,False,True,1,squared_hinge,ovr,l1,0.0022173435996968244,,,,,,,,,,,,,,,,,,,,,,
-27,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.4426097579931897,None,0.0,16,4,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,mean,robust_scaler,,,0.7407323456920554,0.0804756921037867,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,
-28,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5607168729746175,None,0.0,2,3,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.009810589644392154,most_frequent,quantile_transformer,1132,normal,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.06211089008167909,fdr,chi2
-29,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9333237919291875,None,0.0,8,3,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,
-30,none,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.9728532763078892,None,0.0,13,9,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,none,,,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6651071190864879,False,,,,,,,,,,,,,,,
-31,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9561075301148197,None,0.0,6,2,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,
-32,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.557321265077371,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-33,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,1.153931482107762e-10,0.011666190464009397,auto,255,None,29,190,18,loss,1e-07,0.147868307124396,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,
-34,none,adaboost,SAMME,0.6255555220658239,6,446,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.008833097306364968,most_frequent,none,,,,,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,10,None,4,17,1.0,76,,,,,
-35,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.47147939823444396,None,0.0,18,13,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,robust_scaler,,,0.9414880591550729,0.19219631546028484,fast_ica,,,,,,,,,,,parallel,logcosh,1978,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-36,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,3.6797768538445263e-09,0.05511004217375807,auto,255,None,72,62,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,robust_scaler,,,0.7241481576763391,0.0501033899901458,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,36,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-37,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.8680237807500206,None,0.0,6,4,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.021223427667606046,median,standardize,,,,,fast_ica,,,,,,,,,,,deflation,logcosh,364,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-38,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.887898394612674,None,0.0,10,19,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,robust_scaler,,,0.8350698288864961,0.2347163538066168,extra_trees_preproc_for_classification,False,entropy,None,0.49795108918064074,None,0.0,13,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-39,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,1.6343482806592892e-09,0.19537524572679268,auto,255,None,32,29,13,loss,1e-07,0.05061738224302791,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-40,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7088164344533493,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,mean,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,
-41,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,1.943186107984505e-07,0.08852455702036988,auto,255,None,5,72,19,loss,1e-07,0.039837460762271175,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.01591265419367627,median,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,
-42,weighting,adaboost,SAMME,0.5210651042094924,10,401,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,standardize,,,,,feature_agglomeration,,,,,,,,,,,,,,,euclidean,complete,89,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-43,none,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0686374607394679,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,minmax,,,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.8490260132946401,False,,,,,,,,,,,,,,,
-44,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.6931271368243409,None,0.0,11,10,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.362940004438799,most_frequent,robust_scaler,,,0.9177587796305063,0.17084038458597867,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,30.491514144623952,mutual_info,,,
-45,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.9398869869697849,None,0.0,20,15,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,normalize,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,269,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-46,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,92.66144306787051,,,0.04654639302091653,rbf,-1,True,0.0022231544280610005,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.005194838694455963,mean,none,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,15,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-47,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,21.69482701422382,-0.9439355912923322,,0.012708037096662675,sigmoid,-1,True,1.3900680173654052e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-48,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.8095522998548572,None,0.0,17,10,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.031940595071539304,mean,quantile_transformer,1563,normal,,,extra_trees_preproc_for_classification,False,entropy,None,0.9330145598603196,None,0.0,18,10,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-49,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,26.502259985828186,,,9.475662388405154e-05,rbf,-1,False,0.00019690635968217866,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-50,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,2.5360196477156387e-07,0.011018756408558218,auto,255,None,40,122,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.00011749164080669445,most_frequent,quantile_transformer,757,uniform,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,5.097834697156853,False,True,1,squared_hinge,ovr,l1,0.053877834919477424,,,,,,,,,,,,,,,,,,,,,,
-51,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5255991794296913,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-52,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.34697723213923637,None,0.0,15,18,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.1923594385988973,mean,normalize,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,25.77499302945827,chi2,,,
-53,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,23529.225652845635,0.30829978977649053,2,0.0008220498708157514,poly,-1,False,0.0005532556252982188,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.07570228092651136,mean,quantile_transformer,1143,normal,,,fast_ica,,,,,,,,,,,parallel,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-54,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,2.013916546255427e-10,0.08334928961641928,auto,255,None,61,84,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010000000000000004,most_frequent,quantile_transformer,1069,normal,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1740143466708828,fdr,f_classif
-55,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.9523435078770708,0.8748383232154038,4,0.2195992292488049,poly,-1,True,4.849685143365339e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,minmax,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-56,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.7610308078756552,None,0.0,1,2,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.010000000000000004,mean,robust_scaler,,,0.9205545465103644,0.20272761060743316,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-57,weighting,adaboost,SAMME.R,0.03127273017917347,10,496,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,standardize,,,,,fast_ica,,,,,,,,,,,deflation,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-58,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,151,None,,0.0003283777607180646,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.2510527431514227,mean,normalize,,,,,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,-0.96093182232312,,0.05762510964607661,sigmoid,2632,,,,,,,,,,,,,,,,,
-59,none,bernoulli_nb,,,,,0.6524090653939782,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.005164169126808402,most_frequent,quantile_transformer,16,normal,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.033082145443631535,False,True,1,squared_hinge,ovr,l1,0.002615020898325393,,,,,,,,,,,,,,,,,,,,,,
-60,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.6453555269772124,None,0.0,1,4,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,normalize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-61,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,940.9437776449834,,,1.5839338084625942,rbf,-1,False,0.023072781592565438,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,none,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,77.25763685070707,mutual_info,,,
-62,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.20911875507660596,None,0.0,12,8,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,median,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,
-63,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.7326858450533382,0.02513634776687268,2,0.8635510319471909,poly,-1,True,0.0014598272110141993,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.06447266871225255,most_frequent,quantile_transformer,1000,normal,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,79.01961823033078,f_classif,,,
-64,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,29.55731828912983,False,True,1,squared_hinge,ovr,l2,0.0011332486951954968,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,
-65,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,1.1387795876755026e-06,0.12245605190343567,auto,255,None,58,7,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,robust_scaler,,,0.79305915648885,0.1841487406876042,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-66,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.8425560317492612,None,0.0,8,15,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.015276988502366128,median,none,,,,,fast_ica,,,,,,,,,,,parallel,cube,17,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-67,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,0.04818131831746998,0.023221509516730385,auto,255,None,132,2,18,loss,1e-07,0.1318981686801681,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0008132666483686764,most_frequent,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-68,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,925.3053743248586,False,True,1,squared_hinge,ovr,l2,0.02764481198395717,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.3850718018366796,most_frequent,quantile_transformer,786,uniform,,,feature_agglomeration,,,,,,,,,,,,,,,cosine,average,156,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-69,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7573078019937971,None,0.0,2,5,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,robust_scaler,,,0.7265312295801113,0.28332515882236425,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.09660564590146253,fpr,f_classif
-70,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0057657098053164255,False,True,squared_hinge,0.013468519636061941,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.484357187898904,mean,quantile_transformer,675,normal,,,kernel_pca,,,,,,,,,,,,,,,,,,,,,,cosine,1876,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-71,none,adaboost,SAMME.R,0.22802213935525836,6,284,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,robust_scaler,,,0.7468645381288924,0.22838821662367073,feature_agglomeration,,,,,,,,,,,,,,,cosine,average,128,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-72,none,adaboost,SAMME,0.1280458968526125,7,125,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,robust_scaler,,,0.7156460334356771,0.2981902862926971,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,65.16171885308417,chi2,,,
-73,none,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9931779675565967,None,0.0,18,17,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,robust_scaler,,,0.75,0.2945917010472818,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,
-74,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.08646118576686276,None,0.0,4,9,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,most_frequent,quantile_transformer,959,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-75,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,1.2430504242164082e-09,0.03359568307446533,auto,255,None,1609,29,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,standardize,,,,,feature_agglomeration,,,,,,,,,,,,,,,cosine,complete,40,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-76,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7.491039171224502,True,True,squared_hinge,2.8058138048327884e-05,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,minmax,,,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.7754372809893839,False,True,1,squared_hinge,ovr,l1,3.9643594596766314e-05,,,,,,,,,,,,,,,,,,,,,,
-77,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.4557246694559742,None,0.0,2,17,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00044760203401312263,mean,robust_scaler,,,0.7111426078575654,0.25,extra_trees_preproc_for_classification,False,entropy,None,0.11395486180078526,None,0.0,1,7,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-78,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,0.011547155223520492,0.13665554140837183,auto,255,None,13,5,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.1824584492028449,median,minmax,,,,,feature_agglomeration,,,,,,,,,,,,,,,euclidean,average,261,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-79,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.933900673392165,None,0.0,7,4,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,minmax,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-80,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1661.2274747409913,,,0.025821208852387936,rbf,-1,False,0.0017011435104921824,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,none,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,306,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-81,none,adaboost,SAMME,0.011233995624432622,9,477,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.018370622484682127,mean,standardize,,,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6039710338898471,False,,,,,,,,,,,,,,,
-82,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,21.85207494001697,0.18872999589786676,4,1.1863646021868761,poly,-1,False,0.0021338371901294443,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0034080852834770217,mean,normalize,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.11326005109886934,fdr,f_classif
-83,weighting,adaboost,SAMME,0.38341570193537416,9,250,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,minmax,,,,,feature_agglomeration,,,,,,,,,,,,,,,euclidean,average,13,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-84,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,0.037849720944694,0.1268815770019783,auto,255,None,11,99,11,loss,1e-07,0.10654523627313527,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,
-85,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.593728171200453,None,0.0,7,20,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.02830941310359363,mean,normalize,,,,,extra_trees_preproc_for_classification,False,entropy,None,0.7389446586281172,None,0.0,20,11,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-86,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,1,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.019797877010737826,mean,normalize,,,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.8239678741128165,False,,,,,,,,,,,,,,,
-87,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.46057831591617715,False,True,hinge,0.04557857428827514,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00027457445401600137,median,standardize,,,,,extra_trees_preproc_for_classification,True,gini,None,0.48190346970486964,None,0.0,17,18,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-88,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,38,auto,,0.00019398431171606024,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010000000000000004,median,quantile_transformer,1440,normal,,,extra_trees_preproc_for_classification,True,gini,None,0.5126639956845847,None,0.0,16,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-89,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5722917944959481,None,0.0,1,14,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,
-90,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.26356754187780895,None,0.0,4,3,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010966178401758774,mean,robust_scaler,,,0.7817043861798886,0.24371720112407158,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
-91,weighting,adaboost,SAMME.R,0.13547552520095074,4,62,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.011851011202809002,most_frequent,standardize,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4888776875314595,fpr,chi2
-92,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,80.0028902468169,False,True,1,squared_hinge,ovr,l2,0.0024481999829574722,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.15047970487774687,mean,quantile_transformer,1130,normal,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,1.1912646635544712,False,True,1,squared_hinge,ovr,l1,0.00014314728486450857,,,,,,,,,,,,,,,,,,,,,,
-93,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.22879311080499845,None,0.0,8,4,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.014320022547615783,median,minmax,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.238630703440461,mutual_info,,,
-94,weighting,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,1,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,robust_scaler,,,0.7136729468599508,0.2308995232542769,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.636620620188117,False,,,,,,,,,,,,,,,
-95,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.107967527639527e-05,True,True,hinge,0.00448294745544775,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,quantile_transformer,953,normal,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,35.00114345405331,chi2,,,
-96,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.14148638702297855,None,0.0,2,6,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.0038499650338378643,most_frequent,quantile_transformer,1759,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-97,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,1.1522414705935405e-10,0.6132800183073315,auto,255,None,1607,67,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010096911249562407,mean,robust_scaler,,,0.7663493326967942,0.136094217337434,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-98,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.35863733679484333,None,0.0,1,11,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.0009777923655842618,median,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-99,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.479405900046494,,,0.013340778632154543,rbf,-1,False,6.913586060890114e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,robust_scaler,,,0.9953232616818098,0.06450577886132274,fast_ica,,,,,,,,,,,deflation,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-100,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.825844582229201,None,0.0,12,9,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0008422508222149647,median,robust_scaler,,,0.8658803880857064,0.2528423686293341,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-101,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2574971638664341,True,True,hinge,1.8027411099296446e-05,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.00536396328125103,median,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,
-102,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7908336536113714,None,0.0,9,3,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,minmax,,,,,extra_trees_preproc_for_classification,False,entropy,None,0.17440717696897978,None,0.0,5,18,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-103,none,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,128,manual,0.8574560771111307,0.010886139381599514,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.24115007944216788,most_frequent,normalize,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,62.26514170991452,mutual_info,,,
-104,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.6282663389679584,None,0.0,1,3,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,median,minmax,,,,,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-105,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,1.430083734915855e-09,0.08779621406981941,auto,255,None,17,20,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.29749837779115257,mean,robust_scaler,,,0.7236445277814435,0.25,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
-106,none,adaboost,SAMME.R,0.9261146078592859,9,476,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.012453052108117785,median,robust_scaler,,,0.7492076537583867,0.25853088636132765,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,6,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-107,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,39.22980757485479,,,0.13597389869623427,rbf,-1,False,0.00011639060052687874,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.006288306814260224,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-108,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.7922837297504705,False,True,1,squared_hinge,ovr,l2,0.0005476597326342352,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.012674631437254894,most_frequent,none,,,,,extra_trees_preproc_for_classification,False,entropy,None,0.23145146971319638,None,0.0,7,20,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-109,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.18060139887649052,None,0.0,10,12,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.010000000000000004,most_frequent,robust_scaler,,,0.9298612094255864,0.1263597946722308,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
-110,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.4111348710613201,None,0.0,16,8,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.01224348527546641,most_frequent,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,
-111,none,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0313388874611118,False,True,1,squared_hinge,ovr,l2,0.00021502747842956193,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,quantile_transformer,925,uniform,,,kernel_pca,,,,,,,,,,,,,,,,,,,,,,cosine,1907,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-112,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.044449043853721795,True,,,True,,optimal,log,l2,,0.0005323728333290294,no_encoding,no_coalescense,,most_frequent,quantile_transformer,588,normal,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,47.34146395379382,chi2,,,
-113,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,9.029473608568458e-09,0.15779990576964315,auto,255,None,37,71,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.018093177195644097,mean,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,False,,,,,,,,,,,,
-114,weighting,adaboost,SAMME.R,0.23766684501868082,10,97,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.004737885672001703,median,none,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50.0,chi2,,,
-115,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.129143822880219,True,True,hinge,0.00012612093569084375,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0016222546082911887,most_frequent,quantile_transformer,1309,uniform,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8.816417526015055,f_classif,,,
-116,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.4864284879550203,None,0.0,1,4,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.008665854799630503,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-117,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.43699642289829416,None,0.0,6,20,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,quantile_transformer,1604,uniform,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-118,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.6922752267318475,True,True,squared_hinge,0.06299408576730353,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,quantile_transformer,1742,uniform,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,107,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-119,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.44727252219812963,None,0.0,2,10,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,standardize,,,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l1,8.17533189014765e-05,,,,,,,,,,,,,,,,,,,,,,
-120,weighting,extra_trees,,,,,,,,,,,,,,,True,gini,None,0.9999521514529074,None,0.0,3,10,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,minmax,,,,,extra_trees_preproc_for_classification,False,gini,None,0.24745225001074223,None,0.0,14,18,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-121,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.4210978663047845,None,0.0,5,6,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.006365988550833196,most_frequent,robust_scaler,,,0.8348891074186082,0.00974961635730845,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0695919708890599,fpr,f_classif
-122,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9.524191988343434,True,True,hinge,0.0959243684834342,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.011065812213652354,most_frequent,quantile_transformer,913,uniform,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.09034604613454934,fpr,chi2
-123,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.20612848067724507,False,True,1,squared_hinge,ovr,l2,0.00017832980955957004,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0007502816173508168,median,quantile_transformer,1083,uniform,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.02820386332530596,fwe,f_classif
-124,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1561.1002600303584,,,0.0024048551704194507,rbf,-1,False,0.0001936214667496851,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,minmax,,,,,fast_ica,,,,,,,,,,,parallel,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-125,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.087313242780732,0.0636709914987994,3,0.004481917125024585,poly,-1,False,0.01848880618941367,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.002136015035740733,mean,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-126,weighting,adaboost,SAMME,0.029932847627447023,8,483,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,quantile_transformer,1290,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-127,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.6466927589470762,None,0.0,11,2,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.009672465311659264,most_frequent,standardize,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,369,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+1,none,decision_tree,,,,,,,gini,0.7229490531192699,1.0,None,0.0,1,18,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.00013586681143040562,median,quantile_transformer,647,uniform,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+2,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7307322501991852,None,0.0,18,14,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+6,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.7283683762613216,None,0.0,12,20,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,median,standardize,,,,,fast_ica,,,,,,,,,,,deflation,exp,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+8,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,114,manual,0.11778631707605758,0.0029112721339713465,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.015582195257240751,median,normalize,,,,,kernel_pca,,,,,,,,,,,,,,,,,,,0.42909623253327767,3,0.25407612000343777,poly,1689,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+11,weighting,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,2,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.3865962574104293,mean,normalize,,,,,fast_ica,,,,,,,,,,,deflation,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+15,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.21772092350860472,None,0.0,14,17,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.0020488212891693275,median,standardize,,,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,33.74257606530722,False,True,1,squared_hinge,ovr,l1,8.740452966205111e-05,,,,,,,,,,,,,,,,,,,,,,
+18,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,2.4243922131268786e-05,0.07570328162448307,auto,255,None,71,42,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,robust_scaler,,,0.8898729094898769,0.12138152991327475,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,94.96086286583851,chi2,,,
+20,none,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.9866058571747695,None,0.0,16,9,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.1680676743605393,most_frequent,robust_scaler,,,0.8840072797399463,0.08535534311393947,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,False,,,,,,,,,,,,
+23,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,0.00022373024203887573,0.027149343206135476,auto,255,None,49,13,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.00777614320416325,most_frequent,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,
+27,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.41025844265119926,None,0.0,18,15,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,median,quantile_transformer,1911,normal,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,False,,,,,,,,,,,,
+31,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,8.757412830636845e-06,0.10095376320667454,auto,255,None,3,2,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.015401766535365336,median,quantile_transformer,647,normal,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,
+32,none,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,174,manual,0.21501892897746921,2.0347110826262054e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.013754893527625522,most_frequent,normalize,,,,,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,-0.019221513778605592,3,0.10190948951339239,poly,2521,,,,,,,,,,,,,,,,,
+33,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.42232830349206685,None,0.0,1,16,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.013593246624583636,mean,quantile_transformer,1000,uniform,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4899076986656834,fpr,f_classif
+36,none,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,11,manual,0.3996057556961169,8.195866467196482e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.030006941783431516,mean,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,
+39,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.6799215694343422,None,0.0,1,5,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.004724310659953132,most_frequent,quantile_transformer,1037,normal,,,fast_ica,,,,,,,,,,,parallel,exp,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+41,none,extra_trees,,,,,,,,,,,,,,,True,gini,None,0.5033866291997137,None,0.0,2,14,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,minmax,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+43,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.9479098792950027,None,0.0,12,14,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,quantile_transformer,1758,normal,,,feature_agglomeration,,,,,,,,,,,,,,,cosine,complete,244,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+44,none,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l2,2.858526169098402e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0007827505640681123,median,standardize,,,,,extra_trees_preproc_for_classification,False,entropy,None,0.3443310459451119,None,0.0,3,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+49,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.049504283446131e-05,False,,0.0004438773852753879,True,2.4110816673432424e-07,constant,log,elasticnet,,0.01321814853186568,one_hot_encoding,minority_coalescer,0.01510416593631535,most_frequent,none,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,66.65285268958851,chi2,,,
+52,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,6.421059381158897e-08,0.11261253191288709,auto,255,None,45,28,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,
+55,none,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.9822179211645446,None,0.0,14,8,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.013966871874533052,most_frequent,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,
+62,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.9116648490883282,None,0.0,19,2,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,normalize,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,90.20892117286336,f_classif,,,
+65,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,1.2822815440468518e-10,0.12922700208645135,auto,255,None,31,44,13,loss,1e-07,0.08789566421608311,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.012230423571314038,mean,robust_scaler,,,0.71734690066056,0.20283418288016852,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,
+68,weighting,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,minmax,,,,,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+75,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13.284077389890939,-0.2621334760621076,5,0.9060908941898536,poly,-1,False,0.06323853917206591,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0011743021592446861,most_frequent,normalize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,
+82,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,3.79982070767762e-10,0.10307173798074214,auto,255,None,30,25,6,loss,1e-07,0.09705336338563596,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.004889186641013006,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+84,none,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.5187414079994191,None,0.0,3,2,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.13752329517270892,median,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+93,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5012519528536651,None,0.0,1,8,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,mean,standardize,,,,,feature_agglomeration,,,,,,,,,,,,,,,cosine,complete,323,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+96,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5669485364001919,None,0.0,1,3,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.012814772651451747,mean,quantile_transformer,1138,uniform,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.123628236727082,fdr,chi2
+99,none,adaboost,SAMME,0.02285389956672728,5,356,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,standardize,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,74,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+102,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,4.5466130325692643e-10,0.05426809030733158,auto,255,None,12,9,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.01670620916503771,mean,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,
+104,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.492415849053268,None,0.0,1,3,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.20277123995405158,mean,normalize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+106,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,2.047681354714616e-06,0.016073071519249763,auto,255,None,127,60,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.005262156195293648,median,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,
+110,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0021814768111094564,False,True,hinge,8.726644999887521e-05,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,none,,,,,kitchen_sinks,,,,,,,,,,,,,,,,,,,,,,,,0.0010909910768059797,970,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+112,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,0.6187370258614137,0.06602427150798913,auto,255,None,81,22,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010000000000000004,most_frequent,quantile_transformer,1389,uniform,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,
+114,none,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.293954208217794,False,True,1,squared_hinge,ovr,l2,6.765219367412778e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+115,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.463780563279276,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.011474025594258192,mean,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,
+116,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,236,None,,0.08905291537442941,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,normalize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,
+117,weighting,extra_trees,,,,,,,,,,,,,,,True,gini,None,0.40869149566870155,None,0.0,2,17,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0020222414286678626,mean,quantile_transformer,1138,uniform,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.09225930189127593,fdr,chi2
+121,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.08836825866738698,None,0.0,5,19,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0003943043347109662,median,robust_scaler,,,0.7564808439539721,0.23469821956116474,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2206955933275169,fdr,f_classif
+124,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.1475115840890851,None,0.0,13,17,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,robust_scaler,,,0.7895374132523357,0.10850129170589755,fast_ica,,,,,,,,,,,deflation,exp,100,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+126,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,1.4547955251450584e-08,0.03465981099276111,auto,255,None,10,4,7,loss,1e-07,0.12579700249284784,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.001559260617305279,most_frequent,normalize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,False,,,,,,,,,,,,
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diff --git a/autosklearn/metalearning/files/precision_macro_binary.classification_dense/description.txt b/autosklearn/metalearning/files/precision_macro_binary.classification_dense/description.txt
index 5899d9e32b..441c5f4a28 100644
--- a/autosklearn/metalearning/files/precision_macro_binary.classification_dense/description.txt
+++ b/autosklearn/metalearning/files/precision_macro_binary.classification_dense/description.txt
@@ -1,8 +1,9 @@
 features_cutoff_time: 3600
 features_cutoff_memory: 3072
-number_of_feature_steps: 35
+number_of_feature_steps: 42
 feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances
 feature_step LogNumberOfInstances: LogNumberOfInstances
+feature_step NumberOfClasses: NumberOfClasses
 feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures
 feature_step LogNumberOfFeatures: LogNumberOfFeatures
 feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues
@@ -20,6 +21,11 @@ feature_step DatasetRatio: DatasetRatio, LogDatasetRatio
 feature_step LogDatasetRatio: LogDatasetRatio
 feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio
 feature_step LogInverseDatasetRatio: LogInverseDatasetRatio
+feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD
+feature_step ClassProbabilityMin: ClassProbabilityMin
+feature_step ClassProbabilityMax: ClassProbabilityMax
+feature_step ClassProbabilityMean: ClassProbabilityMean
+feature_step ClassProbabilitySTD: ClassProbabilitySTD
 feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum
 feature_step SymbolsMin: SymbolsMin
 feature_step SymbolsMax: SymbolsMax
@@ -36,11 +42,12 @@ feature_step SkewnessMin: SkewnessMin
 feature_step SkewnessMax: SkewnessMax
 feature_step SkewnessMean: SkewnessMean
 feature_step SkewnessSTD: SkewnessSTD
-features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
+feature_step ClassEntropy: ClassEntropy
+features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy
 features_stochastic: 
-default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
+default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy
 
-algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127
+algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235,236,237,238,239,240,241,242,243,244,245,246,247,248,249,250,251,252,253,254,255,256,257,258,259,260,261,262,263,264,265,266,267,268,269,270,271,272,273,274,275,276,277,278,279,280,281,282,283,284,285,286,287,288,289,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,312,313,314,315,316,317,318,319,320,321,322,323,324,325,326,327,328,329,330,331,332,333,334,335,336,337,338,339,340,341,342,343,344,345,346,347,348,349,350,351,352,353,354,355,356,357,358,359,360,361,362,363,364,365,366,367,368,369,370,371,372,373,374,375,376,377,378,379,380,381,382,383,384,385,386,387,388,389,390,391,392,393,394,395,396,397,398,399,400,401,402,403,404,405,406,407,408,409,410,411,412,413,414,415,416,417,418,419,420,421,422,423,424,425,426,427,428,429,430,431,432,433,434,435,436,437,438,439,440,441,442,443,444,445,446,447,448,449,450,451,452,453,454,455,456,457,458,459,460,461,462,463,464
 algorithms_stochastic: 
 performance_measures: precision_macro
 performance_type: solution_quality
diff --git a/autosklearn/metalearning/files/precision_macro_binary.classification_dense/feature_costs.arff b/autosklearn/metalearning/files/precision_macro_binary.classification_dense/feature_costs.arff
index 0e17e20d22..8512fd9275 100644
--- a/autosklearn/metalearning/files/precision_macro_binary.classification_dense/feature_costs.arff
+++ b/autosklearn/metalearning/files/precision_macro_binary.classification_dense/feature_costs.arff
@@ -4,6 +4,7 @@
 @ATTRIBUTE repetition NUMERIC
 @ATTRIBUTE NumberOfInstances NUMERIC
 @ATTRIBUTE LogNumberOfInstances NUMERIC
+@ATTRIBUTE NumberOfClasses NUMERIC
 @ATTRIBUTE NumberOfFeatures NUMERIC
 @ATTRIBUTE LogNumberOfFeatures NUMERIC
 @ATTRIBUTE MissingValues NUMERIC
@@ -21,6 +22,11 @@
 @ATTRIBUTE LogDatasetRatio NUMERIC
 @ATTRIBUTE InverseDatasetRatio NUMERIC
 @ATTRIBUTE LogInverseDatasetRatio NUMERIC
+@ATTRIBUTE ClassOccurences NUMERIC
+@ATTRIBUTE ClassProbabilityMin NUMERIC
+@ATTRIBUTE ClassProbabilityMax NUMERIC
+@ATTRIBUTE ClassProbabilityMean NUMERIC
+@ATTRIBUTE ClassProbabilitySTD NUMERIC
 @ATTRIBUTE NumSymbols NUMERIC
 @ATTRIBUTE SymbolsMin NUMERIC
 @ATTRIBUTE SymbolsMax NUMERIC
@@ -37,162 +43,138 @@
 @ATTRIBUTE SkewnessMax NUMERIC
 @ATTRIBUTE SkewnessMean NUMERIC
 @ATTRIBUTE SkewnessSTD NUMERIC
+@ATTRIBUTE ClassEntropy NUMERIC
 
 @DATA
-75123,1.0,1e-05,1e-05,1e-05,1e-05,0.00253,0.00221,1e-05,0.00024,3e-05,0.00011,0.0,2e-05,2e-05,5e-05,8e-05,2e-05,1e-05,7e-05,1e-05,0.00072,1e-05,1e-05,0.00019,0.00041,0.00011,0.00061,0.00012,9e-05,0.00015,0.00025,0.00067,0.00014,0.00011,0.00016,0.00027
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-2123,1.0,1e-05,1e-05,1e-05,1e-05,0.00097,0.00079,1e-05,0.00013,1e-05,7e-05,0.0,2e-05,2e-05,5e-05,8e-05,2e-05,1e-05,5e-05,1e-05,0.0007,1e-05,0.0,0.00017,0.00042,0.0001,0.00052,6e-05,8e-05,0.00012,0.00026,0.00049,6e-05,0.00011,9e-05,0.00023
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-75217,1.0,1e-05,1e-05,2e-05,1e-05,0.00196,0.00158,1e-05,0.00026,2e-05,0.00016,1e-05,3e-05,3e-05,7e-05,0.0001,3e-05,1e-05,7e-05,2e-05,0.00089,1e-05,1e-05,0.00022,0.00053,0.00013,0.00074,9e-05,0.0001,0.00019,0.00036,0.00065,0.00013,0.0001,0.00011,0.00031
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+75234,1.0,1e-05,1e-05,0.00015,1e-05,1e-05,0.0058,0.00534,1e-05,0.00033,1e-05,0.00016,0.0,2e-05,2e-05,7e-05,0.00204,3e-05,1e-05,5e-05,1e-05,0.00024,3e-05,2e-05,0.0001,9e-05,0.00043,0.0,0.0,0.00012,0.00024,6e-05,0.00156,6e-05,7e-05,0.00121,0.00021,0.00054,6e-05,7e-05,9e-05,0.00032,0.00501
+75235,1.0,1e-05,1e-05,0.0003,2e-05,1e-05,0.00461,0.00385,2e-05,0.00055,2e-05,0.00026,1e-05,3e-05,3e-05,0.0001,0.00011,3e-05,1e-05,5e-05,2e-05,0.00026,3e-05,1e-05,8e-05,0.00014,0.00075,1e-05,1e-05,0.0002,0.00043,0.00011,0.00127,0.00017,0.0002,0.00029,0.00061,0.0008,0.00015,0.00012,0.00016,0.00038,0.00242
+75236,1.0,1e-05,1e-05,0.0001,1e-05,1e-05,0.00218,0.00118,2e-05,0.00065,3e-05,0.00041,1e-05,3e-05,3e-05,9e-05,9e-05,4e-05,1e-05,7e-05,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.0006,1e-05,1e-05,0.00021,0.00032,6e-05,0.00043,7e-05,7e-05,9e-05,0.00019,0.00062,7e-05,8e-05,0.0001,0.00037,0.00048
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+75249,1.0,1e-05,1e-05,0.00011,1e-05,1e-05,0.00192,0.00153,1e-05,0.00025,1e-05,0.00017,1e-05,2e-05,2e-05,5e-05,7e-05,2e-05,1e-05,5e-05,3e-05,0.00014,2e-05,1e-05,3e-05,8e-05,0.00029,1e-05,3e-05,6e-05,0.00012,7e-05,0.00041,6e-05,8e-05,0.0001,0.00017,0.00043,6e-05,7e-05,9e-05,0.00019,0.00092
+75250,1.0,1e-05,1e-05,0.00375,1e-05,1e-05,0.04994,0.047,1e-05,0.00247,1e-05,0.00049,1e-05,2e-05,2e-05,6e-05,7e-05,2e-05,1e-05,3e-05,1e-05,0.00022,4e-05,1e-05,4e-05,0.00012,0.00041,0.0,1e-05,0.00011,0.00023,6e-05,0.00076,0.00012,0.00012,0.00019,0.00032,0.00086,0.00012,0.00013,0.00018,0.00043,0.04178
diff --git a/autosklearn/metalearning/files/precision_macro_binary.classification_dense/feature_runstatus.arff b/autosklearn/metalearning/files/precision_macro_binary.classification_dense/feature_runstatus.arff
index 0034ad2bd6..5fff2dfed3 100644
--- a/autosklearn/metalearning/files/precision_macro_binary.classification_dense/feature_runstatus.arff
+++ b/autosklearn/metalearning/files/precision_macro_binary.classification_dense/feature_runstatus.arff
@@ -4,6 +4,7 @@
 @ATTRIBUTE repetition NUMERIC
 @ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other}
@@ -21,6 +22,11 @@
 @ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other}
@@ -37,162 +43,138 @@
 @ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other}
 
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diff --git a/autosklearn/metalearning/files/precision_macro_binary.classification_dense/feature_values.arff b/autosklearn/metalearning/files/precision_macro_binary.classification_dense/feature_values.arff
index a60400e3ce..736dc64321 100644
--- a/autosklearn/metalearning/files/precision_macro_binary.classification_dense/feature_values.arff
+++ b/autosklearn/metalearning/files/precision_macro_binary.classification_dense/feature_values.arff
@@ -13,7 +13,6 @@
 @ATTRIBUTE KurtosisMean NUMERIC
 @ATTRIBUTE KurtosisMin NUMERIC
 @ATTRIBUTE KurtosisSTD NUMERIC
-@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC
 @ATTRIBUTE LogDatasetRatio NUMERIC
 @ATTRIBUTE LogInverseDatasetRatio NUMERIC
 @ATTRIBUTE LogNumberOfFeatures NUMERIC
@@ -42,160 +41,135 @@
 @ATTRIBUTE SymbolsSum NUMERIC
 
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diff --git a/autosklearn/metalearning/files/precision_macro_binary.classification_sparse/algorithm_runs.arff b/autosklearn/metalearning/files/precision_macro_binary.classification_sparse/algorithm_runs.arff
index 053d8069dd..88af4cb738 100644
--- a/autosklearn/metalearning/files/precision_macro_binary.classification_sparse/algorithm_runs.arff
+++ b/autosklearn/metalearning/files/precision_macro_binary.classification_sparse/algorithm_runs.arff
@@ -7,95 +7,133 @@
 @ATTRIBUTE runstatus {ok, timeout, memout, not_applicable, crash, other}
 
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diff --git a/autosklearn/metalearning/files/precision_macro_binary.classification_sparse/configurations.csv b/autosklearn/metalearning/files/precision_macro_binary.classification_sparse/configurations.csv
index 9fbbdefe5c..d9c7a86138 100644
--- a/autosklearn/metalearning/files/precision_macro_binary.classification_sparse/configurations.csv
+++ b/autosklearn/metalearning/files/precision_macro_binary.classification_sparse/configurations.csv
@@ -1,93 +1,131 @@
 idx,balancing:strategy,classifier:__choice__,classifier:adaboost:algorithm,classifier:adaboost:learning_rate,classifier:adaboost:max_depth,classifier:adaboost:n_estimators,classifier:bernoulli_nb:alpha,classifier:bernoulli_nb:fit_prior,classifier:decision_tree:criterion,classifier:decision_tree:max_depth_factor,classifier:decision_tree:max_features,classifier:decision_tree:max_leaf_nodes,classifier:decision_tree:min_impurity_decrease,classifier:decision_tree:min_samples_leaf,classifier:decision_tree:min_samples_split,classifier:decision_tree:min_weight_fraction_leaf,classifier:extra_trees:bootstrap,classifier:extra_trees:criterion,classifier:extra_trees:max_depth,classifier:extra_trees:max_features,classifier:extra_trees:max_leaf_nodes,classifier:extra_trees:min_impurity_decrease,classifier:extra_trees:min_samples_leaf,classifier:extra_trees:min_samples_split,classifier:extra_trees:min_weight_fraction_leaf,classifier:gradient_boosting:early_stop,classifier:gradient_boosting:l2_regularization,classifier:gradient_boosting:learning_rate,classifier:gradient_boosting:loss,classifier:gradient_boosting:max_bins,classifier:gradient_boosting:max_depth,classifier:gradient_boosting:max_leaf_nodes,classifier:gradient_boosting:min_samples_leaf,classifier:gradient_boosting:n_iter_no_change,classifier:gradient_boosting:scoring,classifier:gradient_boosting:tol,classifier:gradient_boosting:validation_fraction,classifier:k_nearest_neighbors:n_neighbors,classifier:k_nearest_neighbors:p,classifier:k_nearest_neighbors:weights,classifier:lda:n_components,classifier:lda:shrinkage,classifier:lda:shrinkage_factor,classifier:lda:tol,classifier:liblinear_svc:C,classifier:liblinear_svc:dual,classifier:liblinear_svc:fit_intercept,classifier:liblinear_svc:intercept_scaling,classifier:liblinear_svc:loss,classifier:liblinear_svc:multi_class,classifier:liblinear_svc:penalty,classifier:liblinear_svc:tol,classifier:libsvm_svc:C,classifier:libsvm_svc:coef0,classifier:libsvm_svc:degree,classifier:libsvm_svc:gamma,classifier:libsvm_svc:kernel,classifier:libsvm_svc:max_iter,classifier:libsvm_svc:shrinking,classifier:libsvm_svc:tol,classifier:multinomial_nb:alpha,classifier:multinomial_nb:fit_prior,classifier:passive_aggressive:C,classifier:passive_aggressive:average,classifier:passive_aggressive:fit_intercept,classifier:passive_aggressive:loss,classifier:passive_aggressive:tol,classifier:qda:reg_param,classifier:random_forest:bootstrap,classifier:random_forest:criterion,classifier:random_forest:max_depth,classifier:random_forest:max_features,classifier:random_forest:max_leaf_nodes,classifier:random_forest:min_impurity_decrease,classifier:random_forest:min_samples_leaf,classifier:random_forest:min_samples_split,classifier:random_forest:min_weight_fraction_leaf,classifier:sgd:alpha,classifier:sgd:average,classifier:sgd:epsilon,classifier:sgd:eta0,classifier:sgd:fit_intercept,classifier:sgd:l1_ratio,classifier:sgd:learning_rate,classifier:sgd:loss,classifier:sgd:penalty,classifier:sgd:power_t,classifier:sgd:tol,data_preprocessing:categorical_transformer:categorical_encoding:__choice__,data_preprocessing:categorical_transformer:category_coalescence:__choice__,data_preprocessing:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction,data_preprocessing:numerical_transformer:imputation:strategy,data_preprocessing:numerical_transformer:rescaling:__choice__,data_preprocessing:numerical_transformer:rescaling:quantile_transformer:n_quantiles,data_preprocessing:numerical_transformer:rescaling:quantile_transformer:output_distribution,data_preprocessing:numerical_transformer:rescaling:robust_scaler:q_max,data_preprocessing:numerical_transformer:rescaling:robust_scaler:q_min,feature_preprocessor:__choice__,feature_preprocessor:extra_trees_preproc_for_classification:bootstrap,feature_preprocessor:extra_trees_preproc_for_classification:criterion,feature_preprocessor:extra_trees_preproc_for_classification:max_depth,feature_preprocessor:extra_trees_preproc_for_classification:max_features,feature_preprocessor:extra_trees_preproc_for_classification:max_leaf_nodes,feature_preprocessor:extra_trees_preproc_for_classification:min_impurity_decrease,feature_preprocessor:extra_trees_preproc_for_classification:min_samples_leaf,feature_preprocessor:extra_trees_preproc_for_classification:min_samples_split,feature_preprocessor:extra_trees_preproc_for_classification:min_weight_fraction_leaf,feature_preprocessor:extra_trees_preproc_for_classification:n_estimators,feature_preprocessor:kernel_pca:coef0,feature_preprocessor:kernel_pca:degree,feature_preprocessor:kernel_pca:gamma,feature_preprocessor:kernel_pca:kernel,feature_preprocessor:kernel_pca:n_components,feature_preprocessor:kitchen_sinks:gamma,feature_preprocessor:kitchen_sinks:n_components,feature_preprocessor:liblinear_svc_preprocessor:C,feature_preprocessor:liblinear_svc_preprocessor:dual,feature_preprocessor:liblinear_svc_preprocessor:fit_intercept,feature_preprocessor:liblinear_svc_preprocess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-14,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.6039096182414764,None,0.0,2,20,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.1939609823704447,most_frequent,robust_scaler,,,0.7331327008874781,0.29747488162877694,extra_trees_preproc_for_classification,True,entropy,None,0.33401603690569853,None,0.0,4,4,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-15,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7865652301288171,None,0.0,1,4,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,median,robust_scaler,,,0.8623635641193496,0.13298297278730625,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-16,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.905401147837377,-0.32832122529048147,3,1.001896309040499,poly,-1,False,5.1523484258030785e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-17,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.3304182089730454,None,0.0,1,11,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.015634546384927752,median,quantile_transformer,332,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-18,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5010952533165973,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.005838375247601407,mean,normalize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,,
-19,none,decision_tree,,,,,,,gini,1.195211653203929,1.0,None,0.0,1,3,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.011971971469963474,median,robust_scaler,,,0.7659403182030059,0.25,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,223.9949500136512,False,True,1,squared_hinge,ovr,l1,0.0022173435996968244,,,,,,,,,,,,,,,,,,,,,
-20,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.4426097579931897,None,0.0,16,4,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,mean,robust_scaler,,,0.7407323456920554,0.0804756921037867,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,,
-21,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5607168729746175,None,0.0,2,3,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.009810589644392154,most_frequent,quantile_transformer,1132,normal,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.06211089008167909,fdr,chi2,
-22,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9333237919291875,None,0.0,8,3,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,,
-23,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.557321265077371,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-24,none,adaboost,SAMME,0.6255555220658239,6,446,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.008833097306364968,most_frequent,none,,,,,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,10,None,4,17,1.0,76,,,,,,
-25,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.7772914874049878,None,0.0,10,15,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00015285482513309234,mean,robust_scaler,,,0.8373404000074934,0.14303151080827242,extra_trees_preproc_for_classification,True,entropy,None,0.5021713909357703,None,0.0,13,16,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-26,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7422641456052917,None,0.0,8,15,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.005561351266169067,most_frequent,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-27,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.887898394612674,None,0.0,10,19,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,robust_scaler,,,0.8350698288864961,0.2347163538066168,extra_trees_preproc_for_classification,False,entropy,None,0.49795108918064074,None,0.0,13,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-28,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7088164344533493,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,mean,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,,
-29,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,10,1,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0022606253047811435,median,quantile_transformer,1247,normal,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,25.327793585266683,chi2,,,,
-30,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,24.786439230569382,,,0.03411197494635044,rbf,-1,True,0.003003351499585181,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0007994838629325874,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-31,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,21.69482701422382,-0.9439355912923322,,0.012708037096662675,sigmoid,-1,True,1.3900680173654052e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-32,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.8095522998548572,None,0.0,17,10,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.031940595071539304,mean,quantile_transformer,1563,normal,,,extra_trees_preproc_for_classification,False,entropy,None,0.9330145598603196,None,0.0,18,10,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-33,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,26.502259985828186,,,9.475662388405154e-05,rbf,-1,False,0.00019690635968217866,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-34,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.339600708038189,False,True,1,squared_hinge,ovr,l2,9.572641158475304e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,quantile_transformer,1193,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-35,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5255991794296913,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-36,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.34697723213923637,None,0.0,15,18,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.1923594385988973,mean,normalize,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,25.77499302945827,chi2,,,,
-37,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.7610308078756552,None,0.0,1,2,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.010000000000000004,mean,robust_scaler,,,0.9205545465103644,0.20272761060743316,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-38,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,249.12499213127998,,,0.0005283374995918348,rbf,-1,False,0.0016932707039261864,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.48910068367909115,most_frequent,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-39,none,bernoulli_nb,,,,,0.6524090653939782,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.005164169126808402,most_frequent,quantile_transformer,16,normal,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.033082145443631535,False,True,1,squared_hinge,ovr,l1,0.002615020898325393,,,,,,,,,,,,,,,,,,,,,
-40,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.6453555269772124,None,0.0,1,4,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,normalize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-41,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.20911875507660596,None,0.0,12,8,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,median,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,,
-42,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5138705053438934,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.14411802159506462,most_frequent,quantile_transformer,857,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-43,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,29.55731828912983,False,True,1,squared_hinge,ovr,l2,0.0011332486951954968,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,,
-44,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.37801720673725603,True,True,squared_hinge,0.0001255132393635067,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.025993178344125793,most_frequent,standardize,,,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,1.4421737618429475,False,True,1,squared_hinge,ovr,l1,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,
-45,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.10581257327556097,None,0.0,7,12,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.004219392676189224,median,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,,
-46,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.4188764793561401,None,0.0,5,5,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.010589444903605501,mean,robust_scaler,,,0.9796997860990453,0.27262832749694405,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-47,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0057657098053164255,False,True,squared_hinge,0.013468519636061941,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.484357187898904,mean,quantile_transformer,675,normal,,,kernel_pca,,,,,,,,,,,,,,cosine,1876,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-48,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.41019363453549706,None,0.0,15,2,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.0003507389224306018,median,none,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.20614098085343638,fdr,chi2,
-49,none,adaboost,SAMME,0.1280458968526125,7,125,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,robust_scaler,,,0.7156460334356771,0.2981902862926971,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,65.16171885308417,chi2,,,,
-50,none,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9931779675565967,None,0.0,18,17,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,robust_scaler,,,0.75,0.2945917010472818,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,,
-51,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.08646118576686276,None,0.0,4,9,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,most_frequent,quantile_transformer,959,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-52,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.6271213423927893,None,0.0,1,4,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.009403509495453675,mean,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,,
-53,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8.85547546146731,True,True,squared_hinge,4.0111304608748804e-05,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-54,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.4557246694559742,None,0.0,2,17,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00044760203401312263,mean,robust_scaler,,,0.7111426078575654,0.25,extra_trees_preproc_for_classification,False,entropy,None,0.11395486180078526,None,0.0,1,7,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-55,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.8793175987664956,None,0.0,10,5,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,robust_scaler,,,0.7645748130904642,0.2677156877155214,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,130.79089036625916,False,True,1,squared_hinge,ovr,l1,0.005538878421721295,,,,,,,,,,,,,,,,,,,,,
-56,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.014911374426433241,None,0.0,6,2,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,,
-57,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0016941428709087083,True,,,True,,optimal,squared_hinge,l1,,5.124265599423001e-05,no_encoding,no_coalescense,,most_frequent,quantile_transformer,461,uniform,,,extra_trees_preproc_for_classification,True,gini,None,0.31371431180257103,None,0.0,5,17,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-58,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.7591396576940418,-0.02447104417007273,2,0.7325180366342191,poly,-1,True,0.00045922794962860523,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,standardize,,,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,2.057731506094293,False,True,1,squared_hinge,ovr,l1,0.03575998247131604,,,,,,,,,,,,,,,,,,,,,
-59,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.593728171200453,None,0.0,7,20,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.02830941310359363,mean,normalize,,,,,extra_trees_preproc_for_classification,False,entropy,None,0.7389446586281172,None,0.0,20,11,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-60,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5542793596955801,None,0.0,1,3,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,most_frequent,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-61,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.46057831591617715,False,True,hinge,0.04557857428827514,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00027457445401600137,median,standardize,,,,,extra_trees_preproc_for_classification,True,gini,None,0.48190346970486964,None,0.0,17,18,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-62,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.46057831591617715,False,True,hinge,0.04557857428827514,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00027457445401600137,median,standardize,,,,,extra_trees_preproc_for_classification,True,gini,None,0.48190346970486964,None,0.0,17,18,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-63,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.8494314157539654,None,0.0,3,19,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,mean,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,,
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-82,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.044449043853721795,True,,,True,,optimal,log,l2,,0.0005323728333290294,no_encoding,no_coalescense,,most_frequent,quantile_transformer,588,normal,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,47.34146395379382,chi2,,,,
-83,weighting,adaboost,SAMME.R,0.23766684501868082,10,97,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.004737885672001703,median,none,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50.0,chi2,,,,
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-89,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9.524191988343434,True,True,hinge,0.0959243684834342,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.011065812213652354,most_frequent,quantile_transformer,913,uniform,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.09034604613454934,fpr,chi2,
-90,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4834106677905965,False,True,1,squared_hinge,ovr,l2,0.00017625027586147415,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0009157492873444966,most_frequent,quantile_transformer,1150,normal,,,extra_trees_preproc_for_classification,False,entropy,None,0.4981700453792278,None,0.0,9,15,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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+370,weighting,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4,1,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,none,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,53.395519184138294,chi2,,,,
diff --git a/autosklearn/metalearning/files/precision_macro_binary.classification_sparse/description.txt b/autosklearn/metalearning/files/precision_macro_binary.classification_sparse/description.txt
index a28fa30107..b3805a6b37 100644
--- a/autosklearn/metalearning/files/precision_macro_binary.classification_sparse/description.txt
+++ b/autosklearn/metalearning/files/precision_macro_binary.classification_sparse/description.txt
@@ -1,8 +1,9 @@
 features_cutoff_time: 3600
 features_cutoff_memory: 3072
-number_of_feature_steps: 35
+number_of_feature_steps: 42
 feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances
 feature_step LogNumberOfInstances: LogNumberOfInstances
+feature_step NumberOfClasses: NumberOfClasses
 feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures
 feature_step LogNumberOfFeatures: LogNumberOfFeatures
 feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues
@@ -20,6 +21,11 @@ feature_step DatasetRatio: DatasetRatio, LogDatasetRatio
 feature_step LogDatasetRatio: LogDatasetRatio
 feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio
 feature_step LogInverseDatasetRatio: LogInverseDatasetRatio
+feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD
+feature_step ClassProbabilityMin: ClassProbabilityMin
+feature_step ClassProbabilityMax: ClassProbabilityMax
+feature_step ClassProbabilityMean: ClassProbabilityMean
+feature_step ClassProbabilitySTD: ClassProbabilitySTD
 feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum
 feature_step SymbolsMin: SymbolsMin
 feature_step SymbolsMax: SymbolsMax
@@ -36,11 +42,12 @@ feature_step SkewnessMin: SkewnessMin
 feature_step SkewnessMax: SkewnessMax
 feature_step SkewnessMean: SkewnessMean
 feature_step SkewnessSTD: SkewnessSTD
-features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
+feature_step ClassEntropy: ClassEntropy
+features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy
 features_stochastic: 
-default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
+default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy
 
-algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92
+algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235,236,237,238,239,240,241,242,243,244,245,246,247,248,249,250,251,252,253,254,255,256,257,258,259,260,261,262,263,264,265,266,267,268,269,270,271,272,273,274,275,276,277,278,279,280,281,282,283,284,285,286,287,288,289,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,312,313,314,315,316,317,318,319,320,321,322,323,324,325,326,327,328,329,330,331,332,333,334,335,336,337,338,339,340,341,342,343,344,345,346,347,348,349,350,351,352,353,354,355,356,357,358,359,360,361,362,363,364,365,366,367,368,369,370,371,372
 algorithms_stochastic: 
 performance_measures: precision_macro
 performance_type: solution_quality
diff --git a/autosklearn/metalearning/files/precision_macro_binary.classification_sparse/feature_costs.arff b/autosklearn/metalearning/files/precision_macro_binary.classification_sparse/feature_costs.arff
index 0e17e20d22..8512fd9275 100644
--- a/autosklearn/metalearning/files/precision_macro_binary.classification_sparse/feature_costs.arff
+++ b/autosklearn/metalearning/files/precision_macro_binary.classification_sparse/feature_costs.arff
@@ -4,6 +4,7 @@
 @ATTRIBUTE repetition NUMERIC
 @ATTRIBUTE NumberOfInstances NUMERIC
 @ATTRIBUTE LogNumberOfInstances NUMERIC
+@ATTRIBUTE NumberOfClasses NUMERIC
 @ATTRIBUTE NumberOfFeatures NUMERIC
 @ATTRIBUTE LogNumberOfFeatures NUMERIC
 @ATTRIBUTE MissingValues NUMERIC
@@ -21,6 +22,11 @@
 @ATTRIBUTE LogDatasetRatio NUMERIC
 @ATTRIBUTE InverseDatasetRatio NUMERIC
 @ATTRIBUTE LogInverseDatasetRatio NUMERIC
+@ATTRIBUTE ClassOccurences NUMERIC
+@ATTRIBUTE ClassProbabilityMin NUMERIC
+@ATTRIBUTE ClassProbabilityMax NUMERIC
+@ATTRIBUTE ClassProbabilityMean NUMERIC
+@ATTRIBUTE ClassProbabilitySTD NUMERIC
 @ATTRIBUTE NumSymbols NUMERIC
 @ATTRIBUTE SymbolsMin NUMERIC
 @ATTRIBUTE SymbolsMax NUMERIC
@@ -37,162 +43,138 @@
 @ATTRIBUTE SkewnessMax NUMERIC
 @ATTRIBUTE SkewnessMean NUMERIC
 @ATTRIBUTE SkewnessSTD NUMERIC
+@ATTRIBUTE ClassEntropy NUMERIC
 
 @DATA
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+75235,1.0,1e-05,1e-05,0.0003,2e-05,1e-05,0.00461,0.00385,2e-05,0.00055,2e-05,0.00026,1e-05,3e-05,3e-05,0.0001,0.00011,3e-05,1e-05,5e-05,2e-05,0.00026,3e-05,1e-05,8e-05,0.00014,0.00075,1e-05,1e-05,0.0002,0.00043,0.00011,0.00127,0.00017,0.0002,0.00029,0.00061,0.0008,0.00015,0.00012,0.00016,0.00038,0.00242
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+75244,1.0,3e-05,2e-05,0.0004,3e-05,2e-05,0.0173,0.01298,4e-05,0.00267,4e-05,0.00174,1e-05,7e-05,9e-05,0.00024,0.00027,8e-05,2e-05,0.00013,3e-05,0.00045,7e-05,2e-05,0.00011,0.00025,0.00077,6e-05,2e-05,0.00021,0.00034,0.00013,0.00077,0.00014,0.00013,0.00026,0.00023,0.00044,7e-05,8e-05,0.0001,0.00019,0.00649
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+75249,1.0,1e-05,1e-05,0.00011,1e-05,1e-05,0.00192,0.00153,1e-05,0.00025,1e-05,0.00017,1e-05,2e-05,2e-05,5e-05,7e-05,2e-05,1e-05,5e-05,3e-05,0.00014,2e-05,1e-05,3e-05,8e-05,0.00029,1e-05,3e-05,6e-05,0.00012,7e-05,0.00041,6e-05,8e-05,0.0001,0.00017,0.00043,6e-05,7e-05,9e-05,0.00019,0.00092
+75250,1.0,1e-05,1e-05,0.00375,1e-05,1e-05,0.04994,0.047,1e-05,0.00247,1e-05,0.00049,1e-05,2e-05,2e-05,6e-05,7e-05,2e-05,1e-05,3e-05,1e-05,0.00022,4e-05,1e-05,4e-05,0.00012,0.00041,0.0,1e-05,0.00011,0.00023,6e-05,0.00076,0.00012,0.00012,0.00019,0.00032,0.00086,0.00012,0.00013,0.00018,0.00043,0.04178
diff --git a/autosklearn/metalearning/files/precision_macro_binary.classification_sparse/feature_runstatus.arff b/autosklearn/metalearning/files/precision_macro_binary.classification_sparse/feature_runstatus.arff
index 0034ad2bd6..5fff2dfed3 100644
--- a/autosklearn/metalearning/files/precision_macro_binary.classification_sparse/feature_runstatus.arff
+++ b/autosklearn/metalearning/files/precision_macro_binary.classification_sparse/feature_runstatus.arff
@@ -4,6 +4,7 @@
 @ATTRIBUTE repetition NUMERIC
 @ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other}
@@ -21,6 +22,11 @@
 @ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other}
@@ -37,162 +43,138 @@
 @ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other}
 
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diff --git a/autosklearn/metalearning/files/precision_macro_binary.classification_sparse/feature_values.arff b/autosklearn/metalearning/files/precision_macro_binary.classification_sparse/feature_values.arff
index a60400e3ce..736dc64321 100644
--- a/autosklearn/metalearning/files/precision_macro_binary.classification_sparse/feature_values.arff
+++ b/autosklearn/metalearning/files/precision_macro_binary.classification_sparse/feature_values.arff
@@ -13,7 +13,6 @@
 @ATTRIBUTE KurtosisMean NUMERIC
 @ATTRIBUTE KurtosisMin NUMERIC
 @ATTRIBUTE KurtosisSTD NUMERIC
-@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC
 @ATTRIBUTE LogDatasetRatio NUMERIC
 @ATTRIBUTE LogInverseDatasetRatio NUMERIC
 @ATTRIBUTE LogNumberOfFeatures NUMERIC
@@ -42,160 +41,135 @@
 @ATTRIBUTE SymbolsSum NUMERIC
 
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diff --git a/autosklearn/metalearning/files/precision_macro_multiclass.classification_dense/algorithm_runs.arff b/autosklearn/metalearning/files/precision_macro_multiclass.classification_dense/algorithm_runs.arff
index 1741657493..f565c325e1 100644
--- a/autosklearn/metalearning/files/precision_macro_multiclass.classification_dense/algorithm_runs.arff
+++ b/autosklearn/metalearning/files/precision_macro_multiclass.classification_dense/algorithm_runs.arff
@@ -7,130 +7,135 @@
 @ATTRIBUTE runstatus {ok, timeout, memout, not_applicable, crash, other}
 
 @DATA
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diff --git a/autosklearn/metalearning/files/precision_macro_multiclass.classification_dense/configurations.csv b/autosklearn/metalearning/files/precision_macro_multiclass.classification_dense/configurations.csv
index 6509d0ad02..2f0e2cf69e 100644
--- a/autosklearn/metalearning/files/precision_macro_multiclass.classification_dense/configurations.csv
+++ b/autosklearn/metalearning/files/precision_macro_multiclass.classification_dense/configurations.csv
@@ -1,128 +1,133 @@
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-1,weighting,adaboost,SAMME,0.015984291290723266,7,184,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010000000000000004,median,standardize,,,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.9333242689646983,False,,,,,,,,,,,,,,,
-2,none,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.9525269450493825,None,0.0,4,20,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,standardize,,,,,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-3,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,101,auto,,1.1004704874013712e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.009420818612537619,most_frequent,normalize,,,,,kitchen_sinks,,,,,,,,,,,,,,,,,,,,,,,,0.1495664841849168,2334,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-4,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,23916.78290516781,False,True,1,squared_hinge,ovr,l2,0.0006655607726658531,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.008808715242823137,median,none,,,,,kernel_pca,,,,,,,,,,,,,,,,,,,0.2633153190620261,2,0.004770332714291669,poly,1483,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-5,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,2,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,none,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,94.23876232993393,mutual_info,,,
-6,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.933900673392165,None,0.0,7,4,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,minmax,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-7,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.3700222208089159,None,0.0,14,19,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,minmax,,,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,1.0533068843554318,False,True,1,squared_hinge,ovr,l1,0.00618591414610778,,,,,,,,,,,,,,,,,,,,,,
-8,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5238814331135578,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,median,none,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,28,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-9,none,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13,None,,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,none,,,,,feature_agglomeration,,,,,,,,,,,,,,,euclidean,average,74,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-10,none,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,174,None,,0.00019846724974856246,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,robust_scaler,,,0.7443909060108587,0.25416038978170036,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,
-11,weighting,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.3506201797016825,median,minmax,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2449821017072459,fdr,chi2
-12,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.541827838541001,True,True,squared_hinge,0.0001129851063838815,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,standardize,,,,,extra_trees_preproc_for_classification,True,entropy,None,0.5533459595240017,None,0.0,18,4,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-13,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.3652103526812861,None,0.0,6,8,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,quantile_transformer,79,uniform,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.17357047764133562,fwe,chi2
-14,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6185424163558029,False,True,1,squared_hinge,ovr,l2,4.385767395120581e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010397452590017174,median,quantile_transformer,961,normal,,,feature_agglomeration,,,,,,,,,,,,,,,cosine,average,25,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-15,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.5890308372885505,None,0.0,19,10,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.02193054860417746,median,robust_scaler,,,0.75,0.25,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,87.97956633784086,f_classif,,,
-16,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7947177310891914,None,0.0,13,3,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,mean,robust_scaler,,,0.7681430822715739,0.23411421138225857,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,16.747604337291705,f_classif,,,
-17,none,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.1769332447629957,mean,minmax,,,,,fast_ica,,,,,,,,,,,deflation,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-18,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.07518858116896743,None,0.0,7,10,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0027133920607872518,mean,robust_scaler,,,0.7873452530915046,0.16215139072179952,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-19,none,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,200,None,,0.0017457216340699586,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,none,,,,,kernel_pca,,,,,,,,,,,,,,,,,,,0.0,4,0.0007494648939847078,poly,1996,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-20,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.6039096182414764,None,0.0,2,20,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.1939609823704447,most_frequent,robust_scaler,,,0.7331327008874781,0.29747488162877694,extra_trees_preproc_for_classification,True,entropy,None,0.33401603690569853,None,0.0,4,4,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-21,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.8054434875626825,None,0.0,1,20,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,median,standardize,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4241048505416849,fpr,f_classif
-22,weighting,adaboost,SAMME,0.2353770068926199,1,62,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.007159634821655197,most_frequent,quantile_transformer,1616,normal,,,feature_agglomeration,,,,,,,,,,,,,,,cosine,average,151,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-23,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7734.521233796252,,,2.6048661611376747,rbf,-1,True,4.68717607435371e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,none,,,,,feature_agglomeration,,,,,,,,,,,,,,,euclidean,average,25,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-24,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.8367655423352977,None,0.0,1,5,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.01866043949164283,median,standardize,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.3824115704370156,fpr,f_classif
-25,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5010952533165973,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.005838375247601407,mean,normalize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,
-26,none,decision_tree,,,,,,,gini,1.195211653203929,1.0,None,0.0,1,3,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.011971971469963474,median,robust_scaler,,,0.7659403182030059,0.25,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,223.9949500136512,False,True,1,squared_hinge,ovr,l1,0.0022173435996968244,,,,,,,,,,,,,,,,,,,,,,
-27,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.4426097579931897,None,0.0,16,4,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,mean,robust_scaler,,,0.7407323456920554,0.0804756921037867,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,
-28,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5607168729746175,None,0.0,2,3,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.009810589644392154,most_frequent,quantile_transformer,1132,normal,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.06211089008167909,fdr,chi2
-29,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9333237919291875,None,0.0,8,3,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,
-30,none,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.9728532763078892,None,0.0,13,9,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,none,,,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6651071190864879,False,,,,,,,,,,,,,,,
-31,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9561075301148197,None,0.0,6,2,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,
-32,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.557321265077371,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-33,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,1.153931482107762e-10,0.011666190464009397,auto,255,None,29,190,18,loss,1e-07,0.147868307124396,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,
-34,none,adaboost,SAMME,0.6255555220658239,6,446,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.008833097306364968,most_frequent,none,,,,,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,10,None,4,17,1.0,76,,,,,
-35,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.47147939823444396,None,0.0,18,13,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,robust_scaler,,,0.9414880591550729,0.19219631546028484,fast_ica,,,,,,,,,,,parallel,logcosh,1978,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-36,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,3.6797768538445263e-09,0.05511004217375807,auto,255,None,72,62,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,robust_scaler,,,0.7241481576763391,0.0501033899901458,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,36,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-37,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.8680237807500206,None,0.0,6,4,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.021223427667606046,median,standardize,,,,,fast_ica,,,,,,,,,,,deflation,logcosh,364,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-38,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.887898394612674,None,0.0,10,19,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,robust_scaler,,,0.8350698288864961,0.2347163538066168,extra_trees_preproc_for_classification,False,entropy,None,0.49795108918064074,None,0.0,13,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-39,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,1.6343482806592892e-09,0.19537524572679268,auto,255,None,32,29,13,loss,1e-07,0.05061738224302791,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-40,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7088164344533493,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,mean,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,
-41,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,1.943186107984505e-07,0.08852455702036988,auto,255,None,5,72,19,loss,1e-07,0.039837460762271175,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.01591265419367627,median,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,
-42,weighting,adaboost,SAMME,0.5210651042094924,10,401,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,standardize,,,,,feature_agglomeration,,,,,,,,,,,,,,,euclidean,complete,89,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-43,none,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0686374607394679,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,minmax,,,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.8490260132946401,False,,,,,,,,,,,,,,,
-44,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.6931271368243409,None,0.0,11,10,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.362940004438799,most_frequent,robust_scaler,,,0.9177587796305063,0.17084038458597867,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,30.491514144623952,mutual_info,,,
-45,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.9398869869697849,None,0.0,20,15,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,normalize,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,269,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-46,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,92.66144306787051,,,0.04654639302091653,rbf,-1,True,0.0022231544280610005,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.005194838694455963,mean,none,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,15,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-47,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,21.69482701422382,-0.9439355912923322,,0.012708037096662675,sigmoid,-1,True,1.3900680173654052e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-48,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.8095522998548572,None,0.0,17,10,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.031940595071539304,mean,quantile_transformer,1563,normal,,,extra_trees_preproc_for_classification,False,entropy,None,0.9330145598603196,None,0.0,18,10,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-49,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,26.502259985828186,,,9.475662388405154e-05,rbf,-1,False,0.00019690635968217866,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-50,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,2.5360196477156387e-07,0.011018756408558218,auto,255,None,40,122,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.00011749164080669445,most_frequent,quantile_transformer,757,uniform,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,5.097834697156853,False,True,1,squared_hinge,ovr,l1,0.053877834919477424,,,,,,,,,,,,,,,,,,,,,,
-51,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5255991794296913,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-52,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.34697723213923637,None,0.0,15,18,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.1923594385988973,mean,normalize,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,25.77499302945827,chi2,,,
-53,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,23529.225652845635,0.30829978977649053,2,0.0008220498708157514,poly,-1,False,0.0005532556252982188,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.07570228092651136,mean,quantile_transformer,1143,normal,,,fast_ica,,,,,,,,,,,parallel,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-54,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,2.013916546255427e-10,0.08334928961641928,auto,255,None,61,84,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010000000000000004,most_frequent,quantile_transformer,1069,normal,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1740143466708828,fdr,f_classif
-55,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.9523435078770708,0.8748383232154038,4,0.2195992292488049,poly,-1,True,4.849685143365339e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,minmax,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-56,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.7610308078756552,None,0.0,1,2,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.010000000000000004,mean,robust_scaler,,,0.9205545465103644,0.20272761060743316,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-57,weighting,adaboost,SAMME.R,0.03127273017917347,10,496,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,standardize,,,,,fast_ica,,,,,,,,,,,deflation,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-58,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,151,None,,0.0003283777607180646,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.2510527431514227,mean,normalize,,,,,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,-0.96093182232312,,0.05762510964607661,sigmoid,2632,,,,,,,,,,,,,,,,,
-59,none,bernoulli_nb,,,,,0.6524090653939782,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.005164169126808402,most_frequent,quantile_transformer,16,normal,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.033082145443631535,False,True,1,squared_hinge,ovr,l1,0.002615020898325393,,,,,,,,,,,,,,,,,,,,,,
-60,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.6453555269772124,None,0.0,1,4,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,normalize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-61,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,940.9437776449834,,,1.5839338084625942,rbf,-1,False,0.023072781592565438,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,none,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,77.25763685070707,mutual_info,,,
-62,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.20911875507660596,None,0.0,12,8,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,median,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,
-63,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.7326858450533382,0.02513634776687268,2,0.8635510319471909,poly,-1,True,0.0014598272110141993,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.06447266871225255,most_frequent,quantile_transformer,1000,normal,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,79.01961823033078,f_classif,,,
-64,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,29.55731828912983,False,True,1,squared_hinge,ovr,l2,0.0011332486951954968,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,
-65,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,1.1387795876755026e-06,0.12245605190343567,auto,255,None,58,7,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,robust_scaler,,,0.79305915648885,0.1841487406876042,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-66,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.8425560317492612,None,0.0,8,15,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.015276988502366128,median,none,,,,,fast_ica,,,,,,,,,,,parallel,cube,17,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-67,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,0.04818131831746998,0.023221509516730385,auto,255,None,132,2,18,loss,1e-07,0.1318981686801681,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0008132666483686764,most_frequent,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-68,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,925.3053743248586,False,True,1,squared_hinge,ovr,l2,0.02764481198395717,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.3850718018366796,most_frequent,quantile_transformer,786,uniform,,,feature_agglomeration,,,,,,,,,,,,,,,cosine,average,156,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-69,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7573078019937971,None,0.0,2,5,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,robust_scaler,,,0.7265312295801113,0.28332515882236425,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.09660564590146253,fpr,f_classif
-70,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0057657098053164255,False,True,squared_hinge,0.013468519636061941,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.484357187898904,mean,quantile_transformer,675,normal,,,kernel_pca,,,,,,,,,,,,,,,,,,,,,,cosine,1876,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-71,none,adaboost,SAMME.R,0.22802213935525836,6,284,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,robust_scaler,,,0.7468645381288924,0.22838821662367073,feature_agglomeration,,,,,,,,,,,,,,,cosine,average,128,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-72,none,adaboost,SAMME,0.1280458968526125,7,125,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,robust_scaler,,,0.7156460334356771,0.2981902862926971,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,65.16171885308417,chi2,,,
-73,none,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9931779675565967,None,0.0,18,17,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,robust_scaler,,,0.75,0.2945917010472818,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,
-74,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.08646118576686276,None,0.0,4,9,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,most_frequent,quantile_transformer,959,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-75,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,1.2430504242164082e-09,0.03359568307446533,auto,255,None,1609,29,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,standardize,,,,,feature_agglomeration,,,,,,,,,,,,,,,cosine,complete,40,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-76,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7.491039171224502,True,True,squared_hinge,2.8058138048327884e-05,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,minmax,,,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.7754372809893839,False,True,1,squared_hinge,ovr,l1,3.9643594596766314e-05,,,,,,,,,,,,,,,,,,,,,,
-77,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.4557246694559742,None,0.0,2,17,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00044760203401312263,mean,robust_scaler,,,0.7111426078575654,0.25,extra_trees_preproc_for_classification,False,entropy,None,0.11395486180078526,None,0.0,1,7,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-78,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,0.011547155223520492,0.13665554140837183,auto,255,None,13,5,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.1824584492028449,median,minmax,,,,,feature_agglomeration,,,,,,,,,,,,,,,euclidean,average,261,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-79,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.933900673392165,None,0.0,7,4,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,minmax,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-80,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1661.2274747409913,,,0.025821208852387936,rbf,-1,False,0.0017011435104921824,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,none,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,306,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-81,none,adaboost,SAMME,0.011233995624432622,9,477,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.018370622484682127,mean,standardize,,,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6039710338898471,False,,,,,,,,,,,,,,,
-82,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,21.85207494001697,0.18872999589786676,4,1.1863646021868761,poly,-1,False,0.0021338371901294443,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0034080852834770217,mean,normalize,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.11326005109886934,fdr,f_classif
-83,weighting,adaboost,SAMME,0.38341570193537416,9,250,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,minmax,,,,,feature_agglomeration,,,,,,,,,,,,,,,euclidean,average,13,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-84,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,0.037849720944694,0.1268815770019783,auto,255,None,11,99,11,loss,1e-07,0.10654523627313527,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,
-85,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.593728171200453,None,0.0,7,20,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.02830941310359363,mean,normalize,,,,,extra_trees_preproc_for_classification,False,entropy,None,0.7389446586281172,None,0.0,20,11,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-86,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,1,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.019797877010737826,mean,normalize,,,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.8239678741128165,False,,,,,,,,,,,,,,,
-87,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.46057831591617715,False,True,hinge,0.04557857428827514,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00027457445401600137,median,standardize,,,,,extra_trees_preproc_for_classification,True,gini,None,0.48190346970486964,None,0.0,17,18,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-88,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,38,auto,,0.00019398431171606024,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010000000000000004,median,quantile_transformer,1440,normal,,,extra_trees_preproc_for_classification,True,gini,None,0.5126639956845847,None,0.0,16,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-89,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5722917944959481,None,0.0,1,14,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,
-90,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.26356754187780895,None,0.0,4,3,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010966178401758774,mean,robust_scaler,,,0.7817043861798886,0.24371720112407158,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
-91,weighting,adaboost,SAMME.R,0.13547552520095074,4,62,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.011851011202809002,most_frequent,standardize,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4888776875314595,fpr,chi2
-92,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,80.0028902468169,False,True,1,squared_hinge,ovr,l2,0.0024481999829574722,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.15047970487774687,mean,quantile_transformer,1130,normal,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,1.1912646635544712,False,True,1,squared_hinge,ovr,l1,0.00014314728486450857,,,,,,,,,,,,,,,,,,,,,,
-93,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.22879311080499845,None,0.0,8,4,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.014320022547615783,median,minmax,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.238630703440461,mutual_info,,,
-94,weighting,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,1,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,robust_scaler,,,0.7136729468599508,0.2308995232542769,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.636620620188117,False,,,,,,,,,,,,,,,
-95,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.107967527639527e-05,True,True,hinge,0.00448294745544775,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,quantile_transformer,953,normal,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,35.00114345405331,chi2,,,
-96,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.14148638702297855,None,0.0,2,6,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.0038499650338378643,most_frequent,quantile_transformer,1759,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-97,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,1.1522414705935405e-10,0.6132800183073315,auto,255,None,1607,67,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010096911249562407,mean,robust_scaler,,,0.7663493326967942,0.136094217337434,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-98,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.35863733679484333,None,0.0,1,11,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.0009777923655842618,median,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-99,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.479405900046494,,,0.013340778632154543,rbf,-1,False,6.913586060890114e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,robust_scaler,,,0.9953232616818098,0.06450577886132274,fast_ica,,,,,,,,,,,deflation,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-100,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.825844582229201,None,0.0,12,9,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0008422508222149647,median,robust_scaler,,,0.8658803880857064,0.2528423686293341,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-101,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2574971638664341,True,True,hinge,1.8027411099296446e-05,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.00536396328125103,median,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,
-102,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7908336536113714,None,0.0,9,3,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,minmax,,,,,extra_trees_preproc_for_classification,False,entropy,None,0.17440717696897978,None,0.0,5,18,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-103,none,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,128,manual,0.8574560771111307,0.010886139381599514,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.24115007944216788,most_frequent,normalize,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,62.26514170991452,mutual_info,,,
-104,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.6282663389679584,None,0.0,1,3,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,median,minmax,,,,,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-105,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,1.430083734915855e-09,0.08779621406981941,auto,255,None,17,20,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.29749837779115257,mean,robust_scaler,,,0.7236445277814435,0.25,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
-106,none,adaboost,SAMME.R,0.9261146078592859,9,476,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.012453052108117785,median,robust_scaler,,,0.7492076537583867,0.25853088636132765,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,6,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-107,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,39.22980757485479,,,0.13597389869623427,rbf,-1,False,0.00011639060052687874,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.006288306814260224,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-108,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.7922837297504705,False,True,1,squared_hinge,ovr,l2,0.0005476597326342352,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.012674631437254894,most_frequent,none,,,,,extra_trees_preproc_for_classification,False,entropy,None,0.23145146971319638,None,0.0,7,20,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-109,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.18060139887649052,None,0.0,10,12,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.010000000000000004,most_frequent,robust_scaler,,,0.9298612094255864,0.1263597946722308,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
-110,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.4111348710613201,None,0.0,16,8,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.01224348527546641,most_frequent,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,
-111,none,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0313388874611118,False,True,1,squared_hinge,ovr,l2,0.00021502747842956193,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,quantile_transformer,925,uniform,,,kernel_pca,,,,,,,,,,,,,,,,,,,,,,cosine,1907,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-112,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.044449043853721795,True,,,True,,optimal,log,l2,,0.0005323728333290294,no_encoding,no_coalescense,,most_frequent,quantile_transformer,588,normal,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,47.34146395379382,chi2,,,
-113,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,9.029473608568458e-09,0.15779990576964315,auto,255,None,37,71,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.018093177195644097,mean,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,False,,,,,,,,,,,,
-114,weighting,adaboost,SAMME.R,0.23766684501868082,10,97,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.004737885672001703,median,none,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50.0,chi2,,,
-115,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.129143822880219,True,True,hinge,0.00012612093569084375,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0016222546082911887,most_frequent,quantile_transformer,1309,uniform,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8.816417526015055,f_classif,,,
-116,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.4864284879550203,None,0.0,1,4,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.008665854799630503,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-117,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.43699642289829416,None,0.0,6,20,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,quantile_transformer,1604,uniform,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-118,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.6922752267318475,True,True,squared_hinge,0.06299408576730353,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,quantile_transformer,1742,uniform,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,107,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-119,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.44727252219812963,None,0.0,2,10,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,standardize,,,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l1,8.17533189014765e-05,,,,,,,,,,,,,,,,,,,,,,
-120,weighting,extra_trees,,,,,,,,,,,,,,,True,gini,None,0.9999521514529074,None,0.0,3,10,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,minmax,,,,,extra_trees_preproc_for_classification,False,gini,None,0.24745225001074223,None,0.0,14,18,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-121,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.4210978663047845,None,0.0,5,6,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.006365988550833196,most_frequent,robust_scaler,,,0.8348891074186082,0.00974961635730845,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0695919708890599,fpr,f_classif
-122,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9.524191988343434,True,True,hinge,0.0959243684834342,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.011065812213652354,most_frequent,quantile_transformer,913,uniform,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.09034604613454934,fpr,chi2
-123,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.20612848067724507,False,True,1,squared_hinge,ovr,l2,0.00017832980955957004,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0007502816173508168,median,quantile_transformer,1083,uniform,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.02820386332530596,fwe,f_classif
-124,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1561.1002600303584,,,0.0024048551704194507,rbf,-1,False,0.0001936214667496851,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,minmax,,,,,fast_ica,,,,,,,,,,,parallel,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-125,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.087313242780732,0.0636709914987994,3,0.004481917125024585,poly,-1,False,0.01848880618941367,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.002136015035740733,mean,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-126,weighting,adaboost,SAMME,0.029932847627447023,8,483,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,quantile_transformer,1290,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-127,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.6466927589470762,None,0.0,11,2,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.009672465311659264,most_frequent,standardize,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,369,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+1,none,decision_tree,,,,,,,gini,0.7229490531192699,1.0,None,0.0,1,18,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.00013586681143040562,median,quantile_transformer,647,uniform,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+2,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7307322501991852,None,0.0,18,14,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+6,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.7283683762613216,None,0.0,12,20,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,median,standardize,,,,,fast_ica,,,,,,,,,,,deflation,exp,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+8,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,114,manual,0.11778631707605758,0.0029112721339713465,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.015582195257240751,median,normalize,,,,,kernel_pca,,,,,,,,,,,,,,,,,,,0.42909623253327767,3,0.25407612000343777,poly,1689,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+11,weighting,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,2,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.3865962574104293,mean,normalize,,,,,fast_ica,,,,,,,,,,,deflation,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+15,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.21772092350860472,None,0.0,14,17,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.0020488212891693275,median,standardize,,,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,33.74257606530722,False,True,1,squared_hinge,ovr,l1,8.740452966205111e-05,,,,,,,,,,,,,,,,,,,,,,
+18,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,2.4243922131268786e-05,0.07570328162448307,auto,255,None,71,42,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,robust_scaler,,,0.8898729094898769,0.12138152991327475,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,94.96086286583851,chi2,,,
+20,none,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.9866058571747695,None,0.0,16,9,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.1680676743605393,most_frequent,robust_scaler,,,0.8840072797399463,0.08535534311393947,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,False,,,,,,,,,,,,
+23,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,0.00022373024203887573,0.027149343206135476,auto,255,None,49,13,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.00777614320416325,most_frequent,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,
+27,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.41025844265119926,None,0.0,18,15,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,median,quantile_transformer,1911,normal,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,False,,,,,,,,,,,,
+31,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,8.757412830636845e-06,0.10095376320667454,auto,255,None,3,2,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.015401766535365336,median,quantile_transformer,647,normal,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,
+32,none,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,174,manual,0.21501892897746921,2.0347110826262054e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.013754893527625522,most_frequent,normalize,,,,,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,-0.019221513778605592,3,0.10190948951339239,poly,2521,,,,,,,,,,,,,,,,,
+33,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.42232830349206685,None,0.0,1,16,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.013593246624583636,mean,quantile_transformer,1000,uniform,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4899076986656834,fpr,f_classif
+36,none,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,11,manual,0.3996057556961169,8.195866467196482e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.030006941783431516,mean,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,
+39,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.6799215694343422,None,0.0,1,5,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.004724310659953132,most_frequent,quantile_transformer,1037,normal,,,fast_ica,,,,,,,,,,,parallel,exp,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+41,none,extra_trees,,,,,,,,,,,,,,,True,gini,None,0.5033866291997137,None,0.0,2,14,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,minmax,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+43,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.9479098792950027,None,0.0,12,14,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,quantile_transformer,1758,normal,,,feature_agglomeration,,,,,,,,,,,,,,,cosine,complete,244,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+44,none,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l2,2.858526169098402e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0007827505640681123,median,standardize,,,,,extra_trees_preproc_for_classification,False,entropy,None,0.3443310459451119,None,0.0,3,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+49,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.049504283446131e-05,False,,0.0004438773852753879,True,2.4110816673432424e-07,constant,log,elasticnet,,0.01321814853186568,one_hot_encoding,minority_coalescer,0.01510416593631535,most_frequent,none,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,66.65285268958851,chi2,,,
+52,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,6.421059381158897e-08,0.11261253191288709,auto,255,None,45,28,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,
+55,none,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.9822179211645446,None,0.0,14,8,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.013966871874533052,most_frequent,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,
+62,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.9116648490883282,None,0.0,19,2,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,normalize,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,90.20892117286336,f_classif,,,
+65,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,1.2822815440468518e-10,0.12922700208645135,auto,255,None,31,44,13,loss,1e-07,0.08789566421608311,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.012230423571314038,mean,robust_scaler,,,0.71734690066056,0.20283418288016852,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,
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+438,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,6.090138036399552e-10,0.017305568433437588,auto,255,None,202,188,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,robust_scaler,,,0.7526196011313747,0.1563616005702636,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+443,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1,2,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.007409481017911302,median,normalize,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50.80546094817748,chi2,,,
+445,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,68.60833575985336,,,0.03905068757955671,rbf,-1,False,0.02846559747114773,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.04775815990561371,mean,standardize,,,,,extra_trees_preproc_for_classification,False,entropy,None,0.45727987956076366,None,0.0,13,11,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+449,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,2.074711735072034e-07,0.01106799034691714,auto,255,None,13,24,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.020949205931423413,mean,standardize,,,,,extra_trees_preproc_for_classification,True,gini,None,0.43505400875624267,None,0.0,20,8,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+458,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,5.581658271288433e-10,0.06171988234311011,auto,255,None,25,6,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.014859405637537319,most_frequent,robust_scaler,,,0.7983900845251328,0.2545046978458648,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,1.380085213170737,False,True,1,squared_hinge,ovr,l1,0.0061450732015354784,,,,,,,,,,,,,,,,,,,,,,
+462,weighting,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4,1,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,none,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,53.395519184138294,chi2,,,
diff --git a/autosklearn/metalearning/files/precision_macro_multiclass.classification_dense/description.txt b/autosklearn/metalearning/files/precision_macro_multiclass.classification_dense/description.txt
index 5899d9e32b..441c5f4a28 100644
--- a/autosklearn/metalearning/files/precision_macro_multiclass.classification_dense/description.txt
+++ b/autosklearn/metalearning/files/precision_macro_multiclass.classification_dense/description.txt
@@ -1,8 +1,9 @@
 features_cutoff_time: 3600
 features_cutoff_memory: 3072
-number_of_feature_steps: 35
+number_of_feature_steps: 42
 feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances
 feature_step LogNumberOfInstances: LogNumberOfInstances
+feature_step NumberOfClasses: NumberOfClasses
 feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures
 feature_step LogNumberOfFeatures: LogNumberOfFeatures
 feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues
@@ -20,6 +21,11 @@ feature_step DatasetRatio: DatasetRatio, LogDatasetRatio
 feature_step LogDatasetRatio: LogDatasetRatio
 feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio
 feature_step LogInverseDatasetRatio: LogInverseDatasetRatio
+feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD
+feature_step ClassProbabilityMin: ClassProbabilityMin
+feature_step ClassProbabilityMax: ClassProbabilityMax
+feature_step ClassProbabilityMean: ClassProbabilityMean
+feature_step ClassProbabilitySTD: ClassProbabilitySTD
 feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum
 feature_step SymbolsMin: SymbolsMin
 feature_step SymbolsMax: SymbolsMax
@@ -36,11 +42,12 @@ feature_step SkewnessMin: SkewnessMin
 feature_step SkewnessMax: SkewnessMax
 feature_step SkewnessMean: SkewnessMean
 feature_step SkewnessSTD: SkewnessSTD
-features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
+feature_step ClassEntropy: ClassEntropy
+features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy
 features_stochastic: 
-default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
+default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy
 
-algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127
+algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235,236,237,238,239,240,241,242,243,244,245,246,247,248,249,250,251,252,253,254,255,256,257,258,259,260,261,262,263,264,265,266,267,268,269,270,271,272,273,274,275,276,277,278,279,280,281,282,283,284,285,286,287,288,289,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,312,313,314,315,316,317,318,319,320,321,322,323,324,325,326,327,328,329,330,331,332,333,334,335,336,337,338,339,340,341,342,343,344,345,346,347,348,349,350,351,352,353,354,355,356,357,358,359,360,361,362,363,364,365,366,367,368,369,370,371,372,373,374,375,376,377,378,379,380,381,382,383,384,385,386,387,388,389,390,391,392,393,394,395,396,397,398,399,400,401,402,403,404,405,406,407,408,409,410,411,412,413,414,415,416,417,418,419,420,421,422,423,424,425,426,427,428,429,430,431,432,433,434,435,436,437,438,439,440,441,442,443,444,445,446,447,448,449,450,451,452,453,454,455,456,457,458,459,460,461,462,463,464
 algorithms_stochastic: 
 performance_measures: precision_macro
 performance_type: solution_quality
diff --git a/autosklearn/metalearning/files/precision_macro_multiclass.classification_dense/feature_costs.arff b/autosklearn/metalearning/files/precision_macro_multiclass.classification_dense/feature_costs.arff
index 0e17e20d22..8512fd9275 100644
--- a/autosklearn/metalearning/files/precision_macro_multiclass.classification_dense/feature_costs.arff
+++ b/autosklearn/metalearning/files/precision_macro_multiclass.classification_dense/feature_costs.arff
@@ -4,6 +4,7 @@
 @ATTRIBUTE repetition NUMERIC
 @ATTRIBUTE NumberOfInstances NUMERIC
 @ATTRIBUTE LogNumberOfInstances NUMERIC
+@ATTRIBUTE NumberOfClasses NUMERIC
 @ATTRIBUTE NumberOfFeatures NUMERIC
 @ATTRIBUTE LogNumberOfFeatures NUMERIC
 @ATTRIBUTE MissingValues NUMERIC
@@ -21,6 +22,11 @@
 @ATTRIBUTE LogDatasetRatio NUMERIC
 @ATTRIBUTE InverseDatasetRatio NUMERIC
 @ATTRIBUTE LogInverseDatasetRatio NUMERIC
+@ATTRIBUTE ClassOccurences NUMERIC
+@ATTRIBUTE ClassProbabilityMin NUMERIC
+@ATTRIBUTE ClassProbabilityMax NUMERIC
+@ATTRIBUTE ClassProbabilityMean NUMERIC
+@ATTRIBUTE ClassProbabilitySTD NUMERIC
 @ATTRIBUTE NumSymbols NUMERIC
 @ATTRIBUTE SymbolsMin NUMERIC
 @ATTRIBUTE SymbolsMax NUMERIC
@@ -37,162 +43,138 @@
 @ATTRIBUTE SkewnessMax NUMERIC
 @ATTRIBUTE SkewnessMean NUMERIC
 @ATTRIBUTE SkewnessSTD NUMERIC
+@ATTRIBUTE ClassEntropy NUMERIC
 
 @DATA
-75123,1.0,1e-05,1e-05,1e-05,1e-05,0.00253,0.00221,1e-05,0.00024,3e-05,0.00011,0.0,2e-05,2e-05,5e-05,8e-05,2e-05,1e-05,7e-05,1e-05,0.00072,1e-05,1e-05,0.00019,0.00041,0.00011,0.00061,0.00012,9e-05,0.00015,0.00025,0.00067,0.00014,0.00011,0.00016,0.00027
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-2123,1.0,1e-05,1e-05,1e-05,1e-05,0.00097,0.00079,1e-05,0.00013,1e-05,7e-05,0.0,2e-05,2e-05,5e-05,8e-05,2e-05,1e-05,5e-05,1e-05,0.0007,1e-05,0.0,0.00017,0.00042,0.0001,0.00052,6e-05,8e-05,0.00012,0.00026,0.00049,6e-05,0.00011,9e-05,0.00023
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-75101,1.0,1e-05,1e-05,1e-05,1e-05,0.0418,0.03546,1e-05,0.00389,1e-05,0.00248,0.0,2e-05,2e-05,4e-05,6e-05,2e-05,1e-05,5e-05,2e-05,0.00045,1e-05,0.0,0.00011,0.00023,0.0001,0.00041,6e-05,8e-05,0.0001,0.00018,0.00047,6e-05,0.0001,8e-05,0.00023
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-75187,1.0,1e-05,1e-05,2e-05,1e-05,0.00548,0.00473,1e-05,0.0005,2e-05,0.00029,1e-05,2e-05,2e-05,7e-05,9e-05,3e-05,1e-05,6e-05,1e-05,0.00062,1e-05,1e-05,0.00014,0.00033,0.00013,0.00038,7e-05,7e-05,8e-05,0.00016,0.00047,6e-05,9e-05,9e-05,0.00023
-266,1.0,1e-05,1e-05,1e-05,1e-05,0.0018,0.00146,1e-05,0.00023,2e-05,0.00015,2e-05,2e-05,2e-05,6e-05,8e-05,3e-05,1e-05,6e-05,1e-05,0.00059,1e-05,1e-05,0.00014,0.00031,0.00013,0.0004,5e-05,7e-05,8e-05,0.0002,0.00044,8e-05,8e-05,8e-05,0.0002
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-75157,1.0,1e-05,1e-05,1e-05,1e-05,0.00125,0.00106,1e-05,0.00015,2e-05,7e-05,0.0,2e-05,2e-05,5e-05,6e-05,2e-05,1e-05,4e-05,1e-05,0.00048,2e-05,0.0,0.0001,0.00026,0.0001,0.00039,6e-05,7e-05,8e-05,0.00019,0.00043,6e-05,9e-05,8e-05,0.0002
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diff --git a/autosklearn/metalearning/files/precision_macro_multiclass.classification_dense/feature_runstatus.arff b/autosklearn/metalearning/files/precision_macro_multiclass.classification_dense/feature_runstatus.arff
index 0034ad2bd6..5fff2dfed3 100644
--- a/autosklearn/metalearning/files/precision_macro_multiclass.classification_dense/feature_runstatus.arff
+++ b/autosklearn/metalearning/files/precision_macro_multiclass.classification_dense/feature_runstatus.arff
@@ -4,6 +4,7 @@
 @ATTRIBUTE repetition NUMERIC
 @ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other}
@@ -21,6 +22,11 @@
 @ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other}
@@ -37,162 +43,138 @@
 @ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other}
 
 @DATA
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-2119,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok
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-252,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok
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diff --git a/autosklearn/metalearning/files/precision_macro_multiclass.classification_dense/feature_values.arff b/autosklearn/metalearning/files/precision_macro_multiclass.classification_dense/feature_values.arff
index a60400e3ce..736dc64321 100644
--- a/autosklearn/metalearning/files/precision_macro_multiclass.classification_dense/feature_values.arff
+++ b/autosklearn/metalearning/files/precision_macro_multiclass.classification_dense/feature_values.arff
@@ -13,7 +13,6 @@
 @ATTRIBUTE KurtosisMean NUMERIC
 @ATTRIBUTE KurtosisMin NUMERIC
 @ATTRIBUTE KurtosisSTD NUMERIC
-@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC
 @ATTRIBUTE LogDatasetRatio NUMERIC
 @ATTRIBUTE LogInverseDatasetRatio NUMERIC
 @ATTRIBUTE LogNumberOfFeatures NUMERIC
@@ -42,160 +41,135 @@
 @ATTRIBUTE SymbolsSum NUMERIC
 
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diff --git a/autosklearn/metalearning/files/precision_macro_multiclass.classification_sparse/algorithm_runs.arff b/autosklearn/metalearning/files/precision_macro_multiclass.classification_sparse/algorithm_runs.arff
index 053d8069dd..88af4cb738 100644
--- a/autosklearn/metalearning/files/precision_macro_multiclass.classification_sparse/algorithm_runs.arff
+++ b/autosklearn/metalearning/files/precision_macro_multiclass.classification_sparse/algorithm_runs.arff
@@ -7,95 +7,133 @@
 @ATTRIBUTE runstatus {ok, timeout, memout, not_applicable, crash, other}
 
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diff --git a/autosklearn/metalearning/files/precision_macro_multiclass.classification_sparse/configurations.csv b/autosklearn/metalearning/files/precision_macro_multiclass.classification_sparse/configurations.csv
index 9fbbdefe5c..d9c7a86138 100644
--- a/autosklearn/metalearning/files/precision_macro_multiclass.classification_sparse/configurations.csv
+++ b/autosklearn/metalearning/files/precision_macro_multiclass.classification_sparse/configurations.csv
@@ -1,93 +1,131 @@
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-91,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.087313242780732,0.0636709914987994,3,0.004481917125024585,poly,-1,False,0.01848880618941367,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.002136015035740733,mean,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-92,weighting,adaboost,SAMME,0.029932847627447023,8,483,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,quantile_transformer,1290,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+1,none,decision_tree,,,,,,,gini,0.7229490531192699,1.0,None,0.0,1,18,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.00013586681143040562,median,quantile_transformer,647,uniform,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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+5,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.36028513793687317,None,0.0,19,8,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,standardize,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,48.144408204553606,chi2,,,,
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+80,weighting,adaboost,SAMME,0.17983222024694556,4,357,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0008898781197407956,mean,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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+287,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.3850609070926397,None,0.0,1,2,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.013992309764275707,median,normalize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,,
+288,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.5719833791553314,None,0.0,16,3,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.13804007452389966,mean,normalize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+292,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.0479982468066316,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,quantile_transformer,1097,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+293,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7040496889211508,None,0.0,17,19,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.016450784709133564,mean,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+300,none,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.7010137052424128,None,0.0,7,10,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.2761813291805052,most_frequent,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,,
+305,none,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.318789969510999e-05,True,0.00020676665332856436,,True,0.3135652769555971,optimal,modified_huber,elasticnet,,0.00017449676135991563,one_hot_encoding,minority_coalescer,0.002260448647328741,mean,quantile_transformer,1262,uniform,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,,
+307,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,33,None,,0.006687546229372618,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.06231343712708962,mean,none,,,,,kernel_pca,,,,,,,,,,,-0.7000777299811654,3,0.008780219231661734,poly,498,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+312,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,60.6761602925822,,,0.038224035513907226,rbf,-1,False,0.0018214343969776353,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.421025538999516,median,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+313,weighting,adaboost,SAMME,1.2423888874490505,5,284,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.01582563952987033,most_frequent,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+316,weighting,adaboost,SAMME,1.2615483379313783,8,190,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.00017789451703497617,mean,robust_scaler,,,0.7345811776810758,0.054715769043996304,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+317,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.4572679693563934,None,0.0,1,11,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,mean,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,,
+318,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.843098530645432,None,0.0,1,4,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,mean,quantile_transformer,930,normal,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,,
+321,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.5864398404787432,None,0.0,1,14,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0070663750850608895,most_frequent,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,,
+324,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.5240435604277579,None,0.0,1,3,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.05089089351168927,median,robust_scaler,,,0.8952718567345662,0.24647973407695942,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,,
+326,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0001329242404968632,False,True,squared_hinge,0.000416219546803132,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.007568009633214092,median,quantile_transformer,1126,normal,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,29.06367291960815,chi2,,,,
+329,none,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.2447987308509467,False,True,1,squared_hinge,ovr,l2,4.351575929255585e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,quantile_transformer,1959,normal,,,kernel_pca,,,,,,,,,,,,,,cosine,1721,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+331,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.5,None,0.0,1,5,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.15244281596303316,mean,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+336,weighting,adaboost,SAMME,0.1313487280585511,3,189,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,robust_scaler,,,0.8008090310131288,0.2935856792824304,extra_trees_preproc_for_classification,False,entropy,None,0.7722400064297721,None,0.0,5,4,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+339,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.36374112587320206,None,0.0,2,10,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010870084126959173,most_frequent,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,,
+343,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.7139498722492832,None,0.0,14,5,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0006591123720866668,mean,normalize,,,,,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,6,None,2,4,1.0,87,,,,,,
+345,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5321814610661173,None,0.0,1,3,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,standardize,,,,,extra_trees_preproc_for_classification,True,gini,None,0.5,None,0.0,1,15,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+350,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.46506427932919464,None,0.0,6,3,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00023489724601729732,mean,standardize,,,,,extra_trees_preproc_for_classification,False,gini,None,0.49497363265259986,None,0.0,16,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+352,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5988524108452234,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,mean,robust_scaler,,,0.75,0.26961884796358077,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+353,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1,2,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.007409481017911302,median,normalize,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50.80546094817748,chi2,,,,
+355,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,68.60833575985336,,,0.03905068757955671,rbf,-1,False,0.02846559747114773,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.04775815990561371,mean,standardize,,,,,extra_trees_preproc_for_classification,False,entropy,None,0.45727987956076366,None,0.0,13,11,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+359,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.9282407375709691,None,0.0,16,3,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.015995805068365877,mean,standardize,,,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.10042232633120375,False,True,1,squared_hinge,ovr,l1,0.004451009526629745,,,,,,,,,,,,,,,,,,,,,
+367,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.938372431886818e-05,True,True,hinge,0.0016143408148969647,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00024949978971758427,median,quantile_transformer,754,uniform,,,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,-0.22339839702350717,2,0.5718982387915436,poly,5260,,,,,,,,,,,,,,,,
+370,weighting,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4,1,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,none,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,53.395519184138294,chi2,,,,
diff --git a/autosklearn/metalearning/files/precision_macro_multiclass.classification_sparse/description.txt b/autosklearn/metalearning/files/precision_macro_multiclass.classification_sparse/description.txt
index a28fa30107..b3805a6b37 100644
--- a/autosklearn/metalearning/files/precision_macro_multiclass.classification_sparse/description.txt
+++ b/autosklearn/metalearning/files/precision_macro_multiclass.classification_sparse/description.txt
@@ -1,8 +1,9 @@
 features_cutoff_time: 3600
 features_cutoff_memory: 3072
-number_of_feature_steps: 35
+number_of_feature_steps: 42
 feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances
 feature_step LogNumberOfInstances: LogNumberOfInstances
+feature_step NumberOfClasses: NumberOfClasses
 feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures
 feature_step LogNumberOfFeatures: LogNumberOfFeatures
 feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues
@@ -20,6 +21,11 @@ feature_step DatasetRatio: DatasetRatio, LogDatasetRatio
 feature_step LogDatasetRatio: LogDatasetRatio
 feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio
 feature_step LogInverseDatasetRatio: LogInverseDatasetRatio
+feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD
+feature_step ClassProbabilityMin: ClassProbabilityMin
+feature_step ClassProbabilityMax: ClassProbabilityMax
+feature_step ClassProbabilityMean: ClassProbabilityMean
+feature_step ClassProbabilitySTD: ClassProbabilitySTD
 feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum
 feature_step SymbolsMin: SymbolsMin
 feature_step SymbolsMax: SymbolsMax
@@ -36,11 +42,12 @@ feature_step SkewnessMin: SkewnessMin
 feature_step SkewnessMax: SkewnessMax
 feature_step SkewnessMean: SkewnessMean
 feature_step SkewnessSTD: SkewnessSTD
-features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
+feature_step ClassEntropy: ClassEntropy
+features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy
 features_stochastic: 
-default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
+default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy
 
-algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92
+algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235,236,237,238,239,240,241,242,243,244,245,246,247,248,249,250,251,252,253,254,255,256,257,258,259,260,261,262,263,264,265,266,267,268,269,270,271,272,273,274,275,276,277,278,279,280,281,282,283,284,285,286,287,288,289,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,312,313,314,315,316,317,318,319,320,321,322,323,324,325,326,327,328,329,330,331,332,333,334,335,336,337,338,339,340,341,342,343,344,345,346,347,348,349,350,351,352,353,354,355,356,357,358,359,360,361,362,363,364,365,366,367,368,369,370,371,372
 algorithms_stochastic: 
 performance_measures: precision_macro
 performance_type: solution_quality
diff --git a/autosklearn/metalearning/files/precision_macro_multiclass.classification_sparse/feature_costs.arff b/autosklearn/metalearning/files/precision_macro_multiclass.classification_sparse/feature_costs.arff
index 0e17e20d22..8512fd9275 100644
--- a/autosklearn/metalearning/files/precision_macro_multiclass.classification_sparse/feature_costs.arff
+++ b/autosklearn/metalearning/files/precision_macro_multiclass.classification_sparse/feature_costs.arff
@@ -4,6 +4,7 @@
 @ATTRIBUTE repetition NUMERIC
 @ATTRIBUTE NumberOfInstances NUMERIC
 @ATTRIBUTE LogNumberOfInstances NUMERIC
+@ATTRIBUTE NumberOfClasses NUMERIC
 @ATTRIBUTE NumberOfFeatures NUMERIC
 @ATTRIBUTE LogNumberOfFeatures NUMERIC
 @ATTRIBUTE MissingValues NUMERIC
@@ -21,6 +22,11 @@
 @ATTRIBUTE LogDatasetRatio NUMERIC
 @ATTRIBUTE InverseDatasetRatio NUMERIC
 @ATTRIBUTE LogInverseDatasetRatio NUMERIC
+@ATTRIBUTE ClassOccurences NUMERIC
+@ATTRIBUTE ClassProbabilityMin NUMERIC
+@ATTRIBUTE ClassProbabilityMax NUMERIC
+@ATTRIBUTE ClassProbabilityMean NUMERIC
+@ATTRIBUTE ClassProbabilitySTD NUMERIC
 @ATTRIBUTE NumSymbols NUMERIC
 @ATTRIBUTE SymbolsMin NUMERIC
 @ATTRIBUTE SymbolsMax NUMERIC
@@ -37,162 +43,138 @@
 @ATTRIBUTE SkewnessMax NUMERIC
 @ATTRIBUTE SkewnessMean NUMERIC
 @ATTRIBUTE SkewnessSTD NUMERIC
+@ATTRIBUTE ClassEntropy NUMERIC
 
 @DATA
-75123,1.0,1e-05,1e-05,1e-05,1e-05,0.00253,0.00221,1e-05,0.00024,3e-05,0.00011,0.0,2e-05,2e-05,5e-05,8e-05,2e-05,1e-05,7e-05,1e-05,0.00072,1e-05,1e-05,0.00019,0.00041,0.00011,0.00061,0.00012,9e-05,0.00015,0.00025,0.00067,0.00014,0.00011,0.00016,0.00027
-75090,1.0,1e-05,1e-05,2e-05,2e-05,0.00815,0.00368,7e-05,0.00228,9e-05,0.00235,0.0,5e-05,5e-05,0.0001,0.00013,0.00013,1e-05,0.00021,2e-05,0.00064,0.0,0.0,0.00014,0.00038,0.00011,0.00066,0.00011,0.00014,0.00014,0.00026,0.00066,0.00011,0.00013,0.00015,0.00027
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+75236,1.0,1e-05,1e-05,0.0001,1e-05,1e-05,0.00218,0.00118,2e-05,0.00065,3e-05,0.00041,1e-05,3e-05,3e-05,9e-05,9e-05,4e-05,1e-05,7e-05,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.0006,1e-05,1e-05,0.00021,0.00032,6e-05,0.00043,7e-05,7e-05,9e-05,0.00019,0.00062,7e-05,8e-05,0.0001,0.00037,0.00048
+75237,1.0,2e-05,1e-05,0.00785,2e-05,1e-05,0.31031,0.27854,2e-05,0.01161,3e-05,0.02022,1e-05,4e-05,3e-05,9e-05,0.00024,6e-05,2e-05,9e-05,2e-05,0.00062,7e-05,3e-05,0.00017,0.00035,0.0008,1e-05,1e-05,0.00028,0.00043,8e-05,0.00043,8e-05,7e-05,9e-05,0.0002,0.00044,6e-05,8e-05,9e-05,0.00021,0.14028
+75239,1.0,1e-05,1e-05,7e-05,1e-05,1e-05,0.00105,0.00084,1e-05,0.00015,1e-05,8e-05,1e-05,2e-05,5e-05,4e-05,8e-05,2e-05,1e-05,3e-05,1e-05,0.00016,2e-05,1e-05,5e-05,9e-05,0.00049,0.0,0.0,0.00012,0.00026,0.00011,0.00046,0.00013,8e-05,8e-05,0.00018,0.00044,6e-05,9e-05,0.00011,0.00018,0.00065
+75240,1.0,2e-05,1e-05,0.00036,5e-05,4e-05,0.00845,0.00644,6e-05,0.00122,4e-05,0.00091,1e-05,4e-05,4e-05,9e-05,0.00013,5e-05,2e-05,9e-05,5e-05,0.00031,3e-05,1e-05,7e-05,0.0002,0.00073,3e-05,2e-05,0.00019,0.0003,0.00018,0.00055,9e-05,9e-05,0.00011,0.00025,0.0005,7e-05,8e-05,0.00015,0.0002,0.00365
+75243,1.0,1e-05,1e-05,0.00029,1e-05,1e-05,0.00461,0.00415,1e-05,0.00035,1e-05,0.00015,0.0,2e-05,2e-05,5e-05,7e-05,2e-05,1e-05,4e-05,1e-05,0.00018,2e-05,1e-05,4e-05,0.00011,0.00022,1e-05,1e-05,5e-05,0.00011,5e-05,0.0004,6e-05,6e-05,8e-05,0.0002,0.00039,6e-05,6e-05,8e-05,0.00019,0.00265
+75244,1.0,3e-05,2e-05,0.0004,3e-05,2e-05,0.0173,0.01298,4e-05,0.00267,4e-05,0.00174,1e-05,7e-05,9e-05,0.00024,0.00027,8e-05,2e-05,0.00013,3e-05,0.00045,7e-05,2e-05,0.00011,0.00025,0.00077,6e-05,2e-05,0.00021,0.00034,0.00013,0.00077,0.00014,0.00013,0.00026,0.00023,0.00044,7e-05,8e-05,0.0001,0.00019,0.00649
+75248,1.0,1e-05,1e-05,0.00017,1e-05,1e-05,0.00416,0.00305,1e-05,0.0006,2e-05,0.00054,1e-05,2e-05,2e-05,5e-05,7e-05,2e-05,1e-05,4e-05,1e-05,0.00017,2e-05,1e-05,4e-05,0.0001,0.00029,5e-05,1e-05,6e-05,0.00012,6e-05,0.00081,0.00011,0.00015,0.00016,0.00038,0.0005,7e-05,8e-05,0.00012,0.00024,0.00184
+75249,1.0,1e-05,1e-05,0.00011,1e-05,1e-05,0.00192,0.00153,1e-05,0.00025,1e-05,0.00017,1e-05,2e-05,2e-05,5e-05,7e-05,2e-05,1e-05,5e-05,3e-05,0.00014,2e-05,1e-05,3e-05,8e-05,0.00029,1e-05,3e-05,6e-05,0.00012,7e-05,0.00041,6e-05,8e-05,0.0001,0.00017,0.00043,6e-05,7e-05,9e-05,0.00019,0.00092
+75250,1.0,1e-05,1e-05,0.00375,1e-05,1e-05,0.04994,0.047,1e-05,0.00247,1e-05,0.00049,1e-05,2e-05,2e-05,6e-05,7e-05,2e-05,1e-05,3e-05,1e-05,0.00022,4e-05,1e-05,4e-05,0.00012,0.00041,0.0,1e-05,0.00011,0.00023,6e-05,0.00076,0.00012,0.00012,0.00019,0.00032,0.00086,0.00012,0.00013,0.00018,0.00043,0.04178
diff --git a/autosklearn/metalearning/files/precision_macro_multiclass.classification_sparse/feature_runstatus.arff b/autosklearn/metalearning/files/precision_macro_multiclass.classification_sparse/feature_runstatus.arff
index 0034ad2bd6..5fff2dfed3 100644
--- a/autosklearn/metalearning/files/precision_macro_multiclass.classification_sparse/feature_runstatus.arff
+++ b/autosklearn/metalearning/files/precision_macro_multiclass.classification_sparse/feature_runstatus.arff
@@ -4,6 +4,7 @@
 @ATTRIBUTE repetition NUMERIC
 @ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other}
@@ -21,6 +22,11 @@
 @ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other}
@@ -37,162 +43,138 @@
 @ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other}
 
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diff --git a/autosklearn/metalearning/files/precision_macro_multiclass.classification_sparse/feature_values.arff b/autosklearn/metalearning/files/precision_macro_multiclass.classification_sparse/feature_values.arff
index a60400e3ce..736dc64321 100644
--- a/autosklearn/metalearning/files/precision_macro_multiclass.classification_sparse/feature_values.arff
+++ b/autosklearn/metalearning/files/precision_macro_multiclass.classification_sparse/feature_values.arff
@@ -13,7 +13,6 @@
 @ATTRIBUTE KurtosisMean NUMERIC
 @ATTRIBUTE KurtosisMin NUMERIC
 @ATTRIBUTE KurtosisSTD NUMERIC
-@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC
 @ATTRIBUTE LogDatasetRatio NUMERIC
 @ATTRIBUTE LogInverseDatasetRatio NUMERIC
 @ATTRIBUTE LogNumberOfFeatures NUMERIC
@@ -42,160 +41,135 @@
 @ATTRIBUTE SymbolsSum NUMERIC
 
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diff --git a/autosklearn/metalearning/files/precision_micro_binary.classification_dense/algorithm_runs.arff b/autosklearn/metalearning/files/precision_micro_binary.classification_dense/algorithm_runs.arff
index 6db76d33c4..634a83e5c7 100644
--- a/autosklearn/metalearning/files/precision_micro_binary.classification_dense/algorithm_runs.arff
+++ b/autosklearn/metalearning/files/precision_micro_binary.classification_dense/algorithm_runs.arff
@@ -7,130 +7,135 @@
 @ATTRIBUTE runstatus {ok, timeout, memout, not_applicable, crash, other}
 
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diff --git a/autosklearn/metalearning/files/precision_micro_binary.classification_dense/configurations.csv b/autosklearn/metalearning/files/precision_micro_binary.classification_dense/configurations.csv
index 14ccf74d94..85e767fd63 100644
--- a/autosklearn/metalearning/files/precision_micro_binary.classification_dense/configurations.csv
+++ b/autosklearn/metalearning/files/precision_micro_binary.classification_dense/configurations.csv
@@ -1,128 +1,133 @@
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-27,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.4426097579931897,None,0.0,16,4,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,mean,robust_scaler,,,0.7407323456920554,0.0804756921037867,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,
-28,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5607168729746175,None,0.0,2,3,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.009810589644392154,most_frequent,quantile_transformer,1132,normal,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.06211089008167909,fdr,chi2
-29,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9333237919291875,None,0.0,8,3,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,
-30,none,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.9728532763078892,None,0.0,13,9,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,none,,,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6651071190864879,False,,,,,,,,,,,,,,,
-31,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9561075301148197,None,0.0,6,2,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,
-32,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.557321265077371,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-33,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,1.153931482107762e-10,0.011666190464009397,auto,255,None,29,190,18,loss,1e-07,0.147868307124396,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,
-34,none,adaboost,SAMME,0.6255555220658239,6,446,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.008833097306364968,most_frequent,none,,,,,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,10,None,4,17,1.0,76,,,,,
-35,none,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.563056219822946,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.32793677336996485,most_frequent,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-36,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,3.6797768538445263e-09,0.05511004217375807,auto,255,None,72,62,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,robust_scaler,,,0.7241481576763391,0.0501033899901458,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,36,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-37,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.7521969592385672,None,0.0,9,5,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.020402264943872365,median,robust_scaler,,,0.737227213630259,0.20752747663727789,fast_ica,,,,,,,,,,,parallel,exp,312,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-38,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.887898394612674,None,0.0,10,19,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,robust_scaler,,,0.8350698288864961,0.2347163538066168,extra_trees_preproc_for_classification,False,entropy,None,0.49795108918064074,None,0.0,13,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-39,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,1.6343482806592892e-09,0.19537524572679268,auto,255,None,32,29,13,loss,1e-07,0.05061738224302791,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-40,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7088164344533493,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,mean,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,
-41,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,1.943186107984505e-07,0.08852455702036988,auto,255,None,5,72,19,loss,1e-07,0.039837460762271175,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.01591265419367627,median,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,
-42,weighting,adaboost,SAMME,0.5210651042094924,10,401,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,standardize,,,,,feature_agglomeration,,,,,,,,,,,,,,,euclidean,complete,89,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-43,none,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0686374607394679,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,minmax,,,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.8490260132946401,False,,,,,,,,,,,,,,,
-44,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.6931271368243409,None,0.0,11,10,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.362940004438799,most_frequent,robust_scaler,,,0.9177587796305063,0.17084038458597867,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,30.491514144623952,mutual_info,,,
-45,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.9398869869697849,None,0.0,20,15,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,normalize,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,269,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-46,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,92.66144306787051,,,0.04654639302091653,rbf,-1,True,0.0022231544280610005,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.005194838694455963,mean,none,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,15,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-47,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2681.998807317996,-0.9558745731731211,,0.0011299948421773457,sigmoid,-1,False,1.5292507354478086e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-48,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.8095522998548572,None,0.0,17,10,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.031940595071539304,mean,quantile_transformer,1563,normal,,,extra_trees_preproc_for_classification,False,entropy,None,0.9330145598603196,None,0.0,18,10,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-49,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,26.502259985828186,,,9.475662388405154e-05,rbf,-1,False,0.00019690635968217866,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-50,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,2.5360196477156387e-07,0.011018756408558218,auto,255,None,40,122,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.00011749164080669445,most_frequent,quantile_transformer,757,uniform,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,5.097834697156853,False,True,1,squared_hinge,ovr,l1,0.053877834919477424,,,,,,,,,,,,,,,,,,,,,,
-51,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5255991794296913,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-52,weighting,decision_tree,,,,,,,gini,1.7853447328017091,1.0,None,0.0,13,7,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0001265964225445088,median,normalize,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.26399878867042303,,mutual_info_classif
-53,weighting,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.7776312540090575,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0005253715056715056,mean,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,False,,,,,,,,,,,,
-54,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,1.488333818193668e-10,0.09526676471267805,auto,255,None,258,87,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,normalize,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.3561953264350478,fpr,f_classif
-55,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.9523435078770708,0.8748383232154038,4,0.2195992292488049,poly,-1,True,4.849685143365339e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,minmax,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-56,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.7610308078756552,None,0.0,1,2,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.010000000000000004,mean,robust_scaler,,,0.9205545465103644,0.20272761060743316,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-57,weighting,adaboost,SAMME.R,0.03127273017917347,10,496,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,standardize,,,,,fast_ica,,,,,,,,,,,deflation,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-58,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,151,None,,0.0003283777607180646,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.2510527431514227,mean,normalize,,,,,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,-0.96093182232312,,0.05762510964607661,sigmoid,2632,,,,,,,,,,,,,,,,,
-59,none,bernoulli_nb,,,,,0.6524090653939782,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.005164169126808402,most_frequent,quantile_transformer,16,normal,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.033082145443631535,False,True,1,squared_hinge,ovr,l1,0.002615020898325393,,,,,,,,,,,,,,,,,,,,,,
-60,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.6453555269772124,None,0.0,1,4,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,normalize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-61,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,940.9437776449834,,,1.5839338084625942,rbf,-1,False,0.023072781592565438,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,none,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,77.25763685070707,mutual_info,,,
-62,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,220,auto,,0.0013824567992319003,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00021328932075284388,mean,minmax,,,,,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.039972474835314076,,0.01017810882899714,sigmoid,295,,,,,,,,,,,,,,,,,
-63,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.7326858450533382,0.02513634776687268,2,0.8635510319471909,poly,-1,True,0.0014598272110141993,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.06447266871225255,most_frequent,quantile_transformer,1000,normal,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,79.01961823033078,f_classif,,,
-64,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,29.55731828912983,False,True,1,squared_hinge,ovr,l2,0.0011332486951954968,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,
-65,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,1.1387795876755026e-06,0.12245605190343567,auto,255,None,58,7,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,robust_scaler,,,0.79305915648885,0.1841487406876042,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-66,none,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.563056219822946,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.32793677336996485,most_frequent,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-67,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,0.2638627266631576,0.022360820165005443,auto,255,None,171,6,19,loss,1e-07,0.016209060012924756,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.008763072395677399,mean,standardize,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,78,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-68,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,925.3053743248586,False,True,1,squared_hinge,ovr,l2,0.02764481198395717,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.3850718018366796,most_frequent,quantile_transformer,786,uniform,,,feature_agglomeration,,,,,,,,,,,,,,,cosine,average,156,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-69,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7573078019937971,None,0.0,2,5,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,robust_scaler,,,0.7265312295801113,0.28332515882236425,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.09660564590146253,fpr,f_classif
-70,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.005753626505113041,False,True,squared_hinge,0.00924401505172238,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.3192271696445429,median,quantile_transformer,913,normal,,,kernel_pca,,,,,,,,,,,,,,,,,,,,,,cosine,1963,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-71,none,adaboost,SAMME.R,0.22802213935525836,6,284,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,robust_scaler,,,0.7468645381288924,0.22838821662367073,feature_agglomeration,,,,,,,,,,,,,,,cosine,average,128,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-72,none,adaboost,SAMME,0.1280458968526125,7,125,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,robust_scaler,,,0.7156460334356771,0.2981902862926971,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,65.16171885308417,chi2,,,
-73,none,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9931779675565967,None,0.0,18,17,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,robust_scaler,,,0.75,0.2945917010472818,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,
-74,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.08646118576686276,None,0.0,4,9,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,most_frequent,quantile_transformer,959,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-75,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,1.2430504242164082e-09,0.03359568307446533,auto,255,None,1609,29,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,standardize,,,,,feature_agglomeration,,,,,,,,,,,,,,,cosine,complete,40,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-76,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7.491039171224502,True,True,squared_hinge,2.8058138048327884e-05,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,minmax,,,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.7754372809893839,False,True,1,squared_hinge,ovr,l1,3.9643594596766314e-05,,,,,,,,,,,,,,,,,,,,,,
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-79,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.933900673392165,None,0.0,7,4,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,minmax,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-80,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1661.2274747409913,,,0.025821208852387936,rbf,-1,False,0.0017011435104921824,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,none,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,306,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-81,none,adaboost,SAMME,0.011233995624432622,9,477,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.018370622484682127,mean,standardize,,,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6039710338898471,False,,,,,,,,,,,,,,,
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-83,none,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.563056219822946,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.32793677336996485,most_frequent,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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-85,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.593728171200453,None,0.0,7,20,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.02830941310359363,mean,normalize,,,,,extra_trees_preproc_for_classification,False,entropy,None,0.7389446586281172,None,0.0,20,11,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-86,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,1,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.019797877010737826,mean,normalize,,,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.8239678741128165,False,,,,,,,,,,,,,,,
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-88,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,38,auto,,0.00019398431171606024,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010000000000000004,median,quantile_transformer,1440,normal,,,extra_trees_preproc_for_classification,True,gini,None,0.5126639956845847,None,0.0,16,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-89,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5722917944959481,None,0.0,1,14,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,
-90,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.26356754187780895,None,0.0,4,3,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010966178401758774,mean,robust_scaler,,,0.7817043861798886,0.24371720112407158,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
-91,weighting,adaboost,SAMME.R,0.13547552520095074,4,62,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.011851011202809002,most_frequent,standardize,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4888776875314595,fpr,chi2
-92,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,80.0028902468169,False,True,1,squared_hinge,ovr,l2,0.0024481999829574722,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.15047970487774687,mean,quantile_transformer,1130,normal,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,1.1912646635544712,False,True,1,squared_hinge,ovr,l1,0.00014314728486450857,,,,,,,,,,,,,,,,,,,,,,
-93,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.22879311080499845,None,0.0,8,4,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.014320022547615783,median,minmax,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.238630703440461,mutual_info,,,
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-96,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.14148638702297855,None,0.0,2,6,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.0038499650338378643,most_frequent,quantile_transformer,1759,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-97,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,1.1522414705935405e-10,0.6132800183073315,auto,255,None,1607,67,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010096911249562407,mean,robust_scaler,,,0.7663493326967942,0.136094217337434,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-98,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2746221958279041,False,True,1,squared_hinge,ovr,l2,6.546077664669006e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,quantile_transformer,1054,normal,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,
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-100,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.825844582229201,None,0.0,12,9,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0008422508222149647,median,robust_scaler,,,0.8658803880857064,0.2528423686293341,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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-103,none,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,128,manual,0.8574560771111307,0.010886139381599514,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.24115007944216788,most_frequent,normalize,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,62.26514170991452,mutual_info,,,
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-105,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,1.430083734915855e-09,0.08779621406981941,auto,255,None,17,20,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.29749837779115257,mean,robust_scaler,,,0.7236445277814435,0.25,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
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-124,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1561.1002600303584,,,0.0024048551704194507,rbf,-1,False,0.0001936214667496851,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,minmax,,,,,fast_ica,,,,,,,,,,,parallel,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-125,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.087313242780732,0.0636709914987994,3,0.004481917125024585,poly,-1,False,0.01848880618941367,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.002136015035740733,mean,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-126,weighting,adaboost,SAMME.R,1.696588612003168,9,455,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,minmax,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,82.18726095926662,chi2,,,
-127,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.6466927589470762,None,0.0,11,2,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.009672465311659264,most_frequent,standardize,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,369,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+1,none,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1514.059969351301,False,True,1,squared_hinge,ovr,l2,0.002448814309966599,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,standardize,,,,,extra_trees_preproc_for_classification,True,entropy,None,0.8221481751476923,None,0.0,2,3,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+3,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5707458022222313,None,0.0,3,12,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,minmax,,,,,extra_trees_preproc_for_classification,False,gini,None,0.5651056128131684,None,0.0,9,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+6,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.7283683762613216,None,0.0,12,20,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,median,standardize,,,,,fast_ica,,,,,,,,,,,deflation,exp,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+8,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,114,manual,0.11778631707605758,0.0029112721339713465,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.015582195257240751,median,normalize,,,,,kernel_pca,,,,,,,,,,,,,,,,,,,0.42909623253327767,3,0.25407612000343777,poly,1689,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+11,weighting,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,2,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.3865962574104293,mean,normalize,,,,,fast_ica,,,,,,,,,,,deflation,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+13,weighting,adaboost,SAMME,0.17983222024694556,4,357,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0008898781197407956,mean,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+15,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.21772092350860472,None,0.0,14,17,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.0020488212891693275,median,standardize,,,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,33.74257606530722,False,True,1,squared_hinge,ovr,l1,8.740452966205111e-05,,,,,,,,,,,,,,,,,,,,,,
+18,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,2.4243922131268786e-05,0.07570328162448307,auto,255,None,71,42,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,robust_scaler,,,0.8898729094898769,0.12138152991327475,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,94.96086286583851,chi2,,,
+20,none,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.9866058571747695,None,0.0,16,9,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.1680676743605393,most_frequent,robust_scaler,,,0.8840072797399463,0.08535534311393947,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,False,,,,,,,,,,,,
+23,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,0.00022373024203887573,0.027149343206135476,auto,255,None,49,13,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.00777614320416325,most_frequent,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,
+27,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.41025844265119926,None,0.0,18,15,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,median,quantile_transformer,1911,normal,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,False,,,,,,,,,,,,
+29,none,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.563056219822946,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.32793677336996485,most_frequent,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+31,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,8.757412830636845e-06,0.10095376320667454,auto,255,None,3,2,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.015401766535365336,median,quantile_transformer,647,normal,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,
+32,none,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,174,manual,0.21501892897746921,2.0347110826262054e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.013754893527625522,most_frequent,normalize,,,,,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,-0.019221513778605592,3,0.10190948951339239,poly,2521,,,,,,,,,,,,,,,,,
+35,none,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9.608707084865884,False,True,1,squared_hinge,ovr,l2,0.008086910150491696,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.020901889243957545,median,minmax,,,,,kitchen_sinks,,,,,,,,,,,,,,,,,,,,,,,,0.5694399784333516,3230,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+39,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.6799215694343422,None,0.0,1,5,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.004724310659953132,most_frequent,quantile_transformer,1037,normal,,,fast_ica,,,,,,,,,,,parallel,exp,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+41,none,extra_trees,,,,,,,,,,,,,,,True,gini,None,0.5033866291997137,None,0.0,2,14,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,minmax,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+43,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.9018061166635376,None,0.0,7,9,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,quantile_transformer,1802,normal,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,84.54647012968938,chi2,,,
+44,none,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l2,2.858526169098402e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0007827505640681123,median,standardize,,,,,extra_trees_preproc_for_classification,False,entropy,None,0.3443310459451119,None,0.0,3,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+49,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.049504283446131e-05,False,,0.0004438773852753879,True,2.4110816673432424e-07,constant,log,elasticnet,,0.01321814853186568,one_hot_encoding,minority_coalescer,0.01510416593631535,most_frequent,none,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,66.65285268958851,chi2,,,
+52,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,6.421059381158897e-08,0.11261253191288709,auto,255,None,45,28,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,
+55,none,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.9822179211645446,None,0.0,14,8,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.013966871874533052,most_frequent,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,
+58,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5214197490559657,None,0.0,1,2,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.00826182798588248,mean,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,
+63,none,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,130,manual,0.5446276346297756,0.00846307015171434,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.005736348762561667,median,quantile_transformer,973,uniform,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.08483615095863663,fdr,chi2
+65,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,1.2822815440468518e-10,0.12922700208645135,auto,255,None,31,44,13,loss,1e-07,0.08789566421608311,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.012230423571314038,mean,robust_scaler,,,0.71734690066056,0.20283418288016852,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,
+68,weighting,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,minmax,,,,,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+75,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13.284077389890939,-0.2621334760621076,5,0.9060908941898536,poly,-1,False,0.06323853917206591,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0011743021592446861,most_frequent,normalize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,
+82,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,3.79982070767762e-10,0.10307173798074214,auto,255,None,30,25,6,loss,1e-07,0.09705336338563596,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.004889186641013006,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+84,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.5507560981489436,None,0.0,7,4,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,
+91,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,0.0006322356877253852,0.02155479872417127,auto,255,None,6,15,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
+93,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5012519528536651,None,0.0,1,8,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,mean,standardize,,,,,feature_agglomeration,,,,,,,,,,,,,,,cosine,complete,323,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+96,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5602498483292023,None,0.0,1,3,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.3195602228752535,mean,standardize,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.09782235573464702,fdr,chi2
+99,weighting,adaboost,SAMME,0.17983222024694556,4,357,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0008898781197407956,mean,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+105,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.268699692198443e-05,True,,4.1225573912733725e-06,True,,constant,log,l2,,4.729985534114764e-05,no_encoding,no_coalescense,,median,quantile_transformer,246,normal,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,20.73090458774975,chi2,,,
+106,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,2.047681354714616e-06,0.016073071519249763,auto,255,None,127,60,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.005262156195293648,median,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,
+110,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0021814768111094564,False,True,hinge,8.726644999887521e-05,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,none,,,,,kitchen_sinks,,,,,,,,,,,,,,,,,,,,,,,,0.0010909910768059797,970,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+111,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.4990576176544739,None,0.0,6,6,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
+114,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,83,auto,,0.024875973599406742,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,normalize,,,,,kitchen_sinks,,,,,,,,,,,,,,,,,,,,,,,,0.041059688084044955,7269,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+115,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.463780563279276,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.011474025594258192,mean,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,
+116,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,236,None,,0.08905291537442941,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,normalize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,
+121,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.08836825866738698,None,0.0,5,19,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0003943043347109662,median,robust_scaler,,,0.7564808439539721,0.23469821956116474,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2206955933275169,fdr,f_classif
+124,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.3955965159441456,None,0.0,3,7,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,median,minmax,,,,,extra_trees_preproc_for_classification,False,entropy,None,0.029027785145223306,None,0.0,17,7,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+126,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,1.4547955251450584e-08,0.03465981099276111,auto,255,None,10,4,7,loss,1e-07,0.12579700249284784,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.001559260617305279,most_frequent,normalize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,False,,,,,,,,,,,,
+130,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,9.471664460969401e-09,0.05921990503832894,auto,255,None,1357,22,15,loss,1e-07,0.10690431328779236,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.08690713722954983,most_frequent,robust_scaler,,,0.9305626247167209,0.22989015341001506,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.33284956824843637,fwe,f_classif
+136,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,4.136413423055797e-09,0.13801185065309887,auto,255,None,186,1,15,loss,1e-07,0.042836514118739795,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,none,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,372,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+138,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,6.611729692973355e-06,0.057217701969255366,auto,255,None,8,1,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,minmax,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+139,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,10.855406135042964,0.9747671863774952,5,0.2387142708486043,poly,-1,False,0.015747719198070633,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.1749714579410719,median,minmax,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,53.581101959413125,mutual_info,,,
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+352,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.002017899394165588,False,,0.0005911937660372945,True,0.17801822098174286,constant,log,elasticnet,,0.00041682529601986016,no_encoding,no_coalescense,,median,quantile_transformer,1335,normal,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0642825062494559,fpr,chi2
+354,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.47037910442880493,None,0.0,10,3,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.17511596636550678,mean,none,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,87.25836994493127,mutual_info,,,
+362,none,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,147,None,,0.020167732354570436,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,minmax,,,,,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.39502590219018424,rbf,117,,,,,,,,,,,,,,,,,
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+369,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.9347805195624181,None,0.0,2,19,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.1594813217854149,mean,normalize,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,52.65009549586377,f_classif,,,
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+379,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,1.2940289605295536e-09,0.15591343717459372,auto,255,None,7,59,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0005549095909238961,mean,quantile_transformer,297,uniform,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,58.10560700979111,chi2,,,
+388,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,53.67009896349757,,,0.03687753298495132,rbf,-1,False,0.028791286245157307,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.047859755696856916,mean,standardize,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,70,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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+399,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.5864398404787432,None,0.0,1,14,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0070663750850608895,most_frequent,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
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+408,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.6642356723981304,False,True,1,squared_hinge,ovr,l2,7.912577426155621e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.08464913801800422,most_frequent,normalize,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,53.35800942135187,chi2,,,
+413,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,7.194851113809193e-10,0.013882834218943657,auto,255,None,18,2,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0005124238915083231,mean,quantile_transformer,948,normal,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,60.45885911036194,f_classif,,,
+415,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,0.00037966471646911845,0.016422945536911454,auto,255,None,10,56,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.002419439132687246,median,quantile_transformer,828,uniform,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+422,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.36374112587320206,None,0.0,2,10,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010870084126959173,most_frequent,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,
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+430,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5321814610661173,None,0.0,1,3,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,standardize,,,,,extra_trees_preproc_for_classification,True,gini,None,0.5,None,0.0,1,15,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+435,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.44903177399085925,None,0.0,2,17,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,mean,robust_scaler,,,0.7812408516672696,0.12791233577400205,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,
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+462,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6,1,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.017471096998391875,median,none,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,67.7801145492284,f_classif,,,
diff --git a/autosklearn/metalearning/files/precision_micro_binary.classification_dense/description.txt b/autosklearn/metalearning/files/precision_micro_binary.classification_dense/description.txt
index 949e91c3cc..b229283604 100644
--- a/autosklearn/metalearning/files/precision_micro_binary.classification_dense/description.txt
+++ b/autosklearn/metalearning/files/precision_micro_binary.classification_dense/description.txt
@@ -1,8 +1,9 @@
 features_cutoff_time: 3600
 features_cutoff_memory: 3072
-number_of_feature_steps: 35
+number_of_feature_steps: 42
 feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances
 feature_step LogNumberOfInstances: LogNumberOfInstances
+feature_step NumberOfClasses: NumberOfClasses
 feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures
 feature_step LogNumberOfFeatures: LogNumberOfFeatures
 feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues
@@ -20,6 +21,11 @@ feature_step DatasetRatio: DatasetRatio, LogDatasetRatio
 feature_step LogDatasetRatio: LogDatasetRatio
 feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio
 feature_step LogInverseDatasetRatio: LogInverseDatasetRatio
+feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD
+feature_step ClassProbabilityMin: ClassProbabilityMin
+feature_step ClassProbabilityMax: ClassProbabilityMax
+feature_step ClassProbabilityMean: ClassProbabilityMean
+feature_step ClassProbabilitySTD: ClassProbabilitySTD
 feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum
 feature_step SymbolsMin: SymbolsMin
 feature_step SymbolsMax: SymbolsMax
@@ -36,11 +42,12 @@ feature_step SkewnessMin: SkewnessMin
 feature_step SkewnessMax: SkewnessMax
 feature_step SkewnessMean: SkewnessMean
 feature_step SkewnessSTD: SkewnessSTD
-features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
+feature_step ClassEntropy: ClassEntropy
+features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy
 features_stochastic: 
-default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
+default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy
 
-algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127
+algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235,236,237,238,239,240,241,242,243,244,245,246,247,248,249,250,251,252,253,254,255,256,257,258,259,260,261,262,263,264,265,266,267,268,269,270,271,272,273,274,275,276,277,278,279,280,281,282,283,284,285,286,287,288,289,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,312,313,314,315,316,317,318,319,320,321,322,323,324,325,326,327,328,329,330,331,332,333,334,335,336,337,338,339,340,341,342,343,344,345,346,347,348,349,350,351,352,353,354,355,356,357,358,359,360,361,362,363,364,365,366,367,368,369,370,371,372,373,374,375,376,377,378,379,380,381,382,383,384,385,386,387,388,389,390,391,392,393,394,395,396,397,398,399,400,401,402,403,404,405,406,407,408,409,410,411,412,413,414,415,416,417,418,419,420,421,422,423,424,425,426,427,428,429,430,431,432,433,434,435,436,437,438,439,440,441,442,443,444,445,446,447,448,449,450,451,452,453,454,455,456,457,458,459,460,461,462,463,464
 algorithms_stochastic: 
 performance_measures: precision_micro
 performance_type: solution_quality
diff --git a/autosklearn/metalearning/files/precision_micro_binary.classification_dense/feature_costs.arff b/autosklearn/metalearning/files/precision_micro_binary.classification_dense/feature_costs.arff
index 0e17e20d22..8512fd9275 100644
--- a/autosklearn/metalearning/files/precision_micro_binary.classification_dense/feature_costs.arff
+++ b/autosklearn/metalearning/files/precision_micro_binary.classification_dense/feature_costs.arff
@@ -4,6 +4,7 @@
 @ATTRIBUTE repetition NUMERIC
 @ATTRIBUTE NumberOfInstances NUMERIC
 @ATTRIBUTE LogNumberOfInstances NUMERIC
+@ATTRIBUTE NumberOfClasses NUMERIC
 @ATTRIBUTE NumberOfFeatures NUMERIC
 @ATTRIBUTE LogNumberOfFeatures NUMERIC
 @ATTRIBUTE MissingValues NUMERIC
@@ -21,6 +22,11 @@
 @ATTRIBUTE LogDatasetRatio NUMERIC
 @ATTRIBUTE InverseDatasetRatio NUMERIC
 @ATTRIBUTE LogInverseDatasetRatio NUMERIC
+@ATTRIBUTE ClassOccurences NUMERIC
+@ATTRIBUTE ClassProbabilityMin NUMERIC
+@ATTRIBUTE ClassProbabilityMax NUMERIC
+@ATTRIBUTE ClassProbabilityMean NUMERIC
+@ATTRIBUTE ClassProbabilitySTD NUMERIC
 @ATTRIBUTE NumSymbols NUMERIC
 @ATTRIBUTE SymbolsMin NUMERIC
 @ATTRIBUTE SymbolsMax NUMERIC
@@ -37,162 +43,138 @@
 @ATTRIBUTE SkewnessMax NUMERIC
 @ATTRIBUTE SkewnessMean NUMERIC
 @ATTRIBUTE SkewnessSTD NUMERIC
+@ATTRIBUTE ClassEntropy NUMERIC
 
 @DATA
-75123,1.0,1e-05,1e-05,1e-05,1e-05,0.00253,0.00221,1e-05,0.00024,3e-05,0.00011,0.0,2e-05,2e-05,5e-05,8e-05,2e-05,1e-05,7e-05,1e-05,0.00072,1e-05,1e-05,0.00019,0.00041,0.00011,0.00061,0.00012,9e-05,0.00015,0.00025,0.00067,0.00014,0.00011,0.00016,0.00027
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-75217,1.0,1e-05,1e-05,2e-05,1e-05,0.00196,0.00158,1e-05,0.00026,2e-05,0.00016,1e-05,3e-05,3e-05,7e-05,0.0001,3e-05,1e-05,7e-05,2e-05,0.00089,1e-05,1e-05,0.00022,0.00053,0.00013,0.00074,9e-05,0.0001,0.00019,0.00036,0.00065,0.00013,0.0001,0.00011,0.00031
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+75235,1.0,1e-05,1e-05,0.0003,2e-05,1e-05,0.00461,0.00385,2e-05,0.00055,2e-05,0.00026,1e-05,3e-05,3e-05,0.0001,0.00011,3e-05,1e-05,5e-05,2e-05,0.00026,3e-05,1e-05,8e-05,0.00014,0.00075,1e-05,1e-05,0.0002,0.00043,0.00011,0.00127,0.00017,0.0002,0.00029,0.00061,0.0008,0.00015,0.00012,0.00016,0.00038,0.00242
+75236,1.0,1e-05,1e-05,0.0001,1e-05,1e-05,0.00218,0.00118,2e-05,0.00065,3e-05,0.00041,1e-05,3e-05,3e-05,9e-05,9e-05,4e-05,1e-05,7e-05,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.0006,1e-05,1e-05,0.00021,0.00032,6e-05,0.00043,7e-05,7e-05,9e-05,0.00019,0.00062,7e-05,8e-05,0.0001,0.00037,0.00048
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diff --git a/autosklearn/metalearning/files/precision_micro_binary.classification_dense/feature_runstatus.arff b/autosklearn/metalearning/files/precision_micro_binary.classification_dense/feature_runstatus.arff
index 0034ad2bd6..5fff2dfed3 100644
--- a/autosklearn/metalearning/files/precision_micro_binary.classification_dense/feature_runstatus.arff
+++ b/autosklearn/metalearning/files/precision_micro_binary.classification_dense/feature_runstatus.arff
@@ -4,6 +4,7 @@
 @ATTRIBUTE repetition NUMERIC
 @ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other}
@@ -21,6 +22,11 @@
 @ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other}
@@ -37,162 +43,138 @@
 @ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other}
 
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diff --git a/autosklearn/metalearning/files/precision_micro_binary.classification_dense/feature_values.arff b/autosklearn/metalearning/files/precision_micro_binary.classification_dense/feature_values.arff
index a60400e3ce..736dc64321 100644
--- a/autosklearn/metalearning/files/precision_micro_binary.classification_dense/feature_values.arff
+++ b/autosklearn/metalearning/files/precision_micro_binary.classification_dense/feature_values.arff
@@ -13,7 +13,6 @@
 @ATTRIBUTE KurtosisMean NUMERIC
 @ATTRIBUTE KurtosisMin NUMERIC
 @ATTRIBUTE KurtosisSTD NUMERIC
-@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC
 @ATTRIBUTE LogDatasetRatio NUMERIC
 @ATTRIBUTE LogInverseDatasetRatio NUMERIC
 @ATTRIBUTE LogNumberOfFeatures NUMERIC
@@ -42,160 +41,135 @@
 @ATTRIBUTE SymbolsSum NUMERIC
 
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diff --git a/autosklearn/metalearning/files/precision_micro_binary.classification_sparse/algorithm_runs.arff b/autosklearn/metalearning/files/precision_micro_binary.classification_sparse/algorithm_runs.arff
index 09dee5548f..4ab2e22d87 100644
--- a/autosklearn/metalearning/files/precision_micro_binary.classification_sparse/algorithm_runs.arff
+++ b/autosklearn/metalearning/files/precision_micro_binary.classification_sparse/algorithm_runs.arff
@@ -7,95 +7,133 @@
 @ATTRIBUTE runstatus {ok, timeout, memout, not_applicable, crash, other}
 
 @DATA
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diff --git a/autosklearn/metalearning/files/precision_micro_binary.classification_sparse/configurations.csv b/autosklearn/metalearning/files/precision_micro_binary.classification_sparse/configurations.csv
index 75662496d2..36fd991e13 100644
--- a/autosklearn/metalearning/files/precision_micro_binary.classification_sparse/configurations.csv
+++ b/autosklearn/metalearning/files/precision_micro_binary.classification_sparse/configurations.csv
@@ -1,93 +1,131 @@
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-14,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.6039096182414764,None,0.0,2,20,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.1939609823704447,most_frequent,robust_scaler,,,0.7331327008874781,0.29747488162877694,extra_trees_preproc_for_classification,True,entropy,None,0.33401603690569853,None,0.0,4,4,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-15,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7865652301288171,None,0.0,1,4,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,median,robust_scaler,,,0.8623635641193496,0.13298297278730625,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-16,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.905401147837377,-0.32832122529048147,3,1.001896309040499,poly,-1,False,5.1523484258030785e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-17,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.3304182089730454,None,0.0,1,11,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.015634546384927752,median,quantile_transformer,332,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-18,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5010952533165973,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.005838375247601407,mean,normalize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,,
-19,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.7297122444166401,False,True,1,squared_hinge,ovr,l2,0.00023416745444541063,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.03941815152197094,most_frequent,normalize,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0973649546833947,fpr,chi2,
-20,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.4426097579931897,None,0.0,16,4,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,mean,robust_scaler,,,0.7407323456920554,0.0804756921037867,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,,
-21,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5607168729746175,None,0.0,2,3,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.009810589644392154,most_frequent,quantile_transformer,1132,normal,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.06211089008167909,fdr,chi2,
-22,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9333237919291875,None,0.0,8,3,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,,
-23,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.557321265077371,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-24,none,adaboost,SAMME,0.6255555220658239,6,446,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.008833097306364968,most_frequent,none,,,,,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,10,None,4,17,1.0,76,,,,,,
-25,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.7772914874049878,None,0.0,10,15,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00015285482513309234,mean,robust_scaler,,,0.8373404000074934,0.14303151080827242,extra_trees_preproc_for_classification,True,entropy,None,0.5021713909357703,None,0.0,13,16,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-26,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7422641456052917,None,0.0,8,15,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.005561351266169067,most_frequent,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-27,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.887898394612674,None,0.0,10,19,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,robust_scaler,,,0.8350698288864961,0.2347163538066168,extra_trees_preproc_for_classification,False,entropy,None,0.49795108918064074,None,0.0,13,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-28,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7088164344533493,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,mean,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,,
-29,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,10,1,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0022606253047811435,median,quantile_transformer,1247,normal,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,25.327793585266683,chi2,,,,
-30,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,24.786439230569382,,,0.03411197494635044,rbf,-1,True,0.003003351499585181,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0007994838629325874,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-31,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2681.998807317996,-0.9558745731731211,,0.0011299948421773457,sigmoid,-1,False,1.5292507354478086e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-32,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.8095522998548572,None,0.0,17,10,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.031940595071539304,mean,quantile_transformer,1563,normal,,,extra_trees_preproc_for_classification,False,entropy,None,0.9330145598603196,None,0.0,18,10,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-33,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,26.502259985828186,,,9.475662388405154e-05,rbf,-1,False,0.00019690635968217866,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-34,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.339600708038189,False,True,1,squared_hinge,ovr,l2,9.572641158475304e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,quantile_transformer,1193,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-35,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5255991794296913,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-36,weighting,decision_tree,,,,,,,gini,1.7853447328017091,1.0,None,0.0,13,7,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0001265964225445088,median,normalize,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.26399878867042303,,mutual_info_classif,
-37,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.7610308078756552,None,0.0,1,2,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.010000000000000004,mean,robust_scaler,,,0.9205545465103644,0.20272761060743316,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-38,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,249.12499213127998,,,0.0005283374995918348,rbf,-1,False,0.0016932707039261864,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.48910068367909115,most_frequent,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-39,none,bernoulli_nb,,,,,0.6524090653939782,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.005164169126808402,most_frequent,quantile_transformer,16,normal,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.033082145443631535,False,True,1,squared_hinge,ovr,l1,0.002615020898325393,,,,,,,,,,,,,,,,,,,,,
-40,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.6453555269772124,None,0.0,1,4,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,normalize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-41,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.20911875507660596,None,0.0,12,8,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,median,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,,
-42,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5138705053438934,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.14411802159506462,most_frequent,quantile_transformer,857,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-43,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,29.55731828912983,False,True,1,squared_hinge,ovr,l2,0.0011332486951954968,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,,
-44,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.37801720673725603,True,True,squared_hinge,0.0001255132393635067,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.025993178344125793,most_frequent,standardize,,,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,1.4421737618429475,False,True,1,squared_hinge,ovr,l1,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,
-45,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.10581257327556097,None,0.0,7,12,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.004219392676189224,median,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,,
-46,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.4188764793561401,None,0.0,5,5,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.010589444903605501,mean,robust_scaler,,,0.9796997860990453,0.27262832749694405,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-47,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.005753626505113041,False,True,squared_hinge,0.00924401505172238,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.3192271696445429,median,quantile_transformer,913,normal,,,kernel_pca,,,,,,,,,,,,,,cosine,1963,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-48,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.41019363453549706,None,0.0,15,2,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.0003507389224306018,median,none,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.20614098085343638,fdr,chi2,
-49,none,adaboost,SAMME,0.1280458968526125,7,125,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,robust_scaler,,,0.7156460334356771,0.2981902862926971,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,65.16171885308417,chi2,,,,
-50,none,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9931779675565967,None,0.0,18,17,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,robust_scaler,,,0.75,0.2945917010472818,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,,
-51,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.08646118576686276,None,0.0,4,9,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,most_frequent,quantile_transformer,959,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-52,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.6271213423927893,None,0.0,1,4,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.009403509495453675,mean,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,,
-53,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8.85547546146731,True,True,squared_hinge,4.0111304608748804e-05,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-54,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.34936025988397124,None,0.0,2,13,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00018601309647406987,median,robust_scaler,,,0.8634309348843472,0.26976989465367607,extra_trees_preproc_for_classification,False,entropy,None,0.48025175288975036,None,0.0,6,17,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-55,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.8793175987664956,None,0.0,10,5,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,robust_scaler,,,0.7645748130904642,0.2677156877155214,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,130.79089036625916,False,True,1,squared_hinge,ovr,l1,0.005538878421721295,,,,,,,,,,,,,,,,,,,,,
-56,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.014911374426433241,None,0.0,6,2,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,,
-57,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0016941428709087083,True,,,True,,optimal,squared_hinge,l1,,5.124265599423001e-05,no_encoding,no_coalescense,,most_frequent,quantile_transformer,461,uniform,,,extra_trees_preproc_for_classification,True,gini,None,0.31371431180257103,None,0.0,5,17,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-58,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.7591396576940418,-0.02447104417007273,2,0.7325180366342191,poly,-1,True,0.00045922794962860523,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,standardize,,,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,2.057731506094293,False,True,1,squared_hinge,ovr,l1,0.03575998247131604,,,,,,,,,,,,,,,,,,,,,
-59,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.593728171200453,None,0.0,7,20,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.02830941310359363,mean,normalize,,,,,extra_trees_preproc_for_classification,False,entropy,None,0.7389446586281172,None,0.0,20,11,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-60,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5542793596955801,None,0.0,1,3,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,most_frequent,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-61,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.46057831591617715,False,True,hinge,0.04557857428827514,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00027457445401600137,median,standardize,,,,,extra_trees_preproc_for_classification,True,gini,None,0.48190346970486964,None,0.0,17,18,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-62,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.46057831591617715,False,True,hinge,0.04557857428827514,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00027457445401600137,median,standardize,,,,,extra_trees_preproc_for_classification,True,gini,None,0.48190346970486964,None,0.0,17,18,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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+367,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.938372431886818e-05,True,True,hinge,0.0016143408148969647,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00024949978971758427,median,quantile_transformer,754,uniform,,,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,-0.22339839702350717,2,0.5718982387915436,poly,5260,,,,,,,,,,,,,,,,
+371,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4,1,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.01020427226149318,median,none,,,,,extra_trees_preproc_for_classification,True,entropy,None,0.3663460889081009,None,0.0,2,7,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
diff --git a/autosklearn/metalearning/files/precision_micro_binary.classification_sparse/description.txt b/autosklearn/metalearning/files/precision_micro_binary.classification_sparse/description.txt
index ae1b5954e0..000bc8cb75 100644
--- a/autosklearn/metalearning/files/precision_micro_binary.classification_sparse/description.txt
+++ b/autosklearn/metalearning/files/precision_micro_binary.classification_sparse/description.txt
@@ -1,8 +1,9 @@
 features_cutoff_time: 3600
 features_cutoff_memory: 3072
-number_of_feature_steps: 35
+number_of_feature_steps: 42
 feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances
 feature_step LogNumberOfInstances: LogNumberOfInstances
+feature_step NumberOfClasses: NumberOfClasses
 feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures
 feature_step LogNumberOfFeatures: LogNumberOfFeatures
 feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues
@@ -20,6 +21,11 @@ feature_step DatasetRatio: DatasetRatio, LogDatasetRatio
 feature_step LogDatasetRatio: LogDatasetRatio
 feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio
 feature_step LogInverseDatasetRatio: LogInverseDatasetRatio
+feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD
+feature_step ClassProbabilityMin: ClassProbabilityMin
+feature_step ClassProbabilityMax: ClassProbabilityMax
+feature_step ClassProbabilityMean: ClassProbabilityMean
+feature_step ClassProbabilitySTD: ClassProbabilitySTD
 feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum
 feature_step SymbolsMin: SymbolsMin
 feature_step SymbolsMax: SymbolsMax
@@ -36,11 +42,12 @@ feature_step SkewnessMin: SkewnessMin
 feature_step SkewnessMax: SkewnessMax
 feature_step SkewnessMean: SkewnessMean
 feature_step SkewnessSTD: SkewnessSTD
-features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
+feature_step ClassEntropy: ClassEntropy
+features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy
 features_stochastic: 
-default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
+default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy
 
-algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92
+algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235,236,237,238,239,240,241,242,243,244,245,246,247,248,249,250,251,252,253,254,255,256,257,258,259,260,261,262,263,264,265,266,267,268,269,270,271,272,273,274,275,276,277,278,279,280,281,282,283,284,285,286,287,288,289,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,312,313,314,315,316,317,318,319,320,321,322,323,324,325,326,327,328,329,330,331,332,333,334,335,336,337,338,339,340,341,342,343,344,345,346,347,348,349,350,351,352,353,354,355,356,357,358,359,360,361,362,363,364,365,366,367,368,369,370,371,372
 algorithms_stochastic: 
 performance_measures: precision_micro
 performance_type: solution_quality
diff --git a/autosklearn/metalearning/files/precision_micro_binary.classification_sparse/feature_costs.arff b/autosklearn/metalearning/files/precision_micro_binary.classification_sparse/feature_costs.arff
index 0e17e20d22..8512fd9275 100644
--- a/autosklearn/metalearning/files/precision_micro_binary.classification_sparse/feature_costs.arff
+++ b/autosklearn/metalearning/files/precision_micro_binary.classification_sparse/feature_costs.arff
@@ -4,6 +4,7 @@
 @ATTRIBUTE repetition NUMERIC
 @ATTRIBUTE NumberOfInstances NUMERIC
 @ATTRIBUTE LogNumberOfInstances NUMERIC
+@ATTRIBUTE NumberOfClasses NUMERIC
 @ATTRIBUTE NumberOfFeatures NUMERIC
 @ATTRIBUTE LogNumberOfFeatures NUMERIC
 @ATTRIBUTE MissingValues NUMERIC
@@ -21,6 +22,11 @@
 @ATTRIBUTE LogDatasetRatio NUMERIC
 @ATTRIBUTE InverseDatasetRatio NUMERIC
 @ATTRIBUTE LogInverseDatasetRatio NUMERIC
+@ATTRIBUTE ClassOccurences NUMERIC
+@ATTRIBUTE ClassProbabilityMin NUMERIC
+@ATTRIBUTE ClassProbabilityMax NUMERIC
+@ATTRIBUTE ClassProbabilityMean NUMERIC
+@ATTRIBUTE ClassProbabilitySTD NUMERIC
 @ATTRIBUTE NumSymbols NUMERIC
 @ATTRIBUTE SymbolsMin NUMERIC
 @ATTRIBUTE SymbolsMax NUMERIC
@@ -37,162 +43,138 @@
 @ATTRIBUTE SkewnessMax NUMERIC
 @ATTRIBUTE SkewnessMean NUMERIC
 @ATTRIBUTE SkewnessSTD NUMERIC
+@ATTRIBUTE ClassEntropy NUMERIC
 
 @DATA
-75123,1.0,1e-05,1e-05,1e-05,1e-05,0.00253,0.00221,1e-05,0.00024,3e-05,0.00011,0.0,2e-05,2e-05,5e-05,8e-05,2e-05,1e-05,7e-05,1e-05,0.00072,1e-05,1e-05,0.00019,0.00041,0.00011,0.00061,0.00012,9e-05,0.00015,0.00025,0.00067,0.00014,0.00011,0.00016,0.00027
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diff --git a/autosklearn/metalearning/files/precision_micro_binary.classification_sparse/feature_runstatus.arff b/autosklearn/metalearning/files/precision_micro_binary.classification_sparse/feature_runstatus.arff
index 0034ad2bd6..5fff2dfed3 100644
--- a/autosklearn/metalearning/files/precision_micro_binary.classification_sparse/feature_runstatus.arff
+++ b/autosklearn/metalearning/files/precision_micro_binary.classification_sparse/feature_runstatus.arff
@@ -4,6 +4,7 @@
 @ATTRIBUTE repetition NUMERIC
 @ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other}
@@ -21,6 +22,11 @@
 @ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other}
@@ -37,162 +43,138 @@
 @ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other}
 
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+75250,1.0,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,other,ok,ok,ok,ok,ok
diff --git a/autosklearn/metalearning/files/precision_micro_binary.classification_sparse/feature_values.arff b/autosklearn/metalearning/files/precision_micro_binary.classification_sparse/feature_values.arff
index a60400e3ce..736dc64321 100644
--- a/autosklearn/metalearning/files/precision_micro_binary.classification_sparse/feature_values.arff
+++ b/autosklearn/metalearning/files/precision_micro_binary.classification_sparse/feature_values.arff
@@ -13,7 +13,6 @@
 @ATTRIBUTE KurtosisMean NUMERIC
 @ATTRIBUTE KurtosisMin NUMERIC
 @ATTRIBUTE KurtosisSTD NUMERIC
-@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC
 @ATTRIBUTE LogDatasetRatio NUMERIC
 @ATTRIBUTE LogInverseDatasetRatio NUMERIC
 @ATTRIBUTE LogNumberOfFeatures NUMERIC
@@ -42,160 +41,135 @@
 @ATTRIBUTE SymbolsSum NUMERIC
 
 @DATA
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diff --git a/autosklearn/metalearning/files/precision_micro_multiclass.classification_dense/algorithm_runs.arff b/autosklearn/metalearning/files/precision_micro_multiclass.classification_dense/algorithm_runs.arff
index 6db76d33c4..634a83e5c7 100644
--- a/autosklearn/metalearning/files/precision_micro_multiclass.classification_dense/algorithm_runs.arff
+++ b/autosklearn/metalearning/files/precision_micro_multiclass.classification_dense/algorithm_runs.arff
@@ -7,130 +7,135 @@
 @ATTRIBUTE runstatus {ok, timeout, memout, not_applicable, crash, other}
 
 @DATA
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diff --git a/autosklearn/metalearning/files/precision_micro_multiclass.classification_dense/configurations.csv b/autosklearn/metalearning/files/precision_micro_multiclass.classification_dense/configurations.csv
index 14ccf74d94..85e767fd63 100644
--- a/autosklearn/metalearning/files/precision_micro_multiclass.classification_dense/configurations.csv
+++ b/autosklearn/metalearning/files/precision_micro_multiclass.classification_dense/configurations.csv
@@ -1,128 +1,133 @@
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-1,weighting,adaboost,SAMME,0.015984291290723266,7,184,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010000000000000004,median,standardize,,,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.9333242689646983,False,,,,,,,,,,,,,,,
-2,none,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.9525269450493825,None,0.0,4,20,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,standardize,,,,,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-3,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,101,auto,,1.1004704874013712e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.009420818612537619,most_frequent,normalize,,,,,kitchen_sinks,,,,,,,,,,,,,,,,,,,,,,,,0.1495664841849168,2334,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-4,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,23916.78290516781,False,True,1,squared_hinge,ovr,l2,0.0006655607726658531,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.008808715242823137,median,none,,,,,kernel_pca,,,,,,,,,,,,,,,,,,,0.2633153190620261,2,0.004770332714291669,poly,1483,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-5,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,2,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,none,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,94.23876232993393,mutual_info,,,
-6,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.933900673392165,None,0.0,7,4,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,minmax,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-7,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.3700222208089159,None,0.0,14,19,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,minmax,,,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,1.0533068843554318,False,True,1,squared_hinge,ovr,l1,0.00618591414610778,,,,,,,,,,,,,,,,,,,,,,
-8,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5238814331135578,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,median,none,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,28,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-9,none,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13,None,,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,none,,,,,feature_agglomeration,,,,,,,,,,,,,,,euclidean,average,74,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-10,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.5244313211416313,None,0.0,1,2,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.454300236339658,most_frequent,robust_scaler,,,0.7858830918128734,0.2705174720960696,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-11,weighting,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0006294780694492238,most_frequent,standardize,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.063715750591364,fdr,chi2
-12,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.541827838541001,True,True,squared_hinge,0.0001129851063838815,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,standardize,,,,,extra_trees_preproc_for_classification,True,entropy,None,0.5533459595240017,None,0.0,18,4,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-13,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.3652103526812861,None,0.0,6,8,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,quantile_transformer,79,uniform,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.17357047764133562,fwe,chi2
-14,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6185424163558029,False,True,1,squared_hinge,ovr,l2,4.385767395120581e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010397452590017174,median,quantile_transformer,961,normal,,,feature_agglomeration,,,,,,,,,,,,,,,cosine,average,25,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-15,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.643761610990255,None,0.0,20,6,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.039859381630488695,mean,normalize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,
-16,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7947177310891914,None,0.0,13,3,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,mean,robust_scaler,,,0.7681430822715739,0.23411421138225857,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,16.747604337291705,f_classif,,,
-17,none,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.1769332447629957,mean,minmax,,,,,fast_ica,,,,,,,,,,,deflation,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-18,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.07518858116896743,None,0.0,7,10,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0027133920607872518,mean,robust_scaler,,,0.7873452530915046,0.16215139072179952,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-19,none,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,200,None,,0.0017457216340699586,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,none,,,,,kernel_pca,,,,,,,,,,,,,,,,,,,0.0,4,0.0007494648939847078,poly,1996,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-20,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.6039096182414764,None,0.0,2,20,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.1939609823704447,most_frequent,robust_scaler,,,0.7331327008874781,0.29747488162877694,extra_trees_preproc_for_classification,True,entropy,None,0.33401603690569853,None,0.0,4,4,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-21,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.8054434875626825,None,0.0,1,20,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,median,standardize,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4241048505416849,fpr,f_classif
-22,none,adaboost,SAMME,0.011233995624432622,9,477,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.018370622484682127,mean,standardize,,,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6039710338898471,False,,,,,,,,,,,,,,,
-23,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7734.521233796252,,,2.6048661611376747,rbf,-1,True,4.68717607435371e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,none,,,,,feature_agglomeration,,,,,,,,,,,,,,,euclidean,average,25,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-24,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.8367655423352977,None,0.0,1,5,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.01866043949164283,median,standardize,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.3824115704370156,fpr,f_classif
-25,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5010952533165973,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.005838375247601407,mean,normalize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,
-26,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.7297122444166401,False,True,1,squared_hinge,ovr,l2,0.00023416745444541063,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.03941815152197094,most_frequent,normalize,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0973649546833947,fpr,chi2
-27,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.4426097579931897,None,0.0,16,4,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,mean,robust_scaler,,,0.7407323456920554,0.0804756921037867,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,
-28,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5607168729746175,None,0.0,2,3,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.009810589644392154,most_frequent,quantile_transformer,1132,normal,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.06211089008167909,fdr,chi2
-29,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9333237919291875,None,0.0,8,3,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,
-30,none,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.9728532763078892,None,0.0,13,9,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,none,,,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6651071190864879,False,,,,,,,,,,,,,,,
-31,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9561075301148197,None,0.0,6,2,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,
-32,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.557321265077371,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-33,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,1.153931482107762e-10,0.011666190464009397,auto,255,None,29,190,18,loss,1e-07,0.147868307124396,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,
-34,none,adaboost,SAMME,0.6255555220658239,6,446,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.008833097306364968,most_frequent,none,,,,,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,10,None,4,17,1.0,76,,,,,
-35,none,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.563056219822946,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.32793677336996485,most_frequent,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-36,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,3.6797768538445263e-09,0.05511004217375807,auto,255,None,72,62,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,robust_scaler,,,0.7241481576763391,0.0501033899901458,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,36,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-37,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.7521969592385672,None,0.0,9,5,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.020402264943872365,median,robust_scaler,,,0.737227213630259,0.20752747663727789,fast_ica,,,,,,,,,,,parallel,exp,312,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-38,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.887898394612674,None,0.0,10,19,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,robust_scaler,,,0.8350698288864961,0.2347163538066168,extra_trees_preproc_for_classification,False,entropy,None,0.49795108918064074,None,0.0,13,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-39,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,1.6343482806592892e-09,0.19537524572679268,auto,255,None,32,29,13,loss,1e-07,0.05061738224302791,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-40,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7088164344533493,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,mean,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,
-41,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,1.943186107984505e-07,0.08852455702036988,auto,255,None,5,72,19,loss,1e-07,0.039837460762271175,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.01591265419367627,median,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,
-42,weighting,adaboost,SAMME,0.5210651042094924,10,401,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,standardize,,,,,feature_agglomeration,,,,,,,,,,,,,,,euclidean,complete,89,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-43,none,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0686374607394679,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,minmax,,,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.8490260132946401,False,,,,,,,,,,,,,,,
-44,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.6931271368243409,None,0.0,11,10,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.362940004438799,most_frequent,robust_scaler,,,0.9177587796305063,0.17084038458597867,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,30.491514144623952,mutual_info,,,
-45,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.9398869869697849,None,0.0,20,15,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,normalize,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,269,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-46,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,92.66144306787051,,,0.04654639302091653,rbf,-1,True,0.0022231544280610005,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.005194838694455963,mean,none,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,15,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-47,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2681.998807317996,-0.9558745731731211,,0.0011299948421773457,sigmoid,-1,False,1.5292507354478086e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-48,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.8095522998548572,None,0.0,17,10,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.031940595071539304,mean,quantile_transformer,1563,normal,,,extra_trees_preproc_for_classification,False,entropy,None,0.9330145598603196,None,0.0,18,10,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-49,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,26.502259985828186,,,9.475662388405154e-05,rbf,-1,False,0.00019690635968217866,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-50,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,2.5360196477156387e-07,0.011018756408558218,auto,255,None,40,122,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.00011749164080669445,most_frequent,quantile_transformer,757,uniform,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,5.097834697156853,False,True,1,squared_hinge,ovr,l1,0.053877834919477424,,,,,,,,,,,,,,,,,,,,,,
-51,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5255991794296913,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-52,weighting,decision_tree,,,,,,,gini,1.7853447328017091,1.0,None,0.0,13,7,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0001265964225445088,median,normalize,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.26399878867042303,,mutual_info_classif
-53,weighting,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.7776312540090575,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0005253715056715056,mean,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,False,,,,,,,,,,,,
-54,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,1.488333818193668e-10,0.09526676471267805,auto,255,None,258,87,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,normalize,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.3561953264350478,fpr,f_classif
-55,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.9523435078770708,0.8748383232154038,4,0.2195992292488049,poly,-1,True,4.849685143365339e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,minmax,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-56,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.7610308078756552,None,0.0,1,2,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.010000000000000004,mean,robust_scaler,,,0.9205545465103644,0.20272761060743316,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-57,weighting,adaboost,SAMME.R,0.03127273017917347,10,496,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,standardize,,,,,fast_ica,,,,,,,,,,,deflation,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-58,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,151,None,,0.0003283777607180646,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.2510527431514227,mean,normalize,,,,,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,-0.96093182232312,,0.05762510964607661,sigmoid,2632,,,,,,,,,,,,,,,,,
-59,none,bernoulli_nb,,,,,0.6524090653939782,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.005164169126808402,most_frequent,quantile_transformer,16,normal,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.033082145443631535,False,True,1,squared_hinge,ovr,l1,0.002615020898325393,,,,,,,,,,,,,,,,,,,,,,
-60,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.6453555269772124,None,0.0,1,4,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,normalize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-61,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,940.9437776449834,,,1.5839338084625942,rbf,-1,False,0.023072781592565438,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,none,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,77.25763685070707,mutual_info,,,
-62,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,220,auto,,0.0013824567992319003,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00021328932075284388,mean,minmax,,,,,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.039972474835314076,,0.01017810882899714,sigmoid,295,,,,,,,,,,,,,,,,,
-63,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.7326858450533382,0.02513634776687268,2,0.8635510319471909,poly,-1,True,0.0014598272110141993,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.06447266871225255,most_frequent,quantile_transformer,1000,normal,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,79.01961823033078,f_classif,,,
-64,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,29.55731828912983,False,True,1,squared_hinge,ovr,l2,0.0011332486951954968,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,
-65,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,1.1387795876755026e-06,0.12245605190343567,auto,255,None,58,7,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,robust_scaler,,,0.79305915648885,0.1841487406876042,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-66,none,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.563056219822946,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.32793677336996485,most_frequent,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-67,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,0.2638627266631576,0.022360820165005443,auto,255,None,171,6,19,loss,1e-07,0.016209060012924756,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.008763072395677399,mean,standardize,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,78,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-68,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,925.3053743248586,False,True,1,squared_hinge,ovr,l2,0.02764481198395717,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.3850718018366796,most_frequent,quantile_transformer,786,uniform,,,feature_agglomeration,,,,,,,,,,,,,,,cosine,average,156,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-69,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7573078019937971,None,0.0,2,5,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,robust_scaler,,,0.7265312295801113,0.28332515882236425,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.09660564590146253,fpr,f_classif
-70,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.005753626505113041,False,True,squared_hinge,0.00924401505172238,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.3192271696445429,median,quantile_transformer,913,normal,,,kernel_pca,,,,,,,,,,,,,,,,,,,,,,cosine,1963,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-71,none,adaboost,SAMME.R,0.22802213935525836,6,284,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,robust_scaler,,,0.7468645381288924,0.22838821662367073,feature_agglomeration,,,,,,,,,,,,,,,cosine,average,128,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-72,none,adaboost,SAMME,0.1280458968526125,7,125,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,robust_scaler,,,0.7156460334356771,0.2981902862926971,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,65.16171885308417,chi2,,,
-73,none,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9931779675565967,None,0.0,18,17,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,robust_scaler,,,0.75,0.2945917010472818,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,
-74,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.08646118576686276,None,0.0,4,9,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,most_frequent,quantile_transformer,959,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-75,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,1.2430504242164082e-09,0.03359568307446533,auto,255,None,1609,29,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,standardize,,,,,feature_agglomeration,,,,,,,,,,,,,,,cosine,complete,40,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-76,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7.491039171224502,True,True,squared_hinge,2.8058138048327884e-05,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,minmax,,,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.7754372809893839,False,True,1,squared_hinge,ovr,l1,3.9643594596766314e-05,,,,,,,,,,,,,,,,,,,,,,
-77,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.34936025988397124,None,0.0,2,13,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00018601309647406987,median,robust_scaler,,,0.8634309348843472,0.26976989465367607,extra_trees_preproc_for_classification,False,entropy,None,0.48025175288975036,None,0.0,6,17,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-78,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,0.011547155223520492,0.13665554140837183,auto,255,None,13,5,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.1824584492028449,median,minmax,,,,,feature_agglomeration,,,,,,,,,,,,,,,euclidean,average,261,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-79,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.933900673392165,None,0.0,7,4,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,minmax,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-80,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1661.2274747409913,,,0.025821208852387936,rbf,-1,False,0.0017011435104921824,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,none,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,306,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-81,none,adaboost,SAMME,0.011233995624432622,9,477,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.018370622484682127,mean,standardize,,,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6039710338898471,False,,,,,,,,,,,,,,,
-82,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,21.85207494001697,0.18872999589786676,4,1.1863646021868761,poly,-1,False,0.0021338371901294443,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0034080852834770217,mean,normalize,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.11326005109886934,fdr,f_classif
-83,none,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.563056219822946,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.32793677336996485,most_frequent,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-84,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,0.037849720944694,0.1268815770019783,auto,255,None,11,99,11,loss,1e-07,0.10654523627313527,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,
-85,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.593728171200453,None,0.0,7,20,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.02830941310359363,mean,normalize,,,,,extra_trees_preproc_for_classification,False,entropy,None,0.7389446586281172,None,0.0,20,11,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-86,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,1,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.019797877010737826,mean,normalize,,,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.8239678741128165,False,,,,,,,,,,,,,,,
-87,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.46057831591617715,False,True,hinge,0.04557857428827514,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00027457445401600137,median,standardize,,,,,extra_trees_preproc_for_classification,True,gini,None,0.48190346970486964,None,0.0,17,18,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-88,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,38,auto,,0.00019398431171606024,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010000000000000004,median,quantile_transformer,1440,normal,,,extra_trees_preproc_for_classification,True,gini,None,0.5126639956845847,None,0.0,16,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-89,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5722917944959481,None,0.0,1,14,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,
-90,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.26356754187780895,None,0.0,4,3,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010966178401758774,mean,robust_scaler,,,0.7817043861798886,0.24371720112407158,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
-91,weighting,adaboost,SAMME.R,0.13547552520095074,4,62,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.011851011202809002,most_frequent,standardize,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4888776875314595,fpr,chi2
-92,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,80.0028902468169,False,True,1,squared_hinge,ovr,l2,0.0024481999829574722,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.15047970487774687,mean,quantile_transformer,1130,normal,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,1.1912646635544712,False,True,1,squared_hinge,ovr,l1,0.00014314728486450857,,,,,,,,,,,,,,,,,,,,,,
-93,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.22879311080499845,None,0.0,8,4,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.014320022547615783,median,minmax,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.238630703440461,mutual_info,,,
-94,weighting,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,2,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,none,,,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6342574285315175,False,,,,,,,,,,,,,,,
-95,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.107967527639527e-05,True,True,hinge,0.00448294745544775,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,quantile_transformer,953,normal,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,35.00114345405331,chi2,,,
-96,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.14148638702297855,None,0.0,2,6,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.0038499650338378643,most_frequent,quantile_transformer,1759,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-97,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,1.1522414705935405e-10,0.6132800183073315,auto,255,None,1607,67,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010096911249562407,mean,robust_scaler,,,0.7663493326967942,0.136094217337434,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-98,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2746221958279041,False,True,1,squared_hinge,ovr,l2,6.546077664669006e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,quantile_transformer,1054,normal,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,
-99,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.479405900046494,,,0.013340778632154543,rbf,-1,False,6.913586060890114e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,robust_scaler,,,0.9953232616818098,0.06450577886132274,fast_ica,,,,,,,,,,,deflation,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-100,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.825844582229201,None,0.0,12,9,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0008422508222149647,median,robust_scaler,,,0.8658803880857064,0.2528423686293341,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-101,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2574971638664341,True,True,hinge,1.8027411099296446e-05,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.00536396328125103,median,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,
-102,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.7033210835501171,None,0.0,3,19,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,robust_scaler,,,0.7699195245692886,0.29258311638476237,feature_agglomeration,,,,,,,,,,,,,,,euclidean,ward,143,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-103,none,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,128,manual,0.8574560771111307,0.010886139381599514,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.24115007944216788,most_frequent,normalize,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,62.26514170991452,mutual_info,,,
-104,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.6282663389679584,None,0.0,1,3,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,median,minmax,,,,,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-105,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,1.430083734915855e-09,0.08779621406981941,auto,255,None,17,20,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.29749837779115257,mean,robust_scaler,,,0.7236445277814435,0.25,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
-106,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.3294588572341214,None,0.0,1,3,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
-107,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,39.22980757485479,,,0.13597389869623427,rbf,-1,False,0.00011639060052687874,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.006288306814260224,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-108,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.7922837297504705,False,True,1,squared_hinge,ovr,l2,0.0005476597326342352,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.012674631437254894,most_frequent,none,,,,,extra_trees_preproc_for_classification,False,entropy,None,0.23145146971319638,None,0.0,7,20,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-109,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.18060139887649052,None,0.0,10,12,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.010000000000000004,most_frequent,robust_scaler,,,0.9298612094255864,0.1263597946722308,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
-110,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.4111348710613201,None,0.0,16,8,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.01224348527546641,most_frequent,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,
-111,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6427543483503397,True,True,hinge,0.00045318775930916944,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.009338484332306904,median,quantile_transformer,174,normal,,,kernel_pca,,,,,,,,,,,,,,,,,,,,,,cosine,1848,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-112,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.04981080944617379,True,,0.07309386939525211,True,,constant,log,l2,,0.0006721243213777075,no_encoding,no_coalescense,,mean,quantile_transformer,574,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-113,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,9.029473608568458e-09,0.15779990576964315,auto,255,None,37,71,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.018093177195644097,mean,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,False,,,,,,,,,,,,
-114,none,adaboost,SAMME,0.011233995624432622,9,477,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.018370622484682127,mean,standardize,,,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6039710338898471,False,,,,,,,,,,,,,,,
-115,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l2,2.9468189962573666e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.014870368357163507,most_frequent,quantile_transformer,1000,uniform,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,83.43414935094258,f_classif,,,
-116,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.4864284879550203,None,0.0,1,4,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.008665854799630503,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-117,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.43699642289829416,None,0.0,6,20,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,quantile_transformer,1604,uniform,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-118,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.6922752267318475,True,True,squared_hinge,0.06299408576730353,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,quantile_transformer,1742,uniform,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,107,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-119,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.44727252219812963,None,0.0,2,10,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,standardize,,,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l1,8.17533189014765e-05,,,,,,,,,,,,,,,,,,,,,,
-120,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.46057831591617715,False,True,hinge,0.04557857428827514,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00027457445401600137,median,standardize,,,,,extra_trees_preproc_for_classification,True,gini,None,0.48190346970486964,None,0.0,17,18,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-121,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.4210978663047845,None,0.0,5,6,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.006365988550833196,most_frequent,robust_scaler,,,0.8348891074186082,0.00974961635730845,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0695919708890599,fpr,f_classif
-122,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4424195769042466,False,True,hinge,0.02490432909709204,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,mean,quantile_transformer,1000,uniform,,,kernel_pca,,,,,,,,,,,,,,,,,,,,,,cosine,769,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-123,weighting,decision_tree,,,,,,,entropy,1.0435267397863155,1.0,None,0.0,15,3,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.36913659113454333,most_frequent,normalize,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4947786362821451,fpr,f_classif
-124,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1561.1002600303584,,,0.0024048551704194507,rbf,-1,False,0.0001936214667496851,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,minmax,,,,,fast_ica,,,,,,,,,,,parallel,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-125,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.087313242780732,0.0636709914987994,3,0.004481917125024585,poly,-1,False,0.01848880618941367,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.002136015035740733,mean,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-126,weighting,adaboost,SAMME.R,1.696588612003168,9,455,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,minmax,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,82.18726095926662,chi2,,,
-127,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.6466927589470762,None,0.0,11,2,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.009672465311659264,most_frequent,standardize,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,369,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+1,none,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1514.059969351301,False,True,1,squared_hinge,ovr,l2,0.002448814309966599,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,standardize,,,,,extra_trees_preproc_for_classification,True,entropy,None,0.8221481751476923,None,0.0,2,3,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+3,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5707458022222313,None,0.0,3,12,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,minmax,,,,,extra_trees_preproc_for_classification,False,gini,None,0.5651056128131684,None,0.0,9,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+6,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.7283683762613216,None,0.0,12,20,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,median,standardize,,,,,fast_ica,,,,,,,,,,,deflation,exp,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+8,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,114,manual,0.11778631707605758,0.0029112721339713465,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.015582195257240751,median,normalize,,,,,kernel_pca,,,,,,,,,,,,,,,,,,,0.42909623253327767,3,0.25407612000343777,poly,1689,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+11,weighting,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,2,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.3865962574104293,mean,normalize,,,,,fast_ica,,,,,,,,,,,deflation,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+13,weighting,adaboost,SAMME,0.17983222024694556,4,357,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0008898781197407956,mean,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+15,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.21772092350860472,None,0.0,14,17,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.0020488212891693275,median,standardize,,,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,33.74257606530722,False,True,1,squared_hinge,ovr,l1,8.740452966205111e-05,,,,,,,,,,,,,,,,,,,,,,
+18,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,2.4243922131268786e-05,0.07570328162448307,auto,255,None,71,42,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,robust_scaler,,,0.8898729094898769,0.12138152991327475,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,94.96086286583851,chi2,,,
+20,none,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.9866058571747695,None,0.0,16,9,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.1680676743605393,most_frequent,robust_scaler,,,0.8840072797399463,0.08535534311393947,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,False,,,,,,,,,,,,
+23,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,0.00022373024203887573,0.027149343206135476,auto,255,None,49,13,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.00777614320416325,most_frequent,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,
+27,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.41025844265119926,None,0.0,18,15,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,median,quantile_transformer,1911,normal,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,False,,,,,,,,,,,,
+29,none,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.563056219822946,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.32793677336996485,most_frequent,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+31,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,8.757412830636845e-06,0.10095376320667454,auto,255,None,3,2,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.015401766535365336,median,quantile_transformer,647,normal,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,
+32,none,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,174,manual,0.21501892897746921,2.0347110826262054e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.013754893527625522,most_frequent,normalize,,,,,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,-0.019221513778605592,3,0.10190948951339239,poly,2521,,,,,,,,,,,,,,,,,
+35,none,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9.608707084865884,False,True,1,squared_hinge,ovr,l2,0.008086910150491696,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.020901889243957545,median,minmax,,,,,kitchen_sinks,,,,,,,,,,,,,,,,,,,,,,,,0.5694399784333516,3230,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+39,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.6799215694343422,None,0.0,1,5,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.004724310659953132,most_frequent,quantile_transformer,1037,normal,,,fast_ica,,,,,,,,,,,parallel,exp,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+41,none,extra_trees,,,,,,,,,,,,,,,True,gini,None,0.5033866291997137,None,0.0,2,14,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,minmax,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+43,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.9018061166635376,None,0.0,7,9,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,quantile_transformer,1802,normal,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,84.54647012968938,chi2,,,
+44,none,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l2,2.858526169098402e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0007827505640681123,median,standardize,,,,,extra_trees_preproc_for_classification,False,entropy,None,0.3443310459451119,None,0.0,3,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+49,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.049504283446131e-05,False,,0.0004438773852753879,True,2.4110816673432424e-07,constant,log,elasticnet,,0.01321814853186568,one_hot_encoding,minority_coalescer,0.01510416593631535,most_frequent,none,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,66.65285268958851,chi2,,,
+52,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,6.421059381158897e-08,0.11261253191288709,auto,255,None,45,28,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,
+55,none,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.9822179211645446,None,0.0,14,8,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.013966871874533052,most_frequent,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,
+58,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5214197490559657,None,0.0,1,2,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.00826182798588248,mean,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,
+63,none,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,130,manual,0.5446276346297756,0.00846307015171434,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.005736348762561667,median,quantile_transformer,973,uniform,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.08483615095863663,fdr,chi2
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+435,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.44903177399085925,None,0.0,2,17,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,mean,robust_scaler,,,0.7812408516672696,0.12791233577400205,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,
+438,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,6.090138036399552e-10,0.017305568433437588,auto,255,None,202,188,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,robust_scaler,,,0.7526196011313747,0.1563616005702636,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+445,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,68.60833575985336,,,0.03905068757955671,rbf,-1,False,0.02846559747114773,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.04775815990561371,mean,standardize,,,,,extra_trees_preproc_for_classification,False,entropy,None,0.45727987956076366,None,0.0,13,11,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+447,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.3431705351883592,None,0.0,4,16,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.09444471061261189,most_frequent,normalize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+450,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,7.837701348094735e-09,0.05315388879642303,auto,255,None,67,2,10,loss,1e-07,0.11655723047843726,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.001857226018763921,median,none,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.014648191862400293,fdr,f_classif
+458,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,5.581658271288433e-10,0.06171988234311011,auto,255,None,25,6,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.014859405637537319,most_frequent,robust_scaler,,,0.7983900845251328,0.2545046978458648,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,1.380085213170737,False,True,1,squared_hinge,ovr,l1,0.0061450732015354784,,,,,,,,,,,,,,,,,,,,,,
+462,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6,1,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.017471096998391875,median,none,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,67.7801145492284,f_classif,,,
diff --git a/autosklearn/metalearning/files/precision_micro_multiclass.classification_dense/description.txt b/autosklearn/metalearning/files/precision_micro_multiclass.classification_dense/description.txt
index 949e91c3cc..b229283604 100644
--- a/autosklearn/metalearning/files/precision_micro_multiclass.classification_dense/description.txt
+++ b/autosklearn/metalearning/files/precision_micro_multiclass.classification_dense/description.txt
@@ -1,8 +1,9 @@
 features_cutoff_time: 3600
 features_cutoff_memory: 3072
-number_of_feature_steps: 35
+number_of_feature_steps: 42
 feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances
 feature_step LogNumberOfInstances: LogNumberOfInstances
+feature_step NumberOfClasses: NumberOfClasses
 feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures
 feature_step LogNumberOfFeatures: LogNumberOfFeatures
 feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues
@@ -20,6 +21,11 @@ feature_step DatasetRatio: DatasetRatio, LogDatasetRatio
 feature_step LogDatasetRatio: LogDatasetRatio
 feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio
 feature_step LogInverseDatasetRatio: LogInverseDatasetRatio
+feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD
+feature_step ClassProbabilityMin: ClassProbabilityMin
+feature_step ClassProbabilityMax: ClassProbabilityMax
+feature_step ClassProbabilityMean: ClassProbabilityMean
+feature_step ClassProbabilitySTD: ClassProbabilitySTD
 feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum
 feature_step SymbolsMin: SymbolsMin
 feature_step SymbolsMax: SymbolsMax
@@ -36,11 +42,12 @@ feature_step SkewnessMin: SkewnessMin
 feature_step SkewnessMax: SkewnessMax
 feature_step SkewnessMean: SkewnessMean
 feature_step SkewnessSTD: SkewnessSTD
-features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
+feature_step ClassEntropy: ClassEntropy
+features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy
 features_stochastic: 
-default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
+default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy
 
-algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127
+algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235,236,237,238,239,240,241,242,243,244,245,246,247,248,249,250,251,252,253,254,255,256,257,258,259,260,261,262,263,264,265,266,267,268,269,270,271,272,273,274,275,276,277,278,279,280,281,282,283,284,285,286,287,288,289,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,312,313,314,315,316,317,318,319,320,321,322,323,324,325,326,327,328,329,330,331,332,333,334,335,336,337,338,339,340,341,342,343,344,345,346,347,348,349,350,351,352,353,354,355,356,357,358,359,360,361,362,363,364,365,366,367,368,369,370,371,372,373,374,375,376,377,378,379,380,381,382,383,384,385,386,387,388,389,390,391,392,393,394,395,396,397,398,399,400,401,402,403,404,405,406,407,408,409,410,411,412,413,414,415,416,417,418,419,420,421,422,423,424,425,426,427,428,429,430,431,432,433,434,435,436,437,438,439,440,441,442,443,444,445,446,447,448,449,450,451,452,453,454,455,456,457,458,459,460,461,462,463,464
 algorithms_stochastic: 
 performance_measures: precision_micro
 performance_type: solution_quality
diff --git a/autosklearn/metalearning/files/precision_micro_multiclass.classification_dense/feature_costs.arff b/autosklearn/metalearning/files/precision_micro_multiclass.classification_dense/feature_costs.arff
index 0e17e20d22..8512fd9275 100644
--- a/autosklearn/metalearning/files/precision_micro_multiclass.classification_dense/feature_costs.arff
+++ b/autosklearn/metalearning/files/precision_micro_multiclass.classification_dense/feature_costs.arff
@@ -4,6 +4,7 @@
 @ATTRIBUTE repetition NUMERIC
 @ATTRIBUTE NumberOfInstances NUMERIC
 @ATTRIBUTE LogNumberOfInstances NUMERIC
+@ATTRIBUTE NumberOfClasses NUMERIC
 @ATTRIBUTE NumberOfFeatures NUMERIC
 @ATTRIBUTE LogNumberOfFeatures NUMERIC
 @ATTRIBUTE MissingValues NUMERIC
@@ -21,6 +22,11 @@
 @ATTRIBUTE LogDatasetRatio NUMERIC
 @ATTRIBUTE InverseDatasetRatio NUMERIC
 @ATTRIBUTE LogInverseDatasetRatio NUMERIC
+@ATTRIBUTE ClassOccurences NUMERIC
+@ATTRIBUTE ClassProbabilityMin NUMERIC
+@ATTRIBUTE ClassProbabilityMax NUMERIC
+@ATTRIBUTE ClassProbabilityMean NUMERIC
+@ATTRIBUTE ClassProbabilitySTD NUMERIC
 @ATTRIBUTE NumSymbols NUMERIC
 @ATTRIBUTE SymbolsMin NUMERIC
 @ATTRIBUTE SymbolsMax NUMERIC
@@ -37,162 +43,138 @@
 @ATTRIBUTE SkewnessMax NUMERIC
 @ATTRIBUTE SkewnessMean NUMERIC
 @ATTRIBUTE SkewnessSTD NUMERIC
+@ATTRIBUTE ClassEntropy NUMERIC
 
 @DATA
-75123,1.0,1e-05,1e-05,1e-05,1e-05,0.00253,0.00221,1e-05,0.00024,3e-05,0.00011,0.0,2e-05,2e-05,5e-05,8e-05,2e-05,1e-05,7e-05,1e-05,0.00072,1e-05,1e-05,0.00019,0.00041,0.00011,0.00061,0.00012,9e-05,0.00015,0.00025,0.00067,0.00014,0.00011,0.00016,0.00027
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-266,1.0,1e-05,1e-05,1e-05,1e-05,0.0018,0.00146,1e-05,0.00023,2e-05,0.00015,2e-05,2e-05,2e-05,6e-05,8e-05,3e-05,1e-05,6e-05,1e-05,0.00059,1e-05,1e-05,0.00014,0.00031,0.00013,0.0004,5e-05,7e-05,8e-05,0.0002,0.00044,8e-05,8e-05,8e-05,0.0002
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diff --git a/autosklearn/metalearning/files/precision_micro_multiclass.classification_dense/feature_runstatus.arff b/autosklearn/metalearning/files/precision_micro_multiclass.classification_dense/feature_runstatus.arff
index 0034ad2bd6..5fff2dfed3 100644
--- a/autosklearn/metalearning/files/precision_micro_multiclass.classification_dense/feature_runstatus.arff
+++ b/autosklearn/metalearning/files/precision_micro_multiclass.classification_dense/feature_runstatus.arff
@@ -4,6 +4,7 @@
 @ATTRIBUTE repetition NUMERIC
 @ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other}
@@ -21,6 +22,11 @@
 @ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other}
@@ -37,162 +43,138 @@
 @ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other}
 
 @DATA
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diff --git a/autosklearn/metalearning/files/precision_micro_multiclass.classification_dense/feature_values.arff b/autosklearn/metalearning/files/precision_micro_multiclass.classification_dense/feature_values.arff
index a60400e3ce..736dc64321 100644
--- a/autosklearn/metalearning/files/precision_micro_multiclass.classification_dense/feature_values.arff
+++ b/autosklearn/metalearning/files/precision_micro_multiclass.classification_dense/feature_values.arff
@@ -13,7 +13,6 @@
 @ATTRIBUTE KurtosisMean NUMERIC
 @ATTRIBUTE KurtosisMin NUMERIC
 @ATTRIBUTE KurtosisSTD NUMERIC
-@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC
 @ATTRIBUTE LogDatasetRatio NUMERIC
 @ATTRIBUTE LogInverseDatasetRatio NUMERIC
 @ATTRIBUTE LogNumberOfFeatures NUMERIC
@@ -42,160 +41,135 @@
 @ATTRIBUTE SymbolsSum NUMERIC
 
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diff --git a/autosklearn/metalearning/files/precision_micro_multiclass.classification_sparse/algorithm_runs.arff b/autosklearn/metalearning/files/precision_micro_multiclass.classification_sparse/algorithm_runs.arff
index 09dee5548f..4ab2e22d87 100644
--- a/autosklearn/metalearning/files/precision_micro_multiclass.classification_sparse/algorithm_runs.arff
+++ b/autosklearn/metalearning/files/precision_micro_multiclass.classification_sparse/algorithm_runs.arff
@@ -7,95 +7,133 @@
 @ATTRIBUTE runstatus {ok, timeout, memout, not_applicable, crash, other}
 
 @DATA
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diff --git a/autosklearn/metalearning/files/precision_micro_multiclass.classification_sparse/configurations.csv b/autosklearn/metalearning/files/precision_micro_multiclass.classification_sparse/configurations.csv
index 75662496d2..36fd991e13 100644
--- a/autosklearn/metalearning/files/precision_micro_multiclass.classification_sparse/configurations.csv
+++ b/autosklearn/metalearning/files/precision_micro_multiclass.classification_sparse/configurations.csv
@@ -1,93 +1,131 @@
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-91,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.087313242780732,0.0636709914987994,3,0.004481917125024585,poly,-1,False,0.01848880618941367,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.002136015035740733,mean,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-92,weighting,adaboost,SAMME,0.029932847627447023,8,483,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,quantile_transformer,1290,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+1,none,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1514.059969351301,False,True,1,squared_hinge,ovr,l2,0.002448814309966599,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,standardize,,,,,extra_trees_preproc_for_classification,True,entropy,None,0.8221481751476923,None,0.0,2,3,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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+289,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.818514683854827,None,0.0,15,20,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.4928285185491301,most_frequent,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,,
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+293,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7040496889211508,None,0.0,17,19,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.016450784709133564,mean,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+300,none,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.7010137052424128,None,0.0,7,10,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.2761813291805052,most_frequent,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,,
+306,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2677516008848513,True,True,hinge,0.05752557410901496,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0010152857217605206,median,quantile_transformer,1000,uniform,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,51.60974796845029,chi2,,,,
+307,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,33,None,,0.006687546229372618,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.06231343712708962,mean,none,,,,,kernel_pca,,,,,,,,,,,-0.7000777299811654,3,0.008780219231661734,poly,498,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+312,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,60.6761602925822,,,0.038224035513907226,rbf,-1,False,0.0018214343969776353,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.421025538999516,median,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+313,weighting,adaboost,SAMME,1.2423888874490505,5,284,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.01582563952987033,most_frequent,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+316,weighting,adaboost,SAMME,1.2615483379313783,8,190,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.00017789451703497617,mean,robust_scaler,,,0.7345811776810758,0.054715769043996304,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+317,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.4572679693563934,None,0.0,1,11,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,mean,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,,
+318,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.843098530645432,None,0.0,1,4,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,mean,quantile_transformer,930,normal,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,,
+321,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.5864398404787432,None,0.0,1,14,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0070663750850608895,most_frequent,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,,
+326,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0001329242404968632,False,True,squared_hinge,0.000416219546803132,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.007568009633214092,median,quantile_transformer,1126,normal,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,29.06367291960815,chi2,,,,
+329,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.6642356723981304,False,True,1,squared_hinge,ovr,l2,7.912577426155621e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.08464913801800422,most_frequent,normalize,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,53.35800942135187,chi2,,,,
+331,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.5,None,0.0,1,5,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.15244281596303316,mean,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+332,none,adaboost,SAMME,0.22805500898886877,8,172,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.019649593891454008,mean,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+334,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7516795829719571,None,0.0,14,7,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0010013681780844724,mean,none,,,,,extra_trees_preproc_for_classification,True,entropy,None,0.6123323873969666,None,0.0,17,7,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+339,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.36374112587320206,None,0.0,2,10,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010870084126959173,most_frequent,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,,
+343,none,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9062834897256731,None,0.0,20,9,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.005187379382274718,mean,quantile_transformer,349,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+345,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5321814610661173,None,0.0,1,3,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,standardize,,,,,extra_trees_preproc_for_classification,True,gini,None,0.5,None,0.0,1,15,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+350,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.44903177399085925,None,0.0,2,17,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,mean,robust_scaler,,,0.7812408516672696,0.12791233577400205,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,,
+352,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5988524108452234,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,mean,robust_scaler,,,0.75,0.26961884796358077,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+355,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,68.60833575985336,,,0.03905068757955671,rbf,-1,False,0.02846559747114773,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.04775815990561371,mean,standardize,,,,,extra_trees_preproc_for_classification,False,entropy,None,0.45727987956076366,None,0.0,13,11,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+357,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.3431705351883592,None,0.0,4,16,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.09444471061261189,most_frequent,normalize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+359,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.9282407375709691,None,0.0,16,3,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.015995805068365877,mean,standardize,,,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.10042232633120375,False,True,1,squared_hinge,ovr,l1,0.004451009526629745,,,,,,,,,,,,,,,,,,,,,
+367,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.938372431886818e-05,True,True,hinge,0.0016143408148969647,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00024949978971758427,median,quantile_transformer,754,uniform,,,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,-0.22339839702350717,2,0.5718982387915436,poly,5260,,,,,,,,,,,,,,,,
+371,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4,1,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.01020427226149318,median,none,,,,,extra_trees_preproc_for_classification,True,entropy,None,0.3663460889081009,None,0.0,2,7,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
diff --git a/autosklearn/metalearning/files/precision_micro_multiclass.classification_sparse/description.txt b/autosklearn/metalearning/files/precision_micro_multiclass.classification_sparse/description.txt
index ae1b5954e0..000bc8cb75 100644
--- a/autosklearn/metalearning/files/precision_micro_multiclass.classification_sparse/description.txt
+++ b/autosklearn/metalearning/files/precision_micro_multiclass.classification_sparse/description.txt
@@ -1,8 +1,9 @@
 features_cutoff_time: 3600
 features_cutoff_memory: 3072
-number_of_feature_steps: 35
+number_of_feature_steps: 42
 feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances
 feature_step LogNumberOfInstances: LogNumberOfInstances
+feature_step NumberOfClasses: NumberOfClasses
 feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures
 feature_step LogNumberOfFeatures: LogNumberOfFeatures
 feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues
@@ -20,6 +21,11 @@ feature_step DatasetRatio: DatasetRatio, LogDatasetRatio
 feature_step LogDatasetRatio: LogDatasetRatio
 feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio
 feature_step LogInverseDatasetRatio: LogInverseDatasetRatio
+feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD
+feature_step ClassProbabilityMin: ClassProbabilityMin
+feature_step ClassProbabilityMax: ClassProbabilityMax
+feature_step ClassProbabilityMean: ClassProbabilityMean
+feature_step ClassProbabilitySTD: ClassProbabilitySTD
 feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum
 feature_step SymbolsMin: SymbolsMin
 feature_step SymbolsMax: SymbolsMax
@@ -36,11 +42,12 @@ feature_step SkewnessMin: SkewnessMin
 feature_step SkewnessMax: SkewnessMax
 feature_step SkewnessMean: SkewnessMean
 feature_step SkewnessSTD: SkewnessSTD
-features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
+feature_step ClassEntropy: ClassEntropy
+features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy
 features_stochastic: 
-default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
+default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy
 
-algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92
+algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235,236,237,238,239,240,241,242,243,244,245,246,247,248,249,250,251,252,253,254,255,256,257,258,259,260,261,262,263,264,265,266,267,268,269,270,271,272,273,274,275,276,277,278,279,280,281,282,283,284,285,286,287,288,289,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,312,313,314,315,316,317,318,319,320,321,322,323,324,325,326,327,328,329,330,331,332,333,334,335,336,337,338,339,340,341,342,343,344,345,346,347,348,349,350,351,352,353,354,355,356,357,358,359,360,361,362,363,364,365,366,367,368,369,370,371,372
 algorithms_stochastic: 
 performance_measures: precision_micro
 performance_type: solution_quality
diff --git a/autosklearn/metalearning/files/precision_micro_multiclass.classification_sparse/feature_costs.arff b/autosklearn/metalearning/files/precision_micro_multiclass.classification_sparse/feature_costs.arff
index 0e17e20d22..8512fd9275 100644
--- a/autosklearn/metalearning/files/precision_micro_multiclass.classification_sparse/feature_costs.arff
+++ b/autosklearn/metalearning/files/precision_micro_multiclass.classification_sparse/feature_costs.arff
@@ -4,6 +4,7 @@
 @ATTRIBUTE repetition NUMERIC
 @ATTRIBUTE NumberOfInstances NUMERIC
 @ATTRIBUTE LogNumberOfInstances NUMERIC
+@ATTRIBUTE NumberOfClasses NUMERIC
 @ATTRIBUTE NumberOfFeatures NUMERIC
 @ATTRIBUTE LogNumberOfFeatures NUMERIC
 @ATTRIBUTE MissingValues NUMERIC
@@ -21,6 +22,11 @@
 @ATTRIBUTE LogDatasetRatio NUMERIC
 @ATTRIBUTE InverseDatasetRatio NUMERIC
 @ATTRIBUTE LogInverseDatasetRatio NUMERIC
+@ATTRIBUTE ClassOccurences NUMERIC
+@ATTRIBUTE ClassProbabilityMin NUMERIC
+@ATTRIBUTE ClassProbabilityMax NUMERIC
+@ATTRIBUTE ClassProbabilityMean NUMERIC
+@ATTRIBUTE ClassProbabilitySTD NUMERIC
 @ATTRIBUTE NumSymbols NUMERIC
 @ATTRIBUTE SymbolsMin NUMERIC
 @ATTRIBUTE SymbolsMax NUMERIC
@@ -37,162 +43,138 @@
 @ATTRIBUTE SkewnessMax NUMERIC
 @ATTRIBUTE SkewnessMean NUMERIC
 @ATTRIBUTE SkewnessSTD NUMERIC
+@ATTRIBUTE ClassEntropy NUMERIC
 
 @DATA
-75123,1.0,1e-05,1e-05,1e-05,1e-05,0.00253,0.00221,1e-05,0.00024,3e-05,0.00011,0.0,2e-05,2e-05,5e-05,8e-05,2e-05,1e-05,7e-05,1e-05,0.00072,1e-05,1e-05,0.00019,0.00041,0.00011,0.00061,0.00012,9e-05,0.00015,0.00025,0.00067,0.00014,0.00011,0.00016,0.00027
-75090,1.0,1e-05,1e-05,2e-05,2e-05,0.00815,0.00368,7e-05,0.00228,9e-05,0.00235,0.0,5e-05,5e-05,0.0001,0.00013,0.00013,1e-05,0.00021,2e-05,0.00064,0.0,0.0,0.00014,0.00038,0.00011,0.00066,0.00011,0.00014,0.00014,0.00026,0.00066,0.00011,0.00013,0.00015,0.00027
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+75236,1.0,1e-05,1e-05,0.0001,1e-05,1e-05,0.00218,0.00118,2e-05,0.00065,3e-05,0.00041,1e-05,3e-05,3e-05,9e-05,9e-05,4e-05,1e-05,7e-05,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.0006,1e-05,1e-05,0.00021,0.00032,6e-05,0.00043,7e-05,7e-05,9e-05,0.00019,0.00062,7e-05,8e-05,0.0001,0.00037,0.00048
+75237,1.0,2e-05,1e-05,0.00785,2e-05,1e-05,0.31031,0.27854,2e-05,0.01161,3e-05,0.02022,1e-05,4e-05,3e-05,9e-05,0.00024,6e-05,2e-05,9e-05,2e-05,0.00062,7e-05,3e-05,0.00017,0.00035,0.0008,1e-05,1e-05,0.00028,0.00043,8e-05,0.00043,8e-05,7e-05,9e-05,0.0002,0.00044,6e-05,8e-05,9e-05,0.00021,0.14028
+75239,1.0,1e-05,1e-05,7e-05,1e-05,1e-05,0.00105,0.00084,1e-05,0.00015,1e-05,8e-05,1e-05,2e-05,5e-05,4e-05,8e-05,2e-05,1e-05,3e-05,1e-05,0.00016,2e-05,1e-05,5e-05,9e-05,0.00049,0.0,0.0,0.00012,0.00026,0.00011,0.00046,0.00013,8e-05,8e-05,0.00018,0.00044,6e-05,9e-05,0.00011,0.00018,0.00065
+75240,1.0,2e-05,1e-05,0.00036,5e-05,4e-05,0.00845,0.00644,6e-05,0.00122,4e-05,0.00091,1e-05,4e-05,4e-05,9e-05,0.00013,5e-05,2e-05,9e-05,5e-05,0.00031,3e-05,1e-05,7e-05,0.0002,0.00073,3e-05,2e-05,0.00019,0.0003,0.00018,0.00055,9e-05,9e-05,0.00011,0.00025,0.0005,7e-05,8e-05,0.00015,0.0002,0.00365
+75243,1.0,1e-05,1e-05,0.00029,1e-05,1e-05,0.00461,0.00415,1e-05,0.00035,1e-05,0.00015,0.0,2e-05,2e-05,5e-05,7e-05,2e-05,1e-05,4e-05,1e-05,0.00018,2e-05,1e-05,4e-05,0.00011,0.00022,1e-05,1e-05,5e-05,0.00011,5e-05,0.0004,6e-05,6e-05,8e-05,0.0002,0.00039,6e-05,6e-05,8e-05,0.00019,0.00265
+75244,1.0,3e-05,2e-05,0.0004,3e-05,2e-05,0.0173,0.01298,4e-05,0.00267,4e-05,0.00174,1e-05,7e-05,9e-05,0.00024,0.00027,8e-05,2e-05,0.00013,3e-05,0.00045,7e-05,2e-05,0.00011,0.00025,0.00077,6e-05,2e-05,0.00021,0.00034,0.00013,0.00077,0.00014,0.00013,0.00026,0.00023,0.00044,7e-05,8e-05,0.0001,0.00019,0.00649
+75248,1.0,1e-05,1e-05,0.00017,1e-05,1e-05,0.00416,0.00305,1e-05,0.0006,2e-05,0.00054,1e-05,2e-05,2e-05,5e-05,7e-05,2e-05,1e-05,4e-05,1e-05,0.00017,2e-05,1e-05,4e-05,0.0001,0.00029,5e-05,1e-05,6e-05,0.00012,6e-05,0.00081,0.00011,0.00015,0.00016,0.00038,0.0005,7e-05,8e-05,0.00012,0.00024,0.00184
+75249,1.0,1e-05,1e-05,0.00011,1e-05,1e-05,0.00192,0.00153,1e-05,0.00025,1e-05,0.00017,1e-05,2e-05,2e-05,5e-05,7e-05,2e-05,1e-05,5e-05,3e-05,0.00014,2e-05,1e-05,3e-05,8e-05,0.00029,1e-05,3e-05,6e-05,0.00012,7e-05,0.00041,6e-05,8e-05,0.0001,0.00017,0.00043,6e-05,7e-05,9e-05,0.00019,0.00092
+75250,1.0,1e-05,1e-05,0.00375,1e-05,1e-05,0.04994,0.047,1e-05,0.00247,1e-05,0.00049,1e-05,2e-05,2e-05,6e-05,7e-05,2e-05,1e-05,3e-05,1e-05,0.00022,4e-05,1e-05,4e-05,0.00012,0.00041,0.0,1e-05,0.00011,0.00023,6e-05,0.00076,0.00012,0.00012,0.00019,0.00032,0.00086,0.00012,0.00013,0.00018,0.00043,0.04178
diff --git a/autosklearn/metalearning/files/precision_micro_multiclass.classification_sparse/feature_runstatus.arff b/autosklearn/metalearning/files/precision_micro_multiclass.classification_sparse/feature_runstatus.arff
index 0034ad2bd6..5fff2dfed3 100644
--- a/autosklearn/metalearning/files/precision_micro_multiclass.classification_sparse/feature_runstatus.arff
+++ b/autosklearn/metalearning/files/precision_micro_multiclass.classification_sparse/feature_runstatus.arff
@@ -4,6 +4,7 @@
 @ATTRIBUTE repetition NUMERIC
 @ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other}
@@ -21,6 +22,11 @@
 @ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other}
@@ -37,162 +43,138 @@
 @ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other}
 
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diff --git a/autosklearn/metalearning/files/precision_micro_multiclass.classification_sparse/feature_values.arff b/autosklearn/metalearning/files/precision_micro_multiclass.classification_sparse/feature_values.arff
index a60400e3ce..736dc64321 100644
--- a/autosklearn/metalearning/files/precision_micro_multiclass.classification_sparse/feature_values.arff
+++ b/autosklearn/metalearning/files/precision_micro_multiclass.classification_sparse/feature_values.arff
@@ -13,7 +13,6 @@
 @ATTRIBUTE KurtosisMean NUMERIC
 @ATTRIBUTE KurtosisMin NUMERIC
 @ATTRIBUTE KurtosisSTD NUMERIC
-@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC
 @ATTRIBUTE LogDatasetRatio NUMERIC
 @ATTRIBUTE LogInverseDatasetRatio NUMERIC
 @ATTRIBUTE LogNumberOfFeatures NUMERIC
@@ -42,160 +41,135 @@
 @ATTRIBUTE SymbolsSum NUMERIC
 
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+75232,1.0,0.9201702027239149,0.6647807637906648,0.5,0.3352192362093352,0.16478076379066478,0.05799151343705799,17.24390243902439,585.4510498046875,41.48030315044416,-0.2529125213623047,95.17568446650067,-2.847458599192265,2.8474585991922647,3.713572066704308,6.561030665896573,0.0,2.0,41.0,0.0,707.0,0.0,0.0,41.0,0.0,0.0,0.0,0.0,0.0,23.23834228515625,3.832647103316537,-1.5003634691238403,4.508387764393873,0.0,0.0,0.0,0.0,0.0
+75233,1.0,0.8824680010722367,0.6990344325013663,0.5,0.3009655674986336,0.19903443250136635,0.0038258334851521223,261.3809523809524,619.9618265719739,76.9042121753925,1.1050496101379395,139.01256776590665,-5.565978930826466,5.565978930826466,3.044522437723423,8.610501368549889,0.0,2.0,21.0,0.0,5489.0,0.0,0.0,21.0,0.0,0.0,0.0,0.0,0.0,21.77604866027832,5.3457508484522505,-0.7721167802810669,4.832918145056104,0.0,0.0,0.0,0.0,0.0
+75234,1.0,0.9998393017810485,0.5074626865671642,0.5,0.4925373134328358,0.007462686567164201,0.004033884630899556,247.9,1.9309582710266113,1.5332300662994385,1.3253521919250488,0.13647900129618118,-5.513025439041145,5.513025439041145,2.995732273553991,8.508757712595136,0.0,2.0,20.0,0.0,4958.0,0.0,0.0,20.0,0.0,0.0,0.0,0.0,0.0,-0.1076231524348259,-0.23315090499818325,-0.33683285117149353,0.052567253463639066,0.0,0.0,0.0,0.0,0.0
+75235,1.0,1.7067919449664923,0.4105579868708972,0.25,0.05579868708971553,0.1492903085996621,0.006564551422319475,152.33333333333334,13.76956558227539,2.346758103856697,-1.6145207422400454,4.524616106763882,-5.026071102226095,5.026071102226095,3.1780538303479458,8.204124932574041,0.0,4.0,24.0,0.0,3656.0,0.0,0.0,24.0,0.0,0.0,0.0,0.0,0.0,3.779214382171631,1.515954251983203,0.004355928860604763,1.0657977665170062,0.0,0.0,0.0,0.0,0.0
+75236,1.0,3.3208751934243548,0.10486891385767791,0.1,0.09363295880149813,0.003810297743328803,0.2397003745318352,4.171875,22.422517776489258,-0.9225467168815645,-1.9997756481170654,1.9352856443240884,-1.4283655750405777,1.4283655750405777,5.545177444479562,6.97354301952014,0.0,10.0,256.0,0.0,1068.0,0.0,0.0,256.0,0.0,0.0,0.0,0.0,0.0,4.94191312789917,0.8186543448100565,-0.6194983720779419,0.6381679017395948,0.0,0.0,0.0,0.0,0.0
+75237,1.0,0.7378736147990802,0.7918800894091566,0.5,0.20811991059084348,0.29188008940915655,1.8271625991997027e-05,54729.666666666664,-0.5975548261535093,-0.7926032911766772,-0.9140236377716064,0.13929334047064063,-10.910161193614513,10.910161193614513,1.0986122886681098,12.008773482282624,0.0,2.0,3.0,0.0,164189.0,0.0,0.0,3.0,0.0,0.0,0.0,0.0,0.0,-0.039879728108644485,-0.3348021370669206,-0.6223916411399841,0.2378660138627058,0.0,0.0,0.0,0.0,0.0
+75239,1.0,0.9443547030267275,0.6379707916986933,0.5,0.3620292083013067,0.1379707916986933,0.02536510376633359,39.42424242424242,30.96304702758789,5.229958094222913,-1.8830041885375977,9.159290860687747,-3.674380917046025,3.674380917046025,3.4965075614664802,7.170888478512505,0.0,2.0,33.0,0.0,1301.0,0.0,0.0,33.0,0.0,0.0,0.0,0.0,0.0,5.591684341430664,1.5151810998266393,-0.8763390183448792,1.68213778251707,0.0,0.0,0.0,0.0,0.0
+75240,1.0,0.9473321982231081,0.6342759940463534,0.5,0.3657240059536466,0.13427599404635338,0.01275781416117372,78.38333333333334,1911.103482572295,38.512800597597035,-3.0,145.76440092342054,-4.361611319722948,4.361611319722948,4.0943445622221,8.455955881945048,27.0,2.0,60.0,17.0,4703.0,4703.0,29447.0,33.0,0.2833333333333333,1.0,0.10435537600113402,0.8181818181818182,1.2222222222222223,43.144344696960424,3.913912678196276,-22.79380338813253,4.943976022970944,173.0,22.59259259259259,1.0,43.77997111758117,610.0
+75243,1.0,1.7118844784475225,0.33529140750979036,0.25,0.024648698456576825,0.13045500689907696,0.0009214466712739,1085.25,0.37898291200086787,-0.9656219973073414,-1.9999312246360998,0.7132002663612025,-6.989565654179703,6.989565654179703,2.0794415416798357,9.06900719585954,8.0,4.0,8.0,0.0,8682.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.5423951867147632,0.9342552225820131,-0.008293091335592482,0.40192683634075954,5.0,3.375,2.0,0.8569568250501305,27.0
+75244,1.0,0.33901826511567296,0.9370570367870401,0.5,0.06294296321295983,0.43705703678704017,0.009449881876476544,105.82142857142857,1180.200844451951,40.51648623113333,-3.0,132.43484378998235,-4.661753037398361,4.661753037398361,4.02535169073515,8.68710472813351,41.0,2.0,56.0,14.0,5926.0,5926.0,23694.0,15.0,0.25,1.0,0.0713984378766694,2.7333333333333334,0.36585365853658536,34.38314768097812,3.7769009011075547,-8.659484722748674,5.307279668054912,16.0,5.975609756097561,1.0,3.692363938923924,245.0
+75248,1.0,0.4971128374263667,0.8909272183449651,0.5,0.1090727816550349,0.39092721834496513,0.010967098703888335,91.18181818181819,5010.000199441565,104.23108191482348,-3.0,468.735807382562,-4.512855515163565,4.512855515163565,4.007333185232471,8.520188700396035,39.0,2.0,55.0,12.0,5015.0,4938.0,21515.0,16.0,0.21818181818181817,0.9846460618145564,0.07800235656666364,2.4375,0.41025641025641024,70.79548149028699,4.06998328231248,-50.04498375424548,9.467860234489164,14.0,5.769230769230769,1.0,3.407845687351005,225.0
+75249,1.0,0.3891675492147647,0.9236550632911392,0.5,0.07634493670886076,0.4236550632911392,0.011471518987341773,87.17241379310344,416.3357123975682,46.57141260912883,-3.0,84.69777944086748,-4.467887925280274,4.467887925280274,3.367295829986474,7.8351837552667485,22.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,7.0,0.2413793103448276,1.0,0.056048123090353556,3.142857142857143,0.3181818181818182,20.453256767506936,0.8884855226401125,-20.453256767506936,6.7466910388456185,5.0,2.0454545454545454,1.0,0.7056443043754556,45.0
+75250,1.0,3.992354626842552,0.146342438507591,0.04545454545454545,0.005796927628356971,0.03949259311161838,3.997881123004808e-05,25013.25,2.1476065760596077,1.4719570787187288,0.3045189380645752,0.6973345081454226,-10.127160963449944,10.127160963449944,1.3862943611198906,11.513455324569835,0.0,22.0,4.0,0.0,100053.0,0.0,0.0,4.0,0.0,0.0,0.0,0.0,0.0,1.2135857343673706,0.3996680751442909,-0.3896799087524414,0.7616029272290122,0.0,0.0,0.0,0.0,0.0
diff --git a/autosklearn/metalearning/files/precision_multiclass.classification_dense/algorithm_runs.arff b/autosklearn/metalearning/files/precision_multiclass.classification_dense/algorithm_runs.arff
index f1d428d668..9a2e1a5829 100644
--- a/autosklearn/metalearning/files/precision_multiclass.classification_dense/algorithm_runs.arff
+++ b/autosklearn/metalearning/files/precision_multiclass.classification_dense/algorithm_runs.arff
@@ -7,130 +7,135 @@
 @ATTRIBUTE runstatus {ok, timeout, memout, not_applicable, crash, other}
 
 @DATA
-75119,1.0,1,0.004576659038901587,ok
-75212,1.0,2,0.2361809045226131,ok
-252,1.0,3,?,not_applicable
-246,1.0,4,?,not_applicable
-75178,1.0,5,?,not_applicable
-75239,1.0,6,0.0,ok
-75171,1.0,7,0.1652892561983471,ok
-75233,1.0,8,0.039279869067103124,ok
-248,1.0,9,?,not_applicable
-75231,1.0,10,?,not_applicable
-2123,1.0,11,?,not_applicable
-75196,1.0,12,0.01904761904761909,ok
-75188,1.0,13,?,not_applicable
-75092,1.0,14,0.5901639344262295,ok
-75248,1.0,15,0.752823086574655,ok
-75126,1.0,16,0.01385681293302543,ok
-75234,1.0,17,0.027709861450692763,ok
-75150,1.0,18,0.27717391304347827,ok
-258,1.0,19,?,not_applicable
-75168,1.0,20,?,not_applicable
-75235,1.0,21,?,not_applicable
-75159,1.0,22,0.7415730337078652,ok
-244,1.0,23,?,not_applicable
-75221,1.0,24,?,not_applicable
-75219,1.0,25,0.02992633517495391,ok
-75202,1.0,26,?,not_applicable
-3043,1.0,27,0.19318181818181823,ok
-75205,1.0,28,?,not_applicable
-75174,1.0,29,0.22988992379339546,ok
-288,1.0,30,?,not_applicable
-75250,1.0,31,?,not_applicable
-275,1.0,32,?,not_applicable
-75142,1.0,33,0.07092198581560283,ok
-75213,1.0,34,0.14457831325301207,ok
-75099,1.0,35,0.5972222222222222,ok
-75243,1.0,36,?,not_applicable
-75184,1.0,37,0.09474412171507607,ok
-75222,1.0,38,0.5211267605633803,ok
-75175,1.0,39,0.10366766467065869,ok
-233,1.0,40,0.004073319755600768,ok
-75161,1.0,41,0.05748148148148147,ok
-75176,1.0,42,0.014147236049253387,ok
-75090,1.0,43,?,not_applicable
-75114,1.0,44,0.01749999999999996,ok
-260,1.0,45,?,not_applicable
-236,1.0,46,?,not_applicable
-75141,1.0,47,0.07511312217194566,ok
-75107,1.0,48,0.29939393939393943,ok
-262,1.0,49,?,not_applicable
-75146,1.0,50,0.0912992586812329,ok
-75189,1.0,51,0.013769684304798857,ok
-2350,1.0,52,0.5842329404773695,ok
-253,1.0,53,?,not_applicable
-2122,1.0,54,?,not_applicable
-75110,1.0,55,?,not_applicable
-75249,1.0,56,0.02020202020202022,ok
-75108,1.0,57,0.0,ok
-242,1.0,58,?,not_applicable
-75117,1.0,59,0.005494505494505475,ok
-75191,1.0,60,0.08133086876155271,ok
-75226,1.0,61,0.0007194244604316058,ok
-261,1.0,62,0.4301075268817204,ok
-75236,1.0,63,?,not_applicable
-75095,1.0,64,0.24038461538461542,ok
-75148,1.0,65,0.12572533849129597,ok
-75093,1.0,66,0.5923076923076923,ok
-75223,1.0,67,?,not_applicable
-75244,1.0,68,0.7897862232779097,ok
-75109,1.0,69,?,not_applicable
-75197,1.0,70,?,not_applicable
-75127,1.0,71,0.33647102251753414,ok
-75143,1.0,72,0.008919722497522264,ok
-75153,1.0,73,0.0755685986793837,ok
-75173,1.0,74,0.10812539987204095,ok
-75215,1.0,75,0.02931283037001442,ok
-75195,1.0,76,0.0,ok
-75207,1.0,77,?,not_applicable
-266,1.0,78,?,not_applicable
-75225,1.0,79,0.6031746031746033,ok
-75166,1.0,80,0.09728506787330315,ok
-75100,1.0,81,0.5,ok
-75169,1.0,82,?,not_applicable
-75132,1.0,83,0.918880122973981,ok
-273,1.0,84,0.047863247863247915,ok
-75121,1.0,85,0.0,ok
-75098,1.0,86,?,not_applicable
-75115,1.0,87,0.01698513800424628,ok
-75116,1.0,88,0.0023866348448687846,ok
-75185,1.0,89,0.11976047904191611,ok
-2119,1.0,90,?,not_applicable
-75157,1.0,91,0.44099378881987583,ok
-75113,1.0,92,0.08463949843260188,ok
-75134,1.0,93,?,not_applicable
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-75203,1.0,95,?,not_applicable
-75182,1.0,96,0.17280995691718526,ok
-251,1.0,97,?,not_applicable
-75123,1.0,98,?,not_applicable
-75237,1.0,99,0.0,ok
-75125,1.0,100,0.02211302211302213,ok
-75232,1.0,101,0.16666666666666663,ok
-75103,1.0,102,0.06109324758842438,ok
-75192,1.0,103,0.4867109634551495,ok
-75230,1.0,104,?,not_applicable
-75139,1.0,105,0.014101778050275904,ok
-75227,1.0,106,0.15169660678642716,ok
-2120,1.0,107,?,not_applicable
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-75240,1.0,109,0.05513513513513513,ok
-75129,1.0,110,0.6944444444444444,ok
-75198,1.0,111,?,not_applicable
-75201,1.0,112,?,not_applicable
-75112,1.0,113,0.11776753712237586,ok
-75133,1.0,114,0.6388888888888888,ok
-75105,1.0,115,0.9518353490345852,ok
-75154,1.0,116,?,not_applicable
-75177,1.0,117,0.17647058823529416,ok
-2117,1.0,118,0.05458937198067637,ok
-75156,1.0,119,0.19851851851851854,ok
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-75101,1.0,121,0.2653600140836805,ok
-75172,1.0,122,?,not_applicable
-75106,1.0,123,0.8779436152570481,ok
-75179,1.0,124,0.35317460317460314,ok
-75187,1.0,125,0.01266891891891897,ok
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-75193,1.0,127,?,not_applicable
+75192,1.0,1,0.41666666666666663,ok
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+75139,1.0,321,0.01167793484941615,ok
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+246,1.0,8,?,not_applicable
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+75177,1.0,425,0.026666666666666616,ok
+75092,1.0,52,0.34883720930232553,ok
+75239,1.0,15,0.0,ok
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+75112,1.0,42,0.11276127612761278,ok
+75227,1.0,190,0.15127701375245584,ok
+75233,1.0,128,0.03593145384190155,ok
+75182,1.0,138,0.14901165737455646,ok
+253,1.0,27,?,not_applicable
+75157,1.0,331,0.4285714285714286,ok
+75187,1.0,267,0.011824324324324342,ok
+75124,1.0,31,0.3571428571428571,ok
+75090,1.0,32,?,not_applicable
+75222,1.0,33,0.10344827586206895,ok
+75231,1.0,34,?,not_applicable
+75185,1.0,325,0.11234817813765186,ok
+2123,1.0,39,?,not_applicable
+75150,1.0,72,0.297752808988764,ok
+75143,1.0,340,0.007960199004975133,ok
+75196,1.0,44,0.028301886792452824,ok
+75188,1.0,49,?,not_applicable
+75248,1.0,55,0.44999999999999996,ok
+75249,1.0,204,0.01041666666666663,ok
+75113,1.0,335,0.02464788732394363,ok
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+288,1.0,64,?,not_applicable
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+75184,1.0,156,0.03386960203217615,ok
+75234,1.0,70,0.027687296416938123,ok
+258,1.0,73,?,not_applicable
+75166,1.0,281,0.08320251177394034,ok
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+75148,1.0,329,0.1372180451127819,ok
+75235,1.0,81,?,not_applicable
+75159,1.0,84,0.0,ok
+75146,1.0,178,0.0851393188854489,ok
+244,1.0,89,?,not_applicable
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+75219,1.0,101,0.013513513513513487,ok
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+3043,1.0,99,0.013698630136986356,ok
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+75174,1.0,108,0.16874400767018216,ok
+75250,1.0,111,?,not_applicable
+75179,1.0,114,0.25528700906344415,ok
+275,1.0,115,?,not_applicable
+242,1.0,116,?,not_applicable
+75207,1.0,117,?,not_applicable
+75142,1.0,118,0.06557867944303042,ok
+75099,1.0,124,0.0,ok
+75243,1.0,126,?,not_applicable
+75175,1.0,259,0.10056497175141244,ok
+233,1.0,136,0.002044989775051076,ok
+75161,1.0,139,0.05600709324663811,ok
+75176,1.0,328,0.013371788148925035,ok
+262,1.0,146,?,not_applicable
+75129,1.0,339,0.0,ok
+261,1.0,179,0.2678571428571429,ok
+75114,1.0,155,0.012626262626262652,ok
+75093,1.0,157,0.3197278911564626,ok
+260,1.0,158,?,not_applicable
+236,1.0,160,?,not_applicable
+254,1.0,166,0.0,ok
+75107,1.0,169,0.12364425162689807,ok
+75181,1.0,175,?,not_applicable
+75189,1.0,187,0.009679150534749104,ok
+75163,1.0,189,0.04433962264150948,ok
+2350,1.0,192,0.381578947368421,ok
+2122,1.0,195,?,not_applicable
+75110,1.0,198,?,not_applicable
+75213,1.0,424,0.09756097560975607,ok
+75095,1.0,284,0.02941176470588236,ok
+75108,1.0,207,0.0,ok
+75117,1.0,212,0.027837259100642386,ok
+75191,1.0,217,0.0786632744275596,ok
+75226,1.0,220,0.0010857763300760048,ok
+75244,1.0,372,0.15384615384615385,ok
+75236,1.0,223,?,not_applicable
+75169,1.0,227,?,not_applicable
+75116,1.0,320,0.0023474178403756207,ok
+75223,1.0,235,?,not_applicable
+75109,1.0,242,?,not_applicable
+75197,1.0,245,?,not_applicable
+75237,1.0,364,4.677268475206109e-05,ok
+248,1.0,250,?,not_applicable
+2119,1.0,254,?,not_applicable
+75127,1.0,255,0.30221535291087065,ok
+75153,1.0,263,0.07380073800738007,ok
+75195,1.0,271,0.0,ok
+266,1.0,277,?,not_applicable
+75225,1.0,297,0.2857142857142857,ok
+75100,1.0,286,0.5,ok
+75132,1.0,293,0.32793522267206476,ok
+75210,1.0,298,0.0,ok
+273,1.0,300,0.045060658578856105,ok
+75133,1.0,413,0.19999999999999996,ok
+75121,1.0,304,0.0,ok
+75098,1.0,310,?,not_applicable
+75115,1.0,315,0.010638297872340385,ok
+75217,1.0,337,?,not_applicable
+75134,1.0,341,?,not_applicable
+75096,1.0,346,?,not_applicable
+75203,1.0,350,?,not_applicable
+75123,1.0,360,?,not_applicable
+75125,1.0,369,0.026066350710900466,ok
+2120,1.0,371,?,not_applicable
+75232,1.0,374,0.13888888888888884,ok
+75103,1.0,379,0.04870129870129869,ok
+75230,1.0,386,?,not_applicable
+75240,1.0,400,0.050605060506050625,ok
+75198,1.0,404,?,not_applicable
+75201,1.0,407,?,not_applicable
+75105,1.0,415,0.0,ok
+75154,1.0,420,?,not_applicable
+2117,1.0,429,0.04640735343571345,ok
+75156,1.0,433,0.19444444444444442,ok
+75097,1.0,437,0.021655490821149814,ok
+75101,1.0,439,0.2614270449958447,ok
+75172,1.0,443,?,not_applicable
+75106,1.0,449,0.19047619047619047,ok
+75120,1.0,459,0.009009009009009028,ok
+75193,1.0,461,?,not_applicable
diff --git a/autosklearn/metalearning/files/precision_multiclass.classification_dense/configurations.csv b/autosklearn/metalearning/files/precision_multiclass.classification_dense/configurations.csv
index 374eda8a54..05968940e2 100644
--- a/autosklearn/metalearning/files/precision_multiclass.classification_dense/configurations.csv
+++ b/autosklearn/metalearning/files/precision_multiclass.classification_dense/configurations.csv
@@ -1,128 +1,133 @@
 idx,balancing:strategy,classifier:__choice__,classifier:adaboost:algorithm,classifier:adaboost:learning_rate,classifier:adaboost:max_depth,classifier:adaboost:n_estimators,classifier:bernoulli_nb:alpha,classifier:bernoulli_nb:fit_prior,classifier:decision_tree:criterion,classifier:decision_tree:max_depth_factor,classifier:decision_tree:max_features,classifier:decision_tree:max_leaf_nodes,classifier:decision_tree:min_impurity_decrease,classifier:decision_tree:min_samples_leaf,classifier:decision_tree:min_samples_split,classifier:decision_tree:min_weight_fraction_leaf,classifier:extra_trees:bootstrap,classifier:extra_trees:criterion,classifier:extra_trees:max_depth,classifier:extra_trees:max_features,classifier:extra_trees:max_leaf_nodes,classifier:extra_trees:min_impurity_decrease,classifier:extra_trees:min_samples_leaf,classifier:extra_trees:min_samples_split,classifier:extra_trees:min_weight_fraction_leaf,classifier:gradient_boosting:early_stop,classifier:gradient_boosting:l2_regularization,classifier:gradient_boosting:learning_rate,classifier:gradient_boosting:loss,classifier:gradient_boosting:max_bins,classifier:gradient_boosting:max_depth,classifier:gradient_boosting:max_leaf_nodes,classifier:gradient_boosting:min_samples_leaf,classifier:gradient_boosting:n_iter_no_change,classifier:gradient_boosting:scoring,classifier:gradient_boosting:tol,classifier:gradient_boosting:validation_fraction,classifier:k_nearest_neighbors:n_neighbors,classifier:k_nearest_neighbors:p,classifier:k_nearest_neighbors:weights,classifier:lda:n_components,classifier:lda:shrinkage,classifier:lda:shrinkage_factor,classifier:lda:tol,classifier:liblinear_svc:C,classifier:liblinear_svc:dual,classifier:liblinear_svc:fit_intercept,classifier:liblinear_svc:intercept_scaling,classifier:liblinear_svc:loss,classifier:liblinear_svc:multi_class,classifier:liblinear_svc:penalty,classifier:liblinear_svc:tol,classifier:libsvm_svc:C,classifier:libsvm_svc:coef0,classifier:libsvm_svc:degree,classifier:libsvm_svc:gamma,classifier:libsvm_svc:kernel,classifier:libsvm_svc:max_iter,classifier:libsvm_svc:shrinking,classifier:libsvm_svc:tol,classifier:multinomial_nb:alpha,classifier:multinomial_nb:fit_prior,classifier:passive_aggressive:C,classifier:passive_aggressive:average,classifier:passive_aggressive:fit_intercept,classifier:passive_aggressive:loss,classifier:passive_aggressive:tol,classifier:qda:reg_param,classifier:random_forest:bootstrap,classifier:random_forest:criterion,classifier:random_forest:max_depth,classifier:random_forest:max_features,classifier:random_forest:max_leaf_nodes,classifier:random_forest:min_impurity_decrease,classifier:random_forest:min_samples_leaf,classifier:random_forest:min_samples_split,classifier:random_forest:min_weight_fraction_leaf,classifier:sgd:alpha,classifier:sgd:average,classifier:sgd:epsilon,classifier:sgd:eta0,classifier:sgd:fit_intercept,classifier:sgd:l1_ratio,classifier:sgd:learning_rate,classifier:sgd:loss,classifier:sgd:penalty,classifier:sgd:power_t,classifier:sgd:tol,data_preprocessing:categorical_transformer:categorical_encoding:__choice__,data_preprocessing:categorical_transformer:category_coalescence:__choice__,data_preprocessing:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction,data_preprocessing:numerical_transformer:imputation:strategy,data_preprocessing:numerical_transformer:rescaling:__choice__,data_preprocessing:numerical_transformer:rescaling:quantile_transformer:n_quantiles,data_preprocessing:numerical_transformer:rescaling:quantile_transformer:output_distribution,data_preprocessing:numerical_transformer:rescaling:robust_scaler:q_max,data_preprocessing:numerical_transformer:rescaling:robust_scaler:q_min,feature_preprocessor:__choice__,feature_preprocessor:extra_trees_preproc_for_classification:bootstrap,feature_preprocessor:extra_trees_preproc_for_classification:criterion,feature_preprocessor:extra_trees_preproc_for_classification:max_depth,feature_preprocessor:extra_trees_preproc_for_classification:max_features,feature_preprocessor:extra_trees_preproc_for_classification:max_leaf_nodes,feature_preprocessor:extra_trees_preproc_for_classification:min_impurity_decrease,feature_preprocessor:extra_trees_preproc_for_classification:min_samples_leaf,feature_preprocessor:extra_trees_preproc_for_classification:min_samples_split,feature_preprocessor:extra_trees_preproc_for_classification:min_weight_fraction_leaf,feature_preprocessor:extra_trees_preproc_for_classification:n_estimators,feature_preprocessor:fast_ica:algorithm,feature_preprocessor:fast_ica:fun,feature_preprocessor:fast_ica:n_components,feature_preprocessor:fast_ica:whiten,feature_preprocessor:feature_agglomeration:affinity,feature_preprocessor:feature_agglomeration:linkage,feature_preprocessor:feature_agglomeration:n_clusters,feature_preprocessor:feature_agglomeration:pooling_func,feature_preprocessor:kernel_pca:coef0,feature_preprocessor:kernel_pca:degree,feature_preprocessor:kernel_pca:gamma,feature_preprocessor:kernel_pca:kernel,feature_preprocessor:kernel_pca:n_components,feature_preprocessor:kitchen_sinks:gamma,feature_preprocessor:kitchen_sinks:n_components,feature_preprocessor:liblinear_svc_preprocessor:C,feature_preprocessor:liblinear_svc_preprocessor:dual,feature_preprocessor:liblinear_svc_preprocessor:fit_intercept,feature_preprocessor:liblinear_svc_preprocessor:intercept_scaling,feature_preprocessor:liblinear_svc_preprocessor:loss,feature_preprocessor:liblinear_svc_preprocessor:multi_class,feature_preprocessor:liblinear_svc_preprocessor:penalty,feature_preprocessor:liblinear_svc_preprocessor:tol,feature_preprocessor:nystroem_sampler:coef0,feature_preprocessor:nystroem_sampler:degree,feature_preprocessor:nystroem_sampler:gamma,feature_preprocessor:nystroem_sampler:kernel,feature_preprocessor:nystroem_sampler:n_components,feature_preprocessor:pca:keep_variance,feature_preprocessor:pca:whiten,feature_preprocessor:polynomial:degree,feature_preprocessor:polynomial:include_bias,feature_preprocessor:polynomial:interaction_only,feature_preprocessor:random_trees_embedding:bootstrap,feature_preprocessor:random_trees_embedding:max_depth,feature_preprocessor:random_trees_embedding:max_leaf_nodes,feature_preprocessor:random_trees_embedding:min_samples_leaf,feature_preprocessor:random_trees_embedding:min_samples_split,feature_preprocessor:random_trees_embedding:min_weight_fraction_leaf,feature_preprocessor:random_trees_embedding:n_estimators,feature_preprocessor:select_percentile_classification:percentile,feature_preprocessor:select_percentile_classification:score_func,feature_preprocessor:select_rates_classification:alpha,feature_preprocessor:select_rates_classification:mode,feature_preprocessor:select_rates_classification:score_func
-1,none,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,standardize,,,,,extra_trees_preproc_for_classification,False,entropy,None,0.8629651625821806,None,0.0,10,10,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-2,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.3285830024416357,None,0.0,5,16,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.011169833613881398,median,robust_scaler,,,0.9747290462087062,0.18164752380929353,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,57.40271214696761,mutual_info,,,
-3,none,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,188,None,,0.09976330267940166,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,none,,,,,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,-0.05439674489937307,2,0.051865468486041774,poly,727,,,,,,,,,,,,,,,,,
-4,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,23916.78290516781,False,True,1,squared_hinge,ovr,l2,0.0006655607726658531,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.008808715242823137,median,none,,,,,kernel_pca,,,,,,,,,,,,,,,,,,,0.2633153190620261,2,0.004770332714291669,poly,1483,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-5,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,2,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010091246322497477,median,none,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,95.37976833605768,mutual_info,,,
-6,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.933900673392165,None,0.0,7,4,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,minmax,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-7,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3847.325291127572,,,5.0615645862292435,rbf,-1,True,0.0009066251808476257,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,minmax,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.26390607994466947,fwe,f_classif
-8,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.1867279289062114,0.40158855342983024,3,1.6874166311129042,poly,-1,True,0.01266646080615196,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.008174660655642629,mean,minmax,,,,,feature_agglomeration,,,,,,,,,,,,,,,euclidean,complete,291,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-9,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1292.7448076998514,0.004489932344506231,2,1.3088036376373702,poly,-1,False,1.7943262139196537e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,minmax,,,,,feature_agglomeration,,,,,,,,,,,,,,,cosine,average,386,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-10,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.5244313211416313,None,0.0,1,2,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.454300236339658,most_frequent,robust_scaler,,,0.7858830918128734,0.2705174720960696,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-11,weighting,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0006294780694492238,most_frequent,standardize,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.063715750591364,fdr,chi2
-12,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.541827838541001,True,True,squared_hinge,0.0001129851063838815,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,standardize,,,,,extra_trees_preproc_for_classification,True,entropy,None,0.5533459595240017,None,0.0,18,4,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-13,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.25657910619102237,None,0.0,6,6,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.021086303324492208,most_frequent,quantile_transformer,1247,uniform,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.16021572815319468,fpr,chi2
-14,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6185424163558029,False,True,1,squared_hinge,ovr,l2,4.385767395120581e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010397452590017174,median,quantile_transformer,961,normal,,,feature_agglomeration,,,,,,,,,,,,,,,cosine,average,25,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-15,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.643761610990255,None,0.0,20,6,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.039859381630488695,mean,normalize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,
-16,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.8528870159900765,None,0.0,19,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,most_frequent,robust_scaler,,,0.7906395775179057,0.22010064838466728,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.000393619499124,mutual_info,,,
-17,weighting,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.06330379669963868,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.054434091133046744,most_frequent,robust_scaler,,,0.7480112557523932,0.17257870788958515,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-18,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.07518858116896743,None,0.0,7,10,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0027133920607872518,mean,robust_scaler,,,0.7873452530915046,0.16215139072179952,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-19,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,199,None,,0.003239674765949011,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,robust_scaler,,,0.7228447795482008,0.25,kernel_pca,,,,,,,,,,,,,,,,,,,0.09440148939740256,3,0.0008114095405154861,poly,1996,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-20,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.6039096182414764,None,0.0,2,20,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.1939609823704447,most_frequent,robust_scaler,,,0.7331327008874781,0.29747488162877694,extra_trees_preproc_for_classification,True,entropy,None,0.33401603690569853,None,0.0,4,4,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-21,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,2.5882079595129472e-05,0.36125906161027654,auto,255,None,341,119,16,loss,1e-07,0.2924550184445239,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.02338478303505337,most_frequent,robust_scaler,,,0.7866731278723753,0.29543475006822734,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,68,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-22,weighting,adaboost,SAMME,0.2353770068926199,1,62,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.007159634821655197,most_frequent,quantile_transformer,1616,normal,,,feature_agglomeration,,,,,,,,,,,,,,,cosine,average,151,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-23,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7725.740943204492,,,3.122840783722913,rbf,-1,True,1.4116065061807418e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.14280178218510184,most_frequent,none,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,27,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-24,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.21794354428393548,None,0.0,2,16,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0025451910134387575,median,quantile_transformer,1477,uniform,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
-25,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5010952533165973,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.005838375247601407,mean,normalize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,
-26,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5514127836113225,None,0.0,4,20,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.010750756228608195,most_frequent,robust_scaler,,,0.7471420685032172,0.2916152663822578,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.22557315057515023,fwe,chi2
-27,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.5,None,0.0,10,15,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,minmax,,,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l1,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,
-28,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.506204385946851,None,0.0,2,8,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.03433007982751011,mean,none,,,,,extra_trees_preproc_for_classification,False,gini,None,0.32676415738837183,None,0.0,5,6,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-29,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9333237919291875,None,0.0,8,3,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,
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-31,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9561075301148197,None,0.0,6,2,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,
-32,none,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,184,manual,0.5022041786628263,2.3064966255468173e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,most_frequent,standardize,,,,,kernel_pca,,,,,,,,,,,,,,,,,,,-0.32023224593255817,2,0.09479590655633498,poly,1382,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-33,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,1.153931482107762e-10,0.011666190464009397,auto,255,None,29,190,18,loss,1e-07,0.147868307124396,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,
-34,none,adaboost,SAMME,0.6255555220658239,6,446,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.008833097306364968,most_frequent,none,,,,,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,10,None,4,17,1.0,76,,,,,
-35,none,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.563056219822946,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.32793677336996485,most_frequent,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-36,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,0.032732094595234795,0.09918078053098744,auto,255,None,97,54,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.019135985037746533,median,quantile_transformer,1000,uniform,,,feature_agglomeration,,,,,,,,,,,,,,,euclidean,average,37,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-37,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.8680237807500206,None,0.0,6,4,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.021223427667606046,median,standardize,,,,,fast_ica,,,,,,,,,,,deflation,logcosh,364,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-38,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.887898394612674,None,0.0,10,19,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,robust_scaler,,,0.8350698288864961,0.2347163538066168,extra_trees_preproc_for_classification,False,entropy,None,0.49795108918064074,None,0.0,13,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-39,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,0.0029579681987136263,0.03811885694667444,auto,255,None,86,60,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.2946153662568939,most_frequent,quantile_transformer,1646,normal,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,
-40,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7088164344533493,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,mean,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,
-41,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,1.943186107984505e-07,0.08852455702036988,auto,255,None,5,72,19,loss,1e-07,0.039837460762271175,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.01591265419367627,median,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,
-42,weighting,adaboost,SAMME,0.5210651042094924,10,401,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,standardize,,,,,feature_agglomeration,,,,,,,,,,,,,,,euclidean,complete,89,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-43,none,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0686374607394679,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,minmax,,,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.8490260132946401,False,,,,,,,,,,,,,,,
-44,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.6931271368243409,None,0.0,11,10,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.362940004438799,most_frequent,robust_scaler,,,0.9177587796305063,0.17084038458597867,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,30.491514144623952,mutual_info,,,
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-46,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,92.66144306787051,,,0.04654639302091653,rbf,-1,True,0.0022231544280610005,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.005194838694455963,mean,none,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,15,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-47,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,2,8,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,median,robust_scaler,,,0.75,0.25,feature_agglomeration,,,,,,,,,,,,,,,euclidean,complete,12,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-48,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.8095522998548572,None,0.0,17,10,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.031940595071539304,mean,quantile_transformer,1563,normal,,,extra_trees_preproc_for_classification,False,entropy,None,0.9330145598603196,None,0.0,18,10,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-49,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,26.502259985828186,,,9.475662388405154e-05,rbf,-1,False,0.00019690635968217866,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-50,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.339600708038189,False,True,1,squared_hinge,ovr,l2,9.572641158475304e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,quantile_transformer,1193,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-51,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5255991794296913,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-52,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.34697723213923637,None,0.0,15,18,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.1923594385988973,mean,normalize,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,25.77499302945827,chi2,,,
-53,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1252.7997374154156,0.20811431041897355,5,0.0018140593074227453,poly,-1,False,0.03149782358621565,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.001930822730474197,most_frequent,quantile_transformer,233,normal,,,feature_agglomeration,,,,,,,,,,,,,,,euclidean,ward,29,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-54,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,1.5661823810486637e-10,0.08996863554015262,auto,255,None,156,89,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.009331521286159971,median,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,
-55,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.6109468076475573,0.8748383232154038,4,0.41333070422336576,poly,-1,False,3.591268778393482e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.011804925819789203,median,minmax,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-56,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.7610308078756552,None,0.0,1,2,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.010000000000000004,mean,robust_scaler,,,0.9205545465103644,0.20272761060743316,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-57,weighting,adaboost,SAMME.R,0.03127273017917347,10,496,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,standardize,,,,,fast_ica,,,,,,,,,,,deflation,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-58,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,151,None,,0.0003283777607180646,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.2510527431514227,mean,normalize,,,,,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,-0.96093182232312,,0.05762510964607661,sigmoid,2632,,,,,,,,,,,,,,,,,
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-61,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,940.9437776449834,,,1.5839338084625942,rbf,-1,False,0.023072781592565438,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,none,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,77.25763685070707,mutual_info,,,
-62,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,220,auto,,0.0013824567992319003,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00021328932075284388,mean,minmax,,,,,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.039972474835314076,,0.01017810882899714,sigmoid,295,,,,,,,,,,,,,,,,,
-63,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.34534888916828577,0.02513634776687268,2,1.0733091880109409,poly,-1,True,0.0006974263047918307,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,quantile_transformer,1000,normal,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,77.99934218022861,mutual_info,,,
-64,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,29.55731828912983,False,True,1,squared_hinge,ovr,l2,0.0011332486951954968,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,
-65,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,1.2189763538537644e-08,0.11802527280286065,auto,255,None,139,29,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.01616335803518846,median,robust_scaler,,,0.8117197935318263,0.1817743361759944,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-66,none,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.563056219822946,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.32793677336996485,most_frequent,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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-68,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,925.3053743248586,False,True,1,squared_hinge,ovr,l2,0.02764481198395717,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.3850718018366796,most_frequent,quantile_transformer,786,uniform,,,feature_agglomeration,,,,,,,,,,,,,,,cosine,average,156,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-69,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.8278163313806293,None,0.0,3,5,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,robust_scaler,,,0.725835793765774,0.2571431961341018,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.07784020380291123,fdr,f_classif
-70,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0023391956112474004,False,True,squared_hinge,0.011792446147470903,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.08295440990272292,most_frequent,quantile_transformer,962,normal,,,kernel_pca,,,,,,,,,,,,,,,,,,,,,,cosine,1778,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-71,none,adaboost,SAMME,0.011233995624432622,9,477,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.018370622484682127,mean,standardize,,,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6039710338898471,False,,,,,,,,,,,,,,,
-72,none,adaboost,SAMME,0.1280458968526125,7,125,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,robust_scaler,,,0.7156460334356771,0.2981902862926971,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,65.16171885308417,chi2,,,
-73,none,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9931779675565967,None,0.0,18,17,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,robust_scaler,,,0.75,0.2945917010472818,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,
-74,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.01084024283715146,None,0.0,12,3,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,False,,,,,,,,,,,,
-75,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,3.044019499065853e-08,0.030966538899129755,auto,255,None,64,50,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.00860619823050038,median,minmax,,,,,feature_agglomeration,,,,,,,,,,,,,,,cosine,average,36,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-76,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7.491039171224502,True,True,squared_hinge,2.8058138048327884e-05,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,minmax,,,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.7754372809893839,False,True,1,squared_hinge,ovr,l1,3.9643594596766314e-05,,,,,,,,,,,,,,,,,,,,,,
-77,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.34936025988397124,None,0.0,2,13,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00018601309647406987,median,robust_scaler,,,0.8634309348843472,0.26976989465367607,extra_trees_preproc_for_classification,False,entropy,None,0.48025175288975036,None,0.0,6,17,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-78,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,2.925599188129995e-05,0.12560285030651847,auto,255,None,300,193,17,loss,1e-07,0.039442997915546514,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00299400754997757,mean,robust_scaler,,,0.7093292916146515,0.009366983030047793,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-79,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.933900673392165,None,0.0,7,4,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,minmax,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-80,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,128.75020223284181,,,0.09047921703481414,rbf,-1,False,0.0028029846849427493,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,none,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,236,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-81,none,adaboost,SAMME,0.011233995624432622,9,477,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.018370622484682127,mean,standardize,,,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6039710338898471,False,,,,,,,,,,,,,,,
-82,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,21.85207494001697,0.18872999589786676,4,1.1863646021868761,poly,-1,False,0.0021338371901294443,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0034080852834770217,mean,normalize,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.11326005109886934,fdr,f_classif
-83,weighting,adaboost,SAMME,0.38341570193537416,9,250,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,minmax,,,,,feature_agglomeration,,,,,,,,,,,,,,,euclidean,average,13,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-84,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,0.037849720944694,0.1268815770019783,auto,255,None,11,99,11,loss,1e-07,0.10654523627313527,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,
-85,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.593728171200453,None,0.0,7,20,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.02830941310359363,mean,normalize,,,,,extra_trees_preproc_for_classification,False,entropy,None,0.7389446586281172,None,0.0,20,11,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-86,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,1,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.019797877010737826,mean,normalize,,,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.8239678741128165,False,,,,,,,,,,,,,,,
-87,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.46057831591617715,False,True,hinge,0.04557857428827514,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00027457445401600137,median,standardize,,,,,extra_trees_preproc_for_classification,True,gini,None,0.48190346970486964,None,0.0,17,18,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-88,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.46057831591617715,False,True,hinge,0.04557857428827514,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00027457445401600137,median,standardize,,,,,extra_trees_preproc_for_classification,True,gini,None,0.48190346970486964,None,0.0,17,18,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-89,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,64,auto,,0.00021332620906594865,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.026032882800756493,median,standardize,,,,,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,-0.9513223792709335,,0.00029881582960753004,sigmoid,543,,,,,,,,,,,,,,,,,
-90,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.26356754187780895,None,0.0,4,3,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010966178401758774,mean,robust_scaler,,,0.7817043861798886,0.24371720112407158,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
-91,weighting,adaboost,SAMME.R,0.13547552520095074,4,62,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.011851011202809002,most_frequent,standardize,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4888776875314595,fpr,chi2
-92,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,80.0028902468169,False,True,1,squared_hinge,ovr,l2,0.0024481999829574722,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.15047970487774687,mean,quantile_transformer,1130,normal,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,1.1912646635544712,False,True,1,squared_hinge,ovr,l1,0.00014314728486450857,,,,,,,,,,,,,,,,,,,,,,
-93,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.22879311080499845,None,0.0,8,4,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.014320022547615783,median,minmax,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.238630703440461,mutual_info,,,
-94,weighting,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,2,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0073667222552306465,median,robust_scaler,,,0.701595034693991,0.29514640404971515,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6066051105210388,False,,,,,,,,,,,,,,,
-95,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.107967527639527e-05,True,True,hinge,0.00448294745544775,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,quantile_transformer,953,normal,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,35.00114345405331,chi2,,,
-96,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.14148638702297855,None,0.0,2,6,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.0038499650338378643,most_frequent,quantile_transformer,1759,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-97,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,1.1522414705935405e-10,0.6132800183073315,auto,255,None,1607,67,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010096911249562407,mean,robust_scaler,,,0.7663493326967942,0.136094217337434,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-98,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1228.3897711257332,0.6862670093933454,5,1.113863025113022,poly,-1,True,1.9271116749907707e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.018161834200128672,mean,normalize,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.06278549919408385,fpr,chi2
-99,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,184.8462559762274,,,7.224209321190626,rbf,-1,True,0.00019266295692834046,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.008361623516030384,most_frequent,standardize,,,,,fast_ica,,,,,,,,,,,parallel,exp,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-100,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.9815182201168626,None,0.0,15,19,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,robust_scaler,,,0.8878662977383482,0.25666364929970126,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,44.15748219826322,mutual_info,,,
-101,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2574971638664341,True,True,hinge,1.8027411099296446e-05,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.00536396328125103,median,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,
-102,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7908336536113714,None,0.0,9,3,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,minmax,,,,,extra_trees_preproc_for_classification,False,entropy,None,0.17440717696897978,None,0.0,5,18,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-103,none,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,128,manual,0.8574560771111307,0.010886139381599514,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.24115007944216788,most_frequent,normalize,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,62.26514170991452,mutual_info,,,
-104,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.6282663389679584,None,0.0,1,3,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,median,minmax,,,,,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-105,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,8.14394583618027e-09,0.1314274861878701,auto,255,None,27,15,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.3598067419611515,mean,robust_scaler,,,0.7494615433236279,0.260649229829574,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
-106,none,adaboost,SAMME.R,0.9261146078592859,9,476,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.012453052108117785,median,robust_scaler,,,0.7492076537583867,0.25853088636132765,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,6,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-107,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,11.046854203258679,,,0.07809571976769347,rbf,-1,False,0.00036193159909053734,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.006445557635410205,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-108,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.7922837297504705,False,True,1,squared_hinge,ovr,l2,0.0005476597326342352,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.012674631437254894,most_frequent,none,,,,,extra_trees_preproc_for_classification,False,entropy,None,0.23145146971319638,None,0.0,7,20,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-109,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.18060139887649052,None,0.0,10,12,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.010000000000000004,most_frequent,robust_scaler,,,0.9298612094255864,0.1263597946722308,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
-110,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.4111348710613201,None,0.0,16,8,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.01224348527546641,most_frequent,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,
-111,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6427543483503397,True,True,hinge,0.00045318775930916944,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.009338484332306904,median,quantile_transformer,174,normal,,,kernel_pca,,,,,,,,,,,,,,,,,,,,,,cosine,1848,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-112,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.02021502857757827,True,,0.055848937053280076,True,,constant,log,l2,,0.00016356162584832537,no_encoding,no_coalescense,,median,quantile_transformer,27,normal,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.11873149622606233,fwe,chi2
-113,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,9.029473608568458e-09,0.15779990576964315,auto,255,None,37,71,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.018093177195644097,mean,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,False,,,,,,,,,,,,
-114,weighting,adaboost,SAMME.R,0.23766684501868082,10,97,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.004737885672001703,median,none,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50.0,chi2,,,
-115,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.129143822880219,True,True,hinge,0.00012612093569084375,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0016222546082911887,most_frequent,quantile_transformer,1309,uniform,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8.816417526015055,f_classif,,,
-116,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.4864284879550203,None,0.0,1,4,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.008665854799630503,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-117,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.43699642289829416,None,0.0,6,20,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,quantile_transformer,1604,uniform,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-118,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.536709062860521e-07,True,,,True,,optimal,squared_hinge,l1,,1.6934515188687678e-05,one_hot_encoding,no_coalescense,,most_frequent,normalize,,,,,feature_agglomeration,,,,,,,,,,,,,,,cosine,complete,306,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-119,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5508478903876926,None,0.0,5,3,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0020120736976747405,most_frequent,minmax,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-120,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.5603377230962405,False,True,hinge,0.04986267764654238,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00014501962937745386,median,standardize,,,,,extra_trees_preproc_for_classification,True,gini,None,0.44197026095225617,None,0.0,20,4,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-121,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5836006490230695,None,0.0,1,19,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.02127792104027424,most_frequent,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,
-122,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.922871100218626,False,True,squared_hinge,0.09957180737451943,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0016563186856411274,median,quantile_transformer,850,uniform,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.09123695282772847,fpr,chi2
-123,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.20612848067724507,False,True,1,squared_hinge,ovr,l2,0.00017832980955957004,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0007502816173508168,median,quantile_transformer,1083,uniform,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.02820386332530596,fwe,f_classif
-124,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1561.1002600303584,,,0.0024048551704194507,rbf,-1,False,0.0001936214667496851,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,minmax,,,,,fast_ica,,,,,,,,,,,parallel,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-125,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.087313242780732,0.0636709914987994,3,0.004481917125024585,poly,-1,False,0.01848880618941367,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.002136015035740733,mean,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-126,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.03204653439894958,True,True,squared_hinge,0.00013006393401112688,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.013506575694382031,most_frequent,normalize,,,,,feature_agglomeration,,,,,,,,,,,,,,,euclidean,ward,356,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-127,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.6466927589470762,None,0.0,11,2,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.009672465311659264,most_frequent,standardize,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,369,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+1,none,decision_tree,,,,,,,gini,0.7229490531192699,1.0,None,0.0,1,18,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.00013586681143040562,median,quantile_transformer,647,uniform,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+4,none,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,quantile_transformer,111,uniform,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4748887602127273,fdr,f_classif
+7,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.36028513793687317,None,0.0,19,8,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,standardize,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,48.144408204553606,chi2,,,
+8,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,114,manual,0.11778631707605758,0.0029112721339713465,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.015582195257240751,median,normalize,,,,,kernel_pca,,,,,,,,,,,,,,,,,,,0.42909623253327767,3,0.25407612000343777,poly,1689,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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+20,none,extra_trees,,,,,,,,,,,,,,,True,gini,None,0.9004196018665994,None,0.0,18,20,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.392993781506731,median,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,
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+31,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,8.757412830636845e-06,0.10095376320667454,auto,255,None,3,2,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.015401766535365336,median,quantile_transformer,647,normal,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,
+32,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,101,auto,,1.553086088086431e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.01137059177001425,most_frequent,normalize,,,,,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,-0.021621751597165284,4,0.7468597264810062,poly,5078,,,,,,,,,,,,,,,,,
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+34,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,0.06671977293714751,0.04456236025167353,auto,255,None,3,163,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.4333293305857194,mean,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+39,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.6799215694343422,None,0.0,1,5,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.004724310659953132,most_frequent,quantile_transformer,1037,normal,,,fast_ica,,,,,,,,,,,parallel,exp,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+42,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.07116943806938189,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.012264499640288412,mean,robust_scaler,,,0.75,0.25690284198822444,feature_agglomeration,,,,,,,,,,,,,,,cosine,complete,142,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+44,none,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l2,2.858526169098402e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0007827505640681123,median,standardize,,,,,extra_trees_preproc_for_classification,False,entropy,None,0.3443310459451119,None,0.0,3,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+49,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00010123414317660679,False,,0.04011567850168966,True,,constant,log,l1,,0.00014179758438189507,one_hot_encoding,minority_coalescer,0.008920898694379564,mean,robust_scaler,,,0.7712963178784032,0.2702398922671455,kernel_pca,,,,,,,,,,,,,,,,,,,,,,cosine,1090,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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+64,weighting,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.5927254703947399,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.004193053494360822,median,none,,,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.5779022723643873,False,,,,,,,,,,,,,,,
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+337,weighting,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,minmax,,,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,227.42750098824558,False,True,1,squared_hinge,ovr,l1,0.0018060472646546995,,,,,,,,,,,,,,,,,,,,,,
+339,none,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.3588413759490688,None,0.0,1,7,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.02576735869993575,median,quantile_transformer,781,uniform,,,fast_ica,,,,,,,,,,,parallel,exp,14,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+340,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.7302408485410412,None,0.0,8,12,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.027592968439566132,median,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+341,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.513560345508016,None,0.0,3,2,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,median,quantile_transformer,1044,uniform,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.07550342883878675,,mutual_info_classif
+346,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,0.15762740504817374,0.17692452709577644,auto,255,None,282,9,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.005207945885722629,most_frequent,robust_scaler,,,0.75,0.27100271805667314,feature_agglomeration,,,,,,,,,,,,,,,euclidean,complete,106,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+350,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.2318705830210126,True,True,hinge,0.0002994421772278671,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.13310408167721913,most_frequent,quantile_transformer,1036,uniform,,,kernel_pca,,,,,,,,,,,,,,,,,,,,,,cosine,703,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+360,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.5720469164289109,None,0.0,1,17,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.03310727058091163,median,robust_scaler,,,0.8680123006808074,0.024825227678742904,fast_ica,,,,,,,,,,,parallel,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+364,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.11789257899273609,None,0.0,2,3,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.4082847014868829,median,robust_scaler,,,0.9073691418507908,0.1954896244813517,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,
+369,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.9347805195624181,None,0.0,2,19,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.1594813217854149,mean,normalize,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,52.65009549586377,f_classif,,,
+371,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,4.198700105243736e-09,0.011070860038925674,auto,255,None,9,116,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010247554402833578,median,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,
+372,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,0.09219798181799597,0.010664673522048937,auto,255,None,8,100,18,loss,1e-07,0.06150512744434074,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,quantile_transformer,967,uniform,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+374,none,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.7304811343030777,None,0.0,2,3,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,robust_scaler,,,0.7202654714984674,0.21555973677200435,feature_agglomeration,,,,,,,,,,,,,,,euclidean,average,149,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+379,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,1.2940289605295536e-09,0.15591343717459372,auto,255,None,7,59,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0005549095909238961,mean,quantile_transformer,297,uniform,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,58.10560700979111,chi2,,,
+386,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9358939105347388,None,0.0,1,5,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,none,,,,,fast_ica,,,,,,,,,,,parallel,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+400,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,0.0014622110897382257,0.031971624780486804,auto,255,None,195,15,2,loss,1e-07,0.07459887038090841,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.013029979892169268,mean,standardize,,,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.08573874028833707,False,True,1,squared_hinge,ovr,l1,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,
+404,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8.380057363340162,False,True,hinge,0.0020653831067503525,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.05020677662128942,median,quantile_transformer,560,normal,,,kernel_pca,,,,,,,,,,,,,,,,,,,,,,cosine,891,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+407,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.7292011996224884,True,True,hinge,8.162763086383311e-05,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010337761588362885,median,quantile_transformer,877,uniform,,,kernel_pca,,,,,,,,,,,,,,,,,,,,,,cosine,1317,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+413,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,3.562032726117879e-09,0.07935074296383826,auto,255,None,25,20,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0035033827976084825,mean,minmax,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+415,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,0.00037966471646911845,0.016422945536911454,auto,255,None,10,56,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.002419439132687246,median,quantile_transformer,828,uniform,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+420,weighting,adaboost,SAMME.R,0.05920795019389743,8,313,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,standardize,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,104,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+424,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,2.344883102482138e-07,0.0328548789988159,auto,255,None,42,24,16,loss,1e-07,0.13015466346727866,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,most_frequent,robust_scaler,,,0.7010898774332888,0.2534280821273264,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,
+425,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,1.1007644378635384e-06,0.1638498547151709,auto,255,None,601,3,16,loss,1e-07,0.024706703809421183,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.11489180733211171,most_frequent,quantile_transformer,285,uniform,,,feature_agglomeration,,,,,,,,,,,,,,,euclidean,average,332,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+429,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.03329934974307803,False,True,squared_hinge,0.0009304676085290937,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,minmax,,,,,kitchen_sinks,,,,,,,,,,,,,,,,,,,,,,,,0.11746032349918363,139,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+433,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5323411241263409,None,0.0,2,18,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,median,standardize,,,,,extra_trees_preproc_for_classification,True,entropy,None,0.6132742708509392,None,0.0,6,16,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+437,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.758794895040923,None,0.0,18,19,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,quantile_transformer,1000,uniform,,,extra_trees_preproc_for_classification,False,gini,None,0.9944228208678969,None,0.0,20,6,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+439,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.5983462947802015,None,0.0,2,16,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00020635833184954756,most_frequent,minmax,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.3423975961749763,fdr,f_classif
+443,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.6269076084367177,None,0.0,1,7,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.12943250859943486,most_frequent,standardize,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,62.84526554323969,chi2,,,
+449,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,2.074711735072034e-07,0.01106799034691714,auto,255,None,13,24,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.020949205931423413,mean,standardize,,,,,extra_trees_preproc_for_classification,True,gini,None,0.43505400875624267,None,0.0,20,8,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+459,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.745748188733041e-05,True,True,hinge,2.181659959258924e-05,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.13921206193066876,most_frequent,quantile_transformer,60,normal,,,extra_trees_preproc_for_classification,False,entropy,None,0.7619669647366906,None,0.0,3,4,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+461,none,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.9796199105973851,None,0.0,2,13,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.00845950033601661,mean,robust_scaler,,,0.8275228560550734,0.23814615163106348,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
diff --git a/autosklearn/metalearning/files/precision_multiclass.classification_dense/description.txt b/autosklearn/metalearning/files/precision_multiclass.classification_dense/description.txt
index cecf1a1e4d..b9c894dc95 100644
--- a/autosklearn/metalearning/files/precision_multiclass.classification_dense/description.txt
+++ b/autosklearn/metalearning/files/precision_multiclass.classification_dense/description.txt
@@ -1,8 +1,9 @@
 features_cutoff_time: 3600
 features_cutoff_memory: 3072
-number_of_feature_steps: 35
+number_of_feature_steps: 42
 feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances
 feature_step LogNumberOfInstances: LogNumberOfInstances
+feature_step NumberOfClasses: NumberOfClasses
 feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures
 feature_step LogNumberOfFeatures: LogNumberOfFeatures
 feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues
@@ -20,6 +21,11 @@ feature_step DatasetRatio: DatasetRatio, LogDatasetRatio
 feature_step LogDatasetRatio: LogDatasetRatio
 feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio
 feature_step LogInverseDatasetRatio: LogInverseDatasetRatio
+feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD
+feature_step ClassProbabilityMin: ClassProbabilityMin
+feature_step ClassProbabilityMax: ClassProbabilityMax
+feature_step ClassProbabilityMean: ClassProbabilityMean
+feature_step ClassProbabilitySTD: ClassProbabilitySTD
 feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum
 feature_step SymbolsMin: SymbolsMin
 feature_step SymbolsMax: SymbolsMax
@@ -36,11 +42,12 @@ feature_step SkewnessMin: SkewnessMin
 feature_step SkewnessMax: SkewnessMax
 feature_step SkewnessMean: SkewnessMean
 feature_step SkewnessSTD: SkewnessSTD
-features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
+feature_step ClassEntropy: ClassEntropy
+features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy
 features_stochastic: 
-default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
+default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy
 
-algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127
+algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235,236,237,238,239,240,241,242,243,244,245,246,247,248,249,250,251,252,253,254,255,256,257,258,259,260,261,262,263,264,265,266,267,268,269,270,271,272,273,274,275,276,277,278,279,280,281,282,283,284,285,286,287,288,289,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,312,313,314,315,316,317,318,319,320,321,322,323,324,325,326,327,328,329,330,331,332,333,334,335,336,337,338,339,340,341,342,343,344,345,346,347,348,349,350,351,352,353,354,355,356,357,358,359,360,361,362,363,364,365,366,367,368,369,370,371,372,373,374,375,376,377,378,379,380,381,382,383,384,385,386,387,388,389,390,391,392,393,394,395,396,397,398,399,400,401,402,403,404,405,406,407,408,409,410,411,412,413,414,415,416,417,418,419,420,421,422,423,424,425,426,427,428,429,430,431,432,433,434,435,436,437,438,439,440,441,442,443,444,445,446,447,448,449,450,451,452,453,454,455,456,457,458,459,460,461,462,463,464
 algorithms_stochastic: 
 performance_measures: precision
 performance_type: solution_quality
diff --git a/autosklearn/metalearning/files/precision_multiclass.classification_dense/feature_costs.arff b/autosklearn/metalearning/files/precision_multiclass.classification_dense/feature_costs.arff
index 0e17e20d22..8512fd9275 100644
--- a/autosklearn/metalearning/files/precision_multiclass.classification_dense/feature_costs.arff
+++ b/autosklearn/metalearning/files/precision_multiclass.classification_dense/feature_costs.arff
@@ -4,6 +4,7 @@
 @ATTRIBUTE repetition NUMERIC
 @ATTRIBUTE NumberOfInstances NUMERIC
 @ATTRIBUTE LogNumberOfInstances NUMERIC
+@ATTRIBUTE NumberOfClasses NUMERIC
 @ATTRIBUTE NumberOfFeatures NUMERIC
 @ATTRIBUTE LogNumberOfFeatures NUMERIC
 @ATTRIBUTE MissingValues NUMERIC
@@ -21,6 +22,11 @@
 @ATTRIBUTE LogDatasetRatio NUMERIC
 @ATTRIBUTE InverseDatasetRatio NUMERIC
 @ATTRIBUTE LogInverseDatasetRatio NUMERIC
+@ATTRIBUTE ClassOccurences NUMERIC
+@ATTRIBUTE ClassProbabilityMin NUMERIC
+@ATTRIBUTE ClassProbabilityMax NUMERIC
+@ATTRIBUTE ClassProbabilityMean NUMERIC
+@ATTRIBUTE ClassProbabilitySTD NUMERIC
 @ATTRIBUTE NumSymbols NUMERIC
 @ATTRIBUTE SymbolsMin NUMERIC
 @ATTRIBUTE SymbolsMax NUMERIC
@@ -37,162 +43,138 @@
 @ATTRIBUTE SkewnessMax NUMERIC
 @ATTRIBUTE SkewnessMean NUMERIC
 @ATTRIBUTE SkewnessSTD NUMERIC
+@ATTRIBUTE ClassEntropy NUMERIC
 
 @DATA
-75123,1.0,1e-05,1e-05,1e-05,1e-05,0.00253,0.00221,1e-05,0.00024,3e-05,0.00011,0.0,2e-05,2e-05,5e-05,8e-05,2e-05,1e-05,7e-05,1e-05,0.00072,1e-05,1e-05,0.00019,0.00041,0.00011,0.00061,0.00012,9e-05,0.00015,0.00025,0.00067,0.00014,0.00011,0.00016,0.00027
-75090,1.0,1e-05,1e-05,2e-05,2e-05,0.00815,0.00368,7e-05,0.00228,9e-05,0.00235,0.0,5e-05,5e-05,0.0001,0.00013,0.00013,1e-05,0.00021,2e-05,0.00064,0.0,0.0,0.00014,0.00038,0.00011,0.00066,0.00011,0.00014,0.00014,0.00026,0.00066,0.00011,0.00013,0.00015,0.00027
-2123,1.0,1e-05,1e-05,1e-05,1e-05,0.00097,0.00079,1e-05,0.00013,1e-05,7e-05,0.0,2e-05,2e-05,5e-05,8e-05,2e-05,1e-05,5e-05,1e-05,0.0007,1e-05,0.0,0.00017,0.00042,0.0001,0.00052,6e-05,8e-05,0.00012,0.00026,0.00049,6e-05,0.00011,9e-05,0.00023
-248,1.0,1e-05,1e-05,1e-05,1e-05,0.00088,0.00077,1e-05,0.0001,1e-05,4e-05,0.0,1e-05,1e-05,4e-05,5e-05,2e-05,1e-05,4e-05,1e-05,0.00041,1e-05,0.0,9e-05,0.00022,9e-05,0.00046,6e-05,8e-05,9e-05,0.00023,0.00049,7e-05,0.0001,0.0001,0.00021
-75101,1.0,1e-05,1e-05,1e-05,1e-05,0.0418,0.03546,1e-05,0.00389,1e-05,0.00248,0.0,2e-05,2e-05,4e-05,6e-05,2e-05,1e-05,5e-05,2e-05,0.00045,1e-05,0.0,0.00011,0.00023,0.0001,0.00041,6e-05,8e-05,0.0001,0.00018,0.00047,6e-05,0.0001,8e-05,0.00023
-75191,1.0,1e-05,1e-05,3e-05,1e-05,0.53436,0.40777,2e-05,0.09655,3e-05,0.0301,1e-05,2e-05,3e-05,5e-05,0.00011,7e-05,1e-05,0.00011,3e-05,0.00084,5e-05,7e-05,0.00017,0.00033,0.00023,0.00088,0.00012,0.00017,0.00032,0.00026,0.00068,9e-05,0.00012,0.00013,0.00035
-75187,1.0,1e-05,1e-05,2e-05,1e-05,0.00548,0.00473,1e-05,0.0005,2e-05,0.00029,1e-05,2e-05,2e-05,7e-05,9e-05,3e-05,1e-05,6e-05,1e-05,0.00062,1e-05,1e-05,0.00014,0.00033,0.00013,0.00038,7e-05,7e-05,8e-05,0.00016,0.00047,6e-05,9e-05,9e-05,0.00023
-266,1.0,1e-05,1e-05,1e-05,1e-05,0.0018,0.00146,1e-05,0.00023,2e-05,0.00015,2e-05,2e-05,2e-05,6e-05,8e-05,3e-05,1e-05,6e-05,1e-05,0.00059,1e-05,1e-05,0.00014,0.00031,0.00013,0.0004,5e-05,7e-05,8e-05,0.0002,0.00044,8e-05,8e-05,8e-05,0.0002
-253,1.0,1e-05,1e-05,1e-05,1e-05,0.00082,0.00063,1e-05,0.00016,2e-05,5e-05,0.0,1e-05,1e-05,4e-05,5e-05,2e-05,1e-05,5e-05,2e-05,0.00046,1e-05,0.0,9e-05,0.00024,0.00012,0.00044,6e-05,7e-05,8e-05,0.00023,0.00043,6e-05,0.0001,8e-05,0.00019
-75217,1.0,1e-05,1e-05,2e-05,1e-05,0.00196,0.00158,1e-05,0.00026,2e-05,0.00016,1e-05,3e-05,3e-05,7e-05,0.0001,3e-05,1e-05,7e-05,2e-05,0.00089,1e-05,1e-05,0.00022,0.00053,0.00013,0.00074,9e-05,0.0001,0.00019,0.00036,0.00065,0.00013,0.0001,0.00011,0.00031
-75176,1.0,1e-05,1e-05,2e-05,1e-05,0.01406,0.01285,1e-05,0.00094,2e-05,0.00031,1e-05,3e-05,3e-05,6e-05,9e-05,3e-05,1e-05,7e-05,2e-05,0.00069,1e-05,1e-05,0.00017,0.00035,0.00015,0.00085,0.00018,0.00011,0.0002,0.00036,0.0011,0.00022,0.00023,0.00023,0.00042
-2117,1.0,1e-05,1e-05,1e-05,1e-05,0.01735,0.0154,1e-05,0.00141,1e-05,0.00057,0.0,1e-05,1e-05,3e-05,6e-05,2e-05,1e-05,5e-05,1e-05,0.0004,1e-05,0.0,8e-05,0.00021,0.0001,0.00065,0.00012,0.00011,0.00015,0.00026,0.00055,7e-05,0.00014,9e-05,0.00025
-75148,1.0,1e-05,1e-05,2e-05,2e-05,0.00129,0.00115,1e-05,0.00012,1e-05,4e-05,0.0,2e-05,2e-05,4e-05,5e-05,2e-05,1e-05,4e-05,1e-05,0.0004,1e-05,0.0,9e-05,0.00021,9e-05,0.00036,7e-05,7e-05,8e-05,0.00015,0.00041,6e-05,8e-05,8e-05,0.00019
-75103,1.0,1e-05,1e-05,1e-05,1e-05,0.01302,0.00819,3e-05,0.00234,3e-05,0.00255,0.0,2e-05,2e-05,6e-05,0.00012,5e-05,1e-05,0.00011,1e-05,0.0005,1e-05,0.0,9e-05,0.00029,0.00011,0.00054,8e-05,0.00011,0.00011,0.00024,0.00066,7e-05,0.00015,0.00011,0.00032
-75168,1.0,1e-05,0.0,1e-05,1e-05,0.02353,0.00601,0.00016,0.01754,0.00018,0.00031,0.0,0.00013,0.00013,0.00027,0.0003,0.00032,1e-05,0.00042,1e-05,0.0004,1e-05,0.0,8e-05,0.00021,0.0001,0.0011,0.00022,0.00025,0.00026,0.00037,0.00124,0.00022,0.00025,0.00028,0.00049
-75157,1.0,1e-05,1e-05,1e-05,1e-05,0.00125,0.00106,1e-05,0.00015,2e-05,7e-05,0.0,2e-05,2e-05,5e-05,6e-05,2e-05,1e-05,4e-05,1e-05,0.00048,2e-05,0.0,0.0001,0.00026,0.0001,0.00039,6e-05,7e-05,8e-05,0.00019,0.00043,6e-05,9e-05,8e-05,0.0002
-75092,1.0,1e-05,1e-05,1e-05,1e-05,0.00099,0.00072,1e-05,0.00018,1e-05,0.00011,0.0,2e-05,2e-05,4e-05,6e-05,2e-05,1e-05,5e-05,1e-05,0.00058,1e-05,0.0,0.00014,0.00034,9e-05,0.00041,6e-05,8e-05,9e-05,0.00017,0.00045,7e-05,0.0001,9e-05,0.00019
-75213,1.0,1e-05,1e-05,1e-05,1e-05,0.00096,0.00079,1e-05,0.00013,1e-05,7e-05,1e-05,2e-05,2e-05,5e-05,7e-05,2e-05,1e-05,6e-05,1e-05,0.00056,1e-05,1e-05,0.00013,0.00029,0.00012,0.00056,6e-05,8e-05,9e-05,0.00033,0.00063,8e-05,0.00014,0.00013,0.00029
-75236,1.0,1e-05,1e-05,1e-05,1e-05,0.00208,0.00115,2e-05,0.00054,3e-05,0.00044,0.0,3e-05,3e-05,7e-05,8e-05,5e-05,1e-05,8e-05,1e-05,0.00062,0.0,0.0,0.00016,0.00036,0.0001,0.00068,0.0001,0.00014,0.00016,0.00029,0.00072,0.0001,0.00015,0.00015,0.00032
-75119,1.0,1e-05,1e-05,2e-05,1e-05,0.04185,0.01398,0.00063,0.01614,0.0007,0.01306,0.0,0.00046,0.00045,0.00091,0.00097,0.0012,1e-05,0.0015,1e-05,0.00047,1e-05,0.0,8e-05,0.00026,0.00012,0.00421,0.00091,0.00097,0.00103,0.0013,0.00402,0.00082,0.0008,0.00097,0.00144
-75227,1.0,1e-05,1e-05,2e-05,1e-05,0.00416,0.00373,1e-05,0.00038,2e-05,9e-05,1e-05,2e-05,2e-05,6e-05,0.0001,3e-05,1e-05,7e-05,2e-05,0.00063,1e-05,1e-05,0.00015,0.00034,0.00013,0.00037,6e-05,8e-05,8e-05,0.00016,0.00043,6e-05,9e-05,9e-05,0.0002
-75141,1.0,1e-05,1e-05,2e-05,1e-05,0.00631,0.00572,1e-05,0.00047,2e-05,0.00016,1e-05,3e-05,3e-05,8e-05,9e-05,3e-05,1e-05,7e-05,2e-05,0.00068,1e-05,1e-05,0.00015,0.00036,0.00014,0.00039,6e-05,8e-05,9e-05,0.00017,0.00047,8e-05,9e-05,0.0001,0.00021
-75188,1.0,1e-05,1e-05,2e-05,1e-05,0.05522,0.00659,0.00042,0.04887,0.00048,0.00066,0.0,0.00042,0.00037,0.00065,0.00077,0.0009,1e-05,0.00098,2e-05,0.00073,4e-05,7e-05,0.00018,0.00027,0.00017,0.00228,0.0005,0.00055,0.00052,0.00071,0.00253,0.00049,0.00055,0.00058,0.00091
-75159,1.0,2e-05,2e-05,2e-05,2e-05,0.00136,0.00099,7e-05,0.00033,8e-05,0.00021,2e-05,0.0001,0.0001,0.00014,0.00016,0.00016,7e-05,0.00019,8e-05,0.00092,1e-05,9e-05,0.0002,0.00039,0.00023,0.00049,7e-05,8e-05,0.00012,0.00023,0.00043,6e-05,8e-05,8e-05,0.0002
-75192,1.0,1e-05,1e-05,2e-05,1e-05,0.00188,0.00165,1e-05,0.00016,1e-05,9e-05,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,1e-05,4e-05,1e-05,0.00039,1e-05,0.0,9e-05,0.00021,9e-05,0.00048,5e-05,8e-05,0.0001,0.00025,0.00045,6e-05,0.0001,0.0001,0.0002
-262,1.0,1e-05,1e-05,1e-05,1e-05,0.00488,0.00434,1e-05,0.00037,1e-05,0.00019,0.0,2e-05,1e-05,4e-05,6e-05,2e-05,1e-05,4e-05,1e-05,0.0004,1e-05,0.0,9e-05,0.00021,9e-05,0.00037,6e-05,7e-05,8e-05,0.00017,0.00045,6e-05,0.00012,9e-05,0.00018
-233,1.0,2e-05,1e-05,2e-05,1e-05,0.00289,0.0023,2e-05,0.00037,2e-05,0.00026,1e-05,3e-05,3e-05,6e-05,9e-05,3e-05,1e-05,8e-05,3e-05,0.00064,1e-05,1e-05,0.00016,0.00033,0.00014,0.00045,6e-05,9e-05,9e-05,0.00022,0.0005,7e-05,0.0001,9e-05,0.00024
-75177,1.0,1e-05,1e-05,1e-05,1e-05,0.0017,0.0014,1e-05,0.0002,1e-05,0.00013,0.0,2e-05,1e-05,3e-05,5e-05,2e-05,1e-05,4e-05,1e-05,0.00038,1e-05,0.0,8e-05,0.0002,0.0001,0.00038,6e-05,7e-05,9e-05,0.00017,0.00045,6e-05,9e-05,8e-05,0.00021
-261,1.0,2e-05,2e-05,1e-05,1e-05,0.00079,0.00058,1e-05,0.00014,1e-05,0.0001,0.0,2e-05,2e-05,4e-05,8e-05,2e-05,1e-05,5e-05,1e-05,0.00045,1e-05,0.0,0.0001,0.00023,0.0001,0.0005,8e-05,9e-05,0.00011,0.00022,0.00045,6e-05,0.0001,9e-05,0.0002
-2120,1.0,1e-05,1e-05,1e-05,1e-05,0.00459,0.00383,1e-05,0.00045,2e-05,0.00033,1e-05,3e-05,2e-05,7e-05,8e-05,3e-05,1e-05,6e-05,1e-05,0.00058,1e-05,0.0,0.00013,0.00032,0.00012,0.00051,6e-05,9e-05,8e-05,0.00029,0.0005,6e-05,0.0001,0.00011,0.00023
-75132,1.0,1e-05,0.0,2e-05,1e-05,0.45642,0.41356,1e-05,0.03273,1e-05,0.01015,0.0,1e-05,1e-05,3e-05,6e-05,2e-05,1e-05,6e-05,2e-05,0.00049,1e-05,0.0,0.00012,0.00023,0.00013,0.00046,7e-05,8e-05,8e-05,0.00023,0.00053,6e-05,0.0001,9e-05,0.00028
-75212,1.0,1e-05,1e-05,1e-05,1e-05,0.0008,0.00059,1e-05,0.00015,1e-05,9e-05,0.0,2e-05,1e-05,3e-05,5e-05,2e-05,1e-05,4e-05,1e-05,0.00037,0.0,0.0,8e-05,0.00019,9e-05,0.00055,8e-05,0.00012,0.00011,0.00024,0.00047,6e-05,0.0001,9e-05,0.00022
-75233,1.0,1e-05,1e-05,1e-05,1e-05,0.00555,0.00487,1e-05,0.00046,2e-05,0.00026,1e-05,2e-05,2e-05,5e-05,8e-05,3e-05,1e-05,6e-05,1e-05,0.00061,1e-05,0.0,0.00014,0.00033,0.00013,0.00039,6e-05,8e-05,8e-05,0.00017,0.00043,6e-05,8e-05,9e-05,0.0002
-260,1.0,2e-05,1e-05,2e-05,2e-05,0.00413,0.00362,7e-05,0.0005,8e-05,0.00017,1e-05,9e-05,0.0001,0.00014,0.00016,0.00016,7e-05,0.00014,2e-05,0.00094,8e-05,1e-05,0.00027,0.00042,0.00016,0.00036,6e-05,7e-05,8e-05,0.00016,0.00044,6e-05,8e-05,9e-05,0.0002
-75153,1.0,1e-05,1e-05,2e-05,1e-05,0.00606,0.00513,1e-05,0.00054,2e-05,0.00042,1e-05,3e-05,4e-05,6e-05,8e-05,4e-05,1e-05,6e-05,2e-05,0.00061,1e-05,1e-05,0.00015,0.00032,0.00013,0.00038,6e-05,7e-05,8e-05,0.00016,0.00048,6e-05,9e-05,0.0001,0.00023
-75127,1.0,1e-05,1e-05,2e-05,1e-05,0.16654,0.15337,1e-05,0.01043,1e-05,0.00277,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,1e-05,4e-05,2e-05,0.00038,0.0,0.0,8e-05,0.00019,0.0001,0.00045,6e-05,9e-05,8e-05,0.00021,0.00054,7e-05,0.00012,0.0001,0.00026
-75171,1.0,1e-05,0.0,1e-05,1e-05,0.00304,0.00277,1e-05,0.00021,1e-05,8e-05,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,1e-05,4e-05,1e-05,0.00036,1e-05,0.0,7e-05,0.00019,8e-05,0.00048,8e-05,0.0001,0.00011,0.0002,0.00055,8e-05,0.00012,0.00012,0.00023
-75232,1.0,1e-05,1e-05,1e-05,1e-05,0.00108,0.00076,1e-05,0.00021,2e-05,0.00015,1e-05,2e-05,2e-05,5e-05,8e-05,3e-05,1e-05,6e-05,1e-05,0.00055,1e-05,1e-05,0.00013,0.00028,0.00012,0.00039,7e-05,7e-05,8e-05,0.00016,0.00046,6e-05,8e-05,0.0001,0.00022
-75235,1.0,1e-05,1e-05,2e-05,1e-05,0.00408,0.0035,1e-05,0.00039,1e-05,0.00022,1e-05,2e-05,2e-05,5e-05,0.0001,3e-05,1e-05,7e-05,2e-05,0.00067,1e-05,1e-05,0.00013,0.00034,0.00018,0.00039,6e-05,9e-05,8e-05,0.00017,0.00051,6e-05,9e-05,0.00011,0.00024
-75097,1.0,1e-05,1e-05,2e-05,1e-05,0.01132,0.01021,1e-05,0.00084,1e-05,0.0003,0.0,1e-05,1e-05,3e-05,6e-05,2e-05,1e-05,5e-05,2e-05,0.0005,1e-05,0.0,9e-05,0.00027,0.00012,0.00074,0.00014,0.0001,0.00017,0.00033,0.00065,9e-05,0.00018,0.00013,0.00024
-288,1.0,1e-05,1e-05,1e-05,1e-05,0.00248,0.00199,1e-05,0.00029,1e-05,0.00022,0.0,2e-05,2e-05,4e-05,6e-05,2e-05,1e-05,5e-05,1e-05,0.0006,1e-05,0.0,0.00014,0.00035,0.00011,0.00043,7e-05,0.00011,8e-05,0.00017,0.00044,6e-05,8e-05,8e-05,0.00021
-75112,1.0,1e-05,1e-05,2e-05,1e-05,0.0116,0.01047,1e-05,0.00087,1e-05,0.00028,1e-05,2e-05,2e-05,5e-05,8e-05,2e-05,1e-05,7e-05,3e-05,0.00064,1e-05,1e-05,0.00019,0.0003,0.00013,0.00038,6e-05,7e-05,8e-05,0.00017,0.00044,6e-05,8e-05,9e-05,0.00021
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+75234,1.0,1e-05,1e-05,0.00015,1e-05,1e-05,0.0058,0.00534,1e-05,0.00033,1e-05,0.00016,0.0,2e-05,2e-05,7e-05,0.00204,3e-05,1e-05,5e-05,1e-05,0.00024,3e-05,2e-05,0.0001,9e-05,0.00043,0.0,0.0,0.00012,0.00024,6e-05,0.00156,6e-05,7e-05,0.00121,0.00021,0.00054,6e-05,7e-05,9e-05,0.00032,0.00501
+75235,1.0,1e-05,1e-05,0.0003,2e-05,1e-05,0.00461,0.00385,2e-05,0.00055,2e-05,0.00026,1e-05,3e-05,3e-05,0.0001,0.00011,3e-05,1e-05,5e-05,2e-05,0.00026,3e-05,1e-05,8e-05,0.00014,0.00075,1e-05,1e-05,0.0002,0.00043,0.00011,0.00127,0.00017,0.0002,0.00029,0.00061,0.0008,0.00015,0.00012,0.00016,0.00038,0.00242
+75236,1.0,1e-05,1e-05,0.0001,1e-05,1e-05,0.00218,0.00118,2e-05,0.00065,3e-05,0.00041,1e-05,3e-05,3e-05,9e-05,9e-05,4e-05,1e-05,7e-05,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.0006,1e-05,1e-05,0.00021,0.00032,6e-05,0.00043,7e-05,7e-05,9e-05,0.00019,0.00062,7e-05,8e-05,0.0001,0.00037,0.00048
+75237,1.0,2e-05,1e-05,0.00785,2e-05,1e-05,0.31031,0.27854,2e-05,0.01161,3e-05,0.02022,1e-05,4e-05,3e-05,9e-05,0.00024,6e-05,2e-05,9e-05,2e-05,0.00062,7e-05,3e-05,0.00017,0.00035,0.0008,1e-05,1e-05,0.00028,0.00043,8e-05,0.00043,8e-05,7e-05,9e-05,0.0002,0.00044,6e-05,8e-05,9e-05,0.00021,0.14028
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+75249,1.0,1e-05,1e-05,0.00011,1e-05,1e-05,0.00192,0.00153,1e-05,0.00025,1e-05,0.00017,1e-05,2e-05,2e-05,5e-05,7e-05,2e-05,1e-05,5e-05,3e-05,0.00014,2e-05,1e-05,3e-05,8e-05,0.00029,1e-05,3e-05,6e-05,0.00012,7e-05,0.00041,6e-05,8e-05,0.0001,0.00017,0.00043,6e-05,7e-05,9e-05,0.00019,0.00092
+75250,1.0,1e-05,1e-05,0.00375,1e-05,1e-05,0.04994,0.047,1e-05,0.00247,1e-05,0.00049,1e-05,2e-05,2e-05,6e-05,7e-05,2e-05,1e-05,3e-05,1e-05,0.00022,4e-05,1e-05,4e-05,0.00012,0.00041,0.0,1e-05,0.00011,0.00023,6e-05,0.00076,0.00012,0.00012,0.00019,0.00032,0.00086,0.00012,0.00013,0.00018,0.00043,0.04178
diff --git a/autosklearn/metalearning/files/precision_multiclass.classification_dense/feature_runstatus.arff b/autosklearn/metalearning/files/precision_multiclass.classification_dense/feature_runstatus.arff
index 0034ad2bd6..5fff2dfed3 100644
--- a/autosklearn/metalearning/files/precision_multiclass.classification_dense/feature_runstatus.arff
+++ b/autosklearn/metalearning/files/precision_multiclass.classification_dense/feature_runstatus.arff
@@ -4,6 +4,7 @@
 @ATTRIBUTE repetition NUMERIC
 @ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other}
@@ -21,6 +22,11 @@
 @ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other}
@@ -37,162 +43,138 @@
 @ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other}
 
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diff --git a/autosklearn/metalearning/files/precision_multiclass.classification_dense/feature_values.arff b/autosklearn/metalearning/files/precision_multiclass.classification_dense/feature_values.arff
index a60400e3ce..736dc64321 100644
--- a/autosklearn/metalearning/files/precision_multiclass.classification_dense/feature_values.arff
+++ b/autosklearn/metalearning/files/precision_multiclass.classification_dense/feature_values.arff
@@ -13,7 +13,6 @@
 @ATTRIBUTE KurtosisMean NUMERIC
 @ATTRIBUTE KurtosisMin NUMERIC
 @ATTRIBUTE KurtosisSTD NUMERIC
-@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC
 @ATTRIBUTE LogDatasetRatio NUMERIC
 @ATTRIBUTE LogInverseDatasetRatio NUMERIC
 @ATTRIBUTE LogNumberOfFeatures NUMERIC
@@ -42,160 +41,135 @@
 @ATTRIBUTE SymbolsSum NUMERIC
 
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+75239,1.0,0.9443547030267275,0.6379707916986933,0.5,0.3620292083013067,0.1379707916986933,0.02536510376633359,39.42424242424242,30.96304702758789,5.229958094222913,-1.8830041885375977,9.159290860687747,-3.674380917046025,3.674380917046025,3.4965075614664802,7.170888478512505,0.0,2.0,33.0,0.0,1301.0,0.0,0.0,33.0,0.0,0.0,0.0,0.0,0.0,5.591684341430664,1.5151810998266393,-0.8763390183448792,1.68213778251707,0.0,0.0,0.0,0.0,0.0
+75240,1.0,0.9473321982231081,0.6342759940463534,0.5,0.3657240059536466,0.13427599404635338,0.01275781416117372,78.38333333333334,1911.103482572295,38.512800597597035,-3.0,145.76440092342054,-4.361611319722948,4.361611319722948,4.0943445622221,8.455955881945048,27.0,2.0,60.0,17.0,4703.0,4703.0,29447.0,33.0,0.2833333333333333,1.0,0.10435537600113402,0.8181818181818182,1.2222222222222223,43.144344696960424,3.913912678196276,-22.79380338813253,4.943976022970944,173.0,22.59259259259259,1.0,43.77997111758117,610.0
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+75248,1.0,0.4971128374263667,0.8909272183449651,0.5,0.1090727816550349,0.39092721834496513,0.010967098703888335,91.18181818181819,5010.000199441565,104.23108191482348,-3.0,468.735807382562,-4.512855515163565,4.512855515163565,4.007333185232471,8.520188700396035,39.0,2.0,55.0,12.0,5015.0,4938.0,21515.0,16.0,0.21818181818181817,0.9846460618145564,0.07800235656666364,2.4375,0.41025641025641024,70.79548149028699,4.06998328231248,-50.04498375424548,9.467860234489164,14.0,5.769230769230769,1.0,3.407845687351005,225.0
+75249,1.0,0.3891675492147647,0.9236550632911392,0.5,0.07634493670886076,0.4236550632911392,0.011471518987341773,87.17241379310344,416.3357123975682,46.57141260912883,-3.0,84.69777944086748,-4.467887925280274,4.467887925280274,3.367295829986474,7.8351837552667485,22.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,7.0,0.2413793103448276,1.0,0.056048123090353556,3.142857142857143,0.3181818181818182,20.453256767506936,0.8884855226401125,-20.453256767506936,6.7466910388456185,5.0,2.0454545454545454,1.0,0.7056443043754556,45.0
+75250,1.0,3.992354626842552,0.146342438507591,0.04545454545454545,0.005796927628356971,0.03949259311161838,3.997881123004808e-05,25013.25,2.1476065760596077,1.4719570787187288,0.3045189380645752,0.6973345081454226,-10.127160963449944,10.127160963449944,1.3862943611198906,11.513455324569835,0.0,22.0,4.0,0.0,100053.0,0.0,0.0,4.0,0.0,0.0,0.0,0.0,0.0,1.2135857343673706,0.3996680751442909,-0.3896799087524414,0.7616029272290122,0.0,0.0,0.0,0.0,0.0
diff --git a/autosklearn/metalearning/files/precision_multiclass.classification_sparse/algorithm_runs.arff b/autosklearn/metalearning/files/precision_multiclass.classification_sparse/algorithm_runs.arff
index 1266b4164b..bdf3ec4584 100644
--- a/autosklearn/metalearning/files/precision_multiclass.classification_sparse/algorithm_runs.arff
+++ b/autosklearn/metalearning/files/precision_multiclass.classification_sparse/algorithm_runs.arff
@@ -7,95 +7,133 @@
 @ATTRIBUTE runstatus {ok, timeout, memout, not_applicable, crash, other}
 
 @DATA
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-246,1.0,2,?,not_applicable
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-75171,1.0,4,0.16604057099924874,ok
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-75196,1.0,7,0.01904761904761909,ok
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-75248,1.0,9,0.752823086574655,ok
-75126,1.0,10,0.01834862385321101,ok
-75234,1.0,11,0.03255425709515858,ok
-75150,1.0,12,0.27717391304347827,ok
-258,1.0,13,?,not_applicable
-75168,1.0,14,?,not_applicable
-75235,1.0,15,?,not_applicable
-244,1.0,16,?,not_applicable
-75221,1.0,17,?,not_applicable
-75219,1.0,18,0.02992633517495391,ok
-75202,1.0,19,?,not_applicable
-3043,1.0,20,0.19565217391304346,ok
-75205,1.0,21,?,not_applicable
-75174,1.0,22,0.22988992379339546,ok
-275,1.0,23,?,not_applicable
-75213,1.0,24,0.14457831325301207,ok
-75099,1.0,25,0.6680851063829787,ok
-75184,1.0,26,0.19008782936010038,ok
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-233,1.0,28,0.004073319755600768,ok
-75114,1.0,29,0.027295285359801524,ok
-236,1.0,30,?,not_applicable
-75141,1.0,31,0.08148804251550046,ok
-75107,1.0,32,0.29939393939393943,ok
-262,1.0,33,?,not_applicable
-75146,1.0,34,0.0912992586812329,ok
-75189,1.0,35,0.013769684304798857,ok
-2350,1.0,36,0.5842329404773695,ok
-75249,1.0,37,0.02020202020202022,ok
-242,1.0,38,?,not_applicable
-75117,1.0,39,0.005494505494505475,ok
-75191,1.0,40,0.08133086876155271,ok
-261,1.0,41,0.46341463414634143,ok
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-75095,1.0,43,0.24038461538461542,ok
-75093,1.0,44,0.6726495726495727,ok
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-75127,1.0,48,0.3729369997582297,ok
-75143,1.0,49,0.008919722497522264,ok
-75153,1.0,50,0.0755685986793837,ok
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-75215,1.0,52,0.029750479846449185,ok
-75195,1.0,53,0.00037565740045075735,ok
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-75225,1.0,55,0.7352941176470589,ok
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-75100,1.0,57,0.980544747081712,ok
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-75121,1.0,59,0.0,ok
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-75115,1.0,61,0.01698513800424628,ok
-75116,1.0,62,0.0023866348448687846,ok
-75185,1.0,63,0.14503816793893132,ok
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-75157,1.0,65,0.44099378881987583,ok
-75113,1.0,66,0.08463949843260188,ok
-75203,1.0,67,?,not_applicable
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-75125,1.0,71,0.028436018957345932,ok
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-75103,1.0,73,0.1308139534883721,ok
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-75139,1.0,76,0.018404907975460127,ok
-75227,1.0,77,0.15936254980079678,ok
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-75124,1.0,79,0.6305732484076434,ok
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+75177,1.0,11,0.0273972602739726,ok
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+288,1.0,89,?,not_applicable
+75250,1.0,90,?,not_applicable
+75179,1.0,91,0.25528700906344415,ok
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+75142,1.0,94,0.07191176470588234,ok
+75099,1.0,136,0.32499999999999996,ok
+75243,1.0,99,?,not_applicable
+75175,1.0,211,0.11283604695191218,ok
+233,1.0,107,0.004081632653061273,ok
+75161,1.0,109,0.05884092253104667,ok
+75176,1.0,110,0.014139827179890041,ok
+262,1.0,113,?,not_applicable
+75129,1.0,324,0.5714285714285714,ok
+261,1.0,141,0.2678571428571429,ok
+75090,1.0,116,?,not_applicable
+75114,1.0,121,0.012626262626262652,ok
+75093,1.0,123,0.3954802259887006,ok
+260,1.0,124,?,not_applicable
+236,1.0,126,?,not_applicable
+254,1.0,132,0.0,ok
+75107,1.0,134,0.17279999999999995,ok
+75139,1.0,313,0.01411909146715773,ok
+75146,1.0,140,0.0851393188854489,ok
+75189,1.0,148,0.009679150534749104,ok
+75163,1.0,150,0.04609595484477891,ok
+2350,1.0,153,0.381578947368421,ok
+2122,1.0,156,?,not_applicable
+75110,1.0,159,?,not_applicable
+75213,1.0,162,0.1460674157303371,ok
+75095,1.0,230,0.02941176470588236,ok
+75108,1.0,167,0.0,ok
+75117,1.0,170,0.027837259100642386,ok
+75191,1.0,175,0.0786632744275596,ok
+75226,1.0,264,0.0028766630708377816,ok
+75244,1.0,180,0.39534883720930236,ok
+75236,1.0,181,?,not_applicable
+75169,1.0,184,?,not_applicable
+75116,1.0,219,0.004705882352941226,ok
+75223,1.0,189,?,not_applicable
+75109,1.0,196,?,not_applicable
+75197,1.0,199,?,not_applicable
+248,1.0,203,?,not_applicable
+2119,1.0,206,?,not_applicable
+75127,1.0,207,0.30221535291087065,ok
+75153,1.0,215,0.07380073800738007,ok
+75173,1.0,217,0.11082802547770698,ok
+75187,1.0,218,0.014297729184188368,ok
+75195,1.0,223,0.0,ok
+75225,1.0,238,0.2857142857142857,ok
+75100,1.0,232,0.9411764705882353,ok
+75132,1.0,236,0.45614035087719296,ok
+75210,1.0,239,0.0,ok
+273,1.0,241,0.045060658578856105,ok
+75133,1.0,243,0.2857142857142857,ok
+75121,1.0,244,0.002049180327868827,ok
+75098,1.0,248,?,not_applicable
+75115,1.0,255,0.013015184381778733,ok
+266,1.0,258,?,not_applicable
+75134,1.0,274,?,not_applicable
+75096,1.0,277,?,not_applicable
+75203,1.0,280,?,not_applicable
+75123,1.0,288,?,not_applicable
+75237,1.0,291,4.677268475206109e-05,ok
+75125,1.0,296,0.02631578947368418,ok
+2120,1.0,297,?,not_applicable
+75232,1.0,300,0.15841584158415845,ok
+75103,1.0,305,0.0490196078431373,ok
+242,1.0,307,?,not_applicable
+75230,1.0,312,?,not_applicable
+75240,1.0,321,0.05411255411255411,ok
+75198,1.0,325,?,not_applicable
+75201,1.0,328,?,not_applicable
+75112,1.0,331,0.124934106483922,ok
+75105,1.0,336,0.9444444444444444,ok
+75154,1.0,338,?,not_applicable
+2117,1.0,344,0.055772342248025075,ok
+75156,1.0,348,0.19444444444444442,ok
+75097,1.0,351,0.021655490821149814,ok
+75101,1.0,352,0.2686696268957961,ok
+75172,1.0,353,?,not_applicable
+75106,1.0,359,0.25,ok
+75120,1.0,368,0.009009009009009028,ok
+75193,1.0,369,?,not_applicable
diff --git a/autosklearn/metalearning/files/precision_multiclass.classification_sparse/configurations.csv b/autosklearn/metalearning/files/precision_multiclass.classification_sparse/configurations.csv
index 20dc59b32a..a868976598 100644
--- a/autosklearn/metalearning/files/precision_multiclass.classification_sparse/configurations.csv
+++ b/autosklearn/metalearning/files/precision_multiclass.classification_sparse/configurations.csv
@@ -1,93 +1,131 @@
 idx,balancing:strategy,classifier:__choice__,classifier:adaboost:algorithm,classifier:adaboost:learning_rate,classifier:adaboost:max_depth,classifier:adaboost:n_estimators,classifier:bernoulli_nb:alpha,classifier:bernoulli_nb:fit_prior,classifier:decision_tree:criterion,classifier:decision_tree:max_depth_factor,classifier:decision_tree:max_features,classifier:decision_tree:max_leaf_nodes,classifier:decision_tree:min_impurity_decrease,classifier:decision_tree:min_samples_leaf,classifier:decision_tree:min_samples_split,classifier:decision_tree:min_weight_fraction_leaf,classifier:extra_trees:bootstrap,classifier:extra_trees:criterion,classifier:extra_trees:max_depth,classifier:extra_trees:max_features,classifier:extra_trees:max_leaf_nodes,classifier:extra_trees:min_impurity_decrease,classifier:extra_trees:min_samples_leaf,classifier:extra_trees:min_samples_split,classifier:extra_trees:min_weight_fraction_leaf,classifier:gradient_boosting:early_stop,classifier:gradient_boosting:l2_regularization,classifier:gradient_boosting:learning_rate,classifier:gradient_boosting:loss,classifier:gradient_boosting:max_bins,classifier:gradient_boosting:max_depth,classifier:gradient_boosting:max_leaf_nodes,classifier:gradient_boosting:min_samples_leaf,classifier:gradient_boosting:n_iter_no_change,classifier:gradient_boosting:scoring,classifier:gradient_boosting:tol,classifier:gradient_boosting:validation_fraction,classifier:k_nearest_neighbors:n_neighbors,classifier:k_nearest_neighbors:p,classifier:k_nearest_neighbors:weights,classifier:lda:n_components,classifier:lda:shrinkage,classifier:lda:shrinkage_factor,classifier:lda:tol,classifier:liblinear_svc:C,classifier:liblinear_svc:dual,classifier:liblinear_svc:fit_intercept,classifier:liblinear_svc:intercept_scaling,classifier:liblinear_svc:loss,classifier:liblinear_svc:multi_class,classifier:liblinear_svc:penalty,classifier:liblinear_svc:tol,classifier:libsvm_svc:C,classifier:libsvm_svc:coef0,classifier:libsvm_svc:degree,classifier:libsvm_svc:gamma,classifier:libsvm_svc:kernel,classifier:libsvm_svc:max_iter,classifier:libsvm_svc:shrinking,classifier:libsvm_svc:tol,classifier:multinomial_nb:alpha,classifier:multinomial_nb:fit_prior,classifier:passive_aggressive:C,classifier:passive_aggressive:average,classifier:passive_aggressive:fit_intercept,classifier:passive_aggressive:loss,classifier:passive_aggressive:tol,classifier:qda:reg_param,classifier:random_forest:bootstrap,classifier:random_forest:criterion,classifier:random_forest:max_depth,classifier:random_forest:max_features,classifier:random_forest:max_leaf_nodes,classifier:random_forest:min_impurity_decrease,classifier:random_forest:min_samples_leaf,classifier:random_forest:min_samples_split,classifier:random_forest:min_weight_fraction_leaf,classifier:sgd:alpha,classifier:sgd:average,classifier:sgd:epsilon,classifier:sgd:eta0,classifier:sgd:fit_intercept,classifier:sgd:l1_ratio,classifier:sgd:learning_rate,classifier:sgd:loss,classifier:sgd:penalty,classifier:sgd:power_t,classifier:sgd:tol,data_preprocessing:categorical_transformer:categorical_encoding:__choice__,data_preprocessing:categorical_transformer:category_coalescence:__choice__,data_preprocessing:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction,data_preprocessing:numerical_transformer:imputation:strategy,data_preprocessing:numerical_transformer:rescaling:__choice__,data_preprocessing:numerical_transformer:rescaling:quantile_transformer:n_quantiles,data_preprocessing:numerical_transformer:rescaling:quantile_transformer:output_distribution,data_preprocessing:numerical_transformer:rescaling:robust_scaler:q_max,data_preprocessing:numerical_transformer:rescaling:robust_scaler:q_min,feature_preprocessor:__choice__,feature_preprocessor:extra_trees_preproc_for_classification:bootstrap,feature_preprocessor:extra_trees_preproc_for_classification:criterion,feature_preprocessor:extra_trees_preproc_for_classification:max_depth,feature_preprocessor:extra_trees_preproc_for_classification:max_features,feature_preprocessor:extra_trees_preproc_for_classification:max_leaf_nodes,feature_preprocessor:extra_trees_preproc_for_classification:min_impurity_decrease,feature_preprocessor:extra_trees_preproc_for_classification:min_samples_leaf,feature_preprocessor:extra_trees_preproc_for_classification:min_samples_split,feature_preprocessor:extra_trees_preproc_for_classification:min_weight_fraction_leaf,feature_preprocessor:extra_trees_preproc_for_classification:n_estimators,feature_preprocessor:kernel_pca:coef0,feature_preprocessor:kernel_pca:degree,feature_preprocessor:kernel_pca:gamma,feature_preprocessor:kernel_pca:kernel,feature_preprocessor:kernel_pca:n_components,feature_preprocessor:kitchen_sinks:gamma,feature_preprocessor:kitchen_sinks:n_components,feature_preprocessor:liblinear_svc_preprocessor:C,feature_preprocessor:liblinear_svc_preprocessor:dual,feature_preprocessor:liblinear_svc_preprocessor:fit_intercept,feature_preprocessor:liblinear_svc_preprocessor:intercept_scaling,feature_preprocessor:liblinear_svc_preprocessor:loss,feature_preprocessor:liblinear_svc_preprocessor:multi_class,feature_preprocessor:liblinear_svc_preprocessor:penalty,feature_preprocessor:liblinear_svc_preprocessor:tol,feature_preprocessor:nystroem_sampler:coef0,feature_preprocessor:nystroem_sampler:degree,feature_preprocessor:nystroem_sampler:gamma,feature_preprocessor:nystroem_sampler:kernel,feature_preprocessor:nystroem_sampler:n_components,feature_preprocessor:polynomial:degree,feature_preprocessor:polynomial:include_bias,feature_preprocessor:polynomial:interaction_only,feature_preprocessor:random_trees_embedding:bootstrap,feature_preprocessor:random_trees_embedding:max_depth,feature_preprocessor:random_trees_embedding:max_leaf_nodes,feature_preprocessor:random_trees_embedding:min_samples_leaf,feature_preprocessor:random_trees_embedding:min_samples_split,feature_preprocessor:random_trees_embedding:min_weight_fraction_leaf,feature_preprocessor:random_trees_embedding:n_estimators,feature_preprocessor:select_percentile_classification:percentile,feature_preprocessor:select_percentile_classification:score_func,feature_preprocessor:select_rates_classification:alpha,feature_preprocessor:select_rates_classification:mode,feature_preprocessor:select_rates_classification:score_func,feature_preprocessor:truncatedSVD:target_dim
-1,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.3163020577549344,None,0.0,15,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.01322981552715978,most_frequent,robust_scaler,,,0.9746667009556722,0.1974876581478311,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-2,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,23916.78290516781,False,True,1,squared_hinge,ovr,l2,0.0006655607726658531,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.008808715242823137,median,none,,,,,kernel_pca,,,,,,,,,,,0.2633153190620261,2,0.004770332714291669,poly,1483,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-3,weighting,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,88,2,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.254263872179305,median,normalize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-4,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.8295237320761981,None,0.0,14,13,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.4912899096849949,most_frequent,robust_scaler,,,0.8602962469260254,0.1681311280324052,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l1,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,
-5,none,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00011009762574324903,True,,0.031641726094190054,True,,invscaling,hinge,l2,0.2208821470267501,0.007895311091089785,one_hot_encoding,minority_coalescer,0.13807502937372682,mean,quantile_transformer,1340,normal,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,,
-6,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.5244313211416313,None,0.0,1,2,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.454300236339658,most_frequent,robust_scaler,,,0.7858830918128734,0.2705174720960696,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-7,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.541827838541001,True,True,squared_hinge,0.0001129851063838815,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,standardize,,,,,extra_trees_preproc_for_classification,True,entropy,None,0.5533459595240017,None,0.0,18,4,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-8,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.25657910619102237,None,0.0,6,6,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.021086303324492208,most_frequent,quantile_transformer,1247,uniform,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.16021572815319468,fpr,chi2,
-9,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.643761610990255,None,0.0,20,6,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.039859381630488695,mean,normalize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,,
-10,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.8253905225980761,None,0.0,18,20,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.003725255016700434,mean,robust_scaler,,,0.7701705977765594,0.2396520157991559,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.3579038776690903,False,True,1,squared_hinge,ovr,l1,4.994232534734023e-05,,,,,,,,,,,,,,,,,,,,,
-11,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.06263739023781334,None,0.0,18,8,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.02206379295100344,most_frequent,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,,
-12,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.07518858116896743,None,0.0,7,10,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0027133920607872518,mean,robust_scaler,,,0.7873452530915046,0.16215139072179952,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-13,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,199,None,,0.003239674765949011,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,robust_scaler,,,0.7228447795482008,0.25,kernel_pca,,,,,,,,,,,0.09440148939740256,3,0.0008114095405154861,poly,1996,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-14,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.6039096182414764,None,0.0,2,20,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.1939609823704447,most_frequent,robust_scaler,,,0.7331327008874781,0.29747488162877694,extra_trees_preproc_for_classification,True,entropy,None,0.33401603690569853,None,0.0,4,4,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-15,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7865652301288171,None,0.0,1,4,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,median,robust_scaler,,,0.8623635641193496,0.13298297278730625,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-16,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.905401147837377,-0.32832122529048147,3,1.001896309040499,poly,-1,False,5.1523484258030785e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-17,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.21794354428393548,None,0.0,2,16,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0025451910134387575,median,quantile_transformer,1477,uniform,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,,
-18,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5010952533165973,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.005838375247601407,mean,normalize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,,
-19,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5514127836113225,None,0.0,4,20,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.010750756228608195,most_frequent,robust_scaler,,,0.7471420685032172,0.2916152663822578,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.22557315057515023,fwe,chi2,
-20,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.4426097579931897,None,0.0,16,4,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,mean,robust_scaler,,,0.7407323456920554,0.0804756921037867,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,,
-21,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.506204385946851,None,0.0,2,8,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.03433007982751011,mean,none,,,,,extra_trees_preproc_for_classification,False,gini,None,0.32676415738837183,None,0.0,5,6,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-22,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9333237919291875,None,0.0,8,3,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,,
-23,none,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,184,manual,0.5022041786628263,2.3064966255468173e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,most_frequent,standardize,,,,,kernel_pca,,,,,,,,,,,-0.32023224593255817,2,0.09479590655633498,poly,1382,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-24,none,adaboost,SAMME,0.6255555220658239,6,446,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.008833097306364968,most_frequent,none,,,,,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,10,None,4,17,1.0,76,,,,,,
-25,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.7772914874049878,None,0.0,10,15,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00015285482513309234,mean,robust_scaler,,,0.8373404000074934,0.14303151080827242,extra_trees_preproc_for_classification,True,entropy,None,0.5021713909357703,None,0.0,13,16,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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-79,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.7922837297504705,False,True,1,squared_hinge,ovr,l2,0.0005476597326342352,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.012674631437254894,most_frequent,none,,,,,extra_trees_preproc_for_classification,False,entropy,None,0.23145146971319638,None,0.0,7,20,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-80,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.18060139887649052,None,0.0,10,12,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.010000000000000004,most_frequent,robust_scaler,,,0.9298612094255864,0.1263597946722308,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,,
-81,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6427543483503397,True,True,hinge,0.00045318775930916944,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.009338484332306904,median,quantile_transformer,174,normal,,,kernel_pca,,,,,,,,,,,,,,cosine,1848,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-82,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.02021502857757827,True,,0.055848937053280076,True,,constant,log,l2,,0.00016356162584832537,no_encoding,no_coalescense,,median,quantile_transformer,27,normal,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.11873149622606233,fwe,chi2,
-83,weighting,adaboost,SAMME.R,0.23766684501868082,10,97,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.004737885672001703,median,none,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50.0,chi2,,,,
-84,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.4864284879550203,None,0.0,1,4,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.008665854799630503,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-85,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.43699642289829416,None,0.0,6,20,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,quantile_transformer,1604,uniform,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-86,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.8918460088801218,None,0.0,18,19,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,median,normalize,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.3268109430054943,fpr,chi2,
-87,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.44727252219812963,None,0.0,2,10,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,standardize,,,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l1,8.17533189014765e-05,,,,,,,,,,,,,,,,,,,,,
-88,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.5603377230962405,False,True,hinge,0.04986267764654238,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00014501962937745386,median,standardize,,,,,extra_trees_preproc_for_classification,True,gini,None,0.44197026095225617,None,0.0,20,4,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-89,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.922871100218626,False,True,squared_hinge,0.09957180737451943,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0016563186856411274,median,quantile_transformer,850,uniform,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.09123695282772847,fpr,chi2,
-90,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4834106677905965,False,True,1,squared_hinge,ovr,l2,0.00017625027586147415,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0009157492873444966,most_frequent,quantile_transformer,1150,normal,,,extra_trees_preproc_for_classification,False,entropy,None,0.4981700453792278,None,0.0,9,15,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-91,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.087313242780732,0.0636709914987994,3,0.004481917125024585,poly,-1,False,0.01848880618941367,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.002136015035740733,mean,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-92,weighting,adaboost,SAMME,0.028177459966491795,1,484,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,quantile_transformer,875,normal,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,1.502807354196727,False,True,1,squared_hinge,ovr,l1,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,
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+5,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.36028513793687317,None,0.0,19,8,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,standardize,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,48.144408204553606,chi2,,,,
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+40,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00010123414317660679,False,,0.04011567850168966,True,,constant,log,l1,,0.00014179758438189507,one_hot_encoding,minority_coalescer,0.008920898694379564,mean,robust_scaler,,,0.7712963178784032,0.2702398922671455,kernel_pca,,,,,,,,,,,,,,cosine,1090,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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+296,weighting,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,1,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.10025594399125329,median,none,,,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.22421266641512966,False,True,1,squared_hinge,ovr,l1,0.0036601423446597503,,,,,,,,,,,,,,,,,,,,,
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+317,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.4572679693563934,None,0.0,1,11,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,mean,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,,
+321,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.5864398404787432,None,0.0,1,14,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0070663750850608895,most_frequent,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,,
+324,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.5240435604277579,None,0.0,1,3,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.05089089351168927,median,robust_scaler,,,0.8952718567345662,0.24647973407695942,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,,
+325,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8.380057363340162,False,True,hinge,0.0020653831067503525,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.05020677662128942,median,quantile_transformer,560,normal,,,kernel_pca,,,,,,,,,,,,,,cosine,891,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+328,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.7292011996224884,True,True,hinge,8.162763086383311e-05,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010337761588362885,median,quantile_transformer,877,uniform,,,kernel_pca,,,,,,,,,,,,,,cosine,1317,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+331,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.5,None,0.0,1,5,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.15244281596303316,mean,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+336,weighting,adaboost,SAMME,0.1313487280585511,3,189,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,robust_scaler,,,0.8008090310131288,0.2935856792824304,extra_trees_preproc_for_classification,False,entropy,None,0.7722400064297721,None,0.0,5,4,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+338,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.37035966336010345,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.007136588202285817,mean,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+344,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.9039555800017817e-05,False,True,squared_hinge,0.0011536981768375681,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,quantile_transformer,1122,uniform,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,,
+348,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5323411241263409,None,0.0,2,18,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,median,standardize,,,,,extra_trees_preproc_for_classification,True,entropy,None,0.6132742708509392,None,0.0,6,16,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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+352,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5988524108452234,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,mean,robust_scaler,,,0.75,0.26961884796358077,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+353,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.6269076084367177,None,0.0,1,7,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.12943250859943486,most_frequent,standardize,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,62.84526554323969,chi2,,,,
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+369,none,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.9796199105973851,None,0.0,2,13,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.00845950033601661,mean,robust_scaler,,,0.8275228560550734,0.23814615163106348,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
diff --git a/autosklearn/metalearning/files/precision_multiclass.classification_sparse/description.txt b/autosklearn/metalearning/files/precision_multiclass.classification_sparse/description.txt
index 66cde4ec09..45ba5041ea 100644
--- a/autosklearn/metalearning/files/precision_multiclass.classification_sparse/description.txt
+++ b/autosklearn/metalearning/files/precision_multiclass.classification_sparse/description.txt
@@ -1,8 +1,9 @@
 features_cutoff_time: 3600
 features_cutoff_memory: 3072
-number_of_feature_steps: 35
+number_of_feature_steps: 42
 feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances
 feature_step LogNumberOfInstances: LogNumberOfInstances
+feature_step NumberOfClasses: NumberOfClasses
 feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures
 feature_step LogNumberOfFeatures: LogNumberOfFeatures
 feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues
@@ -20,6 +21,11 @@ feature_step DatasetRatio: DatasetRatio, LogDatasetRatio
 feature_step LogDatasetRatio: LogDatasetRatio
 feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio
 feature_step LogInverseDatasetRatio: LogInverseDatasetRatio
+feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD
+feature_step ClassProbabilityMin: ClassProbabilityMin
+feature_step ClassProbabilityMax: ClassProbabilityMax
+feature_step ClassProbabilityMean: ClassProbabilityMean
+feature_step ClassProbabilitySTD: ClassProbabilitySTD
 feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum
 feature_step SymbolsMin: SymbolsMin
 feature_step SymbolsMax: SymbolsMax
@@ -36,11 +42,12 @@ feature_step SkewnessMin: SkewnessMin
 feature_step SkewnessMax: SkewnessMax
 feature_step SkewnessMean: SkewnessMean
 feature_step SkewnessSTD: SkewnessSTD
-features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
+feature_step ClassEntropy: ClassEntropy
+features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy
 features_stochastic: 
-default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
+default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy
 
-algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92
+algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235,236,237,238,239,240,241,242,243,244,245,246,247,248,249,250,251,252,253,254,255,256,257,258,259,260,261,262,263,264,265,266,267,268,269,270,271,272,273,274,275,276,277,278,279,280,281,282,283,284,285,286,287,288,289,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,312,313,314,315,316,317,318,319,320,321,322,323,324,325,326,327,328,329,330,331,332,333,334,335,336,337,338,339,340,341,342,343,344,345,346,347,348,349,350,351,352,353,354,355,356,357,358,359,360,361,362,363,364,365,366,367,368,369,370,371,372
 algorithms_stochastic: 
 performance_measures: precision
 performance_type: solution_quality
diff --git a/autosklearn/metalearning/files/precision_multiclass.classification_sparse/feature_costs.arff b/autosklearn/metalearning/files/precision_multiclass.classification_sparse/feature_costs.arff
index 0e17e20d22..8512fd9275 100644
--- a/autosklearn/metalearning/files/precision_multiclass.classification_sparse/feature_costs.arff
+++ b/autosklearn/metalearning/files/precision_multiclass.classification_sparse/feature_costs.arff
@@ -4,6 +4,7 @@
 @ATTRIBUTE repetition NUMERIC
 @ATTRIBUTE NumberOfInstances NUMERIC
 @ATTRIBUTE LogNumberOfInstances NUMERIC
+@ATTRIBUTE NumberOfClasses NUMERIC
 @ATTRIBUTE NumberOfFeatures NUMERIC
 @ATTRIBUTE LogNumberOfFeatures NUMERIC
 @ATTRIBUTE MissingValues NUMERIC
@@ -21,6 +22,11 @@
 @ATTRIBUTE LogDatasetRatio NUMERIC
 @ATTRIBUTE InverseDatasetRatio NUMERIC
 @ATTRIBUTE LogInverseDatasetRatio NUMERIC
+@ATTRIBUTE ClassOccurences NUMERIC
+@ATTRIBUTE ClassProbabilityMin NUMERIC
+@ATTRIBUTE ClassProbabilityMax NUMERIC
+@ATTRIBUTE ClassProbabilityMean NUMERIC
+@ATTRIBUTE ClassProbabilitySTD NUMERIC
 @ATTRIBUTE NumSymbols NUMERIC
 @ATTRIBUTE SymbolsMin NUMERIC
 @ATTRIBUTE SymbolsMax NUMERIC
@@ -37,162 +43,138 @@
 @ATTRIBUTE SkewnessMax NUMERIC
 @ATTRIBUTE SkewnessMean NUMERIC
 @ATTRIBUTE SkewnessSTD NUMERIC
+@ATTRIBUTE ClassEntropy NUMERIC
 
 @DATA
-75123,1.0,1e-05,1e-05,1e-05,1e-05,0.00253,0.00221,1e-05,0.00024,3e-05,0.00011,0.0,2e-05,2e-05,5e-05,8e-05,2e-05,1e-05,7e-05,1e-05,0.00072,1e-05,1e-05,0.00019,0.00041,0.00011,0.00061,0.00012,9e-05,0.00015,0.00025,0.00067,0.00014,0.00011,0.00016,0.00027
-75090,1.0,1e-05,1e-05,2e-05,2e-05,0.00815,0.00368,7e-05,0.00228,9e-05,0.00235,0.0,5e-05,5e-05,0.0001,0.00013,0.00013,1e-05,0.00021,2e-05,0.00064,0.0,0.0,0.00014,0.00038,0.00011,0.00066,0.00011,0.00014,0.00014,0.00026,0.00066,0.00011,0.00013,0.00015,0.00027
-2123,1.0,1e-05,1e-05,1e-05,1e-05,0.00097,0.00079,1e-05,0.00013,1e-05,7e-05,0.0,2e-05,2e-05,5e-05,8e-05,2e-05,1e-05,5e-05,1e-05,0.0007,1e-05,0.0,0.00017,0.00042,0.0001,0.00052,6e-05,8e-05,0.00012,0.00026,0.00049,6e-05,0.00011,9e-05,0.00023
-248,1.0,1e-05,1e-05,1e-05,1e-05,0.00088,0.00077,1e-05,0.0001,1e-05,4e-05,0.0,1e-05,1e-05,4e-05,5e-05,2e-05,1e-05,4e-05,1e-05,0.00041,1e-05,0.0,9e-05,0.00022,9e-05,0.00046,6e-05,8e-05,9e-05,0.00023,0.00049,7e-05,0.0001,0.0001,0.00021
-75101,1.0,1e-05,1e-05,1e-05,1e-05,0.0418,0.03546,1e-05,0.00389,1e-05,0.00248,0.0,2e-05,2e-05,4e-05,6e-05,2e-05,1e-05,5e-05,2e-05,0.00045,1e-05,0.0,0.00011,0.00023,0.0001,0.00041,6e-05,8e-05,0.0001,0.00018,0.00047,6e-05,0.0001,8e-05,0.00023
-75191,1.0,1e-05,1e-05,3e-05,1e-05,0.53436,0.40777,2e-05,0.09655,3e-05,0.0301,1e-05,2e-05,3e-05,5e-05,0.00011,7e-05,1e-05,0.00011,3e-05,0.00084,5e-05,7e-05,0.00017,0.00033,0.00023,0.00088,0.00012,0.00017,0.00032,0.00026,0.00068,9e-05,0.00012,0.00013,0.00035
-75187,1.0,1e-05,1e-05,2e-05,1e-05,0.00548,0.00473,1e-05,0.0005,2e-05,0.00029,1e-05,2e-05,2e-05,7e-05,9e-05,3e-05,1e-05,6e-05,1e-05,0.00062,1e-05,1e-05,0.00014,0.00033,0.00013,0.00038,7e-05,7e-05,8e-05,0.00016,0.00047,6e-05,9e-05,9e-05,0.00023
-266,1.0,1e-05,1e-05,1e-05,1e-05,0.0018,0.00146,1e-05,0.00023,2e-05,0.00015,2e-05,2e-05,2e-05,6e-05,8e-05,3e-05,1e-05,6e-05,1e-05,0.00059,1e-05,1e-05,0.00014,0.00031,0.00013,0.0004,5e-05,7e-05,8e-05,0.0002,0.00044,8e-05,8e-05,8e-05,0.0002
-253,1.0,1e-05,1e-05,1e-05,1e-05,0.00082,0.00063,1e-05,0.00016,2e-05,5e-05,0.0,1e-05,1e-05,4e-05,5e-05,2e-05,1e-05,5e-05,2e-05,0.00046,1e-05,0.0,9e-05,0.00024,0.00012,0.00044,6e-05,7e-05,8e-05,0.00023,0.00043,6e-05,0.0001,8e-05,0.00019
-75217,1.0,1e-05,1e-05,2e-05,1e-05,0.00196,0.00158,1e-05,0.00026,2e-05,0.00016,1e-05,3e-05,3e-05,7e-05,0.0001,3e-05,1e-05,7e-05,2e-05,0.00089,1e-05,1e-05,0.00022,0.00053,0.00013,0.00074,9e-05,0.0001,0.00019,0.00036,0.00065,0.00013,0.0001,0.00011,0.00031
-75176,1.0,1e-05,1e-05,2e-05,1e-05,0.01406,0.01285,1e-05,0.00094,2e-05,0.00031,1e-05,3e-05,3e-05,6e-05,9e-05,3e-05,1e-05,7e-05,2e-05,0.00069,1e-05,1e-05,0.00017,0.00035,0.00015,0.00085,0.00018,0.00011,0.0002,0.00036,0.0011,0.00022,0.00023,0.00023,0.00042
-2117,1.0,1e-05,1e-05,1e-05,1e-05,0.01735,0.0154,1e-05,0.00141,1e-05,0.00057,0.0,1e-05,1e-05,3e-05,6e-05,2e-05,1e-05,5e-05,1e-05,0.0004,1e-05,0.0,8e-05,0.00021,0.0001,0.00065,0.00012,0.00011,0.00015,0.00026,0.00055,7e-05,0.00014,9e-05,0.00025
-75148,1.0,1e-05,1e-05,2e-05,2e-05,0.00129,0.00115,1e-05,0.00012,1e-05,4e-05,0.0,2e-05,2e-05,4e-05,5e-05,2e-05,1e-05,4e-05,1e-05,0.0004,1e-05,0.0,9e-05,0.00021,9e-05,0.00036,7e-05,7e-05,8e-05,0.00015,0.00041,6e-05,8e-05,8e-05,0.00019
-75103,1.0,1e-05,1e-05,1e-05,1e-05,0.01302,0.00819,3e-05,0.00234,3e-05,0.00255,0.0,2e-05,2e-05,6e-05,0.00012,5e-05,1e-05,0.00011,1e-05,0.0005,1e-05,0.0,9e-05,0.00029,0.00011,0.00054,8e-05,0.00011,0.00011,0.00024,0.00066,7e-05,0.00015,0.00011,0.00032
-75168,1.0,1e-05,0.0,1e-05,1e-05,0.02353,0.00601,0.00016,0.01754,0.00018,0.00031,0.0,0.00013,0.00013,0.00027,0.0003,0.00032,1e-05,0.00042,1e-05,0.0004,1e-05,0.0,8e-05,0.00021,0.0001,0.0011,0.00022,0.00025,0.00026,0.00037,0.00124,0.00022,0.00025,0.00028,0.00049
-75157,1.0,1e-05,1e-05,1e-05,1e-05,0.00125,0.00106,1e-05,0.00015,2e-05,7e-05,0.0,2e-05,2e-05,5e-05,6e-05,2e-05,1e-05,4e-05,1e-05,0.00048,2e-05,0.0,0.0001,0.00026,0.0001,0.00039,6e-05,7e-05,8e-05,0.00019,0.00043,6e-05,9e-05,8e-05,0.0002
-75092,1.0,1e-05,1e-05,1e-05,1e-05,0.00099,0.00072,1e-05,0.00018,1e-05,0.00011,0.0,2e-05,2e-05,4e-05,6e-05,2e-05,1e-05,5e-05,1e-05,0.00058,1e-05,0.0,0.00014,0.00034,9e-05,0.00041,6e-05,8e-05,9e-05,0.00017,0.00045,7e-05,0.0001,9e-05,0.00019
-75213,1.0,1e-05,1e-05,1e-05,1e-05,0.00096,0.00079,1e-05,0.00013,1e-05,7e-05,1e-05,2e-05,2e-05,5e-05,7e-05,2e-05,1e-05,6e-05,1e-05,0.00056,1e-05,1e-05,0.00013,0.00029,0.00012,0.00056,6e-05,8e-05,9e-05,0.00033,0.00063,8e-05,0.00014,0.00013,0.00029
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-75212,1.0,1e-05,1e-05,1e-05,1e-05,0.0008,0.00059,1e-05,0.00015,1e-05,9e-05,0.0,2e-05,1e-05,3e-05,5e-05,2e-05,1e-05,4e-05,1e-05,0.00037,0.0,0.0,8e-05,0.00019,9e-05,0.00055,8e-05,0.00012,0.00011,0.00024,0.00047,6e-05,0.0001,9e-05,0.00022
-75233,1.0,1e-05,1e-05,1e-05,1e-05,0.00555,0.00487,1e-05,0.00046,2e-05,0.00026,1e-05,2e-05,2e-05,5e-05,8e-05,3e-05,1e-05,6e-05,1e-05,0.00061,1e-05,0.0,0.00014,0.00033,0.00013,0.00039,6e-05,8e-05,8e-05,0.00017,0.00043,6e-05,8e-05,9e-05,0.0002
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+75230,1.0,1e-05,0.0,9e-05,1e-05,1e-05,0.00087,0.0006,1e-05,0.00018,1e-05,0.00011,0.0,2e-05,2e-05,4e-05,6e-05,2e-05,1e-05,3e-05,1e-05,0.00015,2e-05,1e-05,4e-05,8e-05,0.00038,0.0,1e-05,0.0001,0.00021,5e-05,0.00037,5e-05,6e-05,8e-05,0.00018,0.00033,5e-05,6e-05,7e-05,0.00015,0.00048
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+75233,1.0,1e-05,1e-05,0.00012,1e-05,0.0,0.00257,0.00222,1e-05,0.00024,1e-05,0.00014,0.0,1e-05,1e-05,3e-05,5e-05,3e-05,1e-05,2e-05,1e-05,9e-05,1e-05,0.0,2e-05,5e-05,0.0003,0.0,0.0,8e-05,0.00017,4e-05,0.00076,0.00012,0.00013,0.00014,0.00037,0.00058,0.00012,8e-05,0.00013,0.00025,0.00119
+75234,1.0,1e-05,1e-05,0.00015,1e-05,1e-05,0.0058,0.00534,1e-05,0.00033,1e-05,0.00016,0.0,2e-05,2e-05,7e-05,0.00204,3e-05,1e-05,5e-05,1e-05,0.00024,3e-05,2e-05,0.0001,9e-05,0.00043,0.0,0.0,0.00012,0.00024,6e-05,0.00156,6e-05,7e-05,0.00121,0.00021,0.00054,6e-05,7e-05,9e-05,0.00032,0.00501
+75235,1.0,1e-05,1e-05,0.0003,2e-05,1e-05,0.00461,0.00385,2e-05,0.00055,2e-05,0.00026,1e-05,3e-05,3e-05,0.0001,0.00011,3e-05,1e-05,5e-05,2e-05,0.00026,3e-05,1e-05,8e-05,0.00014,0.00075,1e-05,1e-05,0.0002,0.00043,0.00011,0.00127,0.00017,0.0002,0.00029,0.00061,0.0008,0.00015,0.00012,0.00016,0.00038,0.00242
+75236,1.0,1e-05,1e-05,0.0001,1e-05,1e-05,0.00218,0.00118,2e-05,0.00065,3e-05,0.00041,1e-05,3e-05,3e-05,9e-05,9e-05,4e-05,1e-05,7e-05,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.0006,1e-05,1e-05,0.00021,0.00032,6e-05,0.00043,7e-05,7e-05,9e-05,0.00019,0.00062,7e-05,8e-05,0.0001,0.00037,0.00048
+75237,1.0,2e-05,1e-05,0.00785,2e-05,1e-05,0.31031,0.27854,2e-05,0.01161,3e-05,0.02022,1e-05,4e-05,3e-05,9e-05,0.00024,6e-05,2e-05,9e-05,2e-05,0.00062,7e-05,3e-05,0.00017,0.00035,0.0008,1e-05,1e-05,0.00028,0.00043,8e-05,0.00043,8e-05,7e-05,9e-05,0.0002,0.00044,6e-05,8e-05,9e-05,0.00021,0.14028
+75239,1.0,1e-05,1e-05,7e-05,1e-05,1e-05,0.00105,0.00084,1e-05,0.00015,1e-05,8e-05,1e-05,2e-05,5e-05,4e-05,8e-05,2e-05,1e-05,3e-05,1e-05,0.00016,2e-05,1e-05,5e-05,9e-05,0.00049,0.0,0.0,0.00012,0.00026,0.00011,0.00046,0.00013,8e-05,8e-05,0.00018,0.00044,6e-05,9e-05,0.00011,0.00018,0.00065
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+75244,1.0,3e-05,2e-05,0.0004,3e-05,2e-05,0.0173,0.01298,4e-05,0.00267,4e-05,0.00174,1e-05,7e-05,9e-05,0.00024,0.00027,8e-05,2e-05,0.00013,3e-05,0.00045,7e-05,2e-05,0.00011,0.00025,0.00077,6e-05,2e-05,0.00021,0.00034,0.00013,0.00077,0.00014,0.00013,0.00026,0.00023,0.00044,7e-05,8e-05,0.0001,0.00019,0.00649
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+75249,1.0,1e-05,1e-05,0.00011,1e-05,1e-05,0.00192,0.00153,1e-05,0.00025,1e-05,0.00017,1e-05,2e-05,2e-05,5e-05,7e-05,2e-05,1e-05,5e-05,3e-05,0.00014,2e-05,1e-05,3e-05,8e-05,0.00029,1e-05,3e-05,6e-05,0.00012,7e-05,0.00041,6e-05,8e-05,0.0001,0.00017,0.00043,6e-05,7e-05,9e-05,0.00019,0.00092
+75250,1.0,1e-05,1e-05,0.00375,1e-05,1e-05,0.04994,0.047,1e-05,0.00247,1e-05,0.00049,1e-05,2e-05,2e-05,6e-05,7e-05,2e-05,1e-05,3e-05,1e-05,0.00022,4e-05,1e-05,4e-05,0.00012,0.00041,0.0,1e-05,0.00011,0.00023,6e-05,0.00076,0.00012,0.00012,0.00019,0.00032,0.00086,0.00012,0.00013,0.00018,0.00043,0.04178
diff --git a/autosklearn/metalearning/files/precision_multiclass.classification_sparse/feature_runstatus.arff b/autosklearn/metalearning/files/precision_multiclass.classification_sparse/feature_runstatus.arff
index 0034ad2bd6..5fff2dfed3 100644
--- a/autosklearn/metalearning/files/precision_multiclass.classification_sparse/feature_runstatus.arff
+++ b/autosklearn/metalearning/files/precision_multiclass.classification_sparse/feature_runstatus.arff
@@ -4,6 +4,7 @@
 @ATTRIBUTE repetition NUMERIC
 @ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other}
@@ -21,6 +22,11 @@
 @ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other}
@@ -37,162 +43,138 @@
 @ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other}
 
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diff --git a/autosklearn/metalearning/files/precision_multiclass.classification_sparse/feature_values.arff b/autosklearn/metalearning/files/precision_multiclass.classification_sparse/feature_values.arff
index a60400e3ce..736dc64321 100644
--- a/autosklearn/metalearning/files/precision_multiclass.classification_sparse/feature_values.arff
+++ b/autosklearn/metalearning/files/precision_multiclass.classification_sparse/feature_values.arff
@@ -13,7 +13,6 @@
 @ATTRIBUTE KurtosisMean NUMERIC
 @ATTRIBUTE KurtosisMin NUMERIC
 @ATTRIBUTE KurtosisSTD NUMERIC
-@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC
 @ATTRIBUTE LogDatasetRatio NUMERIC
 @ATTRIBUTE LogInverseDatasetRatio NUMERIC
 @ATTRIBUTE LogNumberOfFeatures NUMERIC
@@ -42,160 +41,135 @@
 @ATTRIBUTE SymbolsSum NUMERIC
 
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diff --git a/autosklearn/metalearning/files/precision_samples_binary.classification_dense/algorithm_runs.arff b/autosklearn/metalearning/files/precision_samples_binary.classification_dense/algorithm_runs.arff
index e1028d34a5..fd2afc0c6a 100644
--- a/autosklearn/metalearning/files/precision_samples_binary.classification_dense/algorithm_runs.arff
+++ b/autosklearn/metalearning/files/precision_samples_binary.classification_dense/algorithm_runs.arff
@@ -7,130 +7,135 @@
 @ATTRIBUTE runstatus {ok, timeout, memout, not_applicable, crash, other}
 
 @DATA
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-2123,1.0,11,?,not_applicable
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+246,1.0,8,?,not_applicable
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 75092,1.0,14,?,not_applicable
-75248,1.0,15,?,not_applicable
-75126,1.0,16,?,not_applicable
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-75150,1.0,18,?,not_applicable
-258,1.0,19,?,not_applicable
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-75159,1.0,22,?,not_applicable
-244,1.0,23,?,not_applicable
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-75219,1.0,25,?,not_applicable
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-288,1.0,30,?,not_applicable
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-275,1.0,32,?,not_applicable
-75142,1.0,33,?,not_applicable
-75213,1.0,34,?,not_applicable
-75099,1.0,35,?,not_applicable
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-253,1.0,53,?,not_applicable
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-266,1.0,78,?,not_applicable
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-273,1.0,84,?,not_applicable
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-2119,1.0,90,?,not_applicable
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-75113,1.0,92,?,not_applicable
-75134,1.0,93,?,not_applicable
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-75203,1.0,95,?,not_applicable
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-251,1.0,97,?,not_applicable
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-75103,1.0,102,?,not_applicable
-75192,1.0,103,?,not_applicable
-75230,1.0,104,?,not_applicable
-75139,1.0,105,?,not_applicable
-75227,1.0,106,?,not_applicable
-2120,1.0,107,?,not_applicable
-75124,1.0,108,?,not_applicable
-75240,1.0,109,?,not_applicable
-75129,1.0,110,?,not_applicable
-75198,1.0,111,?,not_applicable
-75201,1.0,112,?,not_applicable
-75112,1.0,113,?,not_applicable
-75133,1.0,114,?,not_applicable
-75105,1.0,115,?,not_applicable
-75154,1.0,116,?,not_applicable
-75177,1.0,117,?,not_applicable
-2117,1.0,118,?,not_applicable
-75156,1.0,119,?,not_applicable
-75097,1.0,120,?,not_applicable
-75101,1.0,121,?,not_applicable
-75172,1.0,122,?,not_applicable
-75106,1.0,123,?,not_applicable
-75179,1.0,124,?,not_applicable
-75187,1.0,125,?,not_applicable
-75120,1.0,126,?,not_applicable
-75193,1.0,127,?,not_applicable
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+3043,1.0,98,?,not_applicable
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+75174,1.0,106,?,not_applicable
+75250,1.0,111,?,not_applicable
+75179,1.0,114,?,not_applicable
+275,1.0,115,?,not_applicable
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+75142,1.0,118,?,not_applicable
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+75175,1.0,132,?,not_applicable
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+262,1.0,146,?,not_applicable
+75129,1.0,147,?,not_applicable
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+75114,1.0,152,?,not_applicable
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+260,1.0,158,?,not_applicable
+236,1.0,160,?,not_applicable
+254,1.0,166,?,not_applicable
+75107,1.0,167,?,not_applicable
+75181,1.0,175,?,not_applicable
+75189,1.0,184,?,not_applicable
+75163,1.0,189,?,not_applicable
+2350,1.0,191,?,not_applicable
+2122,1.0,195,?,not_applicable
+75110,1.0,198,?,not_applicable
+75213,1.0,201,?,not_applicable
+75095,1.0,203,?,not_applicable
+75108,1.0,207,?,not_applicable
+75117,1.0,211,?,not_applicable
+75191,1.0,216,?,not_applicable
+75226,1.0,219,?,not_applicable
+75244,1.0,222,?,not_applicable
+75236,1.0,223,?,not_applicable
+75169,1.0,227,?,not_applicable
+75116,1.0,228,?,not_applicable
+75223,1.0,235,?,not_applicable
+75109,1.0,242,?,not_applicable
+75197,1.0,245,?,not_applicable
+75237,1.0,248,?,not_applicable
+248,1.0,250,?,not_applicable
+2119,1.0,254,?,not_applicable
+75127,1.0,255,?,not_applicable
+75153,1.0,260,?,not_applicable
+75195,1.0,271,?,not_applicable
+266,1.0,277,?,not_applicable
+75225,1.0,279,?,not_applicable
+75100,1.0,286,?,not_applicable
+75132,1.0,293,?,not_applicable
+75210,1.0,298,?,not_applicable
+273,1.0,300,?,not_applicable
+75133,1.0,303,?,not_applicable
+75121,1.0,304,?,not_applicable
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+75115,1.0,315,?,not_applicable
+75217,1.0,337,?,not_applicable
+75134,1.0,341,?,not_applicable
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+75232,1.0,374,?,not_applicable
+75103,1.0,378,?,not_applicable
+75230,1.0,386,?,not_applicable
+75240,1.0,398,?,not_applicable
+75198,1.0,404,?,not_applicable
+75201,1.0,407,?,not_applicable
+75105,1.0,415,?,not_applicable
+75154,1.0,420,?,not_applicable
+2117,1.0,426,?,not_applicable
+75156,1.0,430,?,not_applicable
+75097,1.0,434,?,not_applicable
+75101,1.0,438,?,not_applicable
+75172,1.0,443,?,not_applicable
+75106,1.0,448,?,not_applicable
+75120,1.0,456,?,not_applicable
+75193,1.0,461,?,not_applicable
diff --git a/autosklearn/metalearning/files/precision_samples_binary.classification_dense/configurations.csv b/autosklearn/metalearning/files/precision_samples_binary.classification_dense/configurations.csv
index 8d94f66c45..03d7f03e43 100644
--- a/autosklearn/metalearning/files/precision_samples_binary.classification_dense/configurations.csv
+++ b/autosklearn/metalearning/files/precision_samples_binary.classification_dense/configurations.csv
@@ -1,128 +1,133 @@
 idx,balancing:strategy,classifier:__choice__,classifier:adaboost:algorithm,classifier:adaboost:learning_rate,classifier:adaboost:max_depth,classifier:adaboost:n_estimators,classifier:bernoulli_nb:alpha,classifier:bernoulli_nb:fit_prior,classifier:decision_tree:criterion,classifier:decision_tree:max_depth_factor,classifier:decision_tree:max_features,classifier:decision_tree:max_leaf_nodes,classifier:decision_tree:min_impurity_decrease,classifier:decision_tree:min_samples_leaf,classifier:decision_tree:min_samples_split,classifier:decision_tree:min_weight_fraction_leaf,classifier:extra_trees:bootstrap,classifier:extra_trees:criterion,classifier:extra_trees:max_depth,classifier:extra_trees:max_features,classifier:extra_trees:max_leaf_nodes,classifier:extra_trees:min_impurity_decrease,classifier:extra_trees:min_samples_leaf,classifier:extra_trees:min_samples_split,classifier:extra_trees:min_weight_fraction_leaf,classifier:gradient_boosting:early_stop,classifier:gradient_boosting:l2_regularization,classifier:gradient_boosting:learning_rate,classifier:gradient_boosting:loss,classifier:gradient_boosting:max_bins,classifier:gradient_boosting:max_depth,classifier:gradient_boosting:max_leaf_nodes,classifier:gradient_boosting:min_samples_leaf,classifier:gradient_boosting:n_iter_no_change,classifier:gradient_boosting:scoring,classifier:gradient_boosting:tol,classifier:gradient_boosting:validation_fraction,classifier:k_nearest_neighbors:n_neighbors,classifier:k_nearest_neighbors:p,classifier:k_nearest_neighbors:weights,classifier:lda:n_components,classifier:lda:shrinkage,classifier:lda:shrinkage_factor,classifier:lda:tol,classifier:liblinear_svc:C,classifier:liblinear_svc:dual,classifier:liblinear_svc:fit_intercept,classifier:liblinear_svc:intercept_scaling,classifier:liblinear_svc:loss,classifier:liblinear_svc:multi_class,classifier:liblinear_svc:penalty,classifier:liblinear_svc:tol,classifier:libsvm_svc:C,classifier:libsvm_svc:coef0,classifier:libsvm_svc:degree,classifier:libsvm_svc:gamma,classifier:libsvm_svc:kernel,classifier:libsvm_svc:max_iter,classifier:libsvm_svc:shrinking,classifier:libsvm_svc:tol,classifier:multinomial_nb:alpha,classifier:multinomial_nb:fit_prior,classifier:passive_aggressive:C,classifier:passive_aggressive:average,classifier:passive_aggressive:fit_intercept,classifier:passive_aggressive:loss,classifier:passive_aggressive:tol,classifier:qda:reg_param,classifier:random_forest:bootstrap,classifier:random_forest:criterion,classifier:random_forest:max_depth,classifier:random_forest:max_features,classifier:random_forest:max_leaf_nodes,classifier:random_forest:min_impurity_decrease,classifier:random_forest:min_samples_leaf,classifier:random_forest:min_samples_split,classifier:random_forest:min_weight_fraction_leaf,classifier:sgd:alpha,classifier:sgd:average,classifier:sgd:epsilon,classifier:sgd:eta0,classifier:sgd:fit_intercept,classifier:sgd:l1_ratio,classifier:sgd:learning_rate,classifier:sgd:loss,classifier:sgd:penalty,classifier:sgd:power_t,classifier:sgd:tol,data_preprocessing:categorical_transformer:categorical_encoding:__choice__,data_preprocessing:categorical_transformer:category_coalescence:__choice__,data_preprocessing:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction,data_preprocessing:numerical_transformer:imputation:strategy,data_preprocessing:numerical_transformer:rescaling:__choice__,data_preprocessing:numerical_transformer:rescaling:quantile_transformer:n_quantiles,data_preprocessing:numerical_transformer:rescaling:quantile_transformer:output_distribution,data_preprocessing:numerical_transformer:rescaling:robust_scaler:q_max,data_preprocessing:numerical_transformer:rescaling:robust_scaler:q_min,feature_preprocessor:__choice__,feature_preprocessor:extra_trees_preproc_for_classification:bootstrap,feature_preprocessor:extra_trees_preproc_for_classification:criterion,feature_preprocessor:extra_trees_preproc_for_classification:max_depth,feature_preprocessor:extra_trees_preproc_for_classification:max_features,feature_preprocessor:extra_trees_preproc_for_classification:max_leaf_nodes,feature_preprocessor:extra_trees_preproc_for_classification:min_impurity_decrease,feature_preprocessor:extra_trees_preproc_for_classification:min_samples_leaf,feature_preprocessor:extra_trees_preproc_for_classification:min_samples_split,feature_preprocessor:extra_trees_preproc_for_classification:min_weight_fraction_leaf,feature_preprocessor:extra_trees_preproc_for_classification:n_estimators,feature_preprocessor:fast_ica:algorithm,feature_preprocessor:fast_ica:fun,feature_preprocessor:fast_ica:n_components,feature_preprocessor:fast_ica:whiten,feature_preprocessor:feature_agglomeration:affinity,feature_preprocessor:feature_agglomeration:linkage,feature_preprocessor:feature_agglomeration:n_clusters,feature_preprocessor:feature_agglomeration:pooling_func,feature_preprocessor:kernel_pca:coef0,feature_preprocessor:kernel_pca:degree,feature_preprocessor:kernel_pca:gamma,feature_preprocessor:kernel_pca:kernel,feature_preprocessor:kernel_pca:n_components,feature_preprocessor:kitchen_sinks:gamma,feature_preprocessor:kitchen_sinks:n_components,feature_preprocessor:liblinear_svc_preprocessor:C,feature_preprocessor:liblinear_svc_preprocessor:dual,feature_preprocessor:liblinear_svc_preprocessor:fit_intercept,feature_preprocessor:liblinear_svc_preprocessor:intercept_scaling,feature_preprocessor:liblinear_svc_preprocessor:loss,feature_preprocessor:liblinear_svc_preprocessor:multi_class,feature_preprocessor:liblinear_svc_preprocessor:penalty,feature_preprocessor:liblinear_svc_preprocessor:tol,feature_preprocessor:nystroem_sampler:coef0,feature_preprocessor:nystroem_sampler:degree,feature_preprocessor:nystroem_sampler:gamma,feature_preprocessor:nystroem_sampler:kernel,feature_preprocessor:nystroem_sampler:n_components,feature_preprocessor:pca:keep_variance,feature_preprocessor:pca:whiten,feature_preprocessor:polynomial:degree,feature_preprocessor:polynomial:include_bias,feature_preprocessor:polynomial:interaction_only,feature_preprocessor:random_trees_embedding:bootstrap,feature_preprocessor:random_trees_embedding:max_depth,feature_preprocessor:random_trees_embedding:max_leaf_nodes,feature_preprocessor:random_trees_embedding:min_samples_leaf,feature_preprocessor:random_trees_embedding:min_samples_split,feature_preprocessor:random_trees_embedding:min_weight_fraction_leaf,feature_preprocessor:random_trees_embedding:n_estimators,feature_preprocessor:select_percentile_classification:percentile,feature_preprocessor:select_percentile_classification:score_func,feature_preprocessor:select_rates_classification:alpha,feature_preprocessor:select_rates_classification:mode,feature_preprocessor:select_rates_classification:score_func
-1,none,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,standardize,,,,,extra_trees_preproc_for_classification,False,entropy,None,0.8629651625821806,None,0.0,10,10,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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-3,none,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,188,None,,0.09976330267940166,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,none,,,,,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,-0.05439674489937307,2,0.051865468486041774,poly,727,,,,,,,,,,,,,,,,,
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-6,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.933900673392165,None,0.0,7,4,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,minmax,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-7,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,170.37509684994612,0.6734219115470694,3,0.08998608839429428,poly,-1,True,0.024147312348267988,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,none,,,,,extra_trees_preproc_for_classification,False,entropy,None,0.9449154369109399,None,0.0,1,14,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-8,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2981.609927782268,0.12126372002542518,2,0.0009954412712187496,poly,-1,False,9.701828055863241e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,mean,standardize,,,,,feature_agglomeration,,,,,,,,,,,,,,,euclidean,ward,37,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-9,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1292.7448076998514,0.004489932344506231,2,1.3088036376373702,poly,-1,False,1.7943262139196537e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,minmax,,,,,feature_agglomeration,,,,,,,,,,,,,,,cosine,average,386,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-10,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.5244313211416313,None,0.0,1,2,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.454300236339658,most_frequent,robust_scaler,,,0.7858830918128734,0.2705174720960696,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-11,weighting,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0006294780694492238,most_frequent,standardize,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.063715750591364,fdr,chi2
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-13,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.25657910619102237,None,0.0,6,6,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.021086303324492208,most_frequent,quantile_transformer,1247,uniform,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.16021572815319468,fpr,chi2
-14,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.4897130288457967,False,True,1,squared_hinge,ovr,l2,0.00023610948867176137,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.22014262985890154,median,quantile_transformer,1499,normal,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,331,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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-16,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.8172226998998798,None,0.0,17,3,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.006497124806317511,median,robust_scaler,,,0.7090070294600064,0.2287445390747399,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.478031402559985,mutual_info,,,
-17,weighting,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.06330379669963868,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.054434091133046744,most_frequent,robust_scaler,,,0.7480112557523932,0.17257870788958515,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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-19,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,199,None,,0.003239674765949011,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,robust_scaler,,,0.7228447795482008,0.25,kernel_pca,,,,,,,,,,,,,,,,,,,0.09440148939740256,3,0.0008114095405154861,poly,1996,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-20,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.6039096182414764,None,0.0,2,20,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.1939609823704447,most_frequent,robust_scaler,,,0.7331327008874781,0.29747488162877694,extra_trees_preproc_for_classification,True,entropy,None,0.33401603690569853,None,0.0,4,4,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-21,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,2.5882079595129472e-05,0.36125906161027654,auto,255,None,341,119,16,loss,1e-07,0.2924550184445239,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.02338478303505337,most_frequent,robust_scaler,,,0.7866731278723753,0.29543475006822734,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,68,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-22,weighting,adaboost,SAMME,0.24425611020065865,2,50,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0014011297758596516,most_frequent,quantile_transformer,1324,normal,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,338,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-23,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7725.740943204492,,,3.122840783722913,rbf,-1,True,1.4116065061807418e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.14280178218510184,most_frequent,none,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,27,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-24,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.21794354428393548,None,0.0,2,16,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0025451910134387575,median,quantile_transformer,1477,uniform,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
-25,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5010952533165973,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.005838375247601407,mean,normalize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,
-26,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5514127836113225,None,0.0,4,20,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.010750756228608195,most_frequent,robust_scaler,,,0.7471420685032172,0.2916152663822578,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.22557315057515023,fwe,chi2
-27,weighting,decision_tree,,,,,,,gini,1.0778738481485166,1.0,None,0.0,14,7,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.002474357425774638,mean,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,
-28,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.506204385946851,None,0.0,2,8,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.03433007982751011,mean,none,,,,,extra_trees_preproc_for_classification,False,gini,None,0.32676415738837183,None,0.0,5,6,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-29,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9333237919291875,None,0.0,8,3,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,
-30,none,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.9728532763078892,None,0.0,13,9,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,none,,,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6651071190864879,False,,,,,,,,,,,,,,,
-31,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9561075301148197,None,0.0,6,2,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,
-32,none,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,184,manual,0.5022041786628263,2.3064966255468173e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,most_frequent,standardize,,,,,kernel_pca,,,,,,,,,,,,,,,,,,,-0.32023224593255817,2,0.09479590655633498,poly,1382,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-33,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,2.7220799337966617e-08,0.011194310743518529,auto,255,None,38,187,3,loss,1e-07,0.13581959545340272,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0036603728729535916,mean,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,False,,,,,,,,,,,,
-34,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,22194.242946902177,0.45210849669021247,,0.0005669713702171556,sigmoid,-1,False,0.03542142266081451,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,quantile_transformer,291,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-35,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.2697794350876887,None,0.0,20,19,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.01857832166651019,median,robust_scaler,,,0.746850014013939,0.26258045325454815,fast_ica,,,,,,,,,,,deflation,cube,67,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-36,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,0.032732094595234795,0.09918078053098744,auto,255,None,97,54,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.019135985037746533,median,quantile_transformer,1000,uniform,,,feature_agglomeration,,,,,,,,,,,,,,,euclidean,average,37,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-37,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7360659359578277,None,0.0,18,5,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.0032431851910047368,mean,robust_scaler,,,0.7204949497300925,0.2277786352264519,fast_ica,,,,,,,,,,,deflation,exp,307,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-38,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.756167892711991e-05,False,True,squared_hinge,5.235791933868702e-05,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0001632172987320906,most_frequent,standardize,,,,,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,5,None,19,12,1.0,70,,,,,
-39,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,3.6796787331285204e-07,0.14399102828508656,auto,255,None,18,12,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,quantile_transformer,965,normal,,,feature_agglomeration,,,,,,,,,,,,,,,cosine,average,365,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-40,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7005303455027131,None,0.0,1,5,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.027348815457551995,mean,quantile_transformer,789,uniform,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,
-41,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,1.943186107984505e-07,0.08852455702036988,auto,255,None,5,72,19,loss,1e-07,0.039837460762271175,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.01591265419367627,median,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,
-42,weighting,adaboost,SAMME,0.34205296521106404,10,89,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.02025205191860567,most_frequent,minmax,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-43,none,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0686374607394679,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,minmax,,,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.8490260132946401,False,,,,,,,,,,,,,,,
-44,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,10,1,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0022606253047811435,median,quantile_transformer,1247,normal,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,25.327793585266683,chi2,,,
-45,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.3663040729445102,None,0.0,20,19,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.010000000000000004,median,robust_scaler,,,0.7338553275791654,0.25,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,331,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-46,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,92.66144306787051,,,0.04654639302091653,rbf,-1,True,0.0022231544280610005,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.005194838694455963,mean,none,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,15,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-47,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1550807214391849,True,True,hinge,0.00019875748182257,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.05786688075604592,mean,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,
-48,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.4467384469668449,None,0.0,19,19,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.00033708405018786203,median,quantile_transformer,711,normal,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,7.744829113696808,False,True,1,squared_hinge,ovr,l1,0.0001869446320464273,,,,,,,,,,,,,,,,,,,,,,
-49,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,26.502259985828186,,,9.475662388405154e-05,rbf,-1,False,0.00019690635968217866,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-50,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,2.3520913174804126e-07,0.052918150906133596,auto,255,None,16,185,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.00790855487341428,mean,robust_scaler,,,0.746273035018099,0.21296174657639108,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,1.298440997922375,False,True,1,squared_hinge,ovr,l1,0.0018510248972884416,,,,,,,,,,,,,,,,,,,,,,
-51,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5255991794296913,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-52,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.34697723213923637,None,0.0,15,18,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.1923594385988973,mean,normalize,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,25.77499302945827,chi2,,,
-53,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1252.7997374154156,0.20811431041897355,5,0.0018140593074227453,poly,-1,False,0.03149782358621565,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.001930822730474197,most_frequent,quantile_transformer,233,normal,,,feature_agglomeration,,,,,,,,,,,,,,,euclidean,ward,29,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-54,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,1.5661823810486637e-10,0.08996863554015262,auto,255,None,156,89,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.009331521286159971,median,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,
-55,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.6109468076475573,0.8748383232154038,4,0.41333070422336576,poly,-1,False,3.591268778393482e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.011804925819789203,median,minmax,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-56,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.7388247096873266,None,0.0,1,2,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.02005334514549439,most_frequent,robust_scaler,,,0.9347422252540531,0.2909868620945952,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,
-57,weighting,adaboost,SAMME.R,0.03127273017917347,10,496,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,standardize,,,,,fast_ica,,,,,,,,,,,deflation,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-58,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,151,None,,0.0003283777607180646,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.2510527431514227,mean,normalize,,,,,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,-0.96093182232312,,0.05762510964607661,sigmoid,2632,,,,,,,,,,,,,,,,,
-59,none,bernoulli_nb,,,,,1.9593357170002308,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.005109073482613361,mean,quantile_transformer,10,normal,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.03495379340060141,False,True,1,squared_hinge,ovr,l1,0.0033386410851928994,,,,,,,,,,,,,,,,,,,,,,
-60,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.6453555269772124,None,0.0,1,4,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,normalize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-61,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5363.189119763322,,,0.4294659312024404,rbf,-1,True,0.06935839422940453,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.019860129781307266,most_frequent,none,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,92.1980997621979,mutual_info,,,
-62,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.20911875507660596,None,0.0,12,8,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,median,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,
-63,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.34534888916828577,0.02513634776687268,2,1.0733091880109409,poly,-1,True,0.0006974263047918307,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,quantile_transformer,1000,normal,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,77.99934218022861,mutual_info,,,
-64,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,29.55731828912983,False,True,1,squared_hinge,ovr,l2,0.0011332486951954968,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,
-65,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,1.1387795876755026e-06,0.12245605190343567,auto,255,None,58,7,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,robust_scaler,,,0.79305915648885,0.1841487406876042,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-66,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.8425560317492612,None,0.0,8,15,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.015276988502366128,median,none,,,,,fast_ica,,,,,,,,,,,parallel,cube,17,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-67,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,0.2638627266631576,0.022360820165005443,auto,255,None,171,6,19,loss,1e-07,0.016209060012924756,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.008763072395677399,mean,standardize,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,78,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-68,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5664.233390385957,0.3496807683995542,4,0.00017689402501527962,poly,-1,True,0.01756282016660122,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.13312139731266243,median,quantile_transformer,1362,uniform,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,72.39572310756226,mutual_info,,,
-69,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.8278163313806293,None,0.0,3,5,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,robust_scaler,,,0.725835793765774,0.2571431961341018,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.07784020380291123,fdr,f_classif
-70,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0023391956112474004,False,True,squared_hinge,0.011792446147470903,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.08295440990272292,most_frequent,quantile_transformer,962,normal,,,kernel_pca,,,,,,,,,,,,,,,,,,,,,,cosine,1778,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-71,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.4264370333817712,None,0.0,13,3,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,median,minmax,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.14972073341699849,fpr,chi2
-72,none,adaboost,SAMME,0.07025975715693651,5,339,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,median,robust_scaler,,,0.7117466125338451,0.2984609070131515,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.21351971922962878,fdr,chi2
-73,none,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9931779675565967,None,0.0,18,17,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,robust_scaler,,,0.75,0.2945917010472818,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,
-74,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2611084157414942,True,True,hinge,0.0009587835023329391,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.14664248075999733,most_frequent,minmax,,,,,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.509603573308092,rbf,198,,,,,,,,,,,,,,,,,
-75,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,3.044019499065853e-08,0.030966538899129755,auto,255,None,64,50,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.00860619823050038,median,minmax,,,,,feature_agglomeration,,,,,,,,,,,,,,,cosine,average,36,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-76,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7.667945934601232,True,True,squared_hinge,1.5995631297928604e-05,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.004775063434540409,most_frequent,minmax,,,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.28455344994291587,False,True,1,squared_hinge,ovr,l1,6.230255219574715e-05,,,,,,,,,,,,,,,,,,,,,,
-77,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.34936025988397124,None,0.0,2,13,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00018601309647406987,median,robust_scaler,,,0.8634309348843472,0.26976989465367607,extra_trees_preproc_for_classification,False,entropy,None,0.48025175288975036,None,0.0,6,17,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-78,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,2.925599188129995e-05,0.12560285030651847,auto,255,None,300,193,17,loss,1e-07,0.039442997915546514,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00299400754997757,mean,robust_scaler,,,0.7093292916146515,0.009366983030047793,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-79,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.642370445299362,None,0.0,15,20,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.014823484140711048,median,robust_scaler,,,0.780144337652783,0.2309544712129402,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,2.2123747341894098,False,True,1,squared_hinge,ovr,l1,0.0033125984161268183,,,,,,,,,,,,,,,,,,,,,,
-80,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2471.298583654142,,,0.025866749427709803,rbf,-1,False,0.0009481287482516433,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,none,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,323,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-81,weighting,adaboost,SAMME,0.028939256953011736,2,368,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,quantile_transformer,1954,uniform,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,41.04018582009321,mutual_info,,,
-82,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,21.85207494001697,0.18872999589786676,4,1.1863646021868761,poly,-1,False,0.0021338371901294443,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0034080852834770217,mean,normalize,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.11326005109886934,fdr,f_classif
-83,weighting,adaboost,SAMME,0.38341570193537416,9,250,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,minmax,,,,,feature_agglomeration,,,,,,,,,,,,,,,euclidean,average,13,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-84,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,0.023106206735923184,0.12762814679231496,auto,255,None,24,151,16,loss,1e-07,0.08978875361280989,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.007723510861056437,median,quantile_transformer,1217,normal,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,
-85,weighting,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.00012847697769902057,mean,minmax,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,10.088989224596698,mutual_info,,,
-86,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,1,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.019797877010737826,mean,normalize,,,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.8239678741128165,False,,,,,,,,,,,,,,,
-87,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.46057831591617715,False,True,hinge,0.04557857428827514,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00027457445401600137,median,standardize,,,,,extra_trees_preproc_for_classification,True,gini,None,0.48190346970486964,None,0.0,17,18,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-88,none,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,230,auto,,0.0014037334852669801,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,quantile_transformer,848,uniform,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,61.06963527679606,f_classif,,,
-89,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,64,auto,,0.00021332620906594865,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.026032882800756493,median,standardize,,,,,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,-0.9513223792709335,,0.00029881582960753004,sigmoid,543,,,,,,,,,,,,,,,,,
-90,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.26356754187780895,None,0.0,4,3,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010966178401758774,mean,robust_scaler,,,0.7817043861798886,0.24371720112407158,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
-91,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,3.7287807266408084e-10,0.15544439749103187,auto,255,None,28,16,18,loss,1e-07,0.10835808508958217,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0207886310256421,mean,quantile_transformer,1245,normal,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.39383167612281667,fpr,f_classif
-92,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,130.2903387607578,False,True,1,squared_hinge,ovr,l2,0.0020820269897903502,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.08534605541971435,mean,quantile_transformer,613,normal,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,12.936259313956771,False,True,1,squared_hinge,ovr,l1,9.225567050752251e-05,,,,,,,,,,,,,,,,,,,,,,
-93,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.22879311080499845,None,0.0,8,4,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.014320022547615783,median,minmax,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.238630703440461,mutual_info,,,
-94,weighting,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,2,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0073667222552306465,median,robust_scaler,,,0.701595034693991,0.29514640404971515,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6066051105210388,False,,,,,,,,,,,,,,,
-95,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.107967527639527e-05,True,True,hinge,0.00448294745544775,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,quantile_transformer,953,normal,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,35.00114345405331,chi2,,,
-96,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.457928416564218,None,0.0,12,2,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,
-97,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,1.1522414705935405e-10,0.6132800183073315,auto,255,None,1607,67,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010096911249562407,mean,robust_scaler,,,0.7663493326967942,0.136094217337434,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-98,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1228.3897711257332,0.6862670093933454,5,1.113863025113022,poly,-1,True,1.9271116749907707e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.018161834200128672,mean,normalize,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.06278549919408385,fpr,chi2
-99,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.479405900046494,,,0.013340778632154543,rbf,-1,False,6.913586060890114e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,robust_scaler,,,0.9953232616818098,0.06450577886132274,fast_ica,,,,,,,,,,,deflation,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-100,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.9815182201168626,None,0.0,15,19,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,robust_scaler,,,0.8878662977383482,0.25666364929970126,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,44.15748219826322,mutual_info,,,
-101,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1584.207754377407,,,0.020660207249591715,rbf,-1,False,0.03773359340731028,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,quantile_transformer,1055,uniform,,,fast_ica,,,,,,,,,,,parallel,exp,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-102,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.7285597583341324,None,0.0,3,20,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.011700861722450575,most_frequent,robust_scaler,,,0.7911119178158159,0.2999762250828743,feature_agglomeration,,,,,,,,,,,,,,,euclidean,ward,151,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-103,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2897.996028838154,,,0.04621527412391808,rbf,-1,False,0.0017262380676728777,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.07770313303106262,mean,robust_scaler,,,0.7510736797641198,0.23032782852489117,extra_trees_preproc_for_classification,False,gini,None,0.9588902962283966,None,0.0,13,12,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-104,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.6282663389679584,None,0.0,1,3,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,median,minmax,,,,,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-105,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,8.14394583618027e-09,0.1314274861878701,auto,255,None,27,15,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.3598067419611515,mean,robust_scaler,,,0.7494615433236279,0.260649229829574,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
-106,weighting,adaboost,SAMME.R,0.46962472583666215,8,455,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.03708420001141539,most_frequent,robust_scaler,,,0.7364857891944228,0.23021698558282877,feature_agglomeration,,,,,,,,,,,,,,,euclidean,complete,5,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-107,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,11.046854203258679,,,0.07809571976769347,rbf,-1,False,0.00036193159909053734,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.006445557635410205,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-108,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.6728827282646637,None,0.0,18,8,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,robust_scaler,,,0.8512985751386942,0.14092824896182807,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,
-109,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.18060139887649052,None,0.0,10,12,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.010000000000000004,most_frequent,robust_scaler,,,0.9298612094255864,0.1263597946722308,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
-110,none,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.16236660803443026,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,quantile_transformer,377,normal,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2611168895496939,fpr,f_classif
-111,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6427543483503397,True,True,hinge,0.00045318775930916944,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.009338484332306904,median,quantile_transformer,174,normal,,,kernel_pca,,,,,,,,,,,,,,,,,,,,,,cosine,1848,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-112,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.02021502857757827,True,,0.055848937053280076,True,,constant,log,l2,,0.00016356162584832537,no_encoding,no_coalescense,,median,quantile_transformer,27,normal,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.11873149622606233,fwe,chi2
-113,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,9.029473608568458e-09,0.15779990576964315,auto,255,None,37,71,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.018093177195644097,mean,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,False,,,,,,,,,,,,
-114,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8.24807027221455,-0.23458710218209156,,0.0001030034352044511,sigmoid,-1,True,0.0002726309373020207,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,quantile_transformer,1974,normal,,,extra_trees_preproc_for_classification,True,entropy,None,0.9395220389606214,None,0.0,2,20,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-115,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.3747863651224668,True,True,hinge,0.00019106780876479388,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.002442315142014378,median,quantile_transformer,1850,uniform,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.570194774022733,f_classif,,,
-116,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.4864284879550203,None,0.0,1,4,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.008665854799630503,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-117,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.8785519499336585,None,0.0,13,17,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.05156816034494817,most_frequent,quantile_transformer,1243,uniform,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.11082264321263885,fdr,f_classif
-118,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.536709062860521e-07,True,,,True,,optimal,squared_hinge,l1,,1.6934515188687678e-05,one_hot_encoding,no_coalescense,,most_frequent,normalize,,,,,feature_agglomeration,,,,,,,,,,,,,,,cosine,complete,306,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-119,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.6905196223806276,None,0.0,6,5,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,minmax,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-120,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.6154724467600127,None,0.0,19,12,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,mean,minmax,,,,,extra_trees_preproc_for_classification,True,entropy,None,0.7630778936392856,None,0.0,11,20,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-121,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5836006490230695,None,0.0,1,19,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.02127792104027424,most_frequent,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,
-122,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.922871100218626,False,True,squared_hinge,0.09957180737451943,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0016563186856411274,median,quantile_transformer,850,uniform,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.09123695282772847,fpr,chi2
-123,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.06571678518052461,False,True,1,squared_hinge,ovr,l2,3.5551445493853517e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0007406565537349627,median,quantile_transformer,1114,normal,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.13436517316012828,fwe,f_classif
-124,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1561.1002600303584,,,0.0024048551704194507,rbf,-1,False,0.0001936214667496851,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,minmax,,,,,fast_ica,,,,,,,,,,,parallel,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-125,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.087313242780732,0.0636709914987994,3,0.004481917125024585,poly,-1,False,0.01848880618941367,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.002136015035740733,mean,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-126,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.03204653439894958,True,True,squared_hinge,0.00013006393401112688,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.013506575694382031,most_frequent,normalize,,,,,feature_agglomeration,,,,,,,,,,,,,,,euclidean,ward,356,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-127,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.6466927589470762,None,0.0,11,2,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.009672465311659264,most_frequent,standardize,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,369,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+1,none,decision_tree,,,,,,,entropy,1.877536950957045,1.0,None,0.0,10,13,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,quantile_transformer,1275,uniform,,,extra_trees_preproc_for_classification,True,gini,None,0.04910706843318435,None,0.0,12,12,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+2,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.8905944749638223,None,0.0,5,4,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.15463552048633444,most_frequent,robust_scaler,,,0.7545842296967857,0.22898551665984185,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.24780801481682346,fwe,chi2
+5,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,9.83838677007206e-09,0.09269618726619562,auto,255,None,37,16,14,loss,1e-07,0.04208303685387712,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
+6,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,4.267426422904654e-06,0.011555121209024403,auto,255,None,121,34,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010064483887441845,most_frequent,quantile_transformer,1194,uniform,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,
+8,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,114,manual,0.11778631707605758,0.0029112721339713465,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.015582195257240751,median,normalize,,,,,kernel_pca,,,,,,,,,,,,,,,,,,,0.42909623253327767,3,0.25407612000343777,poly,1689,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+9,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.685918004243461,True,True,squared_hinge,1.7784504877380735e-05,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010335606354842895,most_frequent,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,
+10,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.7396493120555564,None,0.0,1,18,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010000000000000004,median,robust_scaler,,,0.8908334215036855,0.08666907249785906,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,
+13,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,0.9907352051102745,0.018115439174701313,auto,255,None,84,5,3,loss,1e-07,0.13697766901072808,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.036524416007602305,mean,robust_scaler,,,0.9824116214519696,0.2911868066464925,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,
+14,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,4.629265598705481e-10,0.03894546521791516,auto,255,None,5,22,18,loss,1e-07,0.0837137336215462,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.008397749525484112,mean,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
+15,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.21772092350860472,None,0.0,14,17,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.0020488212891693275,median,standardize,,,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,33.74257606530722,False,True,1,squared_hinge,ovr,l1,8.740452966205111e-05,,,,,,,,,,,,,,,,,,,,,,
+17,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.6591033863118033,None,0.0,10,10,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,none,,,,,fast_ica,,,,,,,,,,,deflation,exp,1506,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+18,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,1.9110915129429343e-09,0.06380311481632683,auto,255,None,20,62,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,robust_scaler,,,0.7307407504330838,0.2660206566992171,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,
+19,weighting,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,99,2,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010033505607662804,median,none,,,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.069760700053376,False,True,1,squared_hinge,ovr,l1,0.06611177025130774,,,,,,,,,,,,,,,,,,,,,,
+23,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,2.0447842449942555e-09,0.04257026709693251,auto,255,None,47,13,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010000000000000004,median,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,
+24,weighting,adaboost,SAMME.R,0.44886160948796244,9,493,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.031007103864624616,mean,robust_scaler,,,0.7575180367976682,0.26457274298753825,feature_agglomeration,,,,,,,,,,,,,,,cosine,complete,211,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+25,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,0.008942041103505539,0.05229567301622349,auto,255,None,6,24,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.01936143670847451,mean,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,
+26,weighting,adaboost,SAMME.R,0.01561270559347964,4,417,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,quantile_transformer,1617,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+27,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.41025844265119926,None,0.0,18,15,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,median,quantile_transformer,1911,normal,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,False,,,,,,,,,,,,
+29,none,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.9034033923099005,False,True,1,squared_hinge,ovr,l2,1.2159485299684885e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.027586423552745792,most_frequent,none,,,,,kernel_pca,,,,,,,,,,,,,,,,,,,,,0.4034250963681743,rbf,735,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+30,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,490.8047233341048,0.13853827450882505,,0.004044309450174347,sigmoid,-1,False,0.005958796387567885,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.008446690235218424,median,minmax,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,229,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+31,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,3.0538220042587883e-07,0.0704056758356995,auto,255,None,9,3,19,loss,1e-07,0.01927933154167906,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.019804174585488932,median,quantile_transformer,921,normal,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,
+32,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,101,auto,,1.553086088086431e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.01137059177001425,most_frequent,normalize,,,,,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,-0.021621751597165284,4,0.7468597264810062,poly,5078,,,,,,,,,,,,,,,,,
+33,none,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.5769165522851386,None,0.0,1,6,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,none,,,,,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,7,None,9,14,1.0,74,,,,,
+34,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,0.06671977293714751,0.04456236025167353,auto,255,None,3,163,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.4333293305857194,mean,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+37,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0618160212153551,True,True,squared_hinge,1.2370453081062464e-05,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.01448339838697973,mean,standardize,,,,,kitchen_sinks,,,,,,,,,,,,,,,,,,,,,,,,0.018879929475513826,1637,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+39,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.6799215694343422,None,0.0,1,5,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.004724310659953132,most_frequent,quantile_transformer,1037,normal,,,fast_ica,,,,,,,,,,,parallel,exp,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+41,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.6965935192702026,None,0.0,12,16,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,none,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,67.60637688434707,f_classif,,,
+43,none,decision_tree,,,,,,,gini,1.1110863621017264,1.0,None,0.0,16,19,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.00043339385698342055,mean,none,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.20957492283649354,fwe,f_classif
+44,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,2.6159836259985015e-05,0.05532294838858921,auto,255,None,116,4,14,loss,1e-07,0.20422881335187132,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,
+49,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00010123414317660679,False,,0.04011567850168966,True,,constant,log,l1,,0.00014179758438189507,one_hot_encoding,minority_coalescer,0.008920898694379564,mean,robust_scaler,,,0.7712963178784032,0.2702398922671455,kernel_pca,,,,,,,,,,,,,,,,,,,,,,cosine,1090,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+54,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,1.0607494292547588e-08,0.014599960935351135,auto,255,None,5,7,1,loss,1e-07,0.01508309574734627,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
+58,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5547994712622472,None,0.0,1,2,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.008028893322827371,most_frequent,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
+59,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9768.124422997415,False,True,1,squared_hinge,ovr,l2,0.002391133479983495,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,robust_scaler,,,0.7418544320601411,0.258610892841927,fast_ica,,,,,,,,,,,parallel,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+60,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.8757952087292588,None,0.0,3,7,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.00023383575324676886,most_frequent,robust_scaler,,,0.75,0.25885426505910014,feature_agglomeration,,,,,,,,,,,,,,,cosine,average,83,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+64,weighting,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.5927254703947399,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.004193053494360822,median,none,,,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.5779022723643873,False,,,,,,,,,,,,,,,
+65,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,4.427693008576421e-07,0.1480512588831196,auto,255,None,5,46,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.07105836118280727,median,quantile_transformer,875,normal,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,False,,,,,,,,,,,,
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diff --git a/autosklearn/metalearning/files/precision_samples_binary.classification_dense/description.txt b/autosklearn/metalearning/files/precision_samples_binary.classification_dense/description.txt
index 6ed52c6219..cdb82de1b3 100644
--- a/autosklearn/metalearning/files/precision_samples_binary.classification_dense/description.txt
+++ b/autosklearn/metalearning/files/precision_samples_binary.classification_dense/description.txt
@@ -1,8 +1,9 @@
 features_cutoff_time: 3600
 features_cutoff_memory: 3072
-number_of_feature_steps: 35
+number_of_feature_steps: 42
 feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances
 feature_step LogNumberOfInstances: LogNumberOfInstances
+feature_step NumberOfClasses: NumberOfClasses
 feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures
 feature_step LogNumberOfFeatures: LogNumberOfFeatures
 feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues
@@ -20,6 +21,11 @@ feature_step DatasetRatio: DatasetRatio, LogDatasetRatio
 feature_step LogDatasetRatio: LogDatasetRatio
 feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio
 feature_step LogInverseDatasetRatio: LogInverseDatasetRatio
+feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD
+feature_step ClassProbabilityMin: ClassProbabilityMin
+feature_step ClassProbabilityMax: ClassProbabilityMax
+feature_step ClassProbabilityMean: ClassProbabilityMean
+feature_step ClassProbabilitySTD: ClassProbabilitySTD
 feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum
 feature_step SymbolsMin: SymbolsMin
 feature_step SymbolsMax: SymbolsMax
@@ -36,11 +42,12 @@ feature_step SkewnessMin: SkewnessMin
 feature_step SkewnessMax: SkewnessMax
 feature_step SkewnessMean: SkewnessMean
 feature_step SkewnessSTD: SkewnessSTD
-features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
+feature_step ClassEntropy: ClassEntropy
+features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy
 features_stochastic: 
-default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
+default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy
 
-algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127
+algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235,236,237,238,239,240,241,242,243,244,245,246,247,248,249,250,251,252,253,254,255,256,257,258,259,260,261,262,263,264,265,266,267,268,269,270,271,272,273,274,275,276,277,278,279,280,281,282,283,284,285,286,287,288,289,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,312,313,314,315,316,317,318,319,320,321,322,323,324,325,326,327,328,329,330,331,332,333,334,335,336,337,338,339,340,341,342,343,344,345,346,347,348,349,350,351,352,353,354,355,356,357,358,359,360,361,362,363,364,365,366,367,368,369,370,371,372,373,374,375,376,377,378,379,380,381,382,383,384,385,386,387,388,389,390,391,392,393,394,395,396,397,398,399,400,401,402,403,404,405,406,407,408,409,410,411,412,413,414,415,416,417,418,419,420,421,422,423,424,425,426,427,428,429,430,431,432,433,434,435,436,437,438,439,440,441,442,443,444,445,446,447,448,449,450,451,452,453,454,455,456,457,458,459,460,461,462,463,464
 algorithms_stochastic: 
 performance_measures: precision_samples
 performance_type: solution_quality
diff --git a/autosklearn/metalearning/files/precision_samples_binary.classification_dense/feature_costs.arff b/autosklearn/metalearning/files/precision_samples_binary.classification_dense/feature_costs.arff
index 0e17e20d22..8512fd9275 100644
--- a/autosklearn/metalearning/files/precision_samples_binary.classification_dense/feature_costs.arff
+++ b/autosklearn/metalearning/files/precision_samples_binary.classification_dense/feature_costs.arff
@@ -4,6 +4,7 @@
 @ATTRIBUTE repetition NUMERIC
 @ATTRIBUTE NumberOfInstances NUMERIC
 @ATTRIBUTE LogNumberOfInstances NUMERIC
+@ATTRIBUTE NumberOfClasses NUMERIC
 @ATTRIBUTE NumberOfFeatures NUMERIC
 @ATTRIBUTE LogNumberOfFeatures NUMERIC
 @ATTRIBUTE MissingValues NUMERIC
@@ -21,6 +22,11 @@
 @ATTRIBUTE LogDatasetRatio NUMERIC
 @ATTRIBUTE InverseDatasetRatio NUMERIC
 @ATTRIBUTE LogInverseDatasetRatio NUMERIC
+@ATTRIBUTE ClassOccurences NUMERIC
+@ATTRIBUTE ClassProbabilityMin NUMERIC
+@ATTRIBUTE ClassProbabilityMax NUMERIC
+@ATTRIBUTE ClassProbabilityMean NUMERIC
+@ATTRIBUTE ClassProbabilitySTD NUMERIC
 @ATTRIBUTE NumSymbols NUMERIC
 @ATTRIBUTE SymbolsMin NUMERIC
 @ATTRIBUTE SymbolsMax NUMERIC
@@ -37,162 +43,138 @@
 @ATTRIBUTE SkewnessMax NUMERIC
 @ATTRIBUTE SkewnessMean NUMERIC
 @ATTRIBUTE SkewnessSTD NUMERIC
+@ATTRIBUTE ClassEntropy NUMERIC
 
 @DATA
-75123,1.0,1e-05,1e-05,1e-05,1e-05,0.00253,0.00221,1e-05,0.00024,3e-05,0.00011,0.0,2e-05,2e-05,5e-05,8e-05,2e-05,1e-05,7e-05,1e-05,0.00072,1e-05,1e-05,0.00019,0.00041,0.00011,0.00061,0.00012,9e-05,0.00015,0.00025,0.00067,0.00014,0.00011,0.00016,0.00027
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-2123,1.0,1e-05,1e-05,1e-05,1e-05,0.00097,0.00079,1e-05,0.00013,1e-05,7e-05,0.0,2e-05,2e-05,5e-05,8e-05,2e-05,1e-05,5e-05,1e-05,0.0007,1e-05,0.0,0.00017,0.00042,0.0001,0.00052,6e-05,8e-05,0.00012,0.00026,0.00049,6e-05,0.00011,9e-05,0.00023
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-75217,1.0,1e-05,1e-05,2e-05,1e-05,0.00196,0.00158,1e-05,0.00026,2e-05,0.00016,1e-05,3e-05,3e-05,7e-05,0.0001,3e-05,1e-05,7e-05,2e-05,0.00089,1e-05,1e-05,0.00022,0.00053,0.00013,0.00074,9e-05,0.0001,0.00019,0.00036,0.00065,0.00013,0.0001,0.00011,0.00031
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+75235,1.0,1e-05,1e-05,0.0003,2e-05,1e-05,0.00461,0.00385,2e-05,0.00055,2e-05,0.00026,1e-05,3e-05,3e-05,0.0001,0.00011,3e-05,1e-05,5e-05,2e-05,0.00026,3e-05,1e-05,8e-05,0.00014,0.00075,1e-05,1e-05,0.0002,0.00043,0.00011,0.00127,0.00017,0.0002,0.00029,0.00061,0.0008,0.00015,0.00012,0.00016,0.00038,0.00242
+75236,1.0,1e-05,1e-05,0.0001,1e-05,1e-05,0.00218,0.00118,2e-05,0.00065,3e-05,0.00041,1e-05,3e-05,3e-05,9e-05,9e-05,4e-05,1e-05,7e-05,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.0006,1e-05,1e-05,0.00021,0.00032,6e-05,0.00043,7e-05,7e-05,9e-05,0.00019,0.00062,7e-05,8e-05,0.0001,0.00037,0.00048
+75237,1.0,2e-05,1e-05,0.00785,2e-05,1e-05,0.31031,0.27854,2e-05,0.01161,3e-05,0.02022,1e-05,4e-05,3e-05,9e-05,0.00024,6e-05,2e-05,9e-05,2e-05,0.00062,7e-05,3e-05,0.00017,0.00035,0.0008,1e-05,1e-05,0.00028,0.00043,8e-05,0.00043,8e-05,7e-05,9e-05,0.0002,0.00044,6e-05,8e-05,9e-05,0.00021,0.14028
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+75243,1.0,1e-05,1e-05,0.00029,1e-05,1e-05,0.00461,0.00415,1e-05,0.00035,1e-05,0.00015,0.0,2e-05,2e-05,5e-05,7e-05,2e-05,1e-05,4e-05,1e-05,0.00018,2e-05,1e-05,4e-05,0.00011,0.00022,1e-05,1e-05,5e-05,0.00011,5e-05,0.0004,6e-05,6e-05,8e-05,0.0002,0.00039,6e-05,6e-05,8e-05,0.00019,0.00265
+75244,1.0,3e-05,2e-05,0.0004,3e-05,2e-05,0.0173,0.01298,4e-05,0.00267,4e-05,0.00174,1e-05,7e-05,9e-05,0.00024,0.00027,8e-05,2e-05,0.00013,3e-05,0.00045,7e-05,2e-05,0.00011,0.00025,0.00077,6e-05,2e-05,0.00021,0.00034,0.00013,0.00077,0.00014,0.00013,0.00026,0.00023,0.00044,7e-05,8e-05,0.0001,0.00019,0.00649
+75248,1.0,1e-05,1e-05,0.00017,1e-05,1e-05,0.00416,0.00305,1e-05,0.0006,2e-05,0.00054,1e-05,2e-05,2e-05,5e-05,7e-05,2e-05,1e-05,4e-05,1e-05,0.00017,2e-05,1e-05,4e-05,0.0001,0.00029,5e-05,1e-05,6e-05,0.00012,6e-05,0.00081,0.00011,0.00015,0.00016,0.00038,0.0005,7e-05,8e-05,0.00012,0.00024,0.00184
+75249,1.0,1e-05,1e-05,0.00011,1e-05,1e-05,0.00192,0.00153,1e-05,0.00025,1e-05,0.00017,1e-05,2e-05,2e-05,5e-05,7e-05,2e-05,1e-05,5e-05,3e-05,0.00014,2e-05,1e-05,3e-05,8e-05,0.00029,1e-05,3e-05,6e-05,0.00012,7e-05,0.00041,6e-05,8e-05,0.0001,0.00017,0.00043,6e-05,7e-05,9e-05,0.00019,0.00092
+75250,1.0,1e-05,1e-05,0.00375,1e-05,1e-05,0.04994,0.047,1e-05,0.00247,1e-05,0.00049,1e-05,2e-05,2e-05,6e-05,7e-05,2e-05,1e-05,3e-05,1e-05,0.00022,4e-05,1e-05,4e-05,0.00012,0.00041,0.0,1e-05,0.00011,0.00023,6e-05,0.00076,0.00012,0.00012,0.00019,0.00032,0.00086,0.00012,0.00013,0.00018,0.00043,0.04178
diff --git a/autosklearn/metalearning/files/precision_samples_binary.classification_dense/feature_runstatus.arff b/autosklearn/metalearning/files/precision_samples_binary.classification_dense/feature_runstatus.arff
index 0034ad2bd6..5fff2dfed3 100644
--- a/autosklearn/metalearning/files/precision_samples_binary.classification_dense/feature_runstatus.arff
+++ b/autosklearn/metalearning/files/precision_samples_binary.classification_dense/feature_runstatus.arff
@@ -4,6 +4,7 @@
 @ATTRIBUTE repetition NUMERIC
 @ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other}
@@ -21,6 +22,11 @@
 @ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other}
@@ -37,162 +43,138 @@
 @ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other}
 
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diff --git a/autosklearn/metalearning/files/precision_samples_binary.classification_dense/feature_values.arff b/autosklearn/metalearning/files/precision_samples_binary.classification_dense/feature_values.arff
index a60400e3ce..736dc64321 100644
--- a/autosklearn/metalearning/files/precision_samples_binary.classification_dense/feature_values.arff
+++ b/autosklearn/metalearning/files/precision_samples_binary.classification_dense/feature_values.arff
@@ -13,7 +13,6 @@
 @ATTRIBUTE KurtosisMean NUMERIC
 @ATTRIBUTE KurtosisMin NUMERIC
 @ATTRIBUTE KurtosisSTD NUMERIC
-@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC
 @ATTRIBUTE LogDatasetRatio NUMERIC
 @ATTRIBUTE LogInverseDatasetRatio NUMERIC
 @ATTRIBUTE LogNumberOfFeatures NUMERIC
@@ -42,160 +41,135 @@
 @ATTRIBUTE SymbolsSum NUMERIC
 
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diff --git a/autosklearn/metalearning/files/precision_samples_binary.classification_sparse/algorithm_runs.arff b/autosklearn/metalearning/files/precision_samples_binary.classification_sparse/algorithm_runs.arff
index 0b5c150cc0..94702a454e 100644
--- a/autosklearn/metalearning/files/precision_samples_binary.classification_sparse/algorithm_runs.arff
+++ b/autosklearn/metalearning/files/precision_samples_binary.classification_sparse/algorithm_runs.arff
@@ -7,95 +7,133 @@
 @ATTRIBUTE runstatus {ok, timeout, memout, not_applicable, crash, other}
 
 @DATA
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-258,1.0,13,?,not_applicable
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-75221,1.0,17,?,not_applicable
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-3043,1.0,20,?,not_applicable
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-75174,1.0,22,?,not_applicable
-275,1.0,23,?,not_applicable
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-75184,1.0,26,?,not_applicable
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+75177,1.0,11,?,not_applicable
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+253,1.0,23,?,not_applicable
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+75124,1.0,26,?,not_applicable
 75222,1.0,27,?,not_applicable
-233,1.0,28,?,not_applicable
-75114,1.0,29,?,not_applicable
-236,1.0,30,?,not_applicable
-75141,1.0,31,?,not_applicable
-75107,1.0,32,?,not_applicable
-262,1.0,33,?,not_applicable
-75146,1.0,34,?,not_applicable
-75189,1.0,35,?,not_applicable
-2350,1.0,36,?,not_applicable
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-75207,1.0,54,?,not_applicable
-75225,1.0,55,?,not_applicable
-75166,1.0,56,?,not_applicable
-75100,1.0,57,?,not_applicable
-75169,1.0,58,?,not_applicable
-75121,1.0,59,?,not_applicable
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-2119,1.0,64,?,not_applicable
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-75113,1.0,66,?,not_applicable
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-75182,1.0,68,?,not_applicable
-251,1.0,69,?,not_applicable
-75123,1.0,70,?,not_applicable
-75125,1.0,71,?,not_applicable
-75232,1.0,72,?,not_applicable
-75103,1.0,73,?,not_applicable
-75192,1.0,74,?,not_applicable
-75230,1.0,75,?,not_applicable
-75139,1.0,76,?,not_applicable
-75227,1.0,77,?,not_applicable
-2120,1.0,78,?,not_applicable
-75124,1.0,79,?,not_applicable
-75240,1.0,80,?,not_applicable
-75198,1.0,81,?,not_applicable
-75201,1.0,82,?,not_applicable
-75133,1.0,83,?,not_applicable
-75154,1.0,84,?,not_applicable
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-2117,1.0,86,?,not_applicable
-75156,1.0,87,?,not_applicable
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-75172,1.0,89,?,not_applicable
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+288,1.0,89,?,not_applicable
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+275,1.0,92,?,not_applicable
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diff --git a/autosklearn/metalearning/files/precision_samples_binary.classification_sparse/configurations.csv b/autosklearn/metalearning/files/precision_samples_binary.classification_sparse/configurations.csv
index 888e169980..c3d201ba5c 100644
--- a/autosklearn/metalearning/files/precision_samples_binary.classification_sparse/configurations.csv
+++ b/autosklearn/metalearning/files/precision_samples_binary.classification_sparse/configurations.csv
@@ -1,93 +1,131 @@
 idx,balancing:strategy,classifier:__choice__,classifier:adaboost:algorithm,classifier:adaboost:learning_rate,classifier:adaboost:max_depth,classifier:adaboost:n_estimators,classifier:bernoulli_nb:alpha,classifier:bernoulli_nb:fit_prior,classifier:decision_tree:criterion,classifier:decision_tree:max_depth_factor,classifier:decision_tree:max_features,classifier:decision_tree:max_leaf_nodes,classifier:decision_tree:min_impurity_decrease,classifier:decision_tree:min_samples_leaf,classifier:decision_tree:min_samples_split,classifier:decision_tree:min_weight_fraction_leaf,classifier:extra_trees:bootstrap,classifier:extra_trees:criterion,classifier:extra_trees:max_depth,classifier:extra_trees:max_features,classifier:extra_trees:max_leaf_nodes,classifier:extra_trees:min_impurity_decrease,classifier:extra_trees:min_samples_leaf,classifier:extra_trees:min_samples_split,classifier:extra_trees:min_weight_fraction_leaf,classifier:gradient_boosting:early_stop,classifier:gradient_boosting:l2_regularization,classifier:gradient_boosting:learning_rate,classifier:gradient_boosting:loss,classifier:gradient_boosting:max_bins,classifier:gradient_boosting:max_depth,classifier:gradient_boosting:max_leaf_nodes,classifier:gradient_boosting:min_samples_leaf,classifier:gradient_boosting:n_iter_no_change,classifier:gradient_boosting:scoring,classifier:gradient_boosting:tol,classifier:gradient_boosting:validation_fraction,classifier:k_nearest_neighbors:n_neighbors,classifier:k_nearest_neighbors:p,classifier:k_nearest_neighbors:weights,classifier:lda:n_components,classifier:lda:shrinkage,classifier:lda:shrinkage_factor,classifier:lda:tol,classifier:liblinear_svc:C,classifier:liblinear_svc:dual,classifier:liblinear_svc:fit_intercept,classifier:liblinear_svc:intercept_scaling,classifier:liblinear_svc:loss,classifier:liblinear_svc:multi_class,classifier:liblinear_svc:penalty,classifier:liblinear_svc:tol,classifier:libsvm_svc:C,classifier:libsvm_svc:coef0,classifier:libsvm_svc:degree,classifier:libsvm_svc:gamma,classifier:libsvm_svc:kernel,classifier:libsvm_svc:max_iter,classifier:libsvm_svc:shrinking,classifier:libsvm_svc:tol,classifier:multinomial_nb:alpha,classifier:multinomial_nb:fit_prior,classifier:passive_aggressive:C,classifier:passive_aggressive:average,classifier:passive_aggressive:fit_intercept,classifier:passive_aggressive:loss,classifier:passive_aggressive:tol,classifier:qda:reg_param,classifier:random_forest:bootstrap,classifier:random_forest:criterion,classifier:random_forest:max_depth,classifier:random_forest:max_features,classifier:random_forest:max_leaf_nodes,classifier:random_forest:min_impurity_decrease,classifier:random_forest:min_samples_leaf,classifier:random_forest:min_samples_split,classifier:random_forest:min_weight_fraction_leaf,classifier:sgd:alpha,classifier:sgd:average,classifier:sgd:epsilon,classifier:sgd:eta0,classifier:sgd:fit_intercept,classifier:sgd:l1_ratio,classifier:sgd:learning_rate,classifier:sgd:loss,classifier:sgd:penalty,classifier:sgd:power_t,classifier:sgd:tol,data_preprocessing:categorical_transformer:categorical_encoding:__choice__,data_preprocessing:categorical_transformer:category_coalescence:__choice__,data_preprocessing:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction,data_preprocessing:numerical_transformer:imputation:strategy,data_preprocessing:numerical_transformer:rescaling:__choice__,data_preprocessing:numerical_transformer:rescaling:quantile_transformer:n_quantiles,data_preprocessing:numerical_transformer:rescaling:quantile_transformer:output_distribution,data_preprocessing:numerical_transformer:rescaling:robust_scaler:q_max,data_preprocessing:numerical_transformer:rescaling:robust_scaler:q_min,feature_preprocessor:__choice__,feature_preprocessor:extra_trees_preproc_for_classification:bootstrap,feature_preprocessor:extra_trees_preproc_for_classification:criterion,feature_preprocessor:extra_trees_preproc_for_classification:max_depth,feature_preprocessor:extra_trees_preproc_for_classification:max_features,feature_preprocessor:extra_trees_preproc_for_classification:max_leaf_nodes,feature_preprocessor:extra_trees_preproc_for_classification:min_impurity_decrease,feature_preprocessor:extra_trees_preproc_for_classification:min_samples_leaf,feature_preprocessor:extra_trees_preproc_for_classification:min_samples_split,feature_preprocessor:extra_trees_preproc_for_classification:min_weight_fraction_leaf,feature_preprocessor:extra_trees_preproc_for_classification:n_estimators,feature_preprocessor:kernel_pca:coef0,feature_preprocessor:kernel_pca:degree,feature_preprocessor:kernel_pca:gamma,feature_preprocessor:kernel_pca:kernel,feature_preprocessor:kernel_pca:n_components,feature_preprocessor:kitchen_sinks:gamma,feature_preprocessor:kitchen_sinks:n_components,feature_preprocessor:liblinear_svc_preprocessor:C,feature_preprocessor:liblinear_svc_preprocessor:dual,feature_preprocessor:liblinear_svc_preprocessor:fit_intercept,feature_preprocessor:liblinear_svc_preprocessor:intercept_scaling,feature_preprocessor:liblinear_svc_preprocessor:loss,feature_preprocessor:liblinear_svc_preprocessor:multi_class,feature_preprocessor:liblinear_svc_preprocessor:penalty,feature_preprocessor:liblinear_svc_preprocessor:tol,feature_preprocessor:nystroem_sampler:coef0,feature_preprocessor:nystroem_sampler:degree,feature_preprocessor:nystroem_sampler:gamma,feature_preprocessor:nystroem_sampler:kernel,feature_preprocessor:nystroem_sampler:n_components,feature_preprocessor:polynomial:degree,feature_preprocessor:polynomial:include_bias,feature_preprocessor:polynomial:interaction_only,feature_preprocessor:random_trees_embedding:bootstrap,feature_preprocessor:random_trees_embedding:max_depth,feature_preprocessor:random_trees_embedding:max_leaf_nodes,feature_preprocessor:random_trees_embedding:min_samples_leaf,feature_preprocessor:random_trees_embedding:min_samples_split,feature_preprocessor:random_trees_embedding:min_weight_fraction_leaf,feature_preprocessor:random_trees_embedding:n_estimators,feature_preprocessor:select_percentile_classification:percentile,feature_preprocessor:select_percentile_classification:score_func,feature_preprocessor:select_rates_classification:alpha,feature_preprocessor:select_rates_classification:mode,feature_preprocessor:select_rates_classification:score_func,feature_preprocessor:truncatedSVD:target_dim
-1,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.3163020577549344,None,0.0,15,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.01322981552715978,most_frequent,robust_scaler,,,0.9746667009556722,0.1974876581478311,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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-25,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.4752532930653711,None,0.0,9,17,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,robust_scaler,,,0.8538731695642808,0.11963446560467883,extra_trees_preproc_for_classification,True,entropy,None,0.5642223456125496,None,0.0,6,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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-29,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,10,1,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0022606253047811435,median,quantile_transformer,1247,normal,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,25.327793585266683,chi2,,,,
-30,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,45.824472650016915,,,0.04733593018751384,rbf,-1,False,0.0001002563992422035,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.007882457896645694,most_frequent,robust_scaler,,,0.7360778699898849,0.27800774875566153,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-31,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,21.69482701422382,-0.9439355912923322,,0.012708037096662675,sigmoid,-1,True,1.3900680173654052e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-32,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.4467384469668449,None,0.0,19,19,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.00033708405018786203,median,quantile_transformer,711,normal,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,7.744829113696808,False,True,1,squared_hinge,ovr,l1,0.0001869446320464273,,,,,,,,,,,,,,,,,,,,,
-33,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,26.502259985828186,,,9.475662388405154e-05,rbf,-1,False,0.00019690635968217866,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-34,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.339600708038189,False,True,1,squared_hinge,ovr,l2,9.572641158475304e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,quantile_transformer,1193,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-35,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5255991794296913,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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-38,none,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4994182104906286,False,True,1,squared_hinge,ovr,l2,0.006324920556381277,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.06848370232187935,mean,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-39,none,bernoulli_nb,,,,,1.9593357170002308,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.005109073482613361,mean,quantile_transformer,10,normal,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.03495379340060141,False,True,1,squared_hinge,ovr,l1,0.0033386410851928994,,,,,,,,,,,,,,,,,,,,,
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-42,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.49918755377415064,None,0.0,1,2,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.36992315774620843,mean,robust_scaler,,,0.7410494147895569,0.29895918805929733,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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-48,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.41019363453549706,None,0.0,15,2,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.0003507389224306018,median,none,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.20614098085343638,fdr,chi2,
-49,none,adaboost,SAMME,0.07025975715693651,5,339,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,median,robust_scaler,,,0.7117466125338451,0.2984609070131515,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.21351971922962878,fdr,chi2,
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-53,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8.85547546146731,True,True,squared_hinge,4.0111304608748804e-05,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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-57,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0016941428709087083,True,,,True,,optimal,squared_hinge,l1,,5.124265599423001e-05,no_encoding,no_coalescense,,most_frequent,quantile_transformer,461,uniform,,,extra_trees_preproc_for_classification,True,gini,None,0.31371431180257103,None,0.0,5,17,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-58,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.7591396576940418,-0.02447104417007273,2,0.7325180366342191,poly,-1,True,0.00045922794962860523,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,standardize,,,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,2.057731506094293,False,True,1,squared_hinge,ovr,l1,0.03575998247131604,,,,,,,,,,,,,,,,,,,,,
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-79,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.6728827282646637,None,0.0,18,8,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,robust_scaler,,,0.8512985751386942,0.14092824896182807,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,,
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-82,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.02021502857757827,True,,0.055848937053280076,True,,constant,log,l2,,0.00016356162584832537,no_encoding,no_coalescense,,median,quantile_transformer,27,normal,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.11873149622606233,fwe,chi2,
-83,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8.24807027221455,-0.23458710218209156,,0.0001030034352044511,sigmoid,-1,True,0.0002726309373020207,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,quantile_transformer,1974,normal,,,extra_trees_preproc_for_classification,True,entropy,None,0.9395220389606214,None,0.0,2,20,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-84,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.4864284879550203,None,0.0,1,4,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.008665854799630503,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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-87,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.44727252219812963,None,0.0,2,10,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,standardize,,,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l1,8.17533189014765e-05,,,,,,,,,,,,,,,,,,,,,
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-89,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.922871100218626,False,True,squared_hinge,0.09957180737451943,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0016563186856411274,median,quantile_transformer,850,uniform,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.09123695282772847,fpr,chi2,
-90,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4834106677905965,False,True,1,squared_hinge,ovr,l2,0.00017625027586147415,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0009157492873444966,most_frequent,quantile_transformer,1150,normal,,,extra_trees_preproc_for_classification,False,entropy,None,0.4981700453792278,None,0.0,9,15,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-91,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.087313242780732,0.0636709914987994,3,0.004481917125024585,poly,-1,False,0.01848880618941367,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.002136015035740733,mean,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-92,weighting,adaboost,SAMME,0.028177459966491795,1,484,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,quantile_transformer,875,normal,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,1.502807354196727,False,True,1,squared_hinge,ovr,l1,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,
+1,none,decision_tree,,,,,,,entropy,1.877536950957045,1.0,None,0.0,10,13,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,quantile_transformer,1275,uniform,,,extra_trees_preproc_for_classification,True,gini,None,0.04910706843318435,None,0.0,12,12,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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diff --git a/autosklearn/metalearning/files/precision_samples_binary.classification_sparse/description.txt b/autosklearn/metalearning/files/precision_samples_binary.classification_sparse/description.txt
index 60a51ca98b..85c6829e8e 100644
--- a/autosklearn/metalearning/files/precision_samples_binary.classification_sparse/description.txt
+++ b/autosklearn/metalearning/files/precision_samples_binary.classification_sparse/description.txt
@@ -1,8 +1,9 @@
 features_cutoff_time: 3600
 features_cutoff_memory: 3072
-number_of_feature_steps: 35
+number_of_feature_steps: 42
 feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances
 feature_step LogNumberOfInstances: LogNumberOfInstances
+feature_step NumberOfClasses: NumberOfClasses
 feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures
 feature_step LogNumberOfFeatures: LogNumberOfFeatures
 feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues
@@ -20,6 +21,11 @@ feature_step DatasetRatio: DatasetRatio, LogDatasetRatio
 feature_step LogDatasetRatio: LogDatasetRatio
 feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio
 feature_step LogInverseDatasetRatio: LogInverseDatasetRatio
+feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD
+feature_step ClassProbabilityMin: ClassProbabilityMin
+feature_step ClassProbabilityMax: ClassProbabilityMax
+feature_step ClassProbabilityMean: ClassProbabilityMean
+feature_step ClassProbabilitySTD: ClassProbabilitySTD
 feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum
 feature_step SymbolsMin: SymbolsMin
 feature_step SymbolsMax: SymbolsMax
@@ -36,11 +42,12 @@ feature_step SkewnessMin: SkewnessMin
 feature_step SkewnessMax: SkewnessMax
 feature_step SkewnessMean: SkewnessMean
 feature_step SkewnessSTD: SkewnessSTD
-features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
+feature_step ClassEntropy: ClassEntropy
+features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy
 features_stochastic: 
-default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
+default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy
 
-algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92
+algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235,236,237,238,239,240,241,242,243,244,245,246,247,248,249,250,251,252,253,254,255,256,257,258,259,260,261,262,263,264,265,266,267,268,269,270,271,272,273,274,275,276,277,278,279,280,281,282,283,284,285,286,287,288,289,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,312,313,314,315,316,317,318,319,320,321,322,323,324,325,326,327,328,329,330,331,332,333,334,335,336,337,338,339,340,341,342,343,344,345,346,347,348,349,350,351,352,353,354,355,356,357,358,359,360,361,362,363,364,365,366,367,368,369,370,371,372
 algorithms_stochastic: 
 performance_measures: precision_samples
 performance_type: solution_quality
diff --git a/autosklearn/metalearning/files/precision_samples_binary.classification_sparse/feature_costs.arff b/autosklearn/metalearning/files/precision_samples_binary.classification_sparse/feature_costs.arff
index 0e17e20d22..8512fd9275 100644
--- a/autosklearn/metalearning/files/precision_samples_binary.classification_sparse/feature_costs.arff
+++ b/autosklearn/metalearning/files/precision_samples_binary.classification_sparse/feature_costs.arff
@@ -4,6 +4,7 @@
 @ATTRIBUTE repetition NUMERIC
 @ATTRIBUTE NumberOfInstances NUMERIC
 @ATTRIBUTE LogNumberOfInstances NUMERIC
+@ATTRIBUTE NumberOfClasses NUMERIC
 @ATTRIBUTE NumberOfFeatures NUMERIC
 @ATTRIBUTE LogNumberOfFeatures NUMERIC
 @ATTRIBUTE MissingValues NUMERIC
@@ -21,6 +22,11 @@
 @ATTRIBUTE LogDatasetRatio NUMERIC
 @ATTRIBUTE InverseDatasetRatio NUMERIC
 @ATTRIBUTE LogInverseDatasetRatio NUMERIC
+@ATTRIBUTE ClassOccurences NUMERIC
+@ATTRIBUTE ClassProbabilityMin NUMERIC
+@ATTRIBUTE ClassProbabilityMax NUMERIC
+@ATTRIBUTE ClassProbabilityMean NUMERIC
+@ATTRIBUTE ClassProbabilitySTD NUMERIC
 @ATTRIBUTE NumSymbols NUMERIC
 @ATTRIBUTE SymbolsMin NUMERIC
 @ATTRIBUTE SymbolsMax NUMERIC
@@ -37,162 +43,138 @@
 @ATTRIBUTE SkewnessMax NUMERIC
 @ATTRIBUTE SkewnessMean NUMERIC
 @ATTRIBUTE SkewnessSTD NUMERIC
+@ATTRIBUTE ClassEntropy NUMERIC
 
 @DATA
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+75230,1.0,1e-05,0.0,9e-05,1e-05,1e-05,0.00087,0.0006,1e-05,0.00018,1e-05,0.00011,0.0,2e-05,2e-05,4e-05,6e-05,2e-05,1e-05,3e-05,1e-05,0.00015,2e-05,1e-05,4e-05,8e-05,0.00038,0.0,1e-05,0.0001,0.00021,5e-05,0.00037,5e-05,6e-05,8e-05,0.00018,0.00033,5e-05,6e-05,7e-05,0.00015,0.00048
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+75234,1.0,1e-05,1e-05,0.00015,1e-05,1e-05,0.0058,0.00534,1e-05,0.00033,1e-05,0.00016,0.0,2e-05,2e-05,7e-05,0.00204,3e-05,1e-05,5e-05,1e-05,0.00024,3e-05,2e-05,0.0001,9e-05,0.00043,0.0,0.0,0.00012,0.00024,6e-05,0.00156,6e-05,7e-05,0.00121,0.00021,0.00054,6e-05,7e-05,9e-05,0.00032,0.00501
+75235,1.0,1e-05,1e-05,0.0003,2e-05,1e-05,0.00461,0.00385,2e-05,0.00055,2e-05,0.00026,1e-05,3e-05,3e-05,0.0001,0.00011,3e-05,1e-05,5e-05,2e-05,0.00026,3e-05,1e-05,8e-05,0.00014,0.00075,1e-05,1e-05,0.0002,0.00043,0.00011,0.00127,0.00017,0.0002,0.00029,0.00061,0.0008,0.00015,0.00012,0.00016,0.00038,0.00242
+75236,1.0,1e-05,1e-05,0.0001,1e-05,1e-05,0.00218,0.00118,2e-05,0.00065,3e-05,0.00041,1e-05,3e-05,3e-05,9e-05,9e-05,4e-05,1e-05,7e-05,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.0006,1e-05,1e-05,0.00021,0.00032,6e-05,0.00043,7e-05,7e-05,9e-05,0.00019,0.00062,7e-05,8e-05,0.0001,0.00037,0.00048
+75237,1.0,2e-05,1e-05,0.00785,2e-05,1e-05,0.31031,0.27854,2e-05,0.01161,3e-05,0.02022,1e-05,4e-05,3e-05,9e-05,0.00024,6e-05,2e-05,9e-05,2e-05,0.00062,7e-05,3e-05,0.00017,0.00035,0.0008,1e-05,1e-05,0.00028,0.00043,8e-05,0.00043,8e-05,7e-05,9e-05,0.0002,0.00044,6e-05,8e-05,9e-05,0.00021,0.14028
+75239,1.0,1e-05,1e-05,7e-05,1e-05,1e-05,0.00105,0.00084,1e-05,0.00015,1e-05,8e-05,1e-05,2e-05,5e-05,4e-05,8e-05,2e-05,1e-05,3e-05,1e-05,0.00016,2e-05,1e-05,5e-05,9e-05,0.00049,0.0,0.0,0.00012,0.00026,0.00011,0.00046,0.00013,8e-05,8e-05,0.00018,0.00044,6e-05,9e-05,0.00011,0.00018,0.00065
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+75243,1.0,1e-05,1e-05,0.00029,1e-05,1e-05,0.00461,0.00415,1e-05,0.00035,1e-05,0.00015,0.0,2e-05,2e-05,5e-05,7e-05,2e-05,1e-05,4e-05,1e-05,0.00018,2e-05,1e-05,4e-05,0.00011,0.00022,1e-05,1e-05,5e-05,0.00011,5e-05,0.0004,6e-05,6e-05,8e-05,0.0002,0.00039,6e-05,6e-05,8e-05,0.00019,0.00265
+75244,1.0,3e-05,2e-05,0.0004,3e-05,2e-05,0.0173,0.01298,4e-05,0.00267,4e-05,0.00174,1e-05,7e-05,9e-05,0.00024,0.00027,8e-05,2e-05,0.00013,3e-05,0.00045,7e-05,2e-05,0.00011,0.00025,0.00077,6e-05,2e-05,0.00021,0.00034,0.00013,0.00077,0.00014,0.00013,0.00026,0.00023,0.00044,7e-05,8e-05,0.0001,0.00019,0.00649
+75248,1.0,1e-05,1e-05,0.00017,1e-05,1e-05,0.00416,0.00305,1e-05,0.0006,2e-05,0.00054,1e-05,2e-05,2e-05,5e-05,7e-05,2e-05,1e-05,4e-05,1e-05,0.00017,2e-05,1e-05,4e-05,0.0001,0.00029,5e-05,1e-05,6e-05,0.00012,6e-05,0.00081,0.00011,0.00015,0.00016,0.00038,0.0005,7e-05,8e-05,0.00012,0.00024,0.00184
+75249,1.0,1e-05,1e-05,0.00011,1e-05,1e-05,0.00192,0.00153,1e-05,0.00025,1e-05,0.00017,1e-05,2e-05,2e-05,5e-05,7e-05,2e-05,1e-05,5e-05,3e-05,0.00014,2e-05,1e-05,3e-05,8e-05,0.00029,1e-05,3e-05,6e-05,0.00012,7e-05,0.00041,6e-05,8e-05,0.0001,0.00017,0.00043,6e-05,7e-05,9e-05,0.00019,0.00092
+75250,1.0,1e-05,1e-05,0.00375,1e-05,1e-05,0.04994,0.047,1e-05,0.00247,1e-05,0.00049,1e-05,2e-05,2e-05,6e-05,7e-05,2e-05,1e-05,3e-05,1e-05,0.00022,4e-05,1e-05,4e-05,0.00012,0.00041,0.0,1e-05,0.00011,0.00023,6e-05,0.00076,0.00012,0.00012,0.00019,0.00032,0.00086,0.00012,0.00013,0.00018,0.00043,0.04178
diff --git a/autosklearn/metalearning/files/precision_samples_binary.classification_sparse/feature_runstatus.arff b/autosklearn/metalearning/files/precision_samples_binary.classification_sparse/feature_runstatus.arff
index 0034ad2bd6..5fff2dfed3 100644
--- a/autosklearn/metalearning/files/precision_samples_binary.classification_sparse/feature_runstatus.arff
+++ b/autosklearn/metalearning/files/precision_samples_binary.classification_sparse/feature_runstatus.arff
@@ -4,6 +4,7 @@
 @ATTRIBUTE repetition NUMERIC
 @ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other}
@@ -21,6 +22,11 @@
 @ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other}
@@ -37,162 +43,138 @@
 @ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other}
 
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diff --git a/autosklearn/metalearning/files/precision_samples_binary.classification_sparse/feature_values.arff b/autosklearn/metalearning/files/precision_samples_binary.classification_sparse/feature_values.arff
index a60400e3ce..736dc64321 100644
--- a/autosklearn/metalearning/files/precision_samples_binary.classification_sparse/feature_values.arff
+++ b/autosklearn/metalearning/files/precision_samples_binary.classification_sparse/feature_values.arff
@@ -13,7 +13,6 @@
 @ATTRIBUTE KurtosisMean NUMERIC
 @ATTRIBUTE KurtosisMin NUMERIC
 @ATTRIBUTE KurtosisSTD NUMERIC
-@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC
 @ATTRIBUTE LogDatasetRatio NUMERIC
 @ATTRIBUTE LogInverseDatasetRatio NUMERIC
 @ATTRIBUTE LogNumberOfFeatures NUMERIC
@@ -42,160 +41,135 @@
 @ATTRIBUTE SymbolsSum NUMERIC
 
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diff --git a/autosklearn/metalearning/files/precision_samples_multiclass.classification_dense/algorithm_runs.arff b/autosklearn/metalearning/files/precision_samples_multiclass.classification_dense/algorithm_runs.arff
index e1028d34a5..fd2afc0c6a 100644
--- a/autosklearn/metalearning/files/precision_samples_multiclass.classification_dense/algorithm_runs.arff
+++ b/autosklearn/metalearning/files/precision_samples_multiclass.classification_dense/algorithm_runs.arff
@@ -7,130 +7,135 @@
 @ATTRIBUTE runstatus {ok, timeout, memout, not_applicable, crash, other}
 
 @DATA
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-75212,1.0,2,?,not_applicable
-252,1.0,3,?,not_applicable
-246,1.0,4,?,not_applicable
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-75239,1.0,6,?,not_applicable
-75171,1.0,7,?,not_applicable
-75233,1.0,8,?,not_applicable
-248,1.0,9,?,not_applicable
-75231,1.0,10,?,not_applicable
-2123,1.0,11,?,not_applicable
-75196,1.0,12,?,not_applicable
-75188,1.0,13,?,not_applicable
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+75139,1.0,5,?,not_applicable
+75212,1.0,6,?,not_applicable
+246,1.0,8,?,not_applicable
+252,1.0,9,?,not_applicable
+75178,1.0,10,?,not_applicable
+75177,1.0,13,?,not_applicable
 75092,1.0,14,?,not_applicable
-75248,1.0,15,?,not_applicable
-75126,1.0,16,?,not_applicable
-75234,1.0,17,?,not_applicable
-75150,1.0,18,?,not_applicable
-258,1.0,19,?,not_applicable
-75168,1.0,20,?,not_applicable
-75235,1.0,21,?,not_applicable
-75159,1.0,22,?,not_applicable
-244,1.0,23,?,not_applicable
-75221,1.0,24,?,not_applicable
-75219,1.0,25,?,not_applicable
-75202,1.0,26,?,not_applicable
-3043,1.0,27,?,not_applicable
-75205,1.0,28,?,not_applicable
-75174,1.0,29,?,not_applicable
-288,1.0,30,?,not_applicable
-75250,1.0,31,?,not_applicable
-275,1.0,32,?,not_applicable
-75142,1.0,33,?,not_applicable
-75213,1.0,34,?,not_applicable
-75099,1.0,35,?,not_applicable
-75243,1.0,36,?,not_applicable
-75184,1.0,37,?,not_applicable
-75222,1.0,38,?,not_applicable
-75175,1.0,39,?,not_applicable
-233,1.0,40,?,not_applicable
-75161,1.0,41,?,not_applicable
-75176,1.0,42,?,not_applicable
-75090,1.0,43,?,not_applicable
-75114,1.0,44,?,not_applicable
-260,1.0,45,?,not_applicable
-236,1.0,46,?,not_applicable
-75141,1.0,47,?,not_applicable
-75107,1.0,48,?,not_applicable
-262,1.0,49,?,not_applicable
-75146,1.0,50,?,not_applicable
-75189,1.0,51,?,not_applicable
-2350,1.0,52,?,not_applicable
-253,1.0,53,?,not_applicable
-2122,1.0,54,?,not_applicable
-75110,1.0,55,?,not_applicable
-75249,1.0,56,?,not_applicable
-75108,1.0,57,?,not_applicable
-242,1.0,58,?,not_applicable
-75117,1.0,59,?,not_applicable
-75191,1.0,60,?,not_applicable
-75226,1.0,61,?,not_applicable
-261,1.0,62,?,not_applicable
-75236,1.0,63,?,not_applicable
-75095,1.0,64,?,not_applicable
-75148,1.0,65,?,not_applicable
-75093,1.0,66,?,not_applicable
-75223,1.0,67,?,not_applicable
-75244,1.0,68,?,not_applicable
-75109,1.0,69,?,not_applicable
-75197,1.0,70,?,not_applicable
-75127,1.0,71,?,not_applicable
-75143,1.0,72,?,not_applicable
-75153,1.0,73,?,not_applicable
-75173,1.0,74,?,not_applicable
-75215,1.0,75,?,not_applicable
-75195,1.0,76,?,not_applicable
-75207,1.0,77,?,not_applicable
-266,1.0,78,?,not_applicable
-75225,1.0,79,?,not_applicable
-75166,1.0,80,?,not_applicable
-75100,1.0,81,?,not_applicable
-75169,1.0,82,?,not_applicable
-75132,1.0,83,?,not_applicable
-273,1.0,84,?,not_applicable
-75121,1.0,85,?,not_applicable
-75098,1.0,86,?,not_applicable
-75115,1.0,87,?,not_applicable
-75116,1.0,88,?,not_applicable
-75185,1.0,89,?,not_applicable
-2119,1.0,90,?,not_applicable
-75157,1.0,91,?,not_applicable
-75113,1.0,92,?,not_applicable
-75134,1.0,93,?,not_applicable
-75096,1.0,94,?,not_applicable
-75203,1.0,95,?,not_applicable
-75182,1.0,96,?,not_applicable
-251,1.0,97,?,not_applicable
-75123,1.0,98,?,not_applicable
-75237,1.0,99,?,not_applicable
-75125,1.0,100,?,not_applicable
-75232,1.0,101,?,not_applicable
-75103,1.0,102,?,not_applicable
-75192,1.0,103,?,not_applicable
-75230,1.0,104,?,not_applicable
-75139,1.0,105,?,not_applicable
-75227,1.0,106,?,not_applicable
-2120,1.0,107,?,not_applicable
-75124,1.0,108,?,not_applicable
-75240,1.0,109,?,not_applicable
-75129,1.0,110,?,not_applicable
-75198,1.0,111,?,not_applicable
-75201,1.0,112,?,not_applicable
-75112,1.0,113,?,not_applicable
-75133,1.0,114,?,not_applicable
-75105,1.0,115,?,not_applicable
-75154,1.0,116,?,not_applicable
-75177,1.0,117,?,not_applicable
-2117,1.0,118,?,not_applicable
-75156,1.0,119,?,not_applicable
-75097,1.0,120,?,not_applicable
-75101,1.0,121,?,not_applicable
-75172,1.0,122,?,not_applicable
-75106,1.0,123,?,not_applicable
-75179,1.0,124,?,not_applicable
-75187,1.0,125,?,not_applicable
-75120,1.0,126,?,not_applicable
-75193,1.0,127,?,not_applicable
+75239,1.0,15,?,not_applicable
+75173,1.0,17,?,not_applicable
+75215,1.0,18,?,not_applicable
+75171,1.0,19,?,not_applicable
+75112,1.0,23,?,not_applicable
+75227,1.0,24,?,not_applicable
+75233,1.0,25,?,not_applicable
+75182,1.0,26,?,not_applicable
+253,1.0,27,?,not_applicable
+75157,1.0,29,?,not_applicable
+75187,1.0,30,?,not_applicable
+75124,1.0,31,?,not_applicable
+75090,1.0,32,?,not_applicable
+75222,1.0,33,?,not_applicable
+75231,1.0,34,?,not_applicable
+75185,1.0,37,?,not_applicable
+2123,1.0,39,?,not_applicable
+75150,1.0,41,?,not_applicable
+75143,1.0,43,?,not_applicable
+75196,1.0,44,?,not_applicable
+75188,1.0,49,?,not_applicable
+75248,1.0,54,?,not_applicable
+75249,1.0,58,?,not_applicable
+75113,1.0,59,?,not_applicable
+75126,1.0,60,?,not_applicable
+288,1.0,64,?,not_applicable
+251,1.0,65,?,not_applicable
+75184,1.0,66,?,not_applicable
+75234,1.0,67,?,not_applicable
+258,1.0,73,?,not_applicable
+75166,1.0,76,?,not_applicable
+75168,1.0,77,?,not_applicable
+75148,1.0,80,?,not_applicable
+75235,1.0,81,?,not_applicable
+75159,1.0,84,?,not_applicable
+75146,1.0,88,?,not_applicable
+244,1.0,89,?,not_applicable
+75141,1.0,90,?,not_applicable
+75221,1.0,92,?,not_applicable
+75219,1.0,95,?,not_applicable
+75202,1.0,96,?,not_applicable
+3043,1.0,98,?,not_applicable
+75205,1.0,103,?,not_applicable
+75174,1.0,106,?,not_applicable
+75250,1.0,111,?,not_applicable
+75179,1.0,114,?,not_applicable
+275,1.0,115,?,not_applicable
+242,1.0,116,?,not_applicable
+75207,1.0,117,?,not_applicable
+75142,1.0,118,?,not_applicable
+75099,1.0,123,?,not_applicable
+75243,1.0,126,?,not_applicable
+75175,1.0,132,?,not_applicable
+233,1.0,134,?,not_applicable
+75161,1.0,139,?,not_applicable
+75176,1.0,143,?,not_applicable
+262,1.0,146,?,not_applicable
+75129,1.0,147,?,not_applicable
+261,1.0,148,?,not_applicable
+75114,1.0,152,?,not_applicable
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+260,1.0,158,?,not_applicable
+236,1.0,160,?,not_applicable
+254,1.0,166,?,not_applicable
+75107,1.0,167,?,not_applicable
+75181,1.0,175,?,not_applicable
+75189,1.0,184,?,not_applicable
+75163,1.0,189,?,not_applicable
+2350,1.0,191,?,not_applicable
+2122,1.0,195,?,not_applicable
+75110,1.0,198,?,not_applicable
+75213,1.0,201,?,not_applicable
+75095,1.0,203,?,not_applicable
+75108,1.0,207,?,not_applicable
+75117,1.0,211,?,not_applicable
+75191,1.0,216,?,not_applicable
+75226,1.0,219,?,not_applicable
+75244,1.0,222,?,not_applicable
+75236,1.0,223,?,not_applicable
+75169,1.0,227,?,not_applicable
+75116,1.0,228,?,not_applicable
+75223,1.0,235,?,not_applicable
+75109,1.0,242,?,not_applicable
+75197,1.0,245,?,not_applicable
+75237,1.0,248,?,not_applicable
+248,1.0,250,?,not_applicable
+2119,1.0,254,?,not_applicable
+75127,1.0,255,?,not_applicable
+75153,1.0,260,?,not_applicable
+75195,1.0,271,?,not_applicable
+266,1.0,277,?,not_applicable
+75225,1.0,279,?,not_applicable
+75100,1.0,286,?,not_applicable
+75132,1.0,293,?,not_applicable
+75210,1.0,298,?,not_applicable
+273,1.0,300,?,not_applicable
+75133,1.0,303,?,not_applicable
+75121,1.0,304,?,not_applicable
+75098,1.0,310,?,not_applicable
+75115,1.0,315,?,not_applicable
+75217,1.0,337,?,not_applicable
+75134,1.0,341,?,not_applicable
+75096,1.0,346,?,not_applicable
+75203,1.0,350,?,not_applicable
+75123,1.0,360,?,not_applicable
+75125,1.0,367,?,not_applicable
+2120,1.0,371,?,not_applicable
+75232,1.0,374,?,not_applicable
+75103,1.0,378,?,not_applicable
+75230,1.0,386,?,not_applicable
+75240,1.0,398,?,not_applicable
+75198,1.0,404,?,not_applicable
+75201,1.0,407,?,not_applicable
+75105,1.0,415,?,not_applicable
+75154,1.0,420,?,not_applicable
+2117,1.0,426,?,not_applicable
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+75101,1.0,438,?,not_applicable
+75172,1.0,443,?,not_applicable
+75106,1.0,448,?,not_applicable
+75120,1.0,456,?,not_applicable
+75193,1.0,461,?,not_applicable
diff --git a/autosklearn/metalearning/files/precision_samples_multiclass.classification_dense/configurations.csv b/autosklearn/metalearning/files/precision_samples_multiclass.classification_dense/configurations.csv
index 8d94f66c45..03d7f03e43 100644
--- a/autosklearn/metalearning/files/precision_samples_multiclass.classification_dense/configurations.csv
+++ b/autosklearn/metalearning/files/precision_samples_multiclass.classification_dense/configurations.csv
@@ -1,128 +1,133 @@
 idx,balancing:strategy,classifier:__choice__,classifier:adaboost:algorithm,classifier:adaboost:learning_rate,classifier:adaboost:max_depth,classifier:adaboost:n_estimators,classifier:bernoulli_nb:alpha,classifier:bernoulli_nb:fit_prior,classifier:decision_tree:criterion,classifier:decision_tree:max_depth_factor,classifier:decision_tree:max_features,classifier:decision_tree:max_leaf_nodes,classifier:decision_tree:min_impurity_decrease,classifier:decision_tree:min_samples_leaf,classifier:decision_tree:min_samples_split,classifier:decision_tree:min_weight_fraction_leaf,classifier:extra_trees:bootstrap,classifier:extra_trees:criterion,classifier:extra_trees:max_depth,classifier:extra_trees:max_features,classifier:extra_trees:max_leaf_nodes,classifier:extra_trees:min_impurity_decrease,classifier:extra_trees:min_samples_leaf,classifier:extra_trees:min_samples_split,classifier:extra_trees:min_weight_fraction_leaf,classifier:gradient_boosting:early_stop,classifier:gradient_boosting:l2_regularization,classifier:gradient_boosting:learning_rate,classifier:gradient_boosting:loss,classifier:gradient_boosting:max_bins,classifier:gradient_boosting:max_depth,classifier:gradient_boosting:max_leaf_nodes,classifier:gradient_boosting:min_samples_leaf,classifier:gradient_boosting:n_iter_no_change,classifier:gradient_boosting:scoring,classifier:gradient_boosting:tol,classifier:gradient_boosting:validation_fraction,classifier:k_nearest_neighbors:n_neighbors,classifier:k_nearest_neighbors:p,classifier:k_nearest_neighbors:weights,classifier:lda:n_components,classifier:lda:shrinkage,classifier:lda:shrinkage_factor,classifier:lda:tol,classifier:liblinear_svc:C,classifier:liblinear_svc:dual,classifier:liblinear_svc:fit_intercept,classifier:liblinear_svc:intercept_scaling,classifier:liblinear_svc:loss,classifier:liblinear_svc:multi_class,classifier:liblinear_svc:penalty,classifier:liblinear_svc:tol,classifier:libsvm_svc:C,classifier:libsvm_svc:coef0,classifier:libsvm_svc:degree,classifier:libsvm_svc:gamma,classifier:libsvm_svc:kernel,classifier:libsvm_svc:max_iter,classifier:libsvm_svc:shrinking,classifier:libsvm_svc:tol,classifier:multinomial_nb:alpha,classifier:multinomial_nb:fit_prior,classifier:passive_aggressive:C,classifier:passive_aggressive:average,classifier:passive_aggressive:fit_intercept,classifier:passive_aggressive:loss,classifier:passive_aggressive:tol,classifier:qda:reg_param,classifier:random_forest:bootstrap,classifier:random_forest:criterion,classifier:random_forest:max_depth,classifier:random_forest:max_features,classifier:random_forest:max_leaf_nodes,classifier:random_forest:min_impurity_decrease,classifier:random_forest:min_samples_leaf,classifier:random_forest:min_samples_split,classifier:random_forest:min_weight_fraction_leaf,classifier:sgd:alpha,classifier:sgd:average,classifier:sgd:epsilon,classifier:sgd:eta0,classifier:sgd:fit_intercept,classifier:sgd:l1_ratio,classifier:sgd:learning_rate,classifier:sgd:loss,classifier:sgd:penalty,classifier:sgd:power_t,classifier:sgd:tol,data_preprocessing:categorical_transformer:categorical_encoding:__choice__,data_preprocessing:categorical_transformer:category_coalescence:__choice__,data_preprocessing:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction,data_preprocessing:numerical_transformer:imputation:strategy,data_preprocessing:numerical_transformer:rescaling:__choice__,data_preprocessing:numerical_transformer:rescaling:quantile_transformer:n_quantiles,data_preprocessing:numerical_transformer:rescaling:quantile_transformer:output_distribution,data_preprocessing:numerical_transformer:rescaling:robust_scaler:q_max,data_preprocessing:numerical_transformer:rescaling:robust_scaler:q_min,feature_preprocessor:__choice__,feature_preprocessor:extra_trees_preproc_for_classification:bootstrap,feature_preprocessor:extra_trees_preproc_for_classification:criterion,feature_preprocessor:extra_trees_preproc_for_classification:max_depth,feature_preprocessor:extra_trees_preproc_for_classification:max_features,feature_preprocessor:extra_trees_preproc_for_classification:max_leaf_nodes,feature_preprocessor:extra_trees_preproc_for_classification:min_impurity_decrease,feature_preprocessor:extra_trees_preproc_for_classification:min_samples_leaf,feature_preprocessor:extra_trees_preproc_for_classification:min_samples_split,feature_preprocessor:extra_trees_preproc_for_classification:min_weight_fraction_leaf,feature_preprocessor:extra_trees_preproc_for_classification:n_estimators,feature_preprocessor:fast_ica:algorithm,feature_preprocessor:fast_ica:fun,feature_preprocessor:fast_ica:n_components,feature_preprocessor:fast_ica:whiten,feature_preprocessor:feature_agglomeration:affinity,feature_preprocessor:feature_agglomeration:linkage,feature_preprocessor:feature_agglomeration:n_clusters,feature_preprocessor:feature_agglomeration:pooling_func,feature_preprocessor:kernel_pca:coef0,feature_preprocessor:kernel_pca:degree,feature_preprocessor:kernel_pca:gamma,feature_preprocessor:kernel_pca:kernel,feature_preprocessor:kernel_pca:n_components,feature_preprocessor:kitchen_sinks:gamma,feature_preprocessor:kitchen_sinks:n_components,feature_preprocessor:liblinear_svc_preprocessor:C,feature_preprocessor:liblinear_svc_preprocessor:dual,feature_preprocessor:liblinear_svc_preprocessor:fit_intercept,feature_preprocessor:liblinear_svc_preprocessor:intercept_scaling,feature_preprocessor:liblinear_svc_preprocessor:loss,feature_preprocessor:liblinear_svc_preprocessor:multi_class,feature_preprocessor:liblinear_svc_preprocessor:penalty,feature_preprocessor:liblinear_svc_preprocessor:tol,feature_preprocessor:nystroem_sampler:coef0,feature_preprocessor:nystroem_sampler:degree,feature_preprocessor:nystroem_sampler:gamma,feature_preprocessor:nystroem_sampler:kernel,feature_preprocessor:nystroem_sampler:n_components,feature_preprocessor:pca:keep_variance,feature_preprocessor:pca:whiten,feature_preprocessor:polynomial:degree,feature_preprocessor:polynomial:include_bias,feature_preprocessor:polynomial:interaction_only,feature_preprocessor:random_trees_embedding:bootstrap,feature_preprocessor:random_trees_embedding:max_depth,feature_preprocessor:random_trees_embedding:max_leaf_nodes,feature_preprocessor:random_trees_embedding:min_samples_leaf,feature_preprocessor:random_trees_embedding:min_samples_split,feature_preprocessor:random_trees_embedding:min_weight_fraction_leaf,feature_preprocessor:random_trees_embedding:n_estimators,feature_preprocessor:select_percentile_classification:percentile,feature_preprocessor:select_percentile_classification:score_func,feature_preprocessor:select_rates_classification:alpha,feature_preprocessor:select_rates_classification:mode,feature_preprocessor:select_rates_classification:score_func
-1,none,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,standardize,,,,,extra_trees_preproc_for_classification,False,entropy,None,0.8629651625821806,None,0.0,10,10,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-2,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9.971526339963626,False,True,1,squared_hinge,ovr,l2,1.2119829083478464e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.10211544986888581,most_frequent,robust_scaler,,,0.7878089656793734,0.28746519719167807,fast_ica,,,,,,,,,,,parallel,cube,30,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-3,none,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,188,None,,0.09976330267940166,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,none,,,,,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,-0.05439674489937307,2,0.051865468486041774,poly,727,,,,,,,,,,,,,,,,,
-4,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,23916.78290516781,False,True,1,squared_hinge,ovr,l2,0.0006655607726658531,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.008808715242823137,median,none,,,,,kernel_pca,,,,,,,,,,,,,,,,,,,0.2633153190620261,2,0.004770332714291669,poly,1483,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-5,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,2,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010091246322497477,median,none,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,95.37976833605768,mutual_info,,,
-6,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.933900673392165,None,0.0,7,4,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,minmax,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-7,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,170.37509684994612,0.6734219115470694,3,0.08998608839429428,poly,-1,True,0.024147312348267988,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,none,,,,,extra_trees_preproc_for_classification,False,entropy,None,0.9449154369109399,None,0.0,1,14,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-8,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2981.609927782268,0.12126372002542518,2,0.0009954412712187496,poly,-1,False,9.701828055863241e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,mean,standardize,,,,,feature_agglomeration,,,,,,,,,,,,,,,euclidean,ward,37,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-9,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1292.7448076998514,0.004489932344506231,2,1.3088036376373702,poly,-1,False,1.7943262139196537e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,minmax,,,,,feature_agglomeration,,,,,,,,,,,,,,,cosine,average,386,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-10,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.5244313211416313,None,0.0,1,2,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.454300236339658,most_frequent,robust_scaler,,,0.7858830918128734,0.2705174720960696,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-11,weighting,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0006294780694492238,most_frequent,standardize,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.063715750591364,fdr,chi2
-12,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.02299691847304223,False,True,squared_hinge,2.16045124689341e-05,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010708146395792527,most_frequent,standardize,,,,,extra_trees_preproc_for_classification,False,entropy,None,0.5298549662385138,None,0.0,12,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-13,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.25657910619102237,None,0.0,6,6,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.021086303324492208,most_frequent,quantile_transformer,1247,uniform,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.16021572815319468,fpr,chi2
-14,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.4897130288457967,False,True,1,squared_hinge,ovr,l2,0.00023610948867176137,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.22014262985890154,median,quantile_transformer,1499,normal,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,331,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-15,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.643761610990255,None,0.0,20,6,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.039859381630488695,mean,normalize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,
-16,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.8172226998998798,None,0.0,17,3,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.006497124806317511,median,robust_scaler,,,0.7090070294600064,0.2287445390747399,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.478031402559985,mutual_info,,,
-17,weighting,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.06330379669963868,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.054434091133046744,most_frequent,robust_scaler,,,0.7480112557523932,0.17257870788958515,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-18,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00335225820194075,True,True,squared_hinge,2.9136221277715572e-05,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.00021598139496857644,median,minmax,,,,,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,4,None,4,9,1.0,14,,,,,
-19,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,199,None,,0.003239674765949011,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,robust_scaler,,,0.7228447795482008,0.25,kernel_pca,,,,,,,,,,,,,,,,,,,0.09440148939740256,3,0.0008114095405154861,poly,1996,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-20,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.6039096182414764,None,0.0,2,20,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.1939609823704447,most_frequent,robust_scaler,,,0.7331327008874781,0.29747488162877694,extra_trees_preproc_for_classification,True,entropy,None,0.33401603690569853,None,0.0,4,4,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-21,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,2.5882079595129472e-05,0.36125906161027654,auto,255,None,341,119,16,loss,1e-07,0.2924550184445239,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.02338478303505337,most_frequent,robust_scaler,,,0.7866731278723753,0.29543475006822734,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,68,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-22,weighting,adaboost,SAMME,0.24425611020065865,2,50,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0014011297758596516,most_frequent,quantile_transformer,1324,normal,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,338,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-23,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7725.740943204492,,,3.122840783722913,rbf,-1,True,1.4116065061807418e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.14280178218510184,most_frequent,none,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,27,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-24,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.21794354428393548,None,0.0,2,16,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0025451910134387575,median,quantile_transformer,1477,uniform,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
-25,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5010952533165973,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.005838375247601407,mean,normalize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,
-26,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5514127836113225,None,0.0,4,20,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.010750756228608195,most_frequent,robust_scaler,,,0.7471420685032172,0.2916152663822578,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.22557315057515023,fwe,chi2
-27,weighting,decision_tree,,,,,,,gini,1.0778738481485166,1.0,None,0.0,14,7,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.002474357425774638,mean,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,
-28,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.506204385946851,None,0.0,2,8,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.03433007982751011,mean,none,,,,,extra_trees_preproc_for_classification,False,gini,None,0.32676415738837183,None,0.0,5,6,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-29,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9333237919291875,None,0.0,8,3,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,
-30,none,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.9728532763078892,None,0.0,13,9,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,none,,,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6651071190864879,False,,,,,,,,,,,,,,,
-31,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9561075301148197,None,0.0,6,2,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,
-32,none,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,184,manual,0.5022041786628263,2.3064966255468173e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,most_frequent,standardize,,,,,kernel_pca,,,,,,,,,,,,,,,,,,,-0.32023224593255817,2,0.09479590655633498,poly,1382,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-33,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,2.7220799337966617e-08,0.011194310743518529,auto,255,None,38,187,3,loss,1e-07,0.13581959545340272,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0036603728729535916,mean,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,False,,,,,,,,,,,,
-34,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,22194.242946902177,0.45210849669021247,,0.0005669713702171556,sigmoid,-1,False,0.03542142266081451,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,quantile_transformer,291,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-35,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.2697794350876887,None,0.0,20,19,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.01857832166651019,median,robust_scaler,,,0.746850014013939,0.26258045325454815,fast_ica,,,,,,,,,,,deflation,cube,67,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-36,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,0.032732094595234795,0.09918078053098744,auto,255,None,97,54,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.019135985037746533,median,quantile_transformer,1000,uniform,,,feature_agglomeration,,,,,,,,,,,,,,,euclidean,average,37,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-37,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7360659359578277,None,0.0,18,5,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.0032431851910047368,mean,robust_scaler,,,0.7204949497300925,0.2277786352264519,fast_ica,,,,,,,,,,,deflation,exp,307,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-38,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.756167892711991e-05,False,True,squared_hinge,5.235791933868702e-05,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0001632172987320906,most_frequent,standardize,,,,,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,5,None,19,12,1.0,70,,,,,
-39,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,3.6796787331285204e-07,0.14399102828508656,auto,255,None,18,12,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,quantile_transformer,965,normal,,,feature_agglomeration,,,,,,,,,,,,,,,cosine,average,365,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-40,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7005303455027131,None,0.0,1,5,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.027348815457551995,mean,quantile_transformer,789,uniform,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,
-41,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,1.943186107984505e-07,0.08852455702036988,auto,255,None,5,72,19,loss,1e-07,0.039837460762271175,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.01591265419367627,median,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,
-42,weighting,adaboost,SAMME,0.34205296521106404,10,89,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.02025205191860567,most_frequent,minmax,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-43,none,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0686374607394679,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,minmax,,,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.8490260132946401,False,,,,,,,,,,,,,,,
-44,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,10,1,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0022606253047811435,median,quantile_transformer,1247,normal,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,25.327793585266683,chi2,,,
-45,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.3663040729445102,None,0.0,20,19,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.010000000000000004,median,robust_scaler,,,0.7338553275791654,0.25,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,331,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-46,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,92.66144306787051,,,0.04654639302091653,rbf,-1,True,0.0022231544280610005,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.005194838694455963,mean,none,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,15,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-47,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1550807214391849,True,True,hinge,0.00019875748182257,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.05786688075604592,mean,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,
-48,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.4467384469668449,None,0.0,19,19,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.00033708405018786203,median,quantile_transformer,711,normal,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,7.744829113696808,False,True,1,squared_hinge,ovr,l1,0.0001869446320464273,,,,,,,,,,,,,,,,,,,,,,
-49,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,26.502259985828186,,,9.475662388405154e-05,rbf,-1,False,0.00019690635968217866,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-50,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,2.3520913174804126e-07,0.052918150906133596,auto,255,None,16,185,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.00790855487341428,mean,robust_scaler,,,0.746273035018099,0.21296174657639108,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,1.298440997922375,False,True,1,squared_hinge,ovr,l1,0.0018510248972884416,,,,,,,,,,,,,,,,,,,,,,
-51,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5255991794296913,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-52,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.34697723213923637,None,0.0,15,18,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.1923594385988973,mean,normalize,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,25.77499302945827,chi2,,,
-53,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1252.7997374154156,0.20811431041897355,5,0.0018140593074227453,poly,-1,False,0.03149782358621565,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.001930822730474197,most_frequent,quantile_transformer,233,normal,,,feature_agglomeration,,,,,,,,,,,,,,,euclidean,ward,29,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-54,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,1.5661823810486637e-10,0.08996863554015262,auto,255,None,156,89,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.009331521286159971,median,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,
-55,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.6109468076475573,0.8748383232154038,4,0.41333070422336576,poly,-1,False,3.591268778393482e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.011804925819789203,median,minmax,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-56,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.7388247096873266,None,0.0,1,2,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.02005334514549439,most_frequent,robust_scaler,,,0.9347422252540531,0.2909868620945952,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,
-57,weighting,adaboost,SAMME.R,0.03127273017917347,10,496,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,standardize,,,,,fast_ica,,,,,,,,,,,deflation,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-58,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,151,None,,0.0003283777607180646,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.2510527431514227,mean,normalize,,,,,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,-0.96093182232312,,0.05762510964607661,sigmoid,2632,,,,,,,,,,,,,,,,,
-59,none,bernoulli_nb,,,,,1.9593357170002308,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.005109073482613361,mean,quantile_transformer,10,normal,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.03495379340060141,False,True,1,squared_hinge,ovr,l1,0.0033386410851928994,,,,,,,,,,,,,,,,,,,,,,
-60,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.6453555269772124,None,0.0,1,4,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,normalize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-61,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5363.189119763322,,,0.4294659312024404,rbf,-1,True,0.06935839422940453,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.019860129781307266,most_frequent,none,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,92.1980997621979,mutual_info,,,
-62,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.20911875507660596,None,0.0,12,8,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,median,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,
-63,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.34534888916828577,0.02513634776687268,2,1.0733091880109409,poly,-1,True,0.0006974263047918307,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,quantile_transformer,1000,normal,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,77.99934218022861,mutual_info,,,
-64,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,29.55731828912983,False,True,1,squared_hinge,ovr,l2,0.0011332486951954968,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,
-65,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,1.1387795876755026e-06,0.12245605190343567,auto,255,None,58,7,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,robust_scaler,,,0.79305915648885,0.1841487406876042,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-66,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.8425560317492612,None,0.0,8,15,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.015276988502366128,median,none,,,,,fast_ica,,,,,,,,,,,parallel,cube,17,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-67,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,0.2638627266631576,0.022360820165005443,auto,255,None,171,6,19,loss,1e-07,0.016209060012924756,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.008763072395677399,mean,standardize,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,78,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-68,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5664.233390385957,0.3496807683995542,4,0.00017689402501527962,poly,-1,True,0.01756282016660122,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.13312139731266243,median,quantile_transformer,1362,uniform,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,72.39572310756226,mutual_info,,,
-69,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.8278163313806293,None,0.0,3,5,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,robust_scaler,,,0.725835793765774,0.2571431961341018,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.07784020380291123,fdr,f_classif
-70,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0023391956112474004,False,True,squared_hinge,0.011792446147470903,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.08295440990272292,most_frequent,quantile_transformer,962,normal,,,kernel_pca,,,,,,,,,,,,,,,,,,,,,,cosine,1778,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-71,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.4264370333817712,None,0.0,13,3,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,median,minmax,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.14972073341699849,fpr,chi2
-72,none,adaboost,SAMME,0.07025975715693651,5,339,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,median,robust_scaler,,,0.7117466125338451,0.2984609070131515,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.21351971922962878,fdr,chi2
-73,none,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9931779675565967,None,0.0,18,17,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,robust_scaler,,,0.75,0.2945917010472818,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,
-74,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2611084157414942,True,True,hinge,0.0009587835023329391,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.14664248075999733,most_frequent,minmax,,,,,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.509603573308092,rbf,198,,,,,,,,,,,,,,,,,
-75,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,3.044019499065853e-08,0.030966538899129755,auto,255,None,64,50,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.00860619823050038,median,minmax,,,,,feature_agglomeration,,,,,,,,,,,,,,,cosine,average,36,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-76,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7.667945934601232,True,True,squared_hinge,1.5995631297928604e-05,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.004775063434540409,most_frequent,minmax,,,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.28455344994291587,False,True,1,squared_hinge,ovr,l1,6.230255219574715e-05,,,,,,,,,,,,,,,,,,,,,,
-77,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.34936025988397124,None,0.0,2,13,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00018601309647406987,median,robust_scaler,,,0.8634309348843472,0.26976989465367607,extra_trees_preproc_for_classification,False,entropy,None,0.48025175288975036,None,0.0,6,17,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-78,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,2.925599188129995e-05,0.12560285030651847,auto,255,None,300,193,17,loss,1e-07,0.039442997915546514,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00299400754997757,mean,robust_scaler,,,0.7093292916146515,0.009366983030047793,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-79,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.642370445299362,None,0.0,15,20,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.014823484140711048,median,robust_scaler,,,0.780144337652783,0.2309544712129402,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,2.2123747341894098,False,True,1,squared_hinge,ovr,l1,0.0033125984161268183,,,,,,,,,,,,,,,,,,,,,,
-80,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2471.298583654142,,,0.025866749427709803,rbf,-1,False,0.0009481287482516433,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,none,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,323,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-81,weighting,adaboost,SAMME,0.028939256953011736,2,368,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,quantile_transformer,1954,uniform,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,41.04018582009321,mutual_info,,,
-82,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,21.85207494001697,0.18872999589786676,4,1.1863646021868761,poly,-1,False,0.0021338371901294443,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0034080852834770217,mean,normalize,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.11326005109886934,fdr,f_classif
-83,weighting,adaboost,SAMME,0.38341570193537416,9,250,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,minmax,,,,,feature_agglomeration,,,,,,,,,,,,,,,euclidean,average,13,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-84,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,0.023106206735923184,0.12762814679231496,auto,255,None,24,151,16,loss,1e-07,0.08978875361280989,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.007723510861056437,median,quantile_transformer,1217,normal,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,
-85,weighting,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.00012847697769902057,mean,minmax,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,10.088989224596698,mutual_info,,,
-86,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,1,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.019797877010737826,mean,normalize,,,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.8239678741128165,False,,,,,,,,,,,,,,,
-87,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.46057831591617715,False,True,hinge,0.04557857428827514,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00027457445401600137,median,standardize,,,,,extra_trees_preproc_for_classification,True,gini,None,0.48190346970486964,None,0.0,17,18,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-88,none,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,230,auto,,0.0014037334852669801,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,quantile_transformer,848,uniform,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,61.06963527679606,f_classif,,,
-89,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,64,auto,,0.00021332620906594865,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.026032882800756493,median,standardize,,,,,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,-0.9513223792709335,,0.00029881582960753004,sigmoid,543,,,,,,,,,,,,,,,,,
-90,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.26356754187780895,None,0.0,4,3,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010966178401758774,mean,robust_scaler,,,0.7817043861798886,0.24371720112407158,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
-91,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,3.7287807266408084e-10,0.15544439749103187,auto,255,None,28,16,18,loss,1e-07,0.10835808508958217,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0207886310256421,mean,quantile_transformer,1245,normal,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.39383167612281667,fpr,f_classif
-92,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,130.2903387607578,False,True,1,squared_hinge,ovr,l2,0.0020820269897903502,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.08534605541971435,mean,quantile_transformer,613,normal,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,12.936259313956771,False,True,1,squared_hinge,ovr,l1,9.225567050752251e-05,,,,,,,,,,,,,,,,,,,,,,
-93,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.22879311080499845,None,0.0,8,4,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.014320022547615783,median,minmax,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.238630703440461,mutual_info,,,
-94,weighting,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,2,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0073667222552306465,median,robust_scaler,,,0.701595034693991,0.29514640404971515,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6066051105210388,False,,,,,,,,,,,,,,,
-95,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.107967527639527e-05,True,True,hinge,0.00448294745544775,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,quantile_transformer,953,normal,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,35.00114345405331,chi2,,,
-96,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.457928416564218,None,0.0,12,2,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,
-97,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,1.1522414705935405e-10,0.6132800183073315,auto,255,None,1607,67,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010096911249562407,mean,robust_scaler,,,0.7663493326967942,0.136094217337434,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-98,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1228.3897711257332,0.6862670093933454,5,1.113863025113022,poly,-1,True,1.9271116749907707e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.018161834200128672,mean,normalize,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.06278549919408385,fpr,chi2
-99,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.479405900046494,,,0.013340778632154543,rbf,-1,False,6.913586060890114e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,robust_scaler,,,0.9953232616818098,0.06450577886132274,fast_ica,,,,,,,,,,,deflation,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-100,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.9815182201168626,None,0.0,15,19,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,robust_scaler,,,0.8878662977383482,0.25666364929970126,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,44.15748219826322,mutual_info,,,
-101,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1584.207754377407,,,0.020660207249591715,rbf,-1,False,0.03773359340731028,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,quantile_transformer,1055,uniform,,,fast_ica,,,,,,,,,,,parallel,exp,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-102,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.7285597583341324,None,0.0,3,20,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.011700861722450575,most_frequent,robust_scaler,,,0.7911119178158159,0.2999762250828743,feature_agglomeration,,,,,,,,,,,,,,,euclidean,ward,151,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-103,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2897.996028838154,,,0.04621527412391808,rbf,-1,False,0.0017262380676728777,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.07770313303106262,mean,robust_scaler,,,0.7510736797641198,0.23032782852489117,extra_trees_preproc_for_classification,False,gini,None,0.9588902962283966,None,0.0,13,12,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-104,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.6282663389679584,None,0.0,1,3,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,median,minmax,,,,,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-105,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,8.14394583618027e-09,0.1314274861878701,auto,255,None,27,15,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.3598067419611515,mean,robust_scaler,,,0.7494615433236279,0.260649229829574,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
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-107,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,11.046854203258679,,,0.07809571976769347,rbf,-1,False,0.00036193159909053734,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.006445557635410205,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-108,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.6728827282646637,None,0.0,18,8,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,robust_scaler,,,0.8512985751386942,0.14092824896182807,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,
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-111,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6427543483503397,True,True,hinge,0.00045318775930916944,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.009338484332306904,median,quantile_transformer,174,normal,,,kernel_pca,,,,,,,,,,,,,,,,,,,,,,cosine,1848,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-112,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.02021502857757827,True,,0.055848937053280076,True,,constant,log,l2,,0.00016356162584832537,no_encoding,no_coalescense,,median,quantile_transformer,27,normal,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.11873149622606233,fwe,chi2
-113,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,9.029473608568458e-09,0.15779990576964315,auto,255,None,37,71,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.018093177195644097,mean,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,False,,,,,,,,,,,,
-114,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8.24807027221455,-0.23458710218209156,,0.0001030034352044511,sigmoid,-1,True,0.0002726309373020207,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,quantile_transformer,1974,normal,,,extra_trees_preproc_for_classification,True,entropy,None,0.9395220389606214,None,0.0,2,20,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-115,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.3747863651224668,True,True,hinge,0.00019106780876479388,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.002442315142014378,median,quantile_transformer,1850,uniform,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.570194774022733,f_classif,,,
-116,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.4864284879550203,None,0.0,1,4,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.008665854799630503,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-117,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.8785519499336585,None,0.0,13,17,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.05156816034494817,most_frequent,quantile_transformer,1243,uniform,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.11082264321263885,fdr,f_classif
-118,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.536709062860521e-07,True,,,True,,optimal,squared_hinge,l1,,1.6934515188687678e-05,one_hot_encoding,no_coalescense,,most_frequent,normalize,,,,,feature_agglomeration,,,,,,,,,,,,,,,cosine,complete,306,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-119,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.6905196223806276,None,0.0,6,5,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,minmax,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-120,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.6154724467600127,None,0.0,19,12,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,mean,minmax,,,,,extra_trees_preproc_for_classification,True,entropy,None,0.7630778936392856,None,0.0,11,20,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-121,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5836006490230695,None,0.0,1,19,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.02127792104027424,most_frequent,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,
-122,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.922871100218626,False,True,squared_hinge,0.09957180737451943,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0016563186856411274,median,quantile_transformer,850,uniform,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.09123695282772847,fpr,chi2
-123,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.06571678518052461,False,True,1,squared_hinge,ovr,l2,3.5551445493853517e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0007406565537349627,median,quantile_transformer,1114,normal,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.13436517316012828,fwe,f_classif
-124,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1561.1002600303584,,,0.0024048551704194507,rbf,-1,False,0.0001936214667496851,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,minmax,,,,,fast_ica,,,,,,,,,,,parallel,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-125,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.087313242780732,0.0636709914987994,3,0.004481917125024585,poly,-1,False,0.01848880618941367,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.002136015035740733,mean,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-126,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.03204653439894958,True,True,squared_hinge,0.00013006393401112688,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.013506575694382031,most_frequent,normalize,,,,,feature_agglomeration,,,,,,,,,,,,,,,euclidean,ward,356,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-127,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.6466927589470762,None,0.0,11,2,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.009672465311659264,most_frequent,standardize,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,369,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+1,none,decision_tree,,,,,,,entropy,1.877536950957045,1.0,None,0.0,10,13,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,quantile_transformer,1275,uniform,,,extra_trees_preproc_for_classification,True,gini,None,0.04910706843318435,None,0.0,12,12,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+2,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.8905944749638223,None,0.0,5,4,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.15463552048633444,most_frequent,robust_scaler,,,0.7545842296967857,0.22898551665984185,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.24780801481682346,fwe,chi2
+5,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,9.83838677007206e-09,0.09269618726619562,auto,255,None,37,16,14,loss,1e-07,0.04208303685387712,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
+6,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,4.267426422904654e-06,0.011555121209024403,auto,255,None,121,34,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010064483887441845,most_frequent,quantile_transformer,1194,uniform,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,
+8,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,114,manual,0.11778631707605758,0.0029112721339713465,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.015582195257240751,median,normalize,,,,,kernel_pca,,,,,,,,,,,,,,,,,,,0.42909623253327767,3,0.25407612000343777,poly,1689,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+9,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.685918004243461,True,True,squared_hinge,1.7784504877380735e-05,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010335606354842895,most_frequent,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,
+10,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.7396493120555564,None,0.0,1,18,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010000000000000004,median,robust_scaler,,,0.8908334215036855,0.08666907249785906,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,
+13,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,0.9907352051102745,0.018115439174701313,auto,255,None,84,5,3,loss,1e-07,0.13697766901072808,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.036524416007602305,mean,robust_scaler,,,0.9824116214519696,0.2911868066464925,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,
+14,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,4.629265598705481e-10,0.03894546521791516,auto,255,None,5,22,18,loss,1e-07,0.0837137336215462,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.008397749525484112,mean,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
+15,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.21772092350860472,None,0.0,14,17,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.0020488212891693275,median,standardize,,,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,33.74257606530722,False,True,1,squared_hinge,ovr,l1,8.740452966205111e-05,,,,,,,,,,,,,,,,,,,,,,
+17,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.6591033863118033,None,0.0,10,10,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,none,,,,,fast_ica,,,,,,,,,,,deflation,exp,1506,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+18,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,1.9110915129429343e-09,0.06380311481632683,auto,255,None,20,62,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,robust_scaler,,,0.7307407504330838,0.2660206566992171,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,
+19,weighting,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,99,2,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010033505607662804,median,none,,,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.069760700053376,False,True,1,squared_hinge,ovr,l1,0.06611177025130774,,,,,,,,,,,,,,,,,,,,,,
+23,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,2.0447842449942555e-09,0.04257026709693251,auto,255,None,47,13,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010000000000000004,median,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,
+24,weighting,adaboost,SAMME.R,0.44886160948796244,9,493,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.031007103864624616,mean,robust_scaler,,,0.7575180367976682,0.26457274298753825,feature_agglomeration,,,,,,,,,,,,,,,cosine,complete,211,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+25,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,0.008942041103505539,0.05229567301622349,auto,255,None,6,24,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.01936143670847451,mean,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,
+26,weighting,adaboost,SAMME.R,0.01561270559347964,4,417,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,quantile_transformer,1617,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+27,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.41025844265119926,None,0.0,18,15,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,median,quantile_transformer,1911,normal,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,False,,,,,,,,,,,,
+29,none,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.9034033923099005,False,True,1,squared_hinge,ovr,l2,1.2159485299684885e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.027586423552745792,most_frequent,none,,,,,kernel_pca,,,,,,,,,,,,,,,,,,,,,0.4034250963681743,rbf,735,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+30,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,490.8047233341048,0.13853827450882505,,0.004044309450174347,sigmoid,-1,False,0.005958796387567885,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.008446690235218424,median,minmax,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,229,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+31,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,3.0538220042587883e-07,0.0704056758356995,auto,255,None,9,3,19,loss,1e-07,0.01927933154167906,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.019804174585488932,median,quantile_transformer,921,normal,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,
+32,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,101,auto,,1.553086088086431e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.01137059177001425,most_frequent,normalize,,,,,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,-0.021621751597165284,4,0.7468597264810062,poly,5078,,,,,,,,,,,,,,,,,
+33,none,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.5769165522851386,None,0.0,1,6,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,none,,,,,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,7,None,9,14,1.0,74,,,,,
+34,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,0.06671977293714751,0.04456236025167353,auto,255,None,3,163,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.4333293305857194,mean,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+37,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0618160212153551,True,True,squared_hinge,1.2370453081062464e-05,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.01448339838697973,mean,standardize,,,,,kitchen_sinks,,,,,,,,,,,,,,,,,,,,,,,,0.018879929475513826,1637,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+39,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.6799215694343422,None,0.0,1,5,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.004724310659953132,most_frequent,quantile_transformer,1037,normal,,,fast_ica,,,,,,,,,,,parallel,exp,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+41,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.6965935192702026,None,0.0,12,16,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,none,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,67.60637688434707,f_classif,,,
+43,none,decision_tree,,,,,,,gini,1.1110863621017264,1.0,None,0.0,16,19,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.00043339385698342055,mean,none,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.20957492283649354,fwe,f_classif
+44,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,2.6159836259985015e-05,0.05532294838858921,auto,255,None,116,4,14,loss,1e-07,0.20422881335187132,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,
+49,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00010123414317660679,False,,0.04011567850168966,True,,constant,log,l1,,0.00014179758438189507,one_hot_encoding,minority_coalescer,0.008920898694379564,mean,robust_scaler,,,0.7712963178784032,0.2702398922671455,kernel_pca,,,,,,,,,,,,,,,,,,,,,,cosine,1090,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+54,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,1.0607494292547588e-08,0.014599960935351135,auto,255,None,5,7,1,loss,1e-07,0.01508309574734627,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
+58,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5547994712622472,None,0.0,1,2,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.008028893322827371,most_frequent,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
+59,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9768.124422997415,False,True,1,squared_hinge,ovr,l2,0.002391133479983495,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,robust_scaler,,,0.7418544320601411,0.258610892841927,fast_ica,,,,,,,,,,,parallel,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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diff --git a/autosklearn/metalearning/files/precision_samples_multiclass.classification_dense/description.txt b/autosklearn/metalearning/files/precision_samples_multiclass.classification_dense/description.txt
index 6ed52c6219..cdb82de1b3 100644
--- a/autosklearn/metalearning/files/precision_samples_multiclass.classification_dense/description.txt
+++ b/autosklearn/metalearning/files/precision_samples_multiclass.classification_dense/description.txt
@@ -1,8 +1,9 @@
 features_cutoff_time: 3600
 features_cutoff_memory: 3072
-number_of_feature_steps: 35
+number_of_feature_steps: 42
 feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances
 feature_step LogNumberOfInstances: LogNumberOfInstances
+feature_step NumberOfClasses: NumberOfClasses
 feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures
 feature_step LogNumberOfFeatures: LogNumberOfFeatures
 feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues
@@ -20,6 +21,11 @@ feature_step DatasetRatio: DatasetRatio, LogDatasetRatio
 feature_step LogDatasetRatio: LogDatasetRatio
 feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio
 feature_step LogInverseDatasetRatio: LogInverseDatasetRatio
+feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD
+feature_step ClassProbabilityMin: ClassProbabilityMin
+feature_step ClassProbabilityMax: ClassProbabilityMax
+feature_step ClassProbabilityMean: ClassProbabilityMean
+feature_step ClassProbabilitySTD: ClassProbabilitySTD
 feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum
 feature_step SymbolsMin: SymbolsMin
 feature_step SymbolsMax: SymbolsMax
@@ -36,11 +42,12 @@ feature_step SkewnessMin: SkewnessMin
 feature_step SkewnessMax: SkewnessMax
 feature_step SkewnessMean: SkewnessMean
 feature_step SkewnessSTD: SkewnessSTD
-features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
+feature_step ClassEntropy: ClassEntropy
+features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy
 features_stochastic: 
-default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
+default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy
 
-algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127
+algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235,236,237,238,239,240,241,242,243,244,245,246,247,248,249,250,251,252,253,254,255,256,257,258,259,260,261,262,263,264,265,266,267,268,269,270,271,272,273,274,275,276,277,278,279,280,281,282,283,284,285,286,287,288,289,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,312,313,314,315,316,317,318,319,320,321,322,323,324,325,326,327,328,329,330,331,332,333,334,335,336,337,338,339,340,341,342,343,344,345,346,347,348,349,350,351,352,353,354,355,356,357,358,359,360,361,362,363,364,365,366,367,368,369,370,371,372,373,374,375,376,377,378,379,380,381,382,383,384,385,386,387,388,389,390,391,392,393,394,395,396,397,398,399,400,401,402,403,404,405,406,407,408,409,410,411,412,413,414,415,416,417,418,419,420,421,422,423,424,425,426,427,428,429,430,431,432,433,434,435,436,437,438,439,440,441,442,443,444,445,446,447,448,449,450,451,452,453,454,455,456,457,458,459,460,461,462,463,464
 algorithms_stochastic: 
 performance_measures: precision_samples
 performance_type: solution_quality
diff --git a/autosklearn/metalearning/files/precision_samples_multiclass.classification_dense/feature_costs.arff b/autosklearn/metalearning/files/precision_samples_multiclass.classification_dense/feature_costs.arff
index 0e17e20d22..8512fd9275 100644
--- a/autosklearn/metalearning/files/precision_samples_multiclass.classification_dense/feature_costs.arff
+++ b/autosklearn/metalearning/files/precision_samples_multiclass.classification_dense/feature_costs.arff
@@ -4,6 +4,7 @@
 @ATTRIBUTE repetition NUMERIC
 @ATTRIBUTE NumberOfInstances NUMERIC
 @ATTRIBUTE LogNumberOfInstances NUMERIC
+@ATTRIBUTE NumberOfClasses NUMERIC
 @ATTRIBUTE NumberOfFeatures NUMERIC
 @ATTRIBUTE LogNumberOfFeatures NUMERIC
 @ATTRIBUTE MissingValues NUMERIC
@@ -21,6 +22,11 @@
 @ATTRIBUTE LogDatasetRatio NUMERIC
 @ATTRIBUTE InverseDatasetRatio NUMERIC
 @ATTRIBUTE LogInverseDatasetRatio NUMERIC
+@ATTRIBUTE ClassOccurences NUMERIC
+@ATTRIBUTE ClassProbabilityMin NUMERIC
+@ATTRIBUTE ClassProbabilityMax NUMERIC
+@ATTRIBUTE ClassProbabilityMean NUMERIC
+@ATTRIBUTE ClassProbabilitySTD NUMERIC
 @ATTRIBUTE NumSymbols NUMERIC
 @ATTRIBUTE SymbolsMin NUMERIC
 @ATTRIBUTE SymbolsMax NUMERIC
@@ -37,162 +43,138 @@
 @ATTRIBUTE SkewnessMax NUMERIC
 @ATTRIBUTE SkewnessMean NUMERIC
 @ATTRIBUTE SkewnessSTD NUMERIC
+@ATTRIBUTE ClassEntropy NUMERIC
 
 @DATA
-75123,1.0,1e-05,1e-05,1e-05,1e-05,0.00253,0.00221,1e-05,0.00024,3e-05,0.00011,0.0,2e-05,2e-05,5e-05,8e-05,2e-05,1e-05,7e-05,1e-05,0.00072,1e-05,1e-05,0.00019,0.00041,0.00011,0.00061,0.00012,9e-05,0.00015,0.00025,0.00067,0.00014,0.00011,0.00016,0.00027
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+75235,1.0,1e-05,1e-05,0.0003,2e-05,1e-05,0.00461,0.00385,2e-05,0.00055,2e-05,0.00026,1e-05,3e-05,3e-05,0.0001,0.00011,3e-05,1e-05,5e-05,2e-05,0.00026,3e-05,1e-05,8e-05,0.00014,0.00075,1e-05,1e-05,0.0002,0.00043,0.00011,0.00127,0.00017,0.0002,0.00029,0.00061,0.0008,0.00015,0.00012,0.00016,0.00038,0.00242
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+75249,1.0,1e-05,1e-05,0.00011,1e-05,1e-05,0.00192,0.00153,1e-05,0.00025,1e-05,0.00017,1e-05,2e-05,2e-05,5e-05,7e-05,2e-05,1e-05,5e-05,3e-05,0.00014,2e-05,1e-05,3e-05,8e-05,0.00029,1e-05,3e-05,6e-05,0.00012,7e-05,0.00041,6e-05,8e-05,0.0001,0.00017,0.00043,6e-05,7e-05,9e-05,0.00019,0.00092
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diff --git a/autosklearn/metalearning/files/precision_samples_multiclass.classification_dense/feature_runstatus.arff b/autosklearn/metalearning/files/precision_samples_multiclass.classification_dense/feature_runstatus.arff
index 0034ad2bd6..5fff2dfed3 100644
--- a/autosklearn/metalearning/files/precision_samples_multiclass.classification_dense/feature_runstatus.arff
+++ b/autosklearn/metalearning/files/precision_samples_multiclass.classification_dense/feature_runstatus.arff
@@ -4,6 +4,7 @@
 @ATTRIBUTE repetition NUMERIC
 @ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other}
@@ -21,6 +22,11 @@
 @ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other}
@@ -37,162 +43,138 @@
 @ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other}
 
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diff --git a/autosklearn/metalearning/files/precision_samples_multiclass.classification_dense/feature_values.arff b/autosklearn/metalearning/files/precision_samples_multiclass.classification_dense/feature_values.arff
index a60400e3ce..736dc64321 100644
--- a/autosklearn/metalearning/files/precision_samples_multiclass.classification_dense/feature_values.arff
+++ b/autosklearn/metalearning/files/precision_samples_multiclass.classification_dense/feature_values.arff
@@ -13,7 +13,6 @@
 @ATTRIBUTE KurtosisMean NUMERIC
 @ATTRIBUTE KurtosisMin NUMERIC
 @ATTRIBUTE KurtosisSTD NUMERIC
-@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC
 @ATTRIBUTE LogDatasetRatio NUMERIC
 @ATTRIBUTE LogInverseDatasetRatio NUMERIC
 @ATTRIBUTE LogNumberOfFeatures NUMERIC
@@ -42,160 +41,135 @@
 @ATTRIBUTE SymbolsSum NUMERIC
 
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diff --git a/autosklearn/metalearning/files/precision_samples_multiclass.classification_sparse/algorithm_runs.arff b/autosklearn/metalearning/files/precision_samples_multiclass.classification_sparse/algorithm_runs.arff
index 0b5c150cc0..94702a454e 100644
--- a/autosklearn/metalearning/files/precision_samples_multiclass.classification_sparse/algorithm_runs.arff
+++ b/autosklearn/metalearning/files/precision_samples_multiclass.classification_sparse/algorithm_runs.arff
@@ -7,95 +7,133 @@
 @ATTRIBUTE runstatus {ok, timeout, memout, not_applicable, crash, other}
 
 @DATA
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-75126,1.0,10,?,not_applicable
-75234,1.0,11,?,not_applicable
-75150,1.0,12,?,not_applicable
-258,1.0,13,?,not_applicable
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-75235,1.0,15,?,not_applicable
-244,1.0,16,?,not_applicable
-75221,1.0,17,?,not_applicable
-75219,1.0,18,?,not_applicable
-75202,1.0,19,?,not_applicable
-3043,1.0,20,?,not_applicable
-75205,1.0,21,?,not_applicable
-75174,1.0,22,?,not_applicable
-275,1.0,23,?,not_applicable
-75213,1.0,24,?,not_applicable
-75099,1.0,25,?,not_applicable
-75184,1.0,26,?,not_applicable
+75192,1.0,1,?,not_applicable
+75119,1.0,2,?,not_applicable
+75212,1.0,5,?,not_applicable
+246,1.0,6,?,not_applicable
+252,1.0,7,?,not_applicable
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+75177,1.0,11,?,not_applicable
+75092,1.0,12,?,not_applicable
+75239,1.0,13,?,not_applicable
+75215,1.0,15,?,not_applicable
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 75222,1.0,27,?,not_applicable
-233,1.0,28,?,not_applicable
-75114,1.0,29,?,not_applicable
-236,1.0,30,?,not_applicable
-75141,1.0,31,?,not_applicable
-75107,1.0,32,?,not_applicable
-262,1.0,33,?,not_applicable
-75146,1.0,34,?,not_applicable
-75189,1.0,35,?,not_applicable
-2350,1.0,36,?,not_applicable
-75249,1.0,37,?,not_applicable
-242,1.0,38,?,not_applicable
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-75191,1.0,40,?,not_applicable
-261,1.0,41,?,not_applicable
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-75166,1.0,56,?,not_applicable
-75100,1.0,57,?,not_applicable
-75169,1.0,58,?,not_applicable
-75121,1.0,59,?,not_applicable
-75098,1.0,60,?,not_applicable
-75115,1.0,61,?,not_applicable
-75116,1.0,62,?,not_applicable
-75185,1.0,63,?,not_applicable
-2119,1.0,64,?,not_applicable
-75157,1.0,65,?,not_applicable
-75113,1.0,66,?,not_applicable
-75203,1.0,67,?,not_applicable
-75182,1.0,68,?,not_applicable
-251,1.0,69,?,not_applicable
-75123,1.0,70,?,not_applicable
-75125,1.0,71,?,not_applicable
-75232,1.0,72,?,not_applicable
-75103,1.0,73,?,not_applicable
-75192,1.0,74,?,not_applicable
-75230,1.0,75,?,not_applicable
-75139,1.0,76,?,not_applicable
-75227,1.0,77,?,not_applicable
-2120,1.0,78,?,not_applicable
-75124,1.0,79,?,not_applicable
-75240,1.0,80,?,not_applicable
-75198,1.0,81,?,not_applicable
-75201,1.0,82,?,not_applicable
-75133,1.0,83,?,not_applicable
-75154,1.0,84,?,not_applicable
-75177,1.0,85,?,not_applicable
-2117,1.0,86,?,not_applicable
-75156,1.0,87,?,not_applicable
-75097,1.0,88,?,not_applicable
-75172,1.0,89,?,not_applicable
-75106,1.0,90,?,not_applicable
-75187,1.0,91,?,not_applicable
-75120,1.0,92,?,not_applicable
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+288,1.0,89,?,not_applicable
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diff --git a/autosklearn/metalearning/files/precision_samples_multiclass.classification_sparse/configurations.csv b/autosklearn/metalearning/files/precision_samples_multiclass.classification_sparse/configurations.csv
index 888e169980..c3d201ba5c 100644
--- a/autosklearn/metalearning/files/precision_samples_multiclass.classification_sparse/configurations.csv
+++ b/autosklearn/metalearning/files/precision_samples_multiclass.classification_sparse/configurations.csv
@@ -1,93 +1,131 @@
 idx,balancing:strategy,classifier:__choice__,classifier:adaboost:algorithm,classifier:adaboost:learning_rate,classifier:adaboost:max_depth,classifier:adaboost:n_estimators,classifier:bernoulli_nb:alpha,classifier:bernoulli_nb:fit_prior,classifier:decision_tree:criterion,classifier:decision_tree:max_depth_factor,classifier:decision_tree:max_features,classifier:decision_tree:max_leaf_nodes,classifier:decision_tree:min_impurity_decrease,classifier:decision_tree:min_samples_leaf,classifier:decision_tree:min_samples_split,classifier:decision_tree:min_weight_fraction_leaf,classifier:extra_trees:bootstrap,classifier:extra_trees:criterion,classifier:extra_trees:max_depth,classifier:extra_trees:max_features,classifier:extra_trees:max_leaf_nodes,classifier:extra_trees:min_impurity_decrease,classifier:extra_trees:min_samples_leaf,classifier:extra_trees:min_samples_split,classifier:extra_trees:min_weight_fraction_leaf,classifier:gradient_boosting:early_stop,classifier:gradient_boosting:l2_regularization,classifier:gradient_boosting:learning_rate,classifier:gradient_boosting:loss,classifier:gradient_boosting:max_bins,classifier:gradient_boosting:max_depth,classifier:gradient_boosting:max_leaf_nodes,classifier:gradient_boosting:min_samples_leaf,classifier:gradient_boosting:n_iter_no_change,classifier:gradient_boosting:scoring,classifier:gradient_boosting:tol,classifier:gradient_boosting:validation_fraction,classifier:k_nearest_neighbors:n_neighbors,classifier:k_nearest_neighbors:p,classifier:k_nearest_neighbors:weights,classifier:lda:n_components,classifier:lda:shrinkage,classifier:lda:shrinkage_factor,classifier:lda:tol,classifier:liblinear_svc:C,classifier:liblinear_svc:dual,classifier:liblinear_svc:fit_intercept,classifier:liblinear_svc:intercept_scaling,classifier:liblinear_svc:loss,classifier:liblinear_svc:multi_class,classifier:liblinear_svc:penalty,classifier:liblinear_svc:tol,classifier:libsvm_svc:C,classifier:libsvm_svc:coef0,classifier:libsvm_svc:degree,classifier:libsvm_svc:gamma,classifier:libsvm_svc:kernel,classifier:libsvm_svc:max_iter,classifier:libsvm_svc:shrinking,classifier:libsvm_svc:tol,classifier:multinomial_nb:alpha,classifier:multinomial_nb:fit_prior,classifier:passive_aggressive:C,classifier:passive_aggressive:average,classifier:passive_aggressive:fit_intercept,classifier:passive_aggressive:loss,classifier:passive_aggressive:tol,classifier:qda:reg_param,classifier:random_forest:bootstrap,classifier:random_forest:criterion,classifier:random_forest:max_depth,classifier:random_forest:max_features,classifier:random_forest:max_leaf_nodes,classifier:random_forest:min_impurity_decrease,classifier:random_forest:min_samples_leaf,classifier:random_forest:min_samples_split,classifier:random_forest:min_weight_fraction_leaf,classifier:sgd:alpha,classifier:sgd:average,classifier:sgd:epsilon,classifier:sgd:eta0,classifier:sgd:fit_intercept,classifier:sgd:l1_ratio,classifier:sgd:learning_rate,classifier:sgd:loss,classifier:sgd:penalty,classifier:sgd:power_t,classifier:sgd:tol,data_preprocessing:categorical_transformer:categorical_encoding:__choice__,data_preprocessing:categorical_transformer:category_coalescence:__choice__,data_preprocessing:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction,data_preprocessing:numerical_transformer:imputation:strategy,data_preprocessing:numerical_transformer:rescaling:__choice__,data_preprocessing:numerical_transformer:rescaling:quantile_transformer:n_quantiles,data_preprocessing:numerical_transformer:rescaling:quantile_transformer:output_distribution,data_preprocessing:numerical_transformer:rescaling:robust_scaler:q_max,data_preprocessing:numerical_transformer:rescaling:robust_scaler:q_min,feature_preprocessor:__choice__,feature_preprocessor:extra_trees_preproc_for_classification:bootstrap,feature_preprocessor:extra_trees_preproc_for_classification:criterion,feature_preprocessor:extra_trees_preproc_for_classification:max_depth,feature_preprocessor:extra_trees_preproc_for_classification:max_features,feature_preprocessor:extra_trees_preproc_for_classification:max_leaf_nodes,feature_preprocessor:extra_trees_preproc_for_classification:min_impurity_decrease,feature_preprocessor:extra_trees_preproc_for_classification:min_samples_leaf,feature_preprocessor:extra_trees_preproc_for_classification:min_samples_split,feature_preprocessor:extra_trees_preproc_for_classification:min_weight_fraction_leaf,feature_preprocessor:extra_trees_preproc_for_classification:n_estimators,feature_preprocessor:kernel_pca:coef0,feature_preprocessor:kernel_pca:degree,feature_preprocessor:kernel_pca:gamma,feature_preprocessor:kernel_pca:kernel,feature_preprocessor:kernel_pca:n_components,feature_preprocessor:kitchen_sinks:gamma,feature_preprocessor:kitchen_sinks:n_components,feature_preprocessor:liblinear_svc_preprocessor:C,feature_preprocessor:liblinear_svc_preprocessor:dual,feature_preprocessor:liblinear_svc_preprocessor:fit_intercept,feature_preprocessor:liblinear_svc_preprocessor:intercept_scaling,feature_preprocessor:liblinear_svc_preprocessor:loss,feature_preprocessor:liblinear_svc_preprocessor:multi_class,feature_preprocessor:liblinear_svc_preprocessor:penalty,feature_preprocessor:liblinear_svc_preprocessor:tol,feature_preprocessor:nystroem_sampler:coef0,feature_preprocessor:nystroem_sampler:degree,feature_preprocessor:nystroem_sampler:gamma,feature_preprocessor:nystroem_sampler:kernel,feature_preprocessor:nystroem_sampler:n_components,feature_preprocessor:polynomial:degree,feature_preprocessor:polynomial:include_bias,feature_preprocessor:polynomial:interaction_only,feature_preprocessor:random_trees_embedding:bootstrap,feature_preprocessor:random_trees_embedding:max_depth,feature_preprocessor:random_trees_embedding:max_leaf_nodes,feature_preprocessor:random_trees_embedding:min_samples_leaf,feature_preprocessor:random_trees_embedding:min_samples_split,feature_preprocessor:random_trees_embedding:min_weight_fraction_leaf,feature_preprocessor:random_trees_embedding:n_estimators,feature_preprocessor:select_percentile_classification:percentile,feature_preprocessor:select_percentile_classification:score_func,feature_preprocessor:select_rates_classification:alpha,feature_preprocessor:select_rates_classification:mode,feature_preprocessor:select_rates_classification:score_func,feature_preprocessor:truncatedSVD:target_dim
-1,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.3163020577549344,None,0.0,15,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.01322981552715978,most_frequent,robust_scaler,,,0.9746667009556722,0.1974876581478311,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-2,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,23916.78290516781,False,True,1,squared_hinge,ovr,l2,0.0006655607726658531,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.008808715242823137,median,none,,,,,kernel_pca,,,,,,,,,,,0.2633153190620261,2,0.004770332714291669,poly,1483,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-3,weighting,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,88,2,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.254263872179305,median,normalize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-4,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,170.37509684994612,0.6734219115470694,3,0.08998608839429428,poly,-1,True,0.024147312348267988,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,none,,,,,extra_trees_preproc_for_classification,False,entropy,None,0.9449154369109399,None,0.0,1,14,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-5,none,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00011009762574324903,True,,0.031641726094190054,True,,invscaling,hinge,l2,0.2208821470267501,0.007895311091089785,one_hot_encoding,minority_coalescer,0.13807502937372682,mean,quantile_transformer,1340,normal,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,,
-6,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.5244313211416313,None,0.0,1,2,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.454300236339658,most_frequent,robust_scaler,,,0.7858830918128734,0.2705174720960696,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-7,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.02299691847304223,False,True,squared_hinge,2.16045124689341e-05,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010708146395792527,most_frequent,standardize,,,,,extra_trees_preproc_for_classification,False,entropy,None,0.5298549662385138,None,0.0,12,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-8,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.25657910619102237,None,0.0,6,6,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.021086303324492208,most_frequent,quantile_transformer,1247,uniform,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.16021572815319468,fpr,chi2,
-9,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.643761610990255,None,0.0,20,6,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.039859381630488695,mean,normalize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,,
-10,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.8253905225980761,None,0.0,18,20,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.003725255016700434,mean,robust_scaler,,,0.7701705977765594,0.2396520157991559,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.3579038776690903,False,True,1,squared_hinge,ovr,l1,4.994232534734023e-05,,,,,,,,,,,,,,,,,,,,,
-11,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.06263739023781334,None,0.0,18,8,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.02206379295100344,most_frequent,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,,
-12,none,decision_tree,,,,,,,entropy,0.9175027108465073,1.0,None,0.0,18,13,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0005144268441601888,most_frequent,none,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.36269138268682594,fdr,chi2,
-13,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,199,None,,0.003239674765949011,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,robust_scaler,,,0.7228447795482008,0.25,kernel_pca,,,,,,,,,,,0.09440148939740256,3,0.0008114095405154861,poly,1996,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-14,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.6039096182414764,None,0.0,2,20,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.1939609823704447,most_frequent,robust_scaler,,,0.7331327008874781,0.29747488162877694,extra_trees_preproc_for_classification,True,entropy,None,0.33401603690569853,None,0.0,4,4,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-15,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7865652301288171,None,0.0,1,4,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,median,robust_scaler,,,0.8623635641193496,0.13298297278730625,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-16,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.905401147837377,-0.32832122529048147,3,1.001896309040499,poly,-1,False,5.1523484258030785e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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-21,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.506204385946851,None,0.0,2,8,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.03433007982751011,mean,none,,,,,extra_trees_preproc_for_classification,False,gini,None,0.32676415738837183,None,0.0,5,6,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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-29,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,10,1,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0022606253047811435,median,quantile_transformer,1247,normal,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,25.327793585266683,chi2,,,,
-30,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,45.824472650016915,,,0.04733593018751384,rbf,-1,False,0.0001002563992422035,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.007882457896645694,most_frequent,robust_scaler,,,0.7360778699898849,0.27800774875566153,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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-32,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.4467384469668449,None,0.0,19,19,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.00033708405018786203,median,quantile_transformer,711,normal,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,7.744829113696808,False,True,1,squared_hinge,ovr,l1,0.0001869446320464273,,,,,,,,,,,,,,,,,,,,,
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-82,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.02021502857757827,True,,0.055848937053280076,True,,constant,log,l2,,0.00016356162584832537,no_encoding,no_coalescense,,median,quantile_transformer,27,normal,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.11873149622606233,fwe,chi2,
-83,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8.24807027221455,-0.23458710218209156,,0.0001030034352044511,sigmoid,-1,True,0.0002726309373020207,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,quantile_transformer,1974,normal,,,extra_trees_preproc_for_classification,True,entropy,None,0.9395220389606214,None,0.0,2,20,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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-91,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.087313242780732,0.0636709914987994,3,0.004481917125024585,poly,-1,False,0.01848880618941367,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.002136015035740733,mean,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-92,weighting,adaboost,SAMME,0.028177459966491795,1,484,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,quantile_transformer,875,normal,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,1.502807354196727,False,True,1,squared_hinge,ovr,l1,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,
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diff --git a/autosklearn/metalearning/files/precision_samples_multiclass.classification_sparse/description.txt b/autosklearn/metalearning/files/precision_samples_multiclass.classification_sparse/description.txt
index 60a51ca98b..85c6829e8e 100644
--- a/autosklearn/metalearning/files/precision_samples_multiclass.classification_sparse/description.txt
+++ b/autosklearn/metalearning/files/precision_samples_multiclass.classification_sparse/description.txt
@@ -1,8 +1,9 @@
 features_cutoff_time: 3600
 features_cutoff_memory: 3072
-number_of_feature_steps: 35
+number_of_feature_steps: 42
 feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances
 feature_step LogNumberOfInstances: LogNumberOfInstances
+feature_step NumberOfClasses: NumberOfClasses
 feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures
 feature_step LogNumberOfFeatures: LogNumberOfFeatures
 feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues
@@ -20,6 +21,11 @@ feature_step DatasetRatio: DatasetRatio, LogDatasetRatio
 feature_step LogDatasetRatio: LogDatasetRatio
 feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio
 feature_step LogInverseDatasetRatio: LogInverseDatasetRatio
+feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD
+feature_step ClassProbabilityMin: ClassProbabilityMin
+feature_step ClassProbabilityMax: ClassProbabilityMax
+feature_step ClassProbabilityMean: ClassProbabilityMean
+feature_step ClassProbabilitySTD: ClassProbabilitySTD
 feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum
 feature_step SymbolsMin: SymbolsMin
 feature_step SymbolsMax: SymbolsMax
@@ -36,11 +42,12 @@ feature_step SkewnessMin: SkewnessMin
 feature_step SkewnessMax: SkewnessMax
 feature_step SkewnessMean: SkewnessMean
 feature_step SkewnessSTD: SkewnessSTD
-features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
+feature_step ClassEntropy: ClassEntropy
+features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy
 features_stochastic: 
-default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
+default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy
 
-algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92
+algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235,236,237,238,239,240,241,242,243,244,245,246,247,248,249,250,251,252,253,254,255,256,257,258,259,260,261,262,263,264,265,266,267,268,269,270,271,272,273,274,275,276,277,278,279,280,281,282,283,284,285,286,287,288,289,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,312,313,314,315,316,317,318,319,320,321,322,323,324,325,326,327,328,329,330,331,332,333,334,335,336,337,338,339,340,341,342,343,344,345,346,347,348,349,350,351,352,353,354,355,356,357,358,359,360,361,362,363,364,365,366,367,368,369,370,371,372
 algorithms_stochastic: 
 performance_measures: precision_samples
 performance_type: solution_quality
diff --git a/autosklearn/metalearning/files/precision_samples_multiclass.classification_sparse/feature_costs.arff b/autosklearn/metalearning/files/precision_samples_multiclass.classification_sparse/feature_costs.arff
index 0e17e20d22..8512fd9275 100644
--- a/autosklearn/metalearning/files/precision_samples_multiclass.classification_sparse/feature_costs.arff
+++ b/autosklearn/metalearning/files/precision_samples_multiclass.classification_sparse/feature_costs.arff
@@ -4,6 +4,7 @@
 @ATTRIBUTE repetition NUMERIC
 @ATTRIBUTE NumberOfInstances NUMERIC
 @ATTRIBUTE LogNumberOfInstances NUMERIC
+@ATTRIBUTE NumberOfClasses NUMERIC
 @ATTRIBUTE NumberOfFeatures NUMERIC
 @ATTRIBUTE LogNumberOfFeatures NUMERIC
 @ATTRIBUTE MissingValues NUMERIC
@@ -21,6 +22,11 @@
 @ATTRIBUTE LogDatasetRatio NUMERIC
 @ATTRIBUTE InverseDatasetRatio NUMERIC
 @ATTRIBUTE LogInverseDatasetRatio NUMERIC
+@ATTRIBUTE ClassOccurences NUMERIC
+@ATTRIBUTE ClassProbabilityMin NUMERIC
+@ATTRIBUTE ClassProbabilityMax NUMERIC
+@ATTRIBUTE ClassProbabilityMean NUMERIC
+@ATTRIBUTE ClassProbabilitySTD NUMERIC
 @ATTRIBUTE NumSymbols NUMERIC
 @ATTRIBUTE SymbolsMin NUMERIC
 @ATTRIBUTE SymbolsMax NUMERIC
@@ -37,162 +43,138 @@
 @ATTRIBUTE SkewnessMax NUMERIC
 @ATTRIBUTE SkewnessMean NUMERIC
 @ATTRIBUTE SkewnessSTD NUMERIC
+@ATTRIBUTE ClassEntropy NUMERIC
 
 @DATA
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+75234,1.0,1e-05,1e-05,0.00015,1e-05,1e-05,0.0058,0.00534,1e-05,0.00033,1e-05,0.00016,0.0,2e-05,2e-05,7e-05,0.00204,3e-05,1e-05,5e-05,1e-05,0.00024,3e-05,2e-05,0.0001,9e-05,0.00043,0.0,0.0,0.00012,0.00024,6e-05,0.00156,6e-05,7e-05,0.00121,0.00021,0.00054,6e-05,7e-05,9e-05,0.00032,0.00501
+75235,1.0,1e-05,1e-05,0.0003,2e-05,1e-05,0.00461,0.00385,2e-05,0.00055,2e-05,0.00026,1e-05,3e-05,3e-05,0.0001,0.00011,3e-05,1e-05,5e-05,2e-05,0.00026,3e-05,1e-05,8e-05,0.00014,0.00075,1e-05,1e-05,0.0002,0.00043,0.00011,0.00127,0.00017,0.0002,0.00029,0.00061,0.0008,0.00015,0.00012,0.00016,0.00038,0.00242
+75236,1.0,1e-05,1e-05,0.0001,1e-05,1e-05,0.00218,0.00118,2e-05,0.00065,3e-05,0.00041,1e-05,3e-05,3e-05,9e-05,9e-05,4e-05,1e-05,7e-05,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.0006,1e-05,1e-05,0.00021,0.00032,6e-05,0.00043,7e-05,7e-05,9e-05,0.00019,0.00062,7e-05,8e-05,0.0001,0.00037,0.00048
+75237,1.0,2e-05,1e-05,0.00785,2e-05,1e-05,0.31031,0.27854,2e-05,0.01161,3e-05,0.02022,1e-05,4e-05,3e-05,9e-05,0.00024,6e-05,2e-05,9e-05,2e-05,0.00062,7e-05,3e-05,0.00017,0.00035,0.0008,1e-05,1e-05,0.00028,0.00043,8e-05,0.00043,8e-05,7e-05,9e-05,0.0002,0.00044,6e-05,8e-05,9e-05,0.00021,0.14028
+75239,1.0,1e-05,1e-05,7e-05,1e-05,1e-05,0.00105,0.00084,1e-05,0.00015,1e-05,8e-05,1e-05,2e-05,5e-05,4e-05,8e-05,2e-05,1e-05,3e-05,1e-05,0.00016,2e-05,1e-05,5e-05,9e-05,0.00049,0.0,0.0,0.00012,0.00026,0.00011,0.00046,0.00013,8e-05,8e-05,0.00018,0.00044,6e-05,9e-05,0.00011,0.00018,0.00065
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+75243,1.0,1e-05,1e-05,0.00029,1e-05,1e-05,0.00461,0.00415,1e-05,0.00035,1e-05,0.00015,0.0,2e-05,2e-05,5e-05,7e-05,2e-05,1e-05,4e-05,1e-05,0.00018,2e-05,1e-05,4e-05,0.00011,0.00022,1e-05,1e-05,5e-05,0.00011,5e-05,0.0004,6e-05,6e-05,8e-05,0.0002,0.00039,6e-05,6e-05,8e-05,0.00019,0.00265
+75244,1.0,3e-05,2e-05,0.0004,3e-05,2e-05,0.0173,0.01298,4e-05,0.00267,4e-05,0.00174,1e-05,7e-05,9e-05,0.00024,0.00027,8e-05,2e-05,0.00013,3e-05,0.00045,7e-05,2e-05,0.00011,0.00025,0.00077,6e-05,2e-05,0.00021,0.00034,0.00013,0.00077,0.00014,0.00013,0.00026,0.00023,0.00044,7e-05,8e-05,0.0001,0.00019,0.00649
+75248,1.0,1e-05,1e-05,0.00017,1e-05,1e-05,0.00416,0.00305,1e-05,0.0006,2e-05,0.00054,1e-05,2e-05,2e-05,5e-05,7e-05,2e-05,1e-05,4e-05,1e-05,0.00017,2e-05,1e-05,4e-05,0.0001,0.00029,5e-05,1e-05,6e-05,0.00012,6e-05,0.00081,0.00011,0.00015,0.00016,0.00038,0.0005,7e-05,8e-05,0.00012,0.00024,0.00184
+75249,1.0,1e-05,1e-05,0.00011,1e-05,1e-05,0.00192,0.00153,1e-05,0.00025,1e-05,0.00017,1e-05,2e-05,2e-05,5e-05,7e-05,2e-05,1e-05,5e-05,3e-05,0.00014,2e-05,1e-05,3e-05,8e-05,0.00029,1e-05,3e-05,6e-05,0.00012,7e-05,0.00041,6e-05,8e-05,0.0001,0.00017,0.00043,6e-05,7e-05,9e-05,0.00019,0.00092
+75250,1.0,1e-05,1e-05,0.00375,1e-05,1e-05,0.04994,0.047,1e-05,0.00247,1e-05,0.00049,1e-05,2e-05,2e-05,6e-05,7e-05,2e-05,1e-05,3e-05,1e-05,0.00022,4e-05,1e-05,4e-05,0.00012,0.00041,0.0,1e-05,0.00011,0.00023,6e-05,0.00076,0.00012,0.00012,0.00019,0.00032,0.00086,0.00012,0.00013,0.00018,0.00043,0.04178
diff --git a/autosklearn/metalearning/files/precision_samples_multiclass.classification_sparse/feature_runstatus.arff b/autosklearn/metalearning/files/precision_samples_multiclass.classification_sparse/feature_runstatus.arff
index 0034ad2bd6..5fff2dfed3 100644
--- a/autosklearn/metalearning/files/precision_samples_multiclass.classification_sparse/feature_runstatus.arff
+++ b/autosklearn/metalearning/files/precision_samples_multiclass.classification_sparse/feature_runstatus.arff
@@ -4,6 +4,7 @@
 @ATTRIBUTE repetition NUMERIC
 @ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other}
@@ -21,6 +22,11 @@
 @ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other}
@@ -37,162 +43,138 @@
 @ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other}
 
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diff --git a/autosklearn/metalearning/files/precision_samples_multiclass.classification_sparse/feature_values.arff b/autosklearn/metalearning/files/precision_samples_multiclass.classification_sparse/feature_values.arff
index a60400e3ce..736dc64321 100644
--- a/autosklearn/metalearning/files/precision_samples_multiclass.classification_sparse/feature_values.arff
+++ b/autosklearn/metalearning/files/precision_samples_multiclass.classification_sparse/feature_values.arff
@@ -13,7 +13,6 @@
 @ATTRIBUTE KurtosisMean NUMERIC
 @ATTRIBUTE KurtosisMin NUMERIC
 @ATTRIBUTE KurtosisSTD NUMERIC
-@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC
 @ATTRIBUTE LogDatasetRatio NUMERIC
 @ATTRIBUTE LogInverseDatasetRatio NUMERIC
 @ATTRIBUTE LogNumberOfFeatures NUMERIC
@@ -42,160 +41,135 @@
 @ATTRIBUTE SymbolsSum NUMERIC
 
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diff --git a/autosklearn/metalearning/files/precision_weighted_binary.classification_dense/algorithm_runs.arff b/autosklearn/metalearning/files/precision_weighted_binary.classification_dense/algorithm_runs.arff
index b26be3c4ee..96a3893388 100644
--- a/autosklearn/metalearning/files/precision_weighted_binary.classification_dense/algorithm_runs.arff
+++ b/autosklearn/metalearning/files/precision_weighted_binary.classification_dense/algorithm_runs.arff
@@ -7,130 +7,135 @@
 @ATTRIBUTE runstatus {ok, timeout, memout, not_applicable, crash, other}
 
 @DATA
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diff --git a/autosklearn/metalearning/files/precision_weighted_binary.classification_dense/configurations.csv b/autosklearn/metalearning/files/precision_weighted_binary.classification_dense/configurations.csv
index b98e7173f2..bc6e6068cc 100644
--- a/autosklearn/metalearning/files/precision_weighted_binary.classification_dense/configurations.csv
+++ b/autosklearn/metalearning/files/precision_weighted_binary.classification_dense/configurations.csv
@@ -1,128 +1,133 @@
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-1,weighting,adaboost,SAMME,0.015984291290723266,7,184,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010000000000000004,median,standardize,,,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.9333242689646983,False,,,,,,,,,,,,,,,
-2,none,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.9525269450493825,None,0.0,4,20,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,standardize,,,,,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-3,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,101,auto,,1.1004704874013712e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.009420818612537619,most_frequent,normalize,,,,,kitchen_sinks,,,,,,,,,,,,,,,,,,,,,,,,0.1495664841849168,2334,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-4,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,23916.78290516781,False,True,1,squared_hinge,ovr,l2,0.0006655607726658531,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.008808715242823137,median,none,,,,,kernel_pca,,,,,,,,,,,,,,,,,,,0.2633153190620261,2,0.004770332714291669,poly,1483,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-5,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,2,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,none,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,94.23876232993393,mutual_info,,,
-6,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.933900673392165,None,0.0,7,4,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,minmax,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-7,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.3700222208089159,None,0.0,14,19,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,minmax,,,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,1.0533068843554318,False,True,1,squared_hinge,ovr,l1,0.00618591414610778,,,,,,,,,,,,,,,,,,,,,,
-8,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5238814331135578,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,median,none,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,28,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-9,none,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13,None,,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,none,,,,,feature_agglomeration,,,,,,,,,,,,,,,euclidean,average,74,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-10,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.5244313211416313,None,0.0,1,2,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.454300236339658,most_frequent,robust_scaler,,,0.7858830918128734,0.2705174720960696,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-11,weighting,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.3506201797016825,median,minmax,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2449821017072459,fdr,chi2
-12,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.541827838541001,True,True,squared_hinge,0.0001129851063838815,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,standardize,,,,,extra_trees_preproc_for_classification,True,entropy,None,0.5533459595240017,None,0.0,18,4,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-13,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.3652103526812861,None,0.0,6,8,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,quantile_transformer,79,uniform,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.17357047764133562,fwe,chi2
-14,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6185424163558029,False,True,1,squared_hinge,ovr,l2,4.385767395120581e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010397452590017174,median,quantile_transformer,961,normal,,,feature_agglomeration,,,,,,,,,,,,,,,cosine,average,25,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-15,weighting,decision_tree,,,,,,,entropy,0.5508420416465765,1.0,None,0.0,4,15,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0030731686230236793,most_frequent,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,False,,,,,,,,,,,,
-16,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7947177310891914,None,0.0,13,3,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,mean,robust_scaler,,,0.7681430822715739,0.23411421138225857,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,16.747604337291705,f_classif,,,
-17,none,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.1769332447629957,mean,minmax,,,,,fast_ica,,,,,,,,,,,deflation,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-18,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.07518858116896743,None,0.0,7,10,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0027133920607872518,mean,robust_scaler,,,0.7873452530915046,0.16215139072179952,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-19,none,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,200,None,,0.0017457216340699586,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,none,,,,,kernel_pca,,,,,,,,,,,,,,,,,,,0.0,4,0.0007494648939847078,poly,1996,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-20,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.6039096182414764,None,0.0,2,20,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.1939609823704447,most_frequent,robust_scaler,,,0.7331327008874781,0.29747488162877694,extra_trees_preproc_for_classification,True,entropy,None,0.33401603690569853,None,0.0,4,4,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-21,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.8054434875626825,None,0.0,1,20,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,median,standardize,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4241048505416849,fpr,f_classif
-22,weighting,adaboost,SAMME,0.2353770068926199,1,62,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.007159634821655197,most_frequent,quantile_transformer,1616,normal,,,feature_agglomeration,,,,,,,,,,,,,,,cosine,average,151,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-23,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7734.521233796252,,,2.6048661611376747,rbf,-1,True,4.68717607435371e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,none,,,,,feature_agglomeration,,,,,,,,,,,,,,,euclidean,average,25,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-24,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.21794354428393548,None,0.0,2,16,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0025451910134387575,median,quantile_transformer,1477,uniform,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
-25,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5010952533165973,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.005838375247601407,mean,normalize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,
-26,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5185406559495093,None,0.0,3,17,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.010000000000000004,median,normalize,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.13046020673147232,fpr,chi2
-27,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.4426097579931897,None,0.0,16,4,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,mean,robust_scaler,,,0.7407323456920554,0.0804756921037867,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,
-28,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5607168729746175,None,0.0,2,3,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.009810589644392154,most_frequent,quantile_transformer,1132,normal,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.06211089008167909,fdr,chi2
-29,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.992156991246829,None,0.0,19,3,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.007016879673679419,median,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,
-30,none,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.9728532763078892,None,0.0,13,9,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,none,,,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6651071190864879,False,,,,,,,,,,,,,,,
-31,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9561075301148197,None,0.0,6,2,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,
-32,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.557321265077371,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-33,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,1.153931482107762e-10,0.011666190464009397,auto,255,None,29,190,18,loss,1e-07,0.147868307124396,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,
-34,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,22194.242946902177,0.45210849669021247,,0.0005669713702171556,sigmoid,-1,False,0.03542142266081451,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,quantile_transformer,291,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-35,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.8189454407806317,None,0.0,10,10,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,robust_scaler,,,0.7192027795318285,0.24912062393096632,fast_ica,,,,,,,,,,,parallel,logcosh,100,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-36,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,3.6797768538445263e-09,0.05511004217375807,auto,255,None,72,62,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,robust_scaler,,,0.7241481576763391,0.0501033899901458,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,36,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-37,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.7521969592385672,None,0.0,9,5,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.020402264943872365,median,robust_scaler,,,0.737227213630259,0.20752747663727789,fast_ica,,,,,,,,,,,parallel,exp,312,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-38,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.756167892711991e-05,False,True,squared_hinge,5.235791933868702e-05,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0001632172987320906,most_frequent,standardize,,,,,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,5,None,19,12,1.0,70,,,,,
-39,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,1.6343482806592892e-09,0.19537524572679268,auto,255,None,32,29,13,loss,1e-07,0.05061738224302791,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-40,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7088164344533493,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,mean,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,
-41,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,1.943186107984505e-07,0.08852455702036988,auto,255,None,5,72,19,loss,1e-07,0.039837460762271175,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.01591265419367627,median,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,
-42,weighting,adaboost,SAMME,0.5210651042094924,10,401,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,standardize,,,,,feature_agglomeration,,,,,,,,,,,,,,,euclidean,complete,89,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-43,none,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0686374607394679,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,minmax,,,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.8490260132946401,False,,,,,,,,,,,,,,,
-44,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.6931271368243409,None,0.0,11,10,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.362940004438799,most_frequent,robust_scaler,,,0.9177587796305063,0.17084038458597867,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,30.491514144623952,mutual_info,,,
-45,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.9398869869697849,None,0.0,20,15,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,normalize,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,269,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-46,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,92.66144306787051,,,0.04654639302091653,rbf,-1,True,0.0022231544280610005,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.005194838694455963,mean,none,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,15,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-47,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2681.998807317996,-0.9558745731731211,,0.0011299948421773457,sigmoid,-1,False,1.5292507354478086e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-48,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.8095522998548572,None,0.0,17,10,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.031940595071539304,mean,quantile_transformer,1563,normal,,,extra_trees_preproc_for_classification,False,entropy,None,0.9330145598603196,None,0.0,18,10,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-49,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,26.502259985828186,,,9.475662388405154e-05,rbf,-1,False,0.00019690635968217866,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-50,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,2.5360196477156387e-07,0.011018756408558218,auto,255,None,40,122,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.00011749164080669445,most_frequent,quantile_transformer,757,uniform,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,5.097834697156853,False,True,1,squared_hinge,ovr,l1,0.053877834919477424,,,,,,,,,,,,,,,,,,,,,,
-51,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5255991794296913,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-52,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.34697723213923637,None,0.0,15,18,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.1923594385988973,mean,normalize,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,25.77499302945827,chi2,,,
-53,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,23529.225652845635,0.30829978977649053,2,0.0008220498708157514,poly,-1,False,0.0005532556252982188,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.07570228092651136,mean,quantile_transformer,1143,normal,,,fast_ica,,,,,,,,,,,parallel,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-54,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,1.488333818193668e-10,0.09526676471267805,auto,255,None,258,87,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,normalize,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.3561953264350478,fpr,f_classif
-55,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.9523435078770708,0.8748383232154038,4,0.2195992292488049,poly,-1,True,4.849685143365339e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,minmax,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-56,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.7610308078756552,None,0.0,1,2,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.010000000000000004,mean,robust_scaler,,,0.9205545465103644,0.20272761060743316,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-57,weighting,adaboost,SAMME.R,0.03127273017917347,10,496,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,standardize,,,,,fast_ica,,,,,,,,,,,deflation,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-58,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,151,None,,0.0003283777607180646,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.2510527431514227,mean,normalize,,,,,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,-0.96093182232312,,0.05762510964607661,sigmoid,2632,,,,,,,,,,,,,,,,,
-59,none,bernoulli_nb,,,,,0.6524090653939782,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.005164169126808402,most_frequent,quantile_transformer,16,normal,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.033082145443631535,False,True,1,squared_hinge,ovr,l1,0.002615020898325393,,,,,,,,,,,,,,,,,,,,,,
-60,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.6453555269772124,None,0.0,1,4,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,normalize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-61,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,940.9437776449834,,,1.5839338084625942,rbf,-1,False,0.023072781592565438,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,none,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,77.25763685070707,mutual_info,,,
-62,weighting,extra_trees,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,13,18,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0001574994528707389,most_frequent,standardize,,,,,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,3,None,19,19,1.0,77,,,,,
-63,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.34534888916828577,0.02513634776687268,2,1.0733091880109409,poly,-1,True,0.0006974263047918307,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,quantile_transformer,1000,normal,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,77.99934218022861,mutual_info,,,
-64,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,29.55731828912983,False,True,1,squared_hinge,ovr,l2,0.0011332486951954968,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,
-65,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,1.1387795876755026e-06,0.12245605190343567,auto,255,None,58,7,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,robust_scaler,,,0.79305915648885,0.1841487406876042,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-66,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.8425560317492612,None,0.0,8,15,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.015276988502366128,median,none,,,,,fast_ica,,,,,,,,,,,parallel,cube,17,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-67,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,0.2638627266631576,0.022360820165005443,auto,255,None,171,6,19,loss,1e-07,0.016209060012924756,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.008763072395677399,mean,standardize,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,78,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-68,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,925.3053743248586,False,True,1,squared_hinge,ovr,l2,0.02764481198395717,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.3850718018366796,most_frequent,quantile_transformer,786,uniform,,,feature_agglomeration,,,,,,,,,,,,,,,cosine,average,156,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-69,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7573078019937971,None,0.0,2,5,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,robust_scaler,,,0.7265312295801113,0.28332515882236425,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.09660564590146253,fpr,f_classif
-70,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0057657098053164255,False,True,squared_hinge,0.013468519636061941,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.484357187898904,mean,quantile_transformer,675,normal,,,kernel_pca,,,,,,,,,,,,,,,,,,,,,,cosine,1876,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-71,none,adaboost,SAMME.R,0.22802213935525836,6,284,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,robust_scaler,,,0.7468645381288924,0.22838821662367073,feature_agglomeration,,,,,,,,,,,,,,,cosine,average,128,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-72,none,adaboost,SAMME,0.1280458968526125,7,125,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,robust_scaler,,,0.7156460334356771,0.2981902862926971,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,65.16171885308417,chi2,,,
-73,none,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9931779675565967,None,0.0,18,17,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,robust_scaler,,,0.75,0.2945917010472818,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,
-74,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.08646118576686276,None,0.0,4,9,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,most_frequent,quantile_transformer,959,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-75,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,1.2430504242164082e-09,0.03359568307446533,auto,255,None,1609,29,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,standardize,,,,,feature_agglomeration,,,,,,,,,,,,,,,cosine,complete,40,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-76,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7.491039171224502,True,True,squared_hinge,2.8058138048327884e-05,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,minmax,,,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.7754372809893839,False,True,1,squared_hinge,ovr,l1,3.9643594596766314e-05,,,,,,,,,,,,,,,,,,,,,,
-77,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.34936025988397124,None,0.0,2,13,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00018601309647406987,median,robust_scaler,,,0.8634309348843472,0.26976989465367607,extra_trees_preproc_for_classification,False,entropy,None,0.48025175288975036,None,0.0,6,17,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-78,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,0.011547155223520492,0.13665554140837183,auto,255,None,13,5,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.1824584492028449,median,minmax,,,,,feature_agglomeration,,,,,,,,,,,,,,,euclidean,average,261,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-79,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.7308100742740112,None,0.0,18,19,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.014637629296743653,most_frequent,robust_scaler,,,0.7724670628916575,0.2233445340332948,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,29.07440444752882,False,True,1,squared_hinge,ovr,l1,0.00040479645225962257,,,,,,,,,,,,,,,,,,,,,,
-80,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1661.2274747409913,,,0.025821208852387936,rbf,-1,False,0.0017011435104921824,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,none,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,306,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-81,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0016941428709087083,True,,,True,,optimal,squared_hinge,l1,,5.124265599423001e-05,no_encoding,no_coalescense,,most_frequent,quantile_transformer,461,uniform,,,extra_trees_preproc_for_classification,True,gini,None,0.31371431180257103,None,0.0,5,17,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-82,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,21.85207494001697,0.18872999589786676,4,1.1863646021868761,poly,-1,False,0.0021338371901294443,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0034080852834770217,mean,normalize,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.11326005109886934,fdr,f_classif
-83,weighting,adaboost,SAMME,0.38341570193537416,9,250,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,minmax,,,,,feature_agglomeration,,,,,,,,,,,,,,,euclidean,average,13,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-84,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,0.037849720944694,0.1268815770019783,auto,255,None,11,99,11,loss,1e-07,0.10654523627313527,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,
-85,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.593728171200453,None,0.0,7,20,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.02830941310359363,mean,normalize,,,,,extra_trees_preproc_for_classification,False,entropy,None,0.7389446586281172,None,0.0,20,11,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-86,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,1,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.019797877010737826,mean,normalize,,,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.8239678741128165,False,,,,,,,,,,,,,,,
-87,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.46057831591617715,False,True,hinge,0.04557857428827514,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00027457445401600137,median,standardize,,,,,extra_trees_preproc_for_classification,True,gini,None,0.48190346970486964,None,0.0,17,18,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-88,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,38,auto,,0.00019398431171606024,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010000000000000004,median,quantile_transformer,1440,normal,,,extra_trees_preproc_for_classification,True,gini,None,0.5126639956845847,None,0.0,16,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-89,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5722917944959481,None,0.0,1,14,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,
-90,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.26356754187780895,None,0.0,4,3,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010966178401758774,mean,robust_scaler,,,0.7817043861798886,0.24371720112407158,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
-91,weighting,adaboost,SAMME.R,0.13547552520095074,4,62,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.011851011202809002,most_frequent,standardize,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4888776875314595,fpr,chi2
-92,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,80.0028902468169,False,True,1,squared_hinge,ovr,l2,0.0024481999829574722,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.15047970487774687,mean,quantile_transformer,1130,normal,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,1.1912646635544712,False,True,1,squared_hinge,ovr,l1,0.00014314728486450857,,,,,,,,,,,,,,,,,,,,,,
-93,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.22879311080499845,None,0.0,8,4,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.014320022547615783,median,minmax,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.238630703440461,mutual_info,,,
-94,weighting,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,2,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,none,,,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6342574285315175,False,,,,,,,,,,,,,,,
-95,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.107967527639527e-05,True,True,hinge,0.00448294745544775,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,quantile_transformer,953,normal,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,35.00114345405331,chi2,,,
-96,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.14148638702297855,None,0.0,2,6,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.0038499650338378643,most_frequent,quantile_transformer,1759,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-97,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,1.1522414705935405e-10,0.6132800183073315,auto,255,None,1607,67,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010096911249562407,mean,robust_scaler,,,0.7663493326967942,0.136094217337434,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-98,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.35863733679484333,None,0.0,1,11,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.0009777923655842618,median,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-99,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.479405900046494,,,0.013340778632154543,rbf,-1,False,6.913586060890114e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,robust_scaler,,,0.9953232616818098,0.06450577886132274,fast_ica,,,,,,,,,,,deflation,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-100,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.825844582229201,None,0.0,12,9,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0008422508222149647,median,robust_scaler,,,0.8658803880857064,0.2528423686293341,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-101,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2574971638664341,True,True,hinge,1.8027411099296446e-05,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.00536396328125103,median,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,
-102,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.7033210835501171,None,0.0,3,19,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,robust_scaler,,,0.7699195245692886,0.29258311638476237,feature_agglomeration,,,,,,,,,,,,,,,euclidean,ward,143,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-103,none,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,128,manual,0.8574560771111307,0.010886139381599514,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.24115007944216788,most_frequent,normalize,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,62.26514170991452,mutual_info,,,
-104,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.6282663389679584,None,0.0,1,3,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,median,minmax,,,,,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-105,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,1.430083734915855e-09,0.08779621406981941,auto,255,None,17,20,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.29749837779115257,mean,robust_scaler,,,0.7236445277814435,0.25,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
-106,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.3294588572341214,None,0.0,1,3,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
-107,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,11.046854203258679,,,0.07809571976769347,rbf,-1,False,0.00036193159909053734,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.006445557635410205,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-108,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.6728827282646637,None,0.0,18,8,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,robust_scaler,,,0.8512985751386942,0.14092824896182807,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,
-109,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.18060139887649052,None,0.0,10,12,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.010000000000000004,most_frequent,robust_scaler,,,0.9298612094255864,0.1263597946722308,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
-110,none,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.563056219822946,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.32793677336996485,most_frequent,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-111,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6427543483503397,True,True,hinge,0.00045318775930916944,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.009338484332306904,median,quantile_transformer,174,normal,,,kernel_pca,,,,,,,,,,,,,,,,,,,,,,cosine,1848,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-112,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.04981080944617379,True,,0.07309386939525211,True,,constant,log,l2,,0.0006721243213777075,no_encoding,no_coalescense,,mean,quantile_transformer,574,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-113,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,9.029473608568458e-09,0.15779990576964315,auto,255,None,37,71,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.018093177195644097,mean,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,False,,,,,,,,,,,,
-114,weighting,adaboost,SAMME.R,0.10000000000000002,3,50,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,robust_scaler,,,0.7639429738274788,0.25,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,67.72501979095493,f_classif,,,
-115,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.129143822880219,True,True,hinge,0.00012612093569084375,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0016222546082911887,most_frequent,quantile_transformer,1309,uniform,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8.816417526015055,f_classif,,,
-116,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.4864284879550203,None,0.0,1,4,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.008665854799630503,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-117,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.8851946632713493,None,0.0,2,20,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.006095662608922393,median,quantile_transformer,1861,normal,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.10486328449423876,fdr,f_classif
-118,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.6922752267318475,True,True,squared_hinge,0.06299408576730353,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,quantile_transformer,1742,uniform,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,107,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-119,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.44727252219812963,None,0.0,2,10,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,standardize,,,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l1,8.17533189014765e-05,,,,,,,,,,,,,,,,,,,,,,
-120,weighting,extra_trees,,,,,,,,,,,,,,,True,gini,None,0.9999521514529074,None,0.0,3,10,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,minmax,,,,,extra_trees_preproc_for_classification,False,gini,None,0.24745225001074223,None,0.0,14,18,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-121,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.4210978663047845,None,0.0,5,6,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.006365988550833196,most_frequent,robust_scaler,,,0.8348891074186082,0.00974961635730845,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0695919708890599,fpr,f_classif
-122,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4424195769042466,False,True,hinge,0.02490432909709204,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,mean,quantile_transformer,1000,uniform,,,kernel_pca,,,,,,,,,,,,,,,,,,,,,,cosine,769,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-123,weighting,decision_tree,,,,,,,entropy,0.04461432844342683,1.0,None,0.0,9,17,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0011300271286478988,mean,minmax,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.43863282379418345,,mutual_info_classif
-124,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1561.1002600303584,,,0.0024048551704194507,rbf,-1,False,0.0001936214667496851,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,minmax,,,,,fast_ica,,,,,,,,,,,parallel,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-125,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.087313242780732,0.0636709914987994,3,0.004481917125024585,poly,-1,False,0.01848880618941367,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.002136015035740733,mean,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-126,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.03204653439894958,True,True,squared_hinge,0.00013006393401112688,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.013506575694382031,most_frequent,normalize,,,,,feature_agglomeration,,,,,,,,,,,,,,,euclidean,ward,356,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-127,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.6466927589470762,None,0.0,11,2,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.009672465311659264,most_frequent,standardize,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,369,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+1,none,decision_tree,,,,,,,gini,0.7229490531192699,1.0,None,0.0,1,18,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.00013586681143040562,median,quantile_transformer,647,uniform,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+4,none,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,quantile_transformer,111,uniform,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4748887602127273,fdr,f_classif
+6,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.7283683762613216,None,0.0,12,20,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,median,standardize,,,,,fast_ica,,,,,,,,,,,deflation,exp,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+8,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,114,manual,0.11778631707605758,0.0029112721339713465,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.015582195257240751,median,normalize,,,,,kernel_pca,,,,,,,,,,,,,,,,,,,0.42909623253327767,3,0.25407612000343777,poly,1689,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+11,weighting,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,2,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.3865962574104293,mean,normalize,,,,,fast_ica,,,,,,,,,,,deflation,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+13,weighting,adaboost,SAMME,0.17983222024694556,4,357,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0008898781197407956,mean,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+15,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.21772092350860472,None,0.0,14,17,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.0020488212891693275,median,standardize,,,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,33.74257606530722,False,True,1,squared_hinge,ovr,l1,8.740452966205111e-05,,,,,,,,,,,,,,,,,,,,,,
+18,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,2.4243922131268786e-05,0.07570328162448307,auto,255,None,71,42,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,robust_scaler,,,0.8898729094898769,0.12138152991327475,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,94.96086286583851,chi2,,,
+20,none,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.9866058571747695,None,0.0,16,9,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.1680676743605393,most_frequent,robust_scaler,,,0.8840072797399463,0.08535534311393947,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,False,,,,,,,,,,,,
+23,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,0.00022373024203887573,0.027149343206135476,auto,255,None,49,13,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.00777614320416325,most_frequent,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,
+28,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,1.0168466808008568e-09,0.21361657232950118,auto,255,None,463,4,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,robust_scaler,,,0.7965217268158734,0.2446550610908456,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,
+31,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,8.757412830636845e-06,0.10095376320667454,auto,255,None,3,2,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.015401766535365336,median,quantile_transformer,647,normal,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,
+32,none,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,174,manual,0.21501892897746921,2.0347110826262054e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.013754893527625522,most_frequent,normalize,,,,,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,-0.019221513778605592,3,0.10190948951339239,poly,2521,,,,,,,,,,,,,,,,,
+35,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,166,auto,,4.5836048743081545e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,standardize,,,,,kernel_pca,,,,,,,,,,,,,,,,,,,,,0.00805519593731235,rbf,573,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+38,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9025597189684735,None,0.0,18,8,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.007536467725572231,most_frequent,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
+39,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.6799215694343422,None,0.0,1,5,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.004724310659953132,most_frequent,quantile_transformer,1037,normal,,,fast_ica,,,,,,,,,,,parallel,exp,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+41,none,extra_trees,,,,,,,,,,,,,,,True,gini,None,0.5033866291997137,None,0.0,2,14,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,minmax,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+43,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.9479098792950027,None,0.0,12,14,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,quantile_transformer,1758,normal,,,feature_agglomeration,,,,,,,,,,,,,,,cosine,complete,244,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+44,none,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l2,2.858526169098402e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0007827505640681123,median,standardize,,,,,extra_trees_preproc_for_classification,False,entropy,None,0.3443310459451119,None,0.0,3,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+48,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.32752246015541675,None,0.0,16,9,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,quantile_transformer,794,normal,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,
+49,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.049504283446131e-05,False,,0.0004438773852753879,True,2.4110816673432424e-07,constant,log,elasticnet,,0.01321814853186568,one_hot_encoding,minority_coalescer,0.01510416593631535,most_frequent,none,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,66.65285268958851,chi2,,,
+57,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.24407113189971e-05,True,,,True,,optimal,hinge,l2,,0.018411607920920666,one_hot_encoding,minority_coalescer,0.006594668208397739,mean,standardize,,,,,extra_trees_preproc_for_classification,True,entropy,None,0.20369732810864838,None,0.0,16,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+58,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5214197490559657,None,0.0,1,2,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.00826182798588248,mean,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,
+63,none,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,130,manual,0.5446276346297756,0.00846307015171434,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.005736348762561667,median,quantile_transformer,973,uniform,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.08483615095863663,fdr,chi2
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+446,weighting,bernoulli_nb,,,,,2.493274808167669,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.16300567981899747,mean,standardize,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,17.02935019079249,mutual_info,,,
+447,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.3431705351883592,None,0.0,4,16,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.09444471061261189,most_frequent,normalize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+449,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,2.074711735072034e-07,0.01106799034691714,auto,255,None,13,24,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.020949205931423413,mean,standardize,,,,,extra_trees_preproc_for_classification,True,gini,None,0.43505400875624267,None,0.0,20,8,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+453,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,162.34906836693145,,,2.470518094983172,rbf,-1,False,1.5366632541084908e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.33912551828528803,median,none,,,,,fast_ica,,,,,,,,,,,deflation,logcosh,412,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+458,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,5.581658271288433e-10,0.06171988234311011,auto,255,None,25,6,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.014859405637537319,most_frequent,robust_scaler,,,0.7983900845251328,0.2545046978458648,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,1.380085213170737,False,True,1,squared_hinge,ovr,l1,0.0061450732015354784,,,,,,,,,,,,,,,,,,,,,,
+462,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6,1,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.017471096998391875,median,none,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,67.7801145492284,f_classif,,,
diff --git a/autosklearn/metalearning/files/precision_weighted_binary.classification_dense/description.txt b/autosklearn/metalearning/files/precision_weighted_binary.classification_dense/description.txt
index 157f78a702..4d84d2a080 100644
--- a/autosklearn/metalearning/files/precision_weighted_binary.classification_dense/description.txt
+++ b/autosklearn/metalearning/files/precision_weighted_binary.classification_dense/description.txt
@@ -1,8 +1,9 @@
 features_cutoff_time: 3600
 features_cutoff_memory: 3072
-number_of_feature_steps: 35
+number_of_feature_steps: 42
 feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances
 feature_step LogNumberOfInstances: LogNumberOfInstances
+feature_step NumberOfClasses: NumberOfClasses
 feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures
 feature_step LogNumberOfFeatures: LogNumberOfFeatures
 feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues
@@ -20,6 +21,11 @@ feature_step DatasetRatio: DatasetRatio, LogDatasetRatio
 feature_step LogDatasetRatio: LogDatasetRatio
 feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio
 feature_step LogInverseDatasetRatio: LogInverseDatasetRatio
+feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD
+feature_step ClassProbabilityMin: ClassProbabilityMin
+feature_step ClassProbabilityMax: ClassProbabilityMax
+feature_step ClassProbabilityMean: ClassProbabilityMean
+feature_step ClassProbabilitySTD: ClassProbabilitySTD
 feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum
 feature_step SymbolsMin: SymbolsMin
 feature_step SymbolsMax: SymbolsMax
@@ -36,11 +42,12 @@ feature_step SkewnessMin: SkewnessMin
 feature_step SkewnessMax: SkewnessMax
 feature_step SkewnessMean: SkewnessMean
 feature_step SkewnessSTD: SkewnessSTD
-features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
+feature_step ClassEntropy: ClassEntropy
+features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy
 features_stochastic: 
-default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
+default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy
 
-algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127
+algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235,236,237,238,239,240,241,242,243,244,245,246,247,248,249,250,251,252,253,254,255,256,257,258,259,260,261,262,263,264,265,266,267,268,269,270,271,272,273,274,275,276,277,278,279,280,281,282,283,284,285,286,287,288,289,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,312,313,314,315,316,317,318,319,320,321,322,323,324,325,326,327,328,329,330,331,332,333,334,335,336,337,338,339,340,341,342,343,344,345,346,347,348,349,350,351,352,353,354,355,356,357,358,359,360,361,362,363,364,365,366,367,368,369,370,371,372,373,374,375,376,377,378,379,380,381,382,383,384,385,386,387,388,389,390,391,392,393,394,395,396,397,398,399,400,401,402,403,404,405,406,407,408,409,410,411,412,413,414,415,416,417,418,419,420,421,422,423,424,425,426,427,428,429,430,431,432,433,434,435,436,437,438,439,440,441,442,443,444,445,446,447,448,449,450,451,452,453,454,455,456,457,458,459,460,461,462,463,464
 algorithms_stochastic: 
 performance_measures: precision_weighted
 performance_type: solution_quality
diff --git a/autosklearn/metalearning/files/precision_weighted_binary.classification_dense/feature_costs.arff b/autosklearn/metalearning/files/precision_weighted_binary.classification_dense/feature_costs.arff
index 0e17e20d22..8512fd9275 100644
--- a/autosklearn/metalearning/files/precision_weighted_binary.classification_dense/feature_costs.arff
+++ b/autosklearn/metalearning/files/precision_weighted_binary.classification_dense/feature_costs.arff
@@ -4,6 +4,7 @@
 @ATTRIBUTE repetition NUMERIC
 @ATTRIBUTE NumberOfInstances NUMERIC
 @ATTRIBUTE LogNumberOfInstances NUMERIC
+@ATTRIBUTE NumberOfClasses NUMERIC
 @ATTRIBUTE NumberOfFeatures NUMERIC
 @ATTRIBUTE LogNumberOfFeatures NUMERIC
 @ATTRIBUTE MissingValues NUMERIC
@@ -21,6 +22,11 @@
 @ATTRIBUTE LogDatasetRatio NUMERIC
 @ATTRIBUTE InverseDatasetRatio NUMERIC
 @ATTRIBUTE LogInverseDatasetRatio NUMERIC
+@ATTRIBUTE ClassOccurences NUMERIC
+@ATTRIBUTE ClassProbabilityMin NUMERIC
+@ATTRIBUTE ClassProbabilityMax NUMERIC
+@ATTRIBUTE ClassProbabilityMean NUMERIC
+@ATTRIBUTE ClassProbabilitySTD NUMERIC
 @ATTRIBUTE NumSymbols NUMERIC
 @ATTRIBUTE SymbolsMin NUMERIC
 @ATTRIBUTE SymbolsMax NUMERIC
@@ -37,162 +43,138 @@
 @ATTRIBUTE SkewnessMax NUMERIC
 @ATTRIBUTE SkewnessMean NUMERIC
 @ATTRIBUTE SkewnessSTD NUMERIC
+@ATTRIBUTE ClassEntropy NUMERIC
 
 @DATA
-75123,1.0,1e-05,1e-05,1e-05,1e-05,0.00253,0.00221,1e-05,0.00024,3e-05,0.00011,0.0,2e-05,2e-05,5e-05,8e-05,2e-05,1e-05,7e-05,1e-05,0.00072,1e-05,1e-05,0.00019,0.00041,0.00011,0.00061,0.00012,9e-05,0.00015,0.00025,0.00067,0.00014,0.00011,0.00016,0.00027
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diff --git a/autosklearn/metalearning/files/precision_weighted_binary.classification_dense/feature_runstatus.arff b/autosklearn/metalearning/files/precision_weighted_binary.classification_dense/feature_runstatus.arff
index 0034ad2bd6..5fff2dfed3 100644
--- a/autosklearn/metalearning/files/precision_weighted_binary.classification_dense/feature_runstatus.arff
+++ b/autosklearn/metalearning/files/precision_weighted_binary.classification_dense/feature_runstatus.arff
@@ -4,6 +4,7 @@
 @ATTRIBUTE repetition NUMERIC
 @ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other}
@@ -21,6 +22,11 @@
 @ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other}
@@ -37,162 +43,138 @@
 @ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other}
 
 @DATA
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diff --git a/autosklearn/metalearning/files/precision_weighted_binary.classification_dense/feature_values.arff b/autosklearn/metalearning/files/precision_weighted_binary.classification_dense/feature_values.arff
index a60400e3ce..736dc64321 100644
--- a/autosklearn/metalearning/files/precision_weighted_binary.classification_dense/feature_values.arff
+++ b/autosklearn/metalearning/files/precision_weighted_binary.classification_dense/feature_values.arff
@@ -13,7 +13,6 @@
 @ATTRIBUTE KurtosisMean NUMERIC
 @ATTRIBUTE KurtosisMin NUMERIC
 @ATTRIBUTE KurtosisSTD NUMERIC
-@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC
 @ATTRIBUTE LogDatasetRatio NUMERIC
 @ATTRIBUTE LogInverseDatasetRatio NUMERIC
 @ATTRIBUTE LogNumberOfFeatures NUMERIC
@@ -42,160 +41,135 @@
 @ATTRIBUTE SymbolsSum NUMERIC
 
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diff --git a/autosklearn/metalearning/files/precision_weighted_binary.classification_sparse/algorithm_runs.arff b/autosklearn/metalearning/files/precision_weighted_binary.classification_sparse/algorithm_runs.arff
index b72eaae68e..c59d5e88ed 100644
--- a/autosklearn/metalearning/files/precision_weighted_binary.classification_sparse/algorithm_runs.arff
+++ b/autosklearn/metalearning/files/precision_weighted_binary.classification_sparse/algorithm_runs.arff
@@ -7,95 +7,133 @@
 @ATTRIBUTE runstatus {ok, timeout, memout, not_applicable, crash, other}
 
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diff --git a/autosklearn/metalearning/files/precision_weighted_binary.classification_sparse/configurations.csv b/autosklearn/metalearning/files/precision_weighted_binary.classification_sparse/configurations.csv
index ac395001fd..c479556821 100644
--- a/autosklearn/metalearning/files/precision_weighted_binary.classification_sparse/configurations.csv
+++ b/autosklearn/metalearning/files/precision_weighted_binary.classification_sparse/configurations.csv
@@ -1,93 +1,131 @@
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-90,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.638576424400212,False,True,1,squared_hinge,ovr,l2,0.00032665837689080493,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,quantile_transformer,1109,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-91,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.087313242780732,0.0636709914987994,3,0.004481917125024585,poly,-1,False,0.01848880618941367,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.002136015035740733,mean,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-92,weighting,adaboost,SAMME,0.029932847627447023,8,483,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,quantile_transformer,1290,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+1,none,decision_tree,,,,,,,gini,0.7229490531192699,1.0,None,0.0,1,18,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.00013586681143040562,median,quantile_transformer,647,uniform,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+4,none,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.2102248190734206e-05,True,,0.00024104571597220975,True,,constant,hinge,l2,,0.0033891595934872373,no_encoding,minority_coalescer,0.0035621424750633857,mean,standardize,,,,,extra_trees_preproc_for_classification,False,entropy,None,0.9207074443402747,None,0.0,11,9,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+5,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.36028513793687317,None,0.0,19,8,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,standardize,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,48.144408204553606,chi2,,,,
+6,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,114,manual,0.11778631707605758,0.0029112721339713465,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.015582195257240751,median,normalize,,,,,kernel_pca,,,,,,,,,,,0.42909623253327767,3,0.25407612000343777,poly,1689,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+7,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.6701903561065912,None,0.0,2,17,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.006153260190695941,mean,normalize,,,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.6714427490708808,False,True,1,squared_hinge,ovr,l1,0.00015984160549086055,,,,,,,,,,,,,,,,,,,,,
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+11,weighting,adaboost,SAMME,0.17983222024694556,4,357,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0008898781197407956,mean,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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+17,none,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.9866058571747695,None,0.0,16,9,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.1680676743605393,most_frequent,robust_scaler,,,0.8840072797399463,0.08535534311393947,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,False,,,,,,,,,,,,,
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+29,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,166,auto,,4.5836048743081545e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,standardize,,,,,kernel_pca,,,,,,,,,,,,,0.00805519593731235,rbf,573,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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+35,none,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l2,2.858526169098402e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0007827505640681123,median,standardize,,,,,extra_trees_preproc_for_classification,False,entropy,None,0.3443310459451119,None,0.0,3,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+39,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.32752246015541675,None,0.0,16,9,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,quantile_transformer,794,normal,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,,
+40,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.049504283446131e-05,False,,0.0004438773852753879,True,2.4110816673432424e-07,constant,log,elasticnet,,0.01321814853186568,one_hot_encoding,minority_coalescer,0.01510416593631535,most_frequent,none,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,66.65285268958851,chi2,,,,
+48,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.24407113189971e-05,True,,,True,,optimal,hinge,l2,,0.018411607920920666,one_hot_encoding,minority_coalescer,0.006594668208397739,mean,standardize,,,,,extra_trees_preproc_for_classification,True,entropy,None,0.20369732810864838,None,0.0,16,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+49,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5214197490559657,None,0.0,1,2,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.00826182798588248,mean,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,,
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+56,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.5480575602293952,None,0.0,3,4,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.13872434801649672,median,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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+80,weighting,adaboost,SAMME,0.17983222024694556,4,357,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0008898781197407956,mean,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+82,weighting,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4,2,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,mean,normalize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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+287,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.3850609070926397,None,0.0,1,2,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.013992309764275707,median,normalize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,,
+288,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.5719833791553314,None,0.0,16,3,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.13804007452389966,mean,normalize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+292,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.0479982468066316,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,quantile_transformer,1097,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+293,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7040496889211508,None,0.0,17,19,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.016450784709133564,mean,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+300,none,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.7010137052424128,None,0.0,7,10,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.2761813291805052,most_frequent,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,,
+306,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2677516008848513,True,True,hinge,0.05752557410901496,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0010152857217605206,median,quantile_transformer,1000,uniform,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,51.60974796845029,chi2,,,,
+307,none,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,249,None,,0.0005516162390755682,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010000000000000004,mean,quantile_transformer,1350,uniform,,,kernel_pca,,,,,,,,,,,0.162692683181769,3,1.0,poly,487,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+312,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,60.6761602925822,,,0.038224035513907226,rbf,-1,False,0.0018214343969776353,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.421025538999516,median,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+313,weighting,adaboost,SAMME,1.2423888874490505,5,284,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.01582563952987033,most_frequent,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+316,weighting,adaboost,SAMME,1.2615483379313783,8,190,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.00017789451703497617,mean,robust_scaler,,,0.7345811776810758,0.054715769043996304,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+318,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.843098530645432,None,0.0,1,4,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,mean,quantile_transformer,930,normal,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,,
+321,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.5864398404787432,None,0.0,1,14,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0070663750850608895,most_frequent,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,,
+326,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0001329242404968632,False,True,squared_hinge,0.000416219546803132,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.007568009633214092,median,quantile_transformer,1126,normal,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,29.06367291960815,chi2,,,,
+329,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.426289271149965,False,True,1,squared_hinge,ovr,l2,0.00012788535194515696,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,normalize,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,51.45463995444033,chi2,,,,
+331,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.5,None,0.0,1,5,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.15244281596303316,mean,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+332,none,adaboost,SAMME,0.22805500898886877,8,172,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.019649593891454008,mean,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+336,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5501.252565062252,False,True,1,squared_hinge,ovr,l2,0.0006688304268563973,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.40704734431738154,most_frequent,standardize,,,,,extra_trees_preproc_for_classification,False,gini,None,0.08936669006295328,None,0.0,16,8,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+339,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.36374112587320206,None,0.0,2,10,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010870084126959173,most_frequent,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,,
+344,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.9039555800017817e-05,False,True,squared_hinge,0.0011536981768375681,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,quantile_transformer,1122,uniform,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,,
+345,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5321814610661173,None,0.0,1,3,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,standardize,,,,,extra_trees_preproc_for_classification,True,gini,None,0.5,None,0.0,1,15,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+350,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.44903177399085925,None,0.0,2,17,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,mean,robust_scaler,,,0.7812408516672696,0.12791233577400205,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,,
+352,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5988524108452234,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,mean,robust_scaler,,,0.75,0.26961884796358077,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+355,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,68.60833575985336,,,0.03905068757955671,rbf,-1,False,0.02846559747114773,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.04775815990561371,mean,standardize,,,,,extra_trees_preproc_for_classification,False,entropy,None,0.45727987956076366,None,0.0,13,11,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+356,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.01400651528258823,True,True,hinge,1.929818740575615e-05,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,normalize,,,,,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,5,None,3,19,1.0,78,,,,,,
+357,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.3431705351883592,None,0.0,4,16,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.09444471061261189,most_frequent,normalize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+359,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.9282407375709691,None,0.0,16,3,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.015995805068365877,mean,standardize,,,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.10042232633120375,False,True,1,squared_hinge,ovr,l1,0.004451009526629745,,,,,,,,,,,,,,,,,,,,,
+363,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.1393173890820956,False,True,1,squared_hinge,ovr,l2,8.283470008521272e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.015152352014390857,mean,normalize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,False,,,,,,,,,,,,,
+368,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.745748188733041e-05,True,True,hinge,2.181659959258924e-05,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.13921206193066876,most_frequent,quantile_transformer,60,normal,,,extra_trees_preproc_for_classification,False,entropy,None,0.7619669647366906,None,0.0,3,4,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+371,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4,1,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.01020427226149318,median,none,,,,,extra_trees_preproc_for_classification,True,entropy,None,0.3663460889081009,None,0.0,2,7,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
diff --git a/autosklearn/metalearning/files/precision_weighted_binary.classification_sparse/description.txt b/autosklearn/metalearning/files/precision_weighted_binary.classification_sparse/description.txt
index 2086584fd9..faa44e28be 100644
--- a/autosklearn/metalearning/files/precision_weighted_binary.classification_sparse/description.txt
+++ b/autosklearn/metalearning/files/precision_weighted_binary.classification_sparse/description.txt
@@ -1,8 +1,9 @@
 features_cutoff_time: 3600
 features_cutoff_memory: 3072
-number_of_feature_steps: 35
+number_of_feature_steps: 42
 feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances
 feature_step LogNumberOfInstances: LogNumberOfInstances
+feature_step NumberOfClasses: NumberOfClasses
 feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures
 feature_step LogNumberOfFeatures: LogNumberOfFeatures
 feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues
@@ -20,6 +21,11 @@ feature_step DatasetRatio: DatasetRatio, LogDatasetRatio
 feature_step LogDatasetRatio: LogDatasetRatio
 feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio
 feature_step LogInverseDatasetRatio: LogInverseDatasetRatio
+feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD
+feature_step ClassProbabilityMin: ClassProbabilityMin
+feature_step ClassProbabilityMax: ClassProbabilityMax
+feature_step ClassProbabilityMean: ClassProbabilityMean
+feature_step ClassProbabilitySTD: ClassProbabilitySTD
 feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum
 feature_step SymbolsMin: SymbolsMin
 feature_step SymbolsMax: SymbolsMax
@@ -36,11 +42,12 @@ feature_step SkewnessMin: SkewnessMin
 feature_step SkewnessMax: SkewnessMax
 feature_step SkewnessMean: SkewnessMean
 feature_step SkewnessSTD: SkewnessSTD
-features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
+feature_step ClassEntropy: ClassEntropy
+features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy
 features_stochastic: 
-default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
+default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy
 
-algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92
+algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235,236,237,238,239,240,241,242,243,244,245,246,247,248,249,250,251,252,253,254,255,256,257,258,259,260,261,262,263,264,265,266,267,268,269,270,271,272,273,274,275,276,277,278,279,280,281,282,283,284,285,286,287,288,289,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,312,313,314,315,316,317,318,319,320,321,322,323,324,325,326,327,328,329,330,331,332,333,334,335,336,337,338,339,340,341,342,343,344,345,346,347,348,349,350,351,352,353,354,355,356,357,358,359,360,361,362,363,364,365,366,367,368,369,370,371,372
 algorithms_stochastic: 
 performance_measures: precision_weighted
 performance_type: solution_quality
diff --git a/autosklearn/metalearning/files/precision_weighted_binary.classification_sparse/feature_costs.arff b/autosklearn/metalearning/files/precision_weighted_binary.classification_sparse/feature_costs.arff
index 0e17e20d22..8512fd9275 100644
--- a/autosklearn/metalearning/files/precision_weighted_binary.classification_sparse/feature_costs.arff
+++ b/autosklearn/metalearning/files/precision_weighted_binary.classification_sparse/feature_costs.arff
@@ -4,6 +4,7 @@
 @ATTRIBUTE repetition NUMERIC
 @ATTRIBUTE NumberOfInstances NUMERIC
 @ATTRIBUTE LogNumberOfInstances NUMERIC
+@ATTRIBUTE NumberOfClasses NUMERIC
 @ATTRIBUTE NumberOfFeatures NUMERIC
 @ATTRIBUTE LogNumberOfFeatures NUMERIC
 @ATTRIBUTE MissingValues NUMERIC
@@ -21,6 +22,11 @@
 @ATTRIBUTE LogDatasetRatio NUMERIC
 @ATTRIBUTE InverseDatasetRatio NUMERIC
 @ATTRIBUTE LogInverseDatasetRatio NUMERIC
+@ATTRIBUTE ClassOccurences NUMERIC
+@ATTRIBUTE ClassProbabilityMin NUMERIC
+@ATTRIBUTE ClassProbabilityMax NUMERIC
+@ATTRIBUTE ClassProbabilityMean NUMERIC
+@ATTRIBUTE ClassProbabilitySTD NUMERIC
 @ATTRIBUTE NumSymbols NUMERIC
 @ATTRIBUTE SymbolsMin NUMERIC
 @ATTRIBUTE SymbolsMax NUMERIC
@@ -37,162 +43,138 @@
 @ATTRIBUTE SkewnessMax NUMERIC
 @ATTRIBUTE SkewnessMean NUMERIC
 @ATTRIBUTE SkewnessSTD NUMERIC
+@ATTRIBUTE ClassEntropy NUMERIC
 
 @DATA
-75123,1.0,1e-05,1e-05,1e-05,1e-05,0.00253,0.00221,1e-05,0.00024,3e-05,0.00011,0.0,2e-05,2e-05,5e-05,8e-05,2e-05,1e-05,7e-05,1e-05,0.00072,1e-05,1e-05,0.00019,0.00041,0.00011,0.00061,0.00012,9e-05,0.00015,0.00025,0.00067,0.00014,0.00011,0.00016,0.00027
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+75235,1.0,1e-05,1e-05,0.0003,2e-05,1e-05,0.00461,0.00385,2e-05,0.00055,2e-05,0.00026,1e-05,3e-05,3e-05,0.0001,0.00011,3e-05,1e-05,5e-05,2e-05,0.00026,3e-05,1e-05,8e-05,0.00014,0.00075,1e-05,1e-05,0.0002,0.00043,0.00011,0.00127,0.00017,0.0002,0.00029,0.00061,0.0008,0.00015,0.00012,0.00016,0.00038,0.00242
+75236,1.0,1e-05,1e-05,0.0001,1e-05,1e-05,0.00218,0.00118,2e-05,0.00065,3e-05,0.00041,1e-05,3e-05,3e-05,9e-05,9e-05,4e-05,1e-05,7e-05,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.0006,1e-05,1e-05,0.00021,0.00032,6e-05,0.00043,7e-05,7e-05,9e-05,0.00019,0.00062,7e-05,8e-05,0.0001,0.00037,0.00048
+75237,1.0,2e-05,1e-05,0.00785,2e-05,1e-05,0.31031,0.27854,2e-05,0.01161,3e-05,0.02022,1e-05,4e-05,3e-05,9e-05,0.00024,6e-05,2e-05,9e-05,2e-05,0.00062,7e-05,3e-05,0.00017,0.00035,0.0008,1e-05,1e-05,0.00028,0.00043,8e-05,0.00043,8e-05,7e-05,9e-05,0.0002,0.00044,6e-05,8e-05,9e-05,0.00021,0.14028
+75239,1.0,1e-05,1e-05,7e-05,1e-05,1e-05,0.00105,0.00084,1e-05,0.00015,1e-05,8e-05,1e-05,2e-05,5e-05,4e-05,8e-05,2e-05,1e-05,3e-05,1e-05,0.00016,2e-05,1e-05,5e-05,9e-05,0.00049,0.0,0.0,0.00012,0.00026,0.00011,0.00046,0.00013,8e-05,8e-05,0.00018,0.00044,6e-05,9e-05,0.00011,0.00018,0.00065
+75240,1.0,2e-05,1e-05,0.00036,5e-05,4e-05,0.00845,0.00644,6e-05,0.00122,4e-05,0.00091,1e-05,4e-05,4e-05,9e-05,0.00013,5e-05,2e-05,9e-05,5e-05,0.00031,3e-05,1e-05,7e-05,0.0002,0.00073,3e-05,2e-05,0.00019,0.0003,0.00018,0.00055,9e-05,9e-05,0.00011,0.00025,0.0005,7e-05,8e-05,0.00015,0.0002,0.00365
+75243,1.0,1e-05,1e-05,0.00029,1e-05,1e-05,0.00461,0.00415,1e-05,0.00035,1e-05,0.00015,0.0,2e-05,2e-05,5e-05,7e-05,2e-05,1e-05,4e-05,1e-05,0.00018,2e-05,1e-05,4e-05,0.00011,0.00022,1e-05,1e-05,5e-05,0.00011,5e-05,0.0004,6e-05,6e-05,8e-05,0.0002,0.00039,6e-05,6e-05,8e-05,0.00019,0.00265
+75244,1.0,3e-05,2e-05,0.0004,3e-05,2e-05,0.0173,0.01298,4e-05,0.00267,4e-05,0.00174,1e-05,7e-05,9e-05,0.00024,0.00027,8e-05,2e-05,0.00013,3e-05,0.00045,7e-05,2e-05,0.00011,0.00025,0.00077,6e-05,2e-05,0.00021,0.00034,0.00013,0.00077,0.00014,0.00013,0.00026,0.00023,0.00044,7e-05,8e-05,0.0001,0.00019,0.00649
+75248,1.0,1e-05,1e-05,0.00017,1e-05,1e-05,0.00416,0.00305,1e-05,0.0006,2e-05,0.00054,1e-05,2e-05,2e-05,5e-05,7e-05,2e-05,1e-05,4e-05,1e-05,0.00017,2e-05,1e-05,4e-05,0.0001,0.00029,5e-05,1e-05,6e-05,0.00012,6e-05,0.00081,0.00011,0.00015,0.00016,0.00038,0.0005,7e-05,8e-05,0.00012,0.00024,0.00184
+75249,1.0,1e-05,1e-05,0.00011,1e-05,1e-05,0.00192,0.00153,1e-05,0.00025,1e-05,0.00017,1e-05,2e-05,2e-05,5e-05,7e-05,2e-05,1e-05,5e-05,3e-05,0.00014,2e-05,1e-05,3e-05,8e-05,0.00029,1e-05,3e-05,6e-05,0.00012,7e-05,0.00041,6e-05,8e-05,0.0001,0.00017,0.00043,6e-05,7e-05,9e-05,0.00019,0.00092
+75250,1.0,1e-05,1e-05,0.00375,1e-05,1e-05,0.04994,0.047,1e-05,0.00247,1e-05,0.00049,1e-05,2e-05,2e-05,6e-05,7e-05,2e-05,1e-05,3e-05,1e-05,0.00022,4e-05,1e-05,4e-05,0.00012,0.00041,0.0,1e-05,0.00011,0.00023,6e-05,0.00076,0.00012,0.00012,0.00019,0.00032,0.00086,0.00012,0.00013,0.00018,0.00043,0.04178
diff --git a/autosklearn/metalearning/files/precision_weighted_binary.classification_sparse/feature_runstatus.arff b/autosklearn/metalearning/files/precision_weighted_binary.classification_sparse/feature_runstatus.arff
index 0034ad2bd6..5fff2dfed3 100644
--- a/autosklearn/metalearning/files/precision_weighted_binary.classification_sparse/feature_runstatus.arff
+++ b/autosklearn/metalearning/files/precision_weighted_binary.classification_sparse/feature_runstatus.arff
@@ -4,6 +4,7 @@
 @ATTRIBUTE repetition NUMERIC
 @ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other}
@@ -21,6 +22,11 @@
 @ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other}
@@ -37,162 +43,138 @@
 @ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other}
 
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diff --git a/autosklearn/metalearning/files/precision_weighted_binary.classification_sparse/feature_values.arff b/autosklearn/metalearning/files/precision_weighted_binary.classification_sparse/feature_values.arff
index a60400e3ce..736dc64321 100644
--- a/autosklearn/metalearning/files/precision_weighted_binary.classification_sparse/feature_values.arff
+++ b/autosklearn/metalearning/files/precision_weighted_binary.classification_sparse/feature_values.arff
@@ -13,7 +13,6 @@
 @ATTRIBUTE KurtosisMean NUMERIC
 @ATTRIBUTE KurtosisMin NUMERIC
 @ATTRIBUTE KurtosisSTD NUMERIC
-@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC
 @ATTRIBUTE LogDatasetRatio NUMERIC
 @ATTRIBUTE LogInverseDatasetRatio NUMERIC
 @ATTRIBUTE LogNumberOfFeatures NUMERIC
@@ -42,160 +41,135 @@
 @ATTRIBUTE SymbolsSum NUMERIC
 
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diff --git a/autosklearn/metalearning/files/precision_weighted_multiclass.classification_dense/algorithm_runs.arff b/autosklearn/metalearning/files/precision_weighted_multiclass.classification_dense/algorithm_runs.arff
index b26be3c4ee..96a3893388 100644
--- a/autosklearn/metalearning/files/precision_weighted_multiclass.classification_dense/algorithm_runs.arff
+++ b/autosklearn/metalearning/files/precision_weighted_multiclass.classification_dense/algorithm_runs.arff
@@ -7,130 +7,135 @@
 @ATTRIBUTE runstatus {ok, timeout, memout, not_applicable, crash, other}
 
 @DATA
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diff --git a/autosklearn/metalearning/files/precision_weighted_multiclass.classification_dense/configurations.csv b/autosklearn/metalearning/files/precision_weighted_multiclass.classification_dense/configurations.csv
index b98e7173f2..bc6e6068cc 100644
--- a/autosklearn/metalearning/files/precision_weighted_multiclass.classification_dense/configurations.csv
+++ b/autosklearn/metalearning/files/precision_weighted_multiclass.classification_dense/configurations.csv
@@ -1,128 +1,133 @@
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-23,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7734.521233796252,,,2.6048661611376747,rbf,-1,True,4.68717607435371e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,none,,,,,feature_agglomeration,,,,,,,,,,,,,,,euclidean,average,25,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-24,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.21794354428393548,None,0.0,2,16,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0025451910134387575,median,quantile_transformer,1477,uniform,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
-25,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5010952533165973,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.005838375247601407,mean,normalize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,
-26,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5185406559495093,None,0.0,3,17,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.010000000000000004,median,normalize,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.13046020673147232,fpr,chi2
-27,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.4426097579931897,None,0.0,16,4,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,mean,robust_scaler,,,0.7407323456920554,0.0804756921037867,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,
-28,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5607168729746175,None,0.0,2,3,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.009810589644392154,most_frequent,quantile_transformer,1132,normal,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.06211089008167909,fdr,chi2
-29,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.992156991246829,None,0.0,19,3,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.007016879673679419,median,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,
-30,none,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.9728532763078892,None,0.0,13,9,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,none,,,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6651071190864879,False,,,,,,,,,,,,,,,
-31,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9561075301148197,None,0.0,6,2,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,
-32,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.557321265077371,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-33,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,1.153931482107762e-10,0.011666190464009397,auto,255,None,29,190,18,loss,1e-07,0.147868307124396,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,
-34,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,22194.242946902177,0.45210849669021247,,0.0005669713702171556,sigmoid,-1,False,0.03542142266081451,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,quantile_transformer,291,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-35,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.8189454407806317,None,0.0,10,10,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,robust_scaler,,,0.7192027795318285,0.24912062393096632,fast_ica,,,,,,,,,,,parallel,logcosh,100,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-36,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,3.6797768538445263e-09,0.05511004217375807,auto,255,None,72,62,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,robust_scaler,,,0.7241481576763391,0.0501033899901458,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,36,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-37,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.7521969592385672,None,0.0,9,5,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.020402264943872365,median,robust_scaler,,,0.737227213630259,0.20752747663727789,fast_ica,,,,,,,,,,,parallel,exp,312,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-38,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.756167892711991e-05,False,True,squared_hinge,5.235791933868702e-05,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0001632172987320906,most_frequent,standardize,,,,,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,5,None,19,12,1.0,70,,,,,
-39,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,1.6343482806592892e-09,0.19537524572679268,auto,255,None,32,29,13,loss,1e-07,0.05061738224302791,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-40,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7088164344533493,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,mean,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,
-41,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,1.943186107984505e-07,0.08852455702036988,auto,255,None,5,72,19,loss,1e-07,0.039837460762271175,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.01591265419367627,median,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,
-42,weighting,adaboost,SAMME,0.5210651042094924,10,401,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,standardize,,,,,feature_agglomeration,,,,,,,,,,,,,,,euclidean,complete,89,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-43,none,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0686374607394679,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,minmax,,,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.8490260132946401,False,,,,,,,,,,,,,,,
-44,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.6931271368243409,None,0.0,11,10,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.362940004438799,most_frequent,robust_scaler,,,0.9177587796305063,0.17084038458597867,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,30.491514144623952,mutual_info,,,
-45,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.9398869869697849,None,0.0,20,15,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,normalize,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,269,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-46,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,92.66144306787051,,,0.04654639302091653,rbf,-1,True,0.0022231544280610005,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.005194838694455963,mean,none,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,15,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-47,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2681.998807317996,-0.9558745731731211,,0.0011299948421773457,sigmoid,-1,False,1.5292507354478086e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-48,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.8095522998548572,None,0.0,17,10,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.031940595071539304,mean,quantile_transformer,1563,normal,,,extra_trees_preproc_for_classification,False,entropy,None,0.9330145598603196,None,0.0,18,10,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-49,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,26.502259985828186,,,9.475662388405154e-05,rbf,-1,False,0.00019690635968217866,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-50,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,2.5360196477156387e-07,0.011018756408558218,auto,255,None,40,122,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.00011749164080669445,most_frequent,quantile_transformer,757,uniform,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,5.097834697156853,False,True,1,squared_hinge,ovr,l1,0.053877834919477424,,,,,,,,,,,,,,,,,,,,,,
-51,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5255991794296913,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-52,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.34697723213923637,None,0.0,15,18,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.1923594385988973,mean,normalize,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,25.77499302945827,chi2,,,
-53,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,23529.225652845635,0.30829978977649053,2,0.0008220498708157514,poly,-1,False,0.0005532556252982188,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.07570228092651136,mean,quantile_transformer,1143,normal,,,fast_ica,,,,,,,,,,,parallel,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-54,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,1.488333818193668e-10,0.09526676471267805,auto,255,None,258,87,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,normalize,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.3561953264350478,fpr,f_classif
-55,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.9523435078770708,0.8748383232154038,4,0.2195992292488049,poly,-1,True,4.849685143365339e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,minmax,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-56,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.7610308078756552,None,0.0,1,2,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.010000000000000004,mean,robust_scaler,,,0.9205545465103644,0.20272761060743316,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-57,weighting,adaboost,SAMME.R,0.03127273017917347,10,496,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,standardize,,,,,fast_ica,,,,,,,,,,,deflation,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-58,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,151,None,,0.0003283777607180646,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.2510527431514227,mean,normalize,,,,,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,-0.96093182232312,,0.05762510964607661,sigmoid,2632,,,,,,,,,,,,,,,,,
-59,none,bernoulli_nb,,,,,0.6524090653939782,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.005164169126808402,most_frequent,quantile_transformer,16,normal,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.033082145443631535,False,True,1,squared_hinge,ovr,l1,0.002615020898325393,,,,,,,,,,,,,,,,,,,,,,
-60,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.6453555269772124,None,0.0,1,4,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,normalize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-61,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,940.9437776449834,,,1.5839338084625942,rbf,-1,False,0.023072781592565438,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,none,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,77.25763685070707,mutual_info,,,
-62,weighting,extra_trees,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,13,18,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0001574994528707389,most_frequent,standardize,,,,,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,3,None,19,19,1.0,77,,,,,
-63,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.34534888916828577,0.02513634776687268,2,1.0733091880109409,poly,-1,True,0.0006974263047918307,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,quantile_transformer,1000,normal,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,77.99934218022861,mutual_info,,,
-64,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,29.55731828912983,False,True,1,squared_hinge,ovr,l2,0.0011332486951954968,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,
-65,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,1.1387795876755026e-06,0.12245605190343567,auto,255,None,58,7,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,robust_scaler,,,0.79305915648885,0.1841487406876042,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-66,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.8425560317492612,None,0.0,8,15,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.015276988502366128,median,none,,,,,fast_ica,,,,,,,,,,,parallel,cube,17,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-67,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,0.2638627266631576,0.022360820165005443,auto,255,None,171,6,19,loss,1e-07,0.016209060012924756,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.008763072395677399,mean,standardize,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,78,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-68,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,925.3053743248586,False,True,1,squared_hinge,ovr,l2,0.02764481198395717,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.3850718018366796,most_frequent,quantile_transformer,786,uniform,,,feature_agglomeration,,,,,,,,,,,,,,,cosine,average,156,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-69,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7573078019937971,None,0.0,2,5,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,robust_scaler,,,0.7265312295801113,0.28332515882236425,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.09660564590146253,fpr,f_classif
-70,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0057657098053164255,False,True,squared_hinge,0.013468519636061941,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.484357187898904,mean,quantile_transformer,675,normal,,,kernel_pca,,,,,,,,,,,,,,,,,,,,,,cosine,1876,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-71,none,adaboost,SAMME.R,0.22802213935525836,6,284,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,robust_scaler,,,0.7468645381288924,0.22838821662367073,feature_agglomeration,,,,,,,,,,,,,,,cosine,average,128,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-72,none,adaboost,SAMME,0.1280458968526125,7,125,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,robust_scaler,,,0.7156460334356771,0.2981902862926971,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,65.16171885308417,chi2,,,
-73,none,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9931779675565967,None,0.0,18,17,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,robust_scaler,,,0.75,0.2945917010472818,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,
-74,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.08646118576686276,None,0.0,4,9,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,most_frequent,quantile_transformer,959,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-75,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,1.2430504242164082e-09,0.03359568307446533,auto,255,None,1609,29,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,standardize,,,,,feature_agglomeration,,,,,,,,,,,,,,,cosine,complete,40,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-76,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7.491039171224502,True,True,squared_hinge,2.8058138048327884e-05,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,minmax,,,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.7754372809893839,False,True,1,squared_hinge,ovr,l1,3.9643594596766314e-05,,,,,,,,,,,,,,,,,,,,,,
-77,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.34936025988397124,None,0.0,2,13,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00018601309647406987,median,robust_scaler,,,0.8634309348843472,0.26976989465367607,extra_trees_preproc_for_classification,False,entropy,None,0.48025175288975036,None,0.0,6,17,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-78,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,0.011547155223520492,0.13665554140837183,auto,255,None,13,5,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.1824584492028449,median,minmax,,,,,feature_agglomeration,,,,,,,,,,,,,,,euclidean,average,261,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-79,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.7308100742740112,None,0.0,18,19,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.014637629296743653,most_frequent,robust_scaler,,,0.7724670628916575,0.2233445340332948,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,29.07440444752882,False,True,1,squared_hinge,ovr,l1,0.00040479645225962257,,,,,,,,,,,,,,,,,,,,,,
-80,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1661.2274747409913,,,0.025821208852387936,rbf,-1,False,0.0017011435104921824,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,none,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,306,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-81,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0016941428709087083,True,,,True,,optimal,squared_hinge,l1,,5.124265599423001e-05,no_encoding,no_coalescense,,most_frequent,quantile_transformer,461,uniform,,,extra_trees_preproc_for_classification,True,gini,None,0.31371431180257103,None,0.0,5,17,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-82,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,21.85207494001697,0.18872999589786676,4,1.1863646021868761,poly,-1,False,0.0021338371901294443,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0034080852834770217,mean,normalize,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.11326005109886934,fdr,f_classif
-83,weighting,adaboost,SAMME,0.38341570193537416,9,250,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,minmax,,,,,feature_agglomeration,,,,,,,,,,,,,,,euclidean,average,13,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-84,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,0.037849720944694,0.1268815770019783,auto,255,None,11,99,11,loss,1e-07,0.10654523627313527,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,
-85,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.593728171200453,None,0.0,7,20,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.02830941310359363,mean,normalize,,,,,extra_trees_preproc_for_classification,False,entropy,None,0.7389446586281172,None,0.0,20,11,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-86,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,1,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.019797877010737826,mean,normalize,,,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.8239678741128165,False,,,,,,,,,,,,,,,
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-88,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,38,auto,,0.00019398431171606024,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010000000000000004,median,quantile_transformer,1440,normal,,,extra_trees_preproc_for_classification,True,gini,None,0.5126639956845847,None,0.0,16,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-89,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5722917944959481,None,0.0,1,14,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,
-90,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.26356754187780895,None,0.0,4,3,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010966178401758774,mean,robust_scaler,,,0.7817043861798886,0.24371720112407158,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
-91,weighting,adaboost,SAMME.R,0.13547552520095074,4,62,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.011851011202809002,most_frequent,standardize,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4888776875314595,fpr,chi2
-92,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,80.0028902468169,False,True,1,squared_hinge,ovr,l2,0.0024481999829574722,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.15047970487774687,mean,quantile_transformer,1130,normal,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,1.1912646635544712,False,True,1,squared_hinge,ovr,l1,0.00014314728486450857,,,,,,,,,,,,,,,,,,,,,,
-93,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.22879311080499845,None,0.0,8,4,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.014320022547615783,median,minmax,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.238630703440461,mutual_info,,,
-94,weighting,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,2,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,none,,,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6342574285315175,False,,,,,,,,,,,,,,,
-95,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.107967527639527e-05,True,True,hinge,0.00448294745544775,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,quantile_transformer,953,normal,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,35.00114345405331,chi2,,,
-96,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.14148638702297855,None,0.0,2,6,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.0038499650338378643,most_frequent,quantile_transformer,1759,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-97,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,1.1522414705935405e-10,0.6132800183073315,auto,255,None,1607,67,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010096911249562407,mean,robust_scaler,,,0.7663493326967942,0.136094217337434,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-98,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.35863733679484333,None,0.0,1,11,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.0009777923655842618,median,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-99,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.479405900046494,,,0.013340778632154543,rbf,-1,False,6.913586060890114e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,robust_scaler,,,0.9953232616818098,0.06450577886132274,fast_ica,,,,,,,,,,,deflation,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-100,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.825844582229201,None,0.0,12,9,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0008422508222149647,median,robust_scaler,,,0.8658803880857064,0.2528423686293341,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-101,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2574971638664341,True,True,hinge,1.8027411099296446e-05,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.00536396328125103,median,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,
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-103,none,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,128,manual,0.8574560771111307,0.010886139381599514,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.24115007944216788,most_frequent,normalize,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,62.26514170991452,mutual_info,,,
-104,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.6282663389679584,None,0.0,1,3,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,median,minmax,,,,,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-105,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,1.430083734915855e-09,0.08779621406981941,auto,255,None,17,20,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.29749837779115257,mean,robust_scaler,,,0.7236445277814435,0.25,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
-106,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.3294588572341214,None,0.0,1,3,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
-107,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,11.046854203258679,,,0.07809571976769347,rbf,-1,False,0.00036193159909053734,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.006445557635410205,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-108,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.6728827282646637,None,0.0,18,8,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,robust_scaler,,,0.8512985751386942,0.14092824896182807,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,
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-111,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6427543483503397,True,True,hinge,0.00045318775930916944,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.009338484332306904,median,quantile_transformer,174,normal,,,kernel_pca,,,,,,,,,,,,,,,,,,,,,,cosine,1848,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-112,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.04981080944617379,True,,0.07309386939525211,True,,constant,log,l2,,0.0006721243213777075,no_encoding,no_coalescense,,mean,quantile_transformer,574,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-113,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,9.029473608568458e-09,0.15779990576964315,auto,255,None,37,71,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.018093177195644097,mean,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,False,,,,,,,,,,,,
-114,weighting,adaboost,SAMME.R,0.10000000000000002,3,50,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,robust_scaler,,,0.7639429738274788,0.25,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,67.72501979095493,f_classif,,,
-115,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.129143822880219,True,True,hinge,0.00012612093569084375,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0016222546082911887,most_frequent,quantile_transformer,1309,uniform,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8.816417526015055,f_classif,,,
-116,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.4864284879550203,None,0.0,1,4,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.008665854799630503,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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-118,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.6922752267318475,True,True,squared_hinge,0.06299408576730353,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,quantile_transformer,1742,uniform,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,107,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-119,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.44727252219812963,None,0.0,2,10,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,standardize,,,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l1,8.17533189014765e-05,,,,,,,,,,,,,,,,,,,,,,
-120,weighting,extra_trees,,,,,,,,,,,,,,,True,gini,None,0.9999521514529074,None,0.0,3,10,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,minmax,,,,,extra_trees_preproc_for_classification,False,gini,None,0.24745225001074223,None,0.0,14,18,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-121,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.4210978663047845,None,0.0,5,6,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.006365988550833196,most_frequent,robust_scaler,,,0.8348891074186082,0.00974961635730845,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0695919708890599,fpr,f_classif
-122,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4424195769042466,False,True,hinge,0.02490432909709204,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,mean,quantile_transformer,1000,uniform,,,kernel_pca,,,,,,,,,,,,,,,,,,,,,,cosine,769,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-123,weighting,decision_tree,,,,,,,entropy,0.04461432844342683,1.0,None,0.0,9,17,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0011300271286478988,mean,minmax,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.43863282379418345,,mutual_info_classif
-124,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1561.1002600303584,,,0.0024048551704194507,rbf,-1,False,0.0001936214667496851,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,minmax,,,,,fast_ica,,,,,,,,,,,parallel,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-125,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.087313242780732,0.0636709914987994,3,0.004481917125024585,poly,-1,False,0.01848880618941367,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.002136015035740733,mean,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-126,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.03204653439894958,True,True,squared_hinge,0.00013006393401112688,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.013506575694382031,most_frequent,normalize,,,,,feature_agglomeration,,,,,,,,,,,,,,,euclidean,ward,356,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-127,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.6466927589470762,None,0.0,11,2,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.009672465311659264,most_frequent,standardize,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,369,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+1,none,decision_tree,,,,,,,gini,0.7229490531192699,1.0,None,0.0,1,18,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.00013586681143040562,median,quantile_transformer,647,uniform,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+4,none,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,quantile_transformer,111,uniform,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4748887602127273,fdr,f_classif
+6,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.7283683762613216,None,0.0,12,20,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,median,standardize,,,,,fast_ica,,,,,,,,,,,deflation,exp,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+8,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,114,manual,0.11778631707605758,0.0029112721339713465,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.015582195257240751,median,normalize,,,,,kernel_pca,,,,,,,,,,,,,,,,,,,0.42909623253327767,3,0.25407612000343777,poly,1689,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+11,weighting,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,2,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.3865962574104293,mean,normalize,,,,,fast_ica,,,,,,,,,,,deflation,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+13,weighting,adaboost,SAMME,0.17983222024694556,4,357,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0008898781197407956,mean,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+15,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.21772092350860472,None,0.0,14,17,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.0020488212891693275,median,standardize,,,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,33.74257606530722,False,True,1,squared_hinge,ovr,l1,8.740452966205111e-05,,,,,,,,,,,,,,,,,,,,,,
+18,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,2.4243922131268786e-05,0.07570328162448307,auto,255,None,71,42,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,robust_scaler,,,0.8898729094898769,0.12138152991327475,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,94.96086286583851,chi2,,,
+20,none,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.9866058571747695,None,0.0,16,9,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.1680676743605393,most_frequent,robust_scaler,,,0.8840072797399463,0.08535534311393947,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,False,,,,,,,,,,,,
+23,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,0.00022373024203887573,0.027149343206135476,auto,255,None,49,13,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.00777614320416325,most_frequent,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,
+28,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,1.0168466808008568e-09,0.21361657232950118,auto,255,None,463,4,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,robust_scaler,,,0.7965217268158734,0.2446550610908456,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,
+31,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,8.757412830636845e-06,0.10095376320667454,auto,255,None,3,2,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.015401766535365336,median,quantile_transformer,647,normal,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,
+32,none,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,174,manual,0.21501892897746921,2.0347110826262054e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.013754893527625522,most_frequent,normalize,,,,,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,-0.019221513778605592,3,0.10190948951339239,poly,2521,,,,,,,,,,,,,,,,,
+35,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,166,auto,,4.5836048743081545e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,standardize,,,,,kernel_pca,,,,,,,,,,,,,,,,,,,,,0.00805519593731235,rbf,573,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+38,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9025597189684735,None,0.0,18,8,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.007536467725572231,most_frequent,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
+39,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.6799215694343422,None,0.0,1,5,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.004724310659953132,most_frequent,quantile_transformer,1037,normal,,,fast_ica,,,,,,,,,,,parallel,exp,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+41,none,extra_trees,,,,,,,,,,,,,,,True,gini,None,0.5033866291997137,None,0.0,2,14,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,minmax,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+43,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.9479098792950027,None,0.0,12,14,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,quantile_transformer,1758,normal,,,feature_agglomeration,,,,,,,,,,,,,,,cosine,complete,244,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+44,none,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l2,2.858526169098402e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0007827505640681123,median,standardize,,,,,extra_trees_preproc_for_classification,False,entropy,None,0.3443310459451119,None,0.0,3,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+48,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.32752246015541675,None,0.0,16,9,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,quantile_transformer,794,normal,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,
+49,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.049504283446131e-05,False,,0.0004438773852753879,True,2.4110816673432424e-07,constant,log,elasticnet,,0.01321814853186568,one_hot_encoding,minority_coalescer,0.01510416593631535,most_frequent,none,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,66.65285268958851,chi2,,,
+57,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.24407113189971e-05,True,,,True,,optimal,hinge,l2,,0.018411607920920666,one_hot_encoding,minority_coalescer,0.006594668208397739,mean,standardize,,,,,extra_trees_preproc_for_classification,True,entropy,None,0.20369732810864838,None,0.0,16,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+58,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5214197490559657,None,0.0,1,2,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.00826182798588248,mean,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,
+63,none,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,130,manual,0.5446276346297756,0.00846307015171434,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.005736348762561667,median,quantile_transformer,973,uniform,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.08483615095863663,fdr,chi2
+65,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,1.2822815440468518e-10,0.12922700208645135,auto,255,None,31,44,13,loss,1e-07,0.08789566421608311,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.012230423571314038,mean,robust_scaler,,,0.71734690066056,0.20283418288016852,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,
+68,weighting,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,minmax,,,,,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+75,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13.284077389890939,-0.2621334760621076,5,0.9060908941898536,poly,-1,False,0.06323853917206591,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0011743021592446861,most_frequent,normalize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,
+82,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,3.79982070767762e-10,0.10307173798074214,auto,255,None,30,25,6,loss,1e-07,0.09705336338563596,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.004889186641013006,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+84,none,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.5187414079994191,None,0.0,3,2,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.13752329517270892,median,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+87,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.07992258780660727,True,True,squared_hinge,0.022067056607853346,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,robust_scaler,,,0.7602075850543897,0.15799780977101488,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,95.09568612626826,chi2,,,
+91,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,0.0006322356877253852,0.02155479872417127,auto,255,None,6,15,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
+94,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5079964624349133,None,0.0,7,5,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.37073794659062626,mean,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,
+97,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.49127237860427425,None,0.0,5,3,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.0022872939343994768,median,quantile_transformer,1107,normal,,,extra_trees_preproc_for_classification,True,gini,None,0.5673147649132957,None,0.0,4,14,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+99,weighting,adaboost,SAMME,0.17983222024694556,4,357,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0008898781197407956,mean,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+105,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.268699692198443e-05,True,,4.1225573912733725e-06,True,,constant,log,l2,,4.729985534114764e-05,no_encoding,no_coalescense,,median,quantile_transformer,246,normal,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,20.73090458774975,chi2,,,
+106,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,2.047681354714616e-06,0.016073071519249763,auto,255,None,127,60,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.005262156195293648,median,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,
+110,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0021814768111094564,False,True,hinge,8.726644999887521e-05,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,none,,,,,kitchen_sinks,,,,,,,,,,,,,,,,,,,,,,,,0.0010909910768059797,970,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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+115,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.463780563279276,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.011474025594258192,mean,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,
+116,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,236,None,,0.08905291537442941,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,normalize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,
+121,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.08836825866738698,None,0.0,5,19,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0003943043347109662,median,robust_scaler,,,0.7564808439539721,0.23469821956116474,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2206955933275169,fdr,f_classif
+124,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.1475115840890851,None,0.0,13,17,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,robust_scaler,,,0.7895374132523357,0.10850129170589755,fast_ica,,,,,,,,,,,deflation,exp,100,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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+413,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,7.194851113809193e-10,0.013882834218943657,auto,255,None,18,2,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0005124238915083231,mean,quantile_transformer,948,normal,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,60.45885911036194,f_classif,,,
+415,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,0.00037966471646911845,0.016422945536911454,auto,255,None,10,56,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.002419439132687246,median,quantile_transformer,828,uniform,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+422,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.36374112587320206,None,0.0,2,10,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010870084126959173,most_frequent,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,
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+430,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5321814610661173,None,0.0,1,3,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,standardize,,,,,extra_trees_preproc_for_classification,True,gini,None,0.5,None,0.0,1,15,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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+462,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6,1,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.017471096998391875,median,none,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,67.7801145492284,f_classif,,,
diff --git a/autosklearn/metalearning/files/precision_weighted_multiclass.classification_dense/description.txt b/autosklearn/metalearning/files/precision_weighted_multiclass.classification_dense/description.txt
index 157f78a702..4d84d2a080 100644
--- a/autosklearn/metalearning/files/precision_weighted_multiclass.classification_dense/description.txt
+++ b/autosklearn/metalearning/files/precision_weighted_multiclass.classification_dense/description.txt
@@ -1,8 +1,9 @@
 features_cutoff_time: 3600
 features_cutoff_memory: 3072
-number_of_feature_steps: 35
+number_of_feature_steps: 42
 feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances
 feature_step LogNumberOfInstances: LogNumberOfInstances
+feature_step NumberOfClasses: NumberOfClasses
 feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures
 feature_step LogNumberOfFeatures: LogNumberOfFeatures
 feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues
@@ -20,6 +21,11 @@ feature_step DatasetRatio: DatasetRatio, LogDatasetRatio
 feature_step LogDatasetRatio: LogDatasetRatio
 feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio
 feature_step LogInverseDatasetRatio: LogInverseDatasetRatio
+feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD
+feature_step ClassProbabilityMin: ClassProbabilityMin
+feature_step ClassProbabilityMax: ClassProbabilityMax
+feature_step ClassProbabilityMean: ClassProbabilityMean
+feature_step ClassProbabilitySTD: ClassProbabilitySTD
 feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum
 feature_step SymbolsMin: SymbolsMin
 feature_step SymbolsMax: SymbolsMax
@@ -36,11 +42,12 @@ feature_step SkewnessMin: SkewnessMin
 feature_step SkewnessMax: SkewnessMax
 feature_step SkewnessMean: SkewnessMean
 feature_step SkewnessSTD: SkewnessSTD
-features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
+feature_step ClassEntropy: ClassEntropy
+features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy
 features_stochastic: 
-default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
+default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy
 
-algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127
+algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235,236,237,238,239,240,241,242,243,244,245,246,247,248,249,250,251,252,253,254,255,256,257,258,259,260,261,262,263,264,265,266,267,268,269,270,271,272,273,274,275,276,277,278,279,280,281,282,283,284,285,286,287,288,289,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,312,313,314,315,316,317,318,319,320,321,322,323,324,325,326,327,328,329,330,331,332,333,334,335,336,337,338,339,340,341,342,343,344,345,346,347,348,349,350,351,352,353,354,355,356,357,358,359,360,361,362,363,364,365,366,367,368,369,370,371,372,373,374,375,376,377,378,379,380,381,382,383,384,385,386,387,388,389,390,391,392,393,394,395,396,397,398,399,400,401,402,403,404,405,406,407,408,409,410,411,412,413,414,415,416,417,418,419,420,421,422,423,424,425,426,427,428,429,430,431,432,433,434,435,436,437,438,439,440,441,442,443,444,445,446,447,448,449,450,451,452,453,454,455,456,457,458,459,460,461,462,463,464
 algorithms_stochastic: 
 performance_measures: precision_weighted
 performance_type: solution_quality
diff --git a/autosklearn/metalearning/files/precision_weighted_multiclass.classification_dense/feature_costs.arff b/autosklearn/metalearning/files/precision_weighted_multiclass.classification_dense/feature_costs.arff
index 0e17e20d22..8512fd9275 100644
--- a/autosklearn/metalearning/files/precision_weighted_multiclass.classification_dense/feature_costs.arff
+++ b/autosklearn/metalearning/files/precision_weighted_multiclass.classification_dense/feature_costs.arff
@@ -4,6 +4,7 @@
 @ATTRIBUTE repetition NUMERIC
 @ATTRIBUTE NumberOfInstances NUMERIC
 @ATTRIBUTE LogNumberOfInstances NUMERIC
+@ATTRIBUTE NumberOfClasses NUMERIC
 @ATTRIBUTE NumberOfFeatures NUMERIC
 @ATTRIBUTE LogNumberOfFeatures NUMERIC
 @ATTRIBUTE MissingValues NUMERIC
@@ -21,6 +22,11 @@
 @ATTRIBUTE LogDatasetRatio NUMERIC
 @ATTRIBUTE InverseDatasetRatio NUMERIC
 @ATTRIBUTE LogInverseDatasetRatio NUMERIC
+@ATTRIBUTE ClassOccurences NUMERIC
+@ATTRIBUTE ClassProbabilityMin NUMERIC
+@ATTRIBUTE ClassProbabilityMax NUMERIC
+@ATTRIBUTE ClassProbabilityMean NUMERIC
+@ATTRIBUTE ClassProbabilitySTD NUMERIC
 @ATTRIBUTE NumSymbols NUMERIC
 @ATTRIBUTE SymbolsMin NUMERIC
 @ATTRIBUTE SymbolsMax NUMERIC
@@ -37,162 +43,138 @@
 @ATTRIBUTE SkewnessMax NUMERIC
 @ATTRIBUTE SkewnessMean NUMERIC
 @ATTRIBUTE SkewnessSTD NUMERIC
+@ATTRIBUTE ClassEntropy NUMERIC
 
 @DATA
-75123,1.0,1e-05,1e-05,1e-05,1e-05,0.00253,0.00221,1e-05,0.00024,3e-05,0.00011,0.0,2e-05,2e-05,5e-05,8e-05,2e-05,1e-05,7e-05,1e-05,0.00072,1e-05,1e-05,0.00019,0.00041,0.00011,0.00061,0.00012,9e-05,0.00015,0.00025,0.00067,0.00014,0.00011,0.00016,0.00027
-75090,1.0,1e-05,1e-05,2e-05,2e-05,0.00815,0.00368,7e-05,0.00228,9e-05,0.00235,0.0,5e-05,5e-05,0.0001,0.00013,0.00013,1e-05,0.00021,2e-05,0.00064,0.0,0.0,0.00014,0.00038,0.00011,0.00066,0.00011,0.00014,0.00014,0.00026,0.00066,0.00011,0.00013,0.00015,0.00027
-2123,1.0,1e-05,1e-05,1e-05,1e-05,0.00097,0.00079,1e-05,0.00013,1e-05,7e-05,0.0,2e-05,2e-05,5e-05,8e-05,2e-05,1e-05,5e-05,1e-05,0.0007,1e-05,0.0,0.00017,0.00042,0.0001,0.00052,6e-05,8e-05,0.00012,0.00026,0.00049,6e-05,0.00011,9e-05,0.00023
-248,1.0,1e-05,1e-05,1e-05,1e-05,0.00088,0.00077,1e-05,0.0001,1e-05,4e-05,0.0,1e-05,1e-05,4e-05,5e-05,2e-05,1e-05,4e-05,1e-05,0.00041,1e-05,0.0,9e-05,0.00022,9e-05,0.00046,6e-05,8e-05,9e-05,0.00023,0.00049,7e-05,0.0001,0.0001,0.00021
-75101,1.0,1e-05,1e-05,1e-05,1e-05,0.0418,0.03546,1e-05,0.00389,1e-05,0.00248,0.0,2e-05,2e-05,4e-05,6e-05,2e-05,1e-05,5e-05,2e-05,0.00045,1e-05,0.0,0.00011,0.00023,0.0001,0.00041,6e-05,8e-05,0.0001,0.00018,0.00047,6e-05,0.0001,8e-05,0.00023
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-266,1.0,1e-05,1e-05,1e-05,1e-05,0.0018,0.00146,1e-05,0.00023,2e-05,0.00015,2e-05,2e-05,2e-05,6e-05,8e-05,3e-05,1e-05,6e-05,1e-05,0.00059,1e-05,1e-05,0.00014,0.00031,0.00013,0.0004,5e-05,7e-05,8e-05,0.0002,0.00044,8e-05,8e-05,8e-05,0.0002
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-75217,1.0,1e-05,1e-05,2e-05,1e-05,0.00196,0.00158,1e-05,0.00026,2e-05,0.00016,1e-05,3e-05,3e-05,7e-05,0.0001,3e-05,1e-05,7e-05,2e-05,0.00089,1e-05,1e-05,0.00022,0.00053,0.00013,0.00074,9e-05,0.0001,0.00019,0.00036,0.00065,0.00013,0.0001,0.00011,0.00031
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-2117,1.0,1e-05,1e-05,1e-05,1e-05,0.01735,0.0154,1e-05,0.00141,1e-05,0.00057,0.0,1e-05,1e-05,3e-05,6e-05,2e-05,1e-05,5e-05,1e-05,0.0004,1e-05,0.0,8e-05,0.00021,0.0001,0.00065,0.00012,0.00011,0.00015,0.00026,0.00055,7e-05,0.00014,9e-05,0.00025
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-75103,1.0,1e-05,1e-05,1e-05,1e-05,0.01302,0.00819,3e-05,0.00234,3e-05,0.00255,0.0,2e-05,2e-05,6e-05,0.00012,5e-05,1e-05,0.00011,1e-05,0.0005,1e-05,0.0,9e-05,0.00029,0.00011,0.00054,8e-05,0.00011,0.00011,0.00024,0.00066,7e-05,0.00015,0.00011,0.00032
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-75157,1.0,1e-05,1e-05,1e-05,1e-05,0.00125,0.00106,1e-05,0.00015,2e-05,7e-05,0.0,2e-05,2e-05,5e-05,6e-05,2e-05,1e-05,4e-05,1e-05,0.00048,2e-05,0.0,0.0001,0.00026,0.0001,0.00039,6e-05,7e-05,8e-05,0.00019,0.00043,6e-05,9e-05,8e-05,0.0002
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+75235,1.0,1e-05,1e-05,0.0003,2e-05,1e-05,0.00461,0.00385,2e-05,0.00055,2e-05,0.00026,1e-05,3e-05,3e-05,0.0001,0.00011,3e-05,1e-05,5e-05,2e-05,0.00026,3e-05,1e-05,8e-05,0.00014,0.00075,1e-05,1e-05,0.0002,0.00043,0.00011,0.00127,0.00017,0.0002,0.00029,0.00061,0.0008,0.00015,0.00012,0.00016,0.00038,0.00242
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+75249,1.0,1e-05,1e-05,0.00011,1e-05,1e-05,0.00192,0.00153,1e-05,0.00025,1e-05,0.00017,1e-05,2e-05,2e-05,5e-05,7e-05,2e-05,1e-05,5e-05,3e-05,0.00014,2e-05,1e-05,3e-05,8e-05,0.00029,1e-05,3e-05,6e-05,0.00012,7e-05,0.00041,6e-05,8e-05,0.0001,0.00017,0.00043,6e-05,7e-05,9e-05,0.00019,0.00092
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diff --git a/autosklearn/metalearning/files/precision_weighted_multiclass.classification_dense/feature_runstatus.arff b/autosklearn/metalearning/files/precision_weighted_multiclass.classification_dense/feature_runstatus.arff
index 0034ad2bd6..5fff2dfed3 100644
--- a/autosklearn/metalearning/files/precision_weighted_multiclass.classification_dense/feature_runstatus.arff
+++ b/autosklearn/metalearning/files/precision_weighted_multiclass.classification_dense/feature_runstatus.arff
@@ -4,6 +4,7 @@
 @ATTRIBUTE repetition NUMERIC
 @ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other}
@@ -21,6 +22,11 @@
 @ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other}
@@ -37,162 +43,138 @@
 @ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other}
 
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diff --git a/autosklearn/metalearning/files/precision_weighted_multiclass.classification_dense/feature_values.arff b/autosklearn/metalearning/files/precision_weighted_multiclass.classification_dense/feature_values.arff
index a60400e3ce..736dc64321 100644
--- a/autosklearn/metalearning/files/precision_weighted_multiclass.classification_dense/feature_values.arff
+++ b/autosklearn/metalearning/files/precision_weighted_multiclass.classification_dense/feature_values.arff
@@ -13,7 +13,6 @@
 @ATTRIBUTE KurtosisMean NUMERIC
 @ATTRIBUTE KurtosisMin NUMERIC
 @ATTRIBUTE KurtosisSTD NUMERIC
-@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC
 @ATTRIBUTE LogDatasetRatio NUMERIC
 @ATTRIBUTE LogInverseDatasetRatio NUMERIC
 @ATTRIBUTE LogNumberOfFeatures NUMERIC
@@ -42,160 +41,135 @@
 @ATTRIBUTE SymbolsSum NUMERIC
 
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diff --git a/autosklearn/metalearning/files/precision_weighted_multiclass.classification_sparse/algorithm_runs.arff b/autosklearn/metalearning/files/precision_weighted_multiclass.classification_sparse/algorithm_runs.arff
index b72eaae68e..c59d5e88ed 100644
--- a/autosklearn/metalearning/files/precision_weighted_multiclass.classification_sparse/algorithm_runs.arff
+++ b/autosklearn/metalearning/files/precision_weighted_multiclass.classification_sparse/algorithm_runs.arff
@@ -7,95 +7,133 @@
 @ATTRIBUTE runstatus {ok, timeout, memout, not_applicable, crash, other}
 
 @DATA
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diff --git a/autosklearn/metalearning/files/precision_weighted_multiclass.classification_sparse/configurations.csv b/autosklearn/metalearning/files/precision_weighted_multiclass.classification_sparse/configurations.csv
index ac395001fd..c479556821 100644
--- a/autosklearn/metalearning/files/precision_weighted_multiclass.classification_sparse/configurations.csv
+++ b/autosklearn/metalearning/files/precision_weighted_multiclass.classification_sparse/configurations.csv
@@ -1,93 +1,131 @@
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-14,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.6039096182414764,None,0.0,2,20,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.1939609823704447,most_frequent,robust_scaler,,,0.7331327008874781,0.29747488162877694,extra_trees_preproc_for_classification,True,entropy,None,0.33401603690569853,None,0.0,4,4,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-15,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7865652301288171,None,0.0,1,4,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,median,robust_scaler,,,0.8623635641193496,0.13298297278730625,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-16,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.905401147837377,-0.32832122529048147,3,1.001896309040499,poly,-1,False,5.1523484258030785e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-17,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.21794354428393548,None,0.0,2,16,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0025451910134387575,median,quantile_transformer,1477,uniform,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,,
-18,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5010952533165973,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.005838375247601407,mean,normalize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,,
-19,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5185406559495093,None,0.0,3,17,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.010000000000000004,median,normalize,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.13046020673147232,fpr,chi2,
-20,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.4426097579931897,None,0.0,16,4,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,mean,robust_scaler,,,0.7407323456920554,0.0804756921037867,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,,
-21,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5607168729746175,None,0.0,2,3,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.009810589644392154,most_frequent,quantile_transformer,1132,normal,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.06211089008167909,fdr,chi2,
-22,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9333237919291875,None,0.0,8,3,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,,
-23,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.557321265077371,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-24,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,22194.242946902177,0.45210849669021247,,0.0005669713702171556,sigmoid,-1,False,0.03542142266081451,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,quantile_transformer,291,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-25,weighting,extra_trees,,,,,,,,,,,,,,,True,gini,None,0.8424814442101096,None,0.0,17,16,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0012963479651174736,most_frequent,robust_scaler,,,0.7721224171367294,0.019641775400724187,extra_trees_preproc_for_classification,True,entropy,None,0.5260601497769445,None,0.0,2,12,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-26,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7422641456052917,None,0.0,8,15,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.005561351266169067,most_frequent,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-27,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.756167892711991e-05,False,True,squared_hinge,5.235791933868702e-05,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0001632172987320906,most_frequent,standardize,,,,,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,5,None,19,12,1.0,70,,,,,,
-28,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7088164344533493,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,mean,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,,
-29,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,10,1,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0022606253047811435,median,quantile_transformer,1247,normal,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,25.327793585266683,chi2,,,,
-30,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,24.786439230569382,,,0.03411197494635044,rbf,-1,True,0.003003351499585181,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0007994838629325874,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-31,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2681.998807317996,-0.9558745731731211,,0.0011299948421773457,sigmoid,-1,False,1.5292507354478086e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-32,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.8095522998548572,None,0.0,17,10,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.031940595071539304,mean,quantile_transformer,1563,normal,,,extra_trees_preproc_for_classification,False,entropy,None,0.9330145598603196,None,0.0,18,10,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-33,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,26.502259985828186,,,9.475662388405154e-05,rbf,-1,False,0.00019690635968217866,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-34,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.339600708038189,False,True,1,squared_hinge,ovr,l2,9.572641158475304e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,quantile_transformer,1193,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-35,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5255991794296913,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-36,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.34697723213923637,None,0.0,15,18,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.1923594385988973,mean,normalize,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,25.77499302945827,chi2,,,,
-37,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.7610308078756552,None,0.0,1,2,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.010000000000000004,mean,robust_scaler,,,0.9205545465103644,0.20272761060743316,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-38,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,249.12499213127998,,,0.0005283374995918348,rbf,-1,False,0.0016932707039261864,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.48910068367909115,most_frequent,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-39,none,bernoulli_nb,,,,,0.6524090653939782,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.005164169126808402,most_frequent,quantile_transformer,16,normal,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.033082145443631535,False,True,1,squared_hinge,ovr,l1,0.002615020898325393,,,,,,,,,,,,,,,,,,,,,
-40,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.6453555269772124,None,0.0,1,4,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,normalize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-41,weighting,extra_trees,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,13,18,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0001574994528707389,most_frequent,standardize,,,,,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,3,None,19,19,1.0,77,,,,,,
-42,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5138705053438934,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.14411802159506462,most_frequent,quantile_transformer,857,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-43,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,29.55731828912983,False,True,1,squared_hinge,ovr,l2,0.0011332486951954968,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,,
-44,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.37801720673725603,True,True,squared_hinge,0.0001255132393635067,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.025993178344125793,most_frequent,standardize,,,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,1.4421737618429475,False,True,1,squared_hinge,ovr,l1,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,
-45,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.10581257327556097,None,0.0,7,12,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.004219392676189224,median,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,,
-46,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.4188764793561401,None,0.0,5,5,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.010589444903605501,mean,robust_scaler,,,0.9796997860990453,0.27262832749694405,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-47,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0057657098053164255,False,True,squared_hinge,0.013468519636061941,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.484357187898904,mean,quantile_transformer,675,normal,,,kernel_pca,,,,,,,,,,,,,,cosine,1876,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-48,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.41019363453549706,None,0.0,15,2,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.0003507389224306018,median,none,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.20614098085343638,fdr,chi2,
-49,none,adaboost,SAMME,0.1280458968526125,7,125,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,robust_scaler,,,0.7156460334356771,0.2981902862926971,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,65.16171885308417,chi2,,,,
-50,none,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9931779675565967,None,0.0,18,17,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,robust_scaler,,,0.75,0.2945917010472818,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,,
-51,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.08646118576686276,None,0.0,4,9,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,most_frequent,quantile_transformer,959,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-52,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.6271213423927893,None,0.0,1,4,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.009403509495453675,mean,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,,
-53,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8.85547546146731,True,True,squared_hinge,4.0111304608748804e-05,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-54,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.34936025988397124,None,0.0,2,13,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00018601309647406987,median,robust_scaler,,,0.8634309348843472,0.26976989465367607,extra_trees_preproc_for_classification,False,entropy,None,0.48025175288975036,None,0.0,6,17,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-55,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.7308100742740112,None,0.0,18,19,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.014637629296743653,most_frequent,robust_scaler,,,0.7724670628916575,0.2233445340332948,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,29.07440444752882,False,True,1,squared_hinge,ovr,l1,0.00040479645225962257,,,,,,,,,,,,,,,,,,,,,
-56,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.014911374426433241,None,0.0,6,2,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,,
-57,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0016941428709087083,True,,,True,,optimal,squared_hinge,l1,,5.124265599423001e-05,no_encoding,no_coalescense,,most_frequent,quantile_transformer,461,uniform,,,extra_trees_preproc_for_classification,True,gini,None,0.31371431180257103,None,0.0,5,17,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-58,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.7591396576940418,-0.02447104417007273,2,0.7325180366342191,poly,-1,True,0.00045922794962860523,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,standardize,,,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,2.057731506094293,False,True,1,squared_hinge,ovr,l1,0.03575998247131604,,,,,,,,,,,,,,,,,,,,,
-59,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.593728171200453,None,0.0,7,20,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.02830941310359363,mean,normalize,,,,,extra_trees_preproc_for_classification,False,entropy,None,0.7389446586281172,None,0.0,20,11,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-60,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5542793596955801,None,0.0,1,3,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,most_frequent,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-61,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.46057831591617715,False,True,hinge,0.04557857428827514,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00027457445401600137,median,standardize,,,,,extra_trees_preproc_for_classification,True,gini,None,0.48190346970486964,None,0.0,17,18,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-62,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.46057831591617715,False,True,hinge,0.04557857428827514,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00027457445401600137,median,standardize,,,,,extra_trees_preproc_for_classification,True,gini,None,0.48190346970486964,None,0.0,17,18,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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-91,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.087313242780732,0.0636709914987994,3,0.004481917125024585,poly,-1,False,0.01848880618941367,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.002136015035740733,mean,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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+368,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.745748188733041e-05,True,True,hinge,2.181659959258924e-05,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.13921206193066876,most_frequent,quantile_transformer,60,normal,,,extra_trees_preproc_for_classification,False,entropy,None,0.7619669647366906,None,0.0,3,4,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+371,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4,1,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.01020427226149318,median,none,,,,,extra_trees_preproc_for_classification,True,entropy,None,0.3663460889081009,None,0.0,2,7,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
diff --git a/autosklearn/metalearning/files/precision_weighted_multiclass.classification_sparse/description.txt b/autosklearn/metalearning/files/precision_weighted_multiclass.classification_sparse/description.txt
index 2086584fd9..faa44e28be 100644
--- a/autosklearn/metalearning/files/precision_weighted_multiclass.classification_sparse/description.txt
+++ b/autosklearn/metalearning/files/precision_weighted_multiclass.classification_sparse/description.txt
@@ -1,8 +1,9 @@
 features_cutoff_time: 3600
 features_cutoff_memory: 3072
-number_of_feature_steps: 35
+number_of_feature_steps: 42
 feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances
 feature_step LogNumberOfInstances: LogNumberOfInstances
+feature_step NumberOfClasses: NumberOfClasses
 feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures
 feature_step LogNumberOfFeatures: LogNumberOfFeatures
 feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues
@@ -20,6 +21,11 @@ feature_step DatasetRatio: DatasetRatio, LogDatasetRatio
 feature_step LogDatasetRatio: LogDatasetRatio
 feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio
 feature_step LogInverseDatasetRatio: LogInverseDatasetRatio
+feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD
+feature_step ClassProbabilityMin: ClassProbabilityMin
+feature_step ClassProbabilityMax: ClassProbabilityMax
+feature_step ClassProbabilityMean: ClassProbabilityMean
+feature_step ClassProbabilitySTD: ClassProbabilitySTD
 feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum
 feature_step SymbolsMin: SymbolsMin
 feature_step SymbolsMax: SymbolsMax
@@ -36,11 +42,12 @@ feature_step SkewnessMin: SkewnessMin
 feature_step SkewnessMax: SkewnessMax
 feature_step SkewnessMean: SkewnessMean
 feature_step SkewnessSTD: SkewnessSTD
-features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
+feature_step ClassEntropy: ClassEntropy
+features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy
 features_stochastic: 
-default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
+default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy
 
-algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92
+algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235,236,237,238,239,240,241,242,243,244,245,246,247,248,249,250,251,252,253,254,255,256,257,258,259,260,261,262,263,264,265,266,267,268,269,270,271,272,273,274,275,276,277,278,279,280,281,282,283,284,285,286,287,288,289,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,312,313,314,315,316,317,318,319,320,321,322,323,324,325,326,327,328,329,330,331,332,333,334,335,336,337,338,339,340,341,342,343,344,345,346,347,348,349,350,351,352,353,354,355,356,357,358,359,360,361,362,363,364,365,366,367,368,369,370,371,372
 algorithms_stochastic: 
 performance_measures: precision_weighted
 performance_type: solution_quality
diff --git a/autosklearn/metalearning/files/precision_weighted_multiclass.classification_sparse/feature_costs.arff b/autosklearn/metalearning/files/precision_weighted_multiclass.classification_sparse/feature_costs.arff
index 0e17e20d22..8512fd9275 100644
--- a/autosklearn/metalearning/files/precision_weighted_multiclass.classification_sparse/feature_costs.arff
+++ b/autosklearn/metalearning/files/precision_weighted_multiclass.classification_sparse/feature_costs.arff
@@ -4,6 +4,7 @@
 @ATTRIBUTE repetition NUMERIC
 @ATTRIBUTE NumberOfInstances NUMERIC
 @ATTRIBUTE LogNumberOfInstances NUMERIC
+@ATTRIBUTE NumberOfClasses NUMERIC
 @ATTRIBUTE NumberOfFeatures NUMERIC
 @ATTRIBUTE LogNumberOfFeatures NUMERIC
 @ATTRIBUTE MissingValues NUMERIC
@@ -21,6 +22,11 @@
 @ATTRIBUTE LogDatasetRatio NUMERIC
 @ATTRIBUTE InverseDatasetRatio NUMERIC
 @ATTRIBUTE LogInverseDatasetRatio NUMERIC
+@ATTRIBUTE ClassOccurences NUMERIC
+@ATTRIBUTE ClassProbabilityMin NUMERIC
+@ATTRIBUTE ClassProbabilityMax NUMERIC
+@ATTRIBUTE ClassProbabilityMean NUMERIC
+@ATTRIBUTE ClassProbabilitySTD NUMERIC
 @ATTRIBUTE NumSymbols NUMERIC
 @ATTRIBUTE SymbolsMin NUMERIC
 @ATTRIBUTE SymbolsMax NUMERIC
@@ -37,162 +43,138 @@
 @ATTRIBUTE SkewnessMax NUMERIC
 @ATTRIBUTE SkewnessMean NUMERIC
 @ATTRIBUTE SkewnessSTD NUMERIC
+@ATTRIBUTE ClassEntropy NUMERIC
 
 @DATA
-75123,1.0,1e-05,1e-05,1e-05,1e-05,0.00253,0.00221,1e-05,0.00024,3e-05,0.00011,0.0,2e-05,2e-05,5e-05,8e-05,2e-05,1e-05,7e-05,1e-05,0.00072,1e-05,1e-05,0.00019,0.00041,0.00011,0.00061,0.00012,9e-05,0.00015,0.00025,0.00067,0.00014,0.00011,0.00016,0.00027
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-75187,1.0,1e-05,1e-05,2e-05,1e-05,0.00548,0.00473,1e-05,0.0005,2e-05,0.00029,1e-05,2e-05,2e-05,7e-05,9e-05,3e-05,1e-05,6e-05,1e-05,0.00062,1e-05,1e-05,0.00014,0.00033,0.00013,0.00038,7e-05,7e-05,8e-05,0.00016,0.00047,6e-05,9e-05,9e-05,0.00023
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-75217,1.0,1e-05,1e-05,2e-05,1e-05,0.00196,0.00158,1e-05,0.00026,2e-05,0.00016,1e-05,3e-05,3e-05,7e-05,0.0001,3e-05,1e-05,7e-05,2e-05,0.00089,1e-05,1e-05,0.00022,0.00053,0.00013,0.00074,9e-05,0.0001,0.00019,0.00036,0.00065,0.00013,0.0001,0.00011,0.00031
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-75157,1.0,1e-05,1e-05,1e-05,1e-05,0.00125,0.00106,1e-05,0.00015,2e-05,7e-05,0.0,2e-05,2e-05,5e-05,6e-05,2e-05,1e-05,4e-05,1e-05,0.00048,2e-05,0.0,0.0001,0.00026,0.0001,0.00039,6e-05,7e-05,8e-05,0.00019,0.00043,6e-05,9e-05,8e-05,0.0002
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+75243,1.0,1e-05,1e-05,0.00029,1e-05,1e-05,0.00461,0.00415,1e-05,0.00035,1e-05,0.00015,0.0,2e-05,2e-05,5e-05,7e-05,2e-05,1e-05,4e-05,1e-05,0.00018,2e-05,1e-05,4e-05,0.00011,0.00022,1e-05,1e-05,5e-05,0.00011,5e-05,0.0004,6e-05,6e-05,8e-05,0.0002,0.00039,6e-05,6e-05,8e-05,0.00019,0.00265
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+75249,1.0,1e-05,1e-05,0.00011,1e-05,1e-05,0.00192,0.00153,1e-05,0.00025,1e-05,0.00017,1e-05,2e-05,2e-05,5e-05,7e-05,2e-05,1e-05,5e-05,3e-05,0.00014,2e-05,1e-05,3e-05,8e-05,0.00029,1e-05,3e-05,6e-05,0.00012,7e-05,0.00041,6e-05,8e-05,0.0001,0.00017,0.00043,6e-05,7e-05,9e-05,0.00019,0.00092
+75250,1.0,1e-05,1e-05,0.00375,1e-05,1e-05,0.04994,0.047,1e-05,0.00247,1e-05,0.00049,1e-05,2e-05,2e-05,6e-05,7e-05,2e-05,1e-05,3e-05,1e-05,0.00022,4e-05,1e-05,4e-05,0.00012,0.00041,0.0,1e-05,0.00011,0.00023,6e-05,0.00076,0.00012,0.00012,0.00019,0.00032,0.00086,0.00012,0.00013,0.00018,0.00043,0.04178
diff --git a/autosklearn/metalearning/files/precision_weighted_multiclass.classification_sparse/feature_runstatus.arff b/autosklearn/metalearning/files/precision_weighted_multiclass.classification_sparse/feature_runstatus.arff
index 0034ad2bd6..5fff2dfed3 100644
--- a/autosklearn/metalearning/files/precision_weighted_multiclass.classification_sparse/feature_runstatus.arff
+++ b/autosklearn/metalearning/files/precision_weighted_multiclass.classification_sparse/feature_runstatus.arff
@@ -4,6 +4,7 @@
 @ATTRIBUTE repetition NUMERIC
 @ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other}
@@ -21,6 +22,11 @@
 @ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other}
@@ -37,162 +43,138 @@
 @ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other}
 
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diff --git a/autosklearn/metalearning/files/precision_weighted_multiclass.classification_sparse/feature_values.arff b/autosklearn/metalearning/files/precision_weighted_multiclass.classification_sparse/feature_values.arff
index a60400e3ce..736dc64321 100644
--- a/autosklearn/metalearning/files/precision_weighted_multiclass.classification_sparse/feature_values.arff
+++ b/autosklearn/metalearning/files/precision_weighted_multiclass.classification_sparse/feature_values.arff
@@ -13,7 +13,6 @@
 @ATTRIBUTE KurtosisMean NUMERIC
 @ATTRIBUTE KurtosisMin NUMERIC
 @ATTRIBUTE KurtosisSTD NUMERIC
-@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC
 @ATTRIBUTE LogDatasetRatio NUMERIC
 @ATTRIBUTE LogInverseDatasetRatio NUMERIC
 @ATTRIBUTE LogNumberOfFeatures NUMERIC
@@ -42,160 +41,135 @@
 @ATTRIBUTE SymbolsSum NUMERIC
 
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diff --git a/autosklearn/metalearning/files/r2_regression_dense/algorithm_runs.arff b/autosklearn/metalearning/files/r2_regression_dense/algorithm_runs.arff
index 669f2fe5f8..cb9164820c 100644
--- a/autosklearn/metalearning/files/r2_regression_dense/algorithm_runs.arff
+++ b/autosklearn/metalearning/files/r2_regression_dense/algorithm_runs.arff
@@ -7,27 +7,28 @@
 @ATTRIBUTE runstatus {ok, timeout, memout, not_applicable, crash, other}
 
 @DATA
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diff --git a/autosklearn/metalearning/files/r2_regression_dense/configurations.csv b/autosklearn/metalearning/files/r2_regression_dense/configurations.csv
index 0dc194dd38..c300d7f3da 100644
--- a/autosklearn/metalearning/files/r2_regression_dense/configurations.csv
+++ b/autosklearn/metalearning/files/r2_regression_dense/configurations.csv
@@ -1,25 +1,26 @@
 idx,data_preprocessing:categorical_transformer:categorical_encoding:__choice__,data_preprocessing:categorical_transformer:category_coalescence:__choice__,data_preprocessing:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction,data_preprocessing:numerical_transformer:imputation:strategy,data_preprocessing:numerical_transformer:rescaling:__choice__,data_preprocessing:numerical_transformer:rescaling:quantile_transformer:n_quantiles,data_preprocessing:numerical_transformer:rescaling:quantile_transformer:output_distribution,data_preprocessing:numerical_transformer:rescaling:robust_scaler:q_max,data_preprocessing:numerical_transformer:rescaling:robust_scaler:q_min,feature_preprocessor:__choice__,feature_preprocessor:extra_trees_preproc_for_regression:bootstrap,feature_preprocessor:extra_trees_preproc_for_regression:criterion,feature_preprocessor:extra_trees_preproc_for_regression:max_depth,feature_preprocessor:extra_trees_preproc_for_regression:max_features,feature_preprocessor:extra_trees_preproc_for_regression:max_leaf_nodes,feature_preprocessor:extra_trees_preproc_for_regression:min_samples_leaf,feature_preprocessor:extra_trees_preproc_for_regression:min_samples_split,feature_preprocessor:extra_trees_preproc_for_regression:min_weight_fraction_leaf,feature_preprocessor:extra_trees_preproc_for_regression:n_estimators,feature_preprocessor:fast_ica:algorithm,feature_preprocessor:fast_ica:fun,feature_preprocessor:fast_ica:n_components,feature_preprocessor:fast_ica:whiten,feature_preprocessor:feature_agglomeration:affinity,feature_preprocessor:feature_agglomeration:linkage,feature_preprocessor:feature_agglomeration:n_clusters,feature_preprocessor:feature_agglomeration:pooling_func,feature_preprocessor:kernel_pca:coef0,feature_preprocessor:kernel_pca:degree,feature_preprocessor:kernel_pca:gamma,feature_preprocessor:kernel_pca:kernel,feature_preprocessor:kernel_pca:n_components,feature_preprocessor:kitchen_sinks:gamma,feature_preprocessor:kitchen_sinks:n_components,feature_preprocessor:nystroem_sampler:coef0,feature_preprocessor:nystroem_sampler:degree,feature_preprocessor:nystroem_sampler:gamma,feature_preprocessor:nystroem_sampler:kernel,feature_preprocessor:nystroem_sampler:n_components,feature_preprocessor:pca:keep_variance,feature_preprocessor:pca:whiten,feature_preprocessor:polynomial:degree,feature_preprocessor:polynomial:include_bias,feature_preprocessor:polynomial:interaction_only,feature_preprocessor:random_trees_embedding:bootstrap,feature_preprocessor:random_trees_embedding:max_depth,feature_preprocessor:random_trees_embedding:max_leaf_nodes,feature_preprocessor:random_trees_embedding:min_samples_leaf,feature_preprocessor:random_trees_embedding:min_samples_split,feature_preprocessor:random_trees_embedding:min_weight_fraction_leaf,feature_preprocessor:random_trees_embedding:n_estimators,feature_preprocessor:select_percentile_regression:percentile,feature_preprocessor:select_percentile_regression:score_func,feature_preprocessor:select_rates_regression:alpha,feature_preprocessor:select_rates_regression:mode,feature_preprocessor:select_rates_regression:score_func,regressor:__choice__,regressor:adaboost:learning_rate,regressor:adaboost:loss,regressor:adaboost:max_depth,regressor:adaboost:n_estimators,regressor:ard_regression:alpha_1,regressor:ard_regression:alpha_2,regressor:ard_regression:fit_intercept,regressor:ard_regression:lambda_1,regressor:ard_regression:lambda_2,regressor:ard_regression:n_iter,regressor:ard_regression:threshold_lambda,regressor:ard_regression:tol,regressor:decision_tree:criterion,regressor:decision_tree:max_depth_factor,regressor:decision_tree:max_features,regressor:decision_tree:max_leaf_nodes,regressor:decision_tree:min_impurity_decrease,regressor:decision_tree:min_samples_leaf,regressor:decision_tree:min_samples_split,regressor:decision_tree:min_weight_fraction_leaf,regressor:extra_trees:bootstrap,regressor:extra_trees:criterion,regressor:extra_trees:max_depth,regressor:extra_trees:max_features,regressor:extra_trees:max_leaf_nodes,regressor:extra_trees:min_impurity_decrease,regressor:extra_trees:min_samples_leaf,regressor:extra_trees:min_samples_split,regressor:extra_trees:min_weight_fraction_leaf,regressor:gaussian_process:alpha,regressor:gaussian_process:thetaL,regressor:gaussian_process:thetaU,regressor:gradient_boosting:early_stop,regressor:gradient_boosting:l2_regularization,regressor:gradient_boosting:learning_rate,regressor:gradient_boosting:loss,regressor:gradient_boosting:max_bins,regressor:gradient_boosting:max_depth,regressor:gradient_boosting:max_leaf_nodes,regressor:gradient_boosting:min_samples_leaf,regressor:gradient_boosting:n_iter_no_change,regressor:gradient_boosting:scoring,regressor:gradient_boosting:tol,regressor:gradient_boosting:validation_fraction,regressor:k_nearest_neighbors:n_neighbors,regressor:k_nearest_neighbors:p,regressor:k_nearest_neighbors:weights,regressor:liblinear_svr:C,regressor:liblinear_svr:dual,regressor:liblinear_svr:epsilon,regressor:liblinear_svr:fit_intercept,regressor:liblinear_svr:intercept_scaling,regressor:liblinear_svr:loss,regressor:liblinear_svr:tol,regressor:libsvm_svr:C,regressor:libsvm_svr:coef0,regressor:libsvm_svr:degree,regressor:libsvm_svr:epsilon,regressor:libsvm_svr:gamma,regressor:libsvm_svr:kernel,regressor:libsvm_svr:max_iter,regressor:libsvm_svr:shrinking,regressor:libsvm_svr:tol,regressor:random_forest:bootstrap,regressor:random_forest:criterion,regressor:random_forest:max_depth,regressor:random_forest:max_features,regressor:random_forest:max_leaf_nodes,regressor:random_forest:min_impurity_decrease,regressor:random_forest:min_samples_leaf,regressor:random_forest:min_samples_split,regressor:random_forest:min_weight_fraction_leaf,regressor:sgd:alpha,regressor:sgd:average,regressor:sgd:epsilon,regressor:sgd:eta0,regressor:sgd:fit_intercept,regressor:sgd:l1_ratio,regressor:sgd:learning_rate,regressor:sgd:loss,regressor:sgd:penalty,regressor:sgd:power_t,regressor:sgd:tol
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+15,no_encoding,no_coalescense,,most_frequent,none,,,,,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00025844019785412086,rbf,305,,,,,,,,,,,,,,,,,,liblinear_svr,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,18080.177016227895,False,0.0017155220175954669,True,1,squared_epsilon_insensitive,7.035440846539455e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+17,no_encoding,no_coalescense,,most_frequent,robust_scaler,,,0.8280417820125114,0.0781634653874277,kitchen_sinks,,,,,,,,,,,,,,,,,,,,,,,8.438432240830361e-05,2984,,,,,,,,,,,,,,,,,,,,,,,ard_regression,,,,,0.00044036509169446026,4.039147500668822e-10,True,8.922721154590444e-05,3.0105431227198885e-05,300,1899.168836704701,0.011611373742389547,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+19,one_hot_encoding,no_coalescense,,mean,quantile_transformer,148,uniform,,,select_rates_regression,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.10789818795901182,fwe,f_regression,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,off,0.0002841741346637058,0.024253735132716756,least_squares,255,None,36,1,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+22,one_hot_encoding,no_coalescense,,most_frequent,standardize,,,,,fast_ica,,,,,,,,,,parallel,logcosh,215,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,off,4.242786820644205e-10,0.0953131401479951,least_squares,255,None,16,17,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+24,no_encoding,no_coalescense,,median,quantile_transformer,1150,uniform,,,kitchen_sinks,,,,,,,,,,,,,,,,,,,,,,,1.1605111479672479,5499,,,,,,,,,,,,,,,,,,,,,,,libsvm_svr,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,272.83550277553246,0.1471721071594263,2,0.18104912091393513,0.09926116813777154,poly,-1,True,0.06084368714049917,,,,,,,,,,,,,,,,,,,,
+30,no_encoding,no_coalescense,,most_frequent,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,friedman_mse,None,0.2961138557020546,None,0.0,14,12,0.0,,,,,,,,,,,
+34,no_encoding,no_coalescense,,most_frequent,robust_scaler,,,0.7049491520337817,0.23602880130851764,fast_ica,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,off,4.696295762197504e-10,0.03269357604116943,least_squares,255,None,32,14,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+36,one_hot_encoding,no_coalescense,,most_frequent,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,mse,None,0.1686847993740384,None,0.0,1,2,0.0,,,,,,,,,,,
+38,no_encoding,no_coalescense,,median,none,,,,,select_rates_regression,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.13294870400704156,fwe,f_regression,adaboost,0.47982456668237294,square,10,143,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+42,no_encoding,minority_coalescer,0.0074923422486941615,mean,quantile_transformer,919,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,mse,None,0.7102443468613595,None,0.0,20,4,0.0,,,,,,,,,,,
+45,no_encoding,minority_coalescer,0.00010476041523839592,median,standardize,,,,,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,-0.8964384900187328,,0.015021411517772181,sigmoid,7415,,,,,,,,,,,,,,,,,,ard_regression,,,,,4.544163312418526e-05,0.0003398091634662838,True,6.154987146709e-06,1.2041910604531783e-05,300,21462.056617690938,0.07078995384093524,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+48,one_hot_encoding,no_coalescense,,mean,quantile_transformer,148,uniform,,,select_rates_regression,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.10789818795901182,fwe,f_regression,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,off,0.0002841741346637058,0.024253735132716756,least_squares,255,None,36,1,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+51,one_hot_encoding,no_coalescense,,median,minmax,,,,,select_rates_regression,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.32764403627543237,fdr,f_regression,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,off,0.000153437102327148,0.01698754273672824,least_squares,255,None,47,76,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+52,no_encoding,no_coalescense,,most_frequent,quantile_transformer,1425,uniform,,,feature_agglomeration,,,,,,,,,,,,,,cosine,complete,276,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,off,0.00018459550741867383,0.04757728173371449,least_squares,255,None,16,6,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+57,one_hot_encoding,minority_coalescer,0.00924813449669181,mean,quantile_transformer,987,uniform,,,select_rates_regression,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fwe,f_regression,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,off,2.5833231171101403e-10,0.10682575320034993,least_squares,255,None,31,12,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+59,no_encoding,minority_coalescer,0.010000000000000004,most_frequent,robust_scaler,,,0.7414290235319753,0.2631696868735969,extra_trees_preproc_for_regression,True,mse,None,0.8529069417035176,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,extra_trees,,,,,,,,,,,,,,,,,,,,,True,mse,None,0.9698708516736436,None,0.0,4,2,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+62,one_hot_encoding,minority_coalescer,0.027537836751886195,median,robust_scaler,,,0.9003944797470034,0.2566610073208164,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,5,None,1,2,1.0,10,,,,,,decision_tree,,,,,,,,,,,,,friedman_mse,0.23884067765544847,1.0,None,0.0,11,13,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+66,no_encoding,minority_coalescer,0.006136459298464629,mean,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,extra_trees,,,,,,,,,,,,,,,,,,,,,False,friedman_mse,None,0.9490464657381125,None,0.0,1,6,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+68,one_hot_encoding,minority_coalescer,0.00924813449669181,mean,quantile_transformer,987,uniform,,,select_rates_regression,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fwe,f_regression,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,off,2.5833231171101403e-10,0.10682575320034993,least_squares,255,None,31,12,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+70,no_encoding,no_coalescense,,median,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,mse,None,0.9234161475923298,None,0.0,6,13,0.0,,,,,,,,,,,
diff --git a/autosklearn/metalearning/files/r2_regression_dense/description.txt b/autosklearn/metalearning/files/r2_regression_dense/description.txt
index 1260180cea..0768854d6d 100644
--- a/autosklearn/metalearning/files/r2_regression_dense/description.txt
+++ b/autosklearn/metalearning/files/r2_regression_dense/description.txt
@@ -40,7 +40,7 @@ features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfFeature
 features_stochastic: 
 default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
 
-algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24
+algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73
 algorithms_stochastic: 
 performance_measures: r2
 performance_type: solution_quality
diff --git a/autosklearn/metalearning/files/r2_regression_dense/feature_costs.arff b/autosklearn/metalearning/files/r2_regression_dense/feature_costs.arff
index 0e17e20d22..2c1c95a875 100644
--- a/autosklearn/metalearning/files/r2_regression_dense/feature_costs.arff
+++ b/autosklearn/metalearning/files/r2_regression_dense/feature_costs.arff
@@ -39,160 +39,28 @@
 @ATTRIBUTE SkewnessSTD NUMERIC
 
 @DATA
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-75217,1.0,1e-05,1e-05,2e-05,1e-05,0.00196,0.00158,1e-05,0.00026,2e-05,0.00016,1e-05,3e-05,3e-05,7e-05,0.0001,3e-05,1e-05,7e-05,2e-05,0.00089,1e-05,1e-05,0.00022,0.00053,0.00013,0.00074,9e-05,0.0001,0.00019,0.00036,0.00065,0.00013,0.0001,0.00011,0.00031
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diff --git a/autosklearn/metalearning/files/r2_regression_dense/feature_runstatus.arff b/autosklearn/metalearning/files/r2_regression_dense/feature_runstatus.arff
index 0034ad2bd6..0166848554 100644
--- a/autosklearn/metalearning/files/r2_regression_dense/feature_runstatus.arff
+++ b/autosklearn/metalearning/files/r2_regression_dense/feature_runstatus.arff
@@ -39,160 +39,28 @@
 @ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other}
 
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diff --git a/autosklearn/metalearning/files/r2_regression_dense/feature_values.arff b/autosklearn/metalearning/files/r2_regression_dense/feature_values.arff
index a60400e3ce..ee955516b5 100644
--- a/autosklearn/metalearning/files/r2_regression_dense/feature_values.arff
+++ b/autosklearn/metalearning/files/r2_regression_dense/feature_values.arff
@@ -2,24 +2,17 @@
 
 @ATTRIBUTE instance_id STRING
 @ATTRIBUTE repetition NUMERIC
-@ATTRIBUTE ClassEntropy NUMERIC
-@ATTRIBUTE ClassProbabilityMax NUMERIC
-@ATTRIBUTE ClassProbabilityMean NUMERIC
-@ATTRIBUTE ClassProbabilityMin NUMERIC
-@ATTRIBUTE ClassProbabilitySTD NUMERIC
 @ATTRIBUTE DatasetRatio NUMERIC
 @ATTRIBUTE InverseDatasetRatio NUMERIC
 @ATTRIBUTE KurtosisMax NUMERIC
 @ATTRIBUTE KurtosisMean NUMERIC
 @ATTRIBUTE KurtosisMin NUMERIC
 @ATTRIBUTE KurtosisSTD NUMERIC
-@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC
 @ATTRIBUTE LogDatasetRatio NUMERIC
 @ATTRIBUTE LogInverseDatasetRatio NUMERIC
 @ATTRIBUTE LogNumberOfFeatures NUMERIC
 @ATTRIBUTE LogNumberOfInstances NUMERIC
 @ATTRIBUTE NumberOfCategoricalFeatures NUMERIC
-@ATTRIBUTE NumberOfClasses NUMERIC
 @ATTRIBUTE NumberOfFeatures NUMERIC
 @ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC
 @ATTRIBUTE NumberOfInstances NUMERIC
@@ -42,160 +35,28 @@
 @ATTRIBUTE SymbolsSum NUMERIC
 
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diff --git a/autosklearn/metalearning/files/r2_regression_sparse/algorithm_runs.arff b/autosklearn/metalearning/files/r2_regression_sparse/algorithm_runs.arff
index 669f2fe5f8..cb9164820c 100644
--- a/autosklearn/metalearning/files/r2_regression_sparse/algorithm_runs.arff
+++ b/autosklearn/metalearning/files/r2_regression_sparse/algorithm_runs.arff
@@ -7,27 +7,28 @@
 @ATTRIBUTE runstatus {ok, timeout, memout, not_applicable, crash, other}
 
 @DATA
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diff --git a/autosklearn/metalearning/files/r2_regression_sparse/configurations.csv b/autosklearn/metalearning/files/r2_regression_sparse/configurations.csv
index 0dc194dd38..c300d7f3da 100644
--- a/autosklearn/metalearning/files/r2_regression_sparse/configurations.csv
+++ b/autosklearn/metalearning/files/r2_regression_sparse/configurations.csv
@@ -1,25 +1,26 @@
 idx,data_preprocessing:categorical_transformer:categorical_encoding:__choice__,data_preprocessing:categorical_transformer:category_coalescence:__choice__,data_preprocessing:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction,data_preprocessing:numerical_transformer:imputation:strategy,data_preprocessing:numerical_transformer:rescaling:__choice__,data_preprocessing:numerical_transformer:rescaling:quantile_transformer:n_quantiles,data_preprocessing:numerical_transformer:rescaling:quantile_transformer:output_distribution,data_preprocessing:numerical_transformer:rescaling:robust_scaler:q_max,data_preprocessing:numerical_transformer:rescaling:robust_scaler:q_min,feature_preprocessor:__choice__,feature_preprocessor:extra_trees_preproc_for_regression:bootstrap,feature_preprocessor:extra_trees_preproc_for_regression:criterion,feature_preprocessor:extra_trees_preproc_for_regression:max_depth,feature_preprocessor:extra_trees_preproc_for_regression:max_features,feature_preprocessor:extra_trees_preproc_for_regression:max_leaf_nodes,feature_preprocessor:extra_trees_preproc_for_regression:min_samples_leaf,feature_preprocessor:extra_trees_preproc_for_regression:min_samples_split,feature_preprocessor:extra_trees_preproc_for_regression:min_weight_fraction_leaf,feature_preprocessor:extra_trees_preproc_for_regression:n_estimators,feature_preprocessor:fast_ica:algorithm,feature_preprocessor:fast_ica:fun,feature_preprocessor:fast_ica:n_components,feature_preprocessor:fast_ica:whiten,feature_preprocessor:feature_agglomeration:affinity,feature_preprocessor:feature_agglomeration:linkage,feature_preprocessor:feature_agglomeration:n_clusters,feature_preprocessor:feature_agglomeration:pooling_func,feature_preprocessor:kernel_pca:coef0,feature_preprocessor:kernel_pca:degree,feature_preprocessor:kernel_pca:gamma,feature_preprocessor:kernel_pca:kernel,feature_preprocessor:kernel_pca:n_components,feature_preprocessor:kitchen_sinks:gamma,feature_preprocessor:kitchen_sinks:n_components,feature_preprocessor:nystroem_sampler:coef0,feature_preprocessor:nystroem_sampler:degree,feature_preprocessor:nystroem_sampler:gamma,feature_preprocessor:nystroem_sampler:kernel,feature_preprocessor:nystroem_sampler:n_components,feature_preprocessor:pca:keep_variance,feature_preprocessor:pca:whiten,feature_preprocessor:polynomial:degree,feature_preprocessor:polynomial:include_bias,feature_preprocessor:polynomial:interaction_only,feature_preprocessor:random_trees_embedding:bootstrap,feature_preprocessor:random_trees_embedding:max_depth,feature_preprocessor:random_trees_embedding:max_leaf_nodes,feature_preprocessor:random_trees_embedding:min_samples_leaf,feature_preprocessor:random_trees_embedding:min_samples_split,feature_preprocessor:random_trees_embedding:min_weight_fraction_leaf,feature_preprocessor:random_trees_embedding:n_estimators,feature_preprocessor:select_percentile_regression:percentile,feature_preprocessor:select_percentile_regression:score_func,feature_preprocessor:select_rates_regression:alpha,feature_preprocessor:select_rates_regression:mode,feature_preprocessor:select_rates_regression:score_func,regressor:__choice__,regressor:adaboost:learning_rate,regressor:adaboost:loss,regressor:adaboost:max_depth,regressor:adaboost:n_estimators,regressor:ard_regression:alpha_1,regressor:ard_regression:alpha_2,regressor:ard_regression:fit_intercept,regressor:ard_regression:lambda_1,regressor:ard_regression:lambda_2,regressor:ard_regression:n_iter,regressor:ard_regression:threshold_lambda,regressor:ard_regression:tol,regressor:decision_tree:criterion,regressor:decision_tree:max_depth_factor,regressor:decision_tree:max_features,regressor:decision_tree:max_leaf_nodes,regressor:decision_tree:min_impurity_decrease,regressor:decision_tree:min_samples_leaf,regressor:decision_tree:min_samples_split,regressor:decision_tree:min_weight_fraction_leaf,regressor:extra_trees:bootstrap,regressor:extra_trees:criterion,regressor:extra_trees:max_depth,regressor:extra_trees:max_features,regressor:extra_trees:max_leaf_nodes,regressor:extra_trees:min_impurity_decrease,regressor:extra_trees:min_samples_leaf,regressor:extra_trees:min_samples_split,regressor:extra_trees:min_weight_fraction_leaf,regressor:gaussian_process:alpha,regressor:gaussian_process:thetaL,regressor:gaussian_process:thetaU,regressor:gradient_boosting:early_stop,regressor:gradient_boosting:l2_regularization,regressor:gradient_boosting:learning_rate,regressor:gradient_boosting:loss,regressor:gradient_boosting:max_bins,regressor:gradient_boosting:max_depth,regressor:gradient_boosting:max_leaf_nodes,regressor:gradient_boosting:min_samples_leaf,regressor:gradient_boosting:n_iter_no_change,regressor:gradient_boosting:scoring,regressor:gradient_boosting:tol,regressor:gradient_boosting:validation_fraction,regressor:k_nearest_neighbors:n_neighbors,regressor:k_nearest_neighbors:p,regressor:k_nearest_neighbors:weights,regressor:liblinear_svr:C,regressor:liblinear_svr:dual,regressor:liblinear_svr:epsilon,regressor:liblinear_svr:fit_intercept,regressor:liblinear_svr:intercept_scaling,regressor:liblinear_svr:loss,regressor:liblinear_svr:tol,regressor:libsvm_svr:C,regressor:libsvm_svr:coef0,regressor:libsvm_svr:degree,regressor:libsvm_svr:epsilon,regressor:libsvm_svr:gamma,regressor:libsvm_svr:kernel,regressor:libsvm_svr:max_iter,regressor:libsvm_svr:shrinking,regressor:libsvm_svr:tol,regressor:random_forest:bootstrap,regressor:random_forest:criterion,regressor:random_forest:max_depth,regressor:random_forest:max_features,regressor:random_forest:max_leaf_nodes,regressor:random_forest:min_impurity_decrease,regressor:random_forest:min_samples_leaf,regressor:random_forest:min_samples_split,regressor:random_forest:min_weight_fraction_leaf,regressor:sgd:alpha,regressor:sgd:average,regressor:sgd:epsilon,regressor:sgd:eta0,regressor:sgd:fit_intercept,regressor:sgd:l1_ratio,regressor:sgd:learning_rate,regressor:sgd:loss,regressor:sgd:penalty,regressor:sgd:power_t,regressor:sgd:tol
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-20,one_hot_encoding,no_coalescense,,mean,robust_scaler,,,0.8821344085141198,0.20696951294776308,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,10,None,3,15,1.0,33,,,,,,decision_tree,,,,,,,,,,,,,friedman_mse,0.8666369072341655,1.0,None,0.0,19,7,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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+1,one_hot_encoding,no_coalescense,,mean,standardize,,,,,select_rates_regression,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.40612979303062824,fdr,f_regression,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,off,2.2742572602302616e-08,0.02539518548794612,least_squares,255,None,57,10,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+2,one_hot_encoding,minority_coalescer,0.010000000000000004,most_frequent,quantile_transformer,1818,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,mse,None,0.9744859278597818,None,0.0,11,2,0.0,,,,,,,,,,,
+5,no_encoding,minority_coalescer,0.06912558240513342,median,quantile_transformer,119,uniform,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,off,3.79271698524212e-09,0.025159826241956527,least_squares,255,None,42,29,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+9,no_encoding,minority_coalescer,0.014087234899150132,most_frequent,minmax,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,libsvm_svr,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,243.80018215429655,,5,0.0010313680328976054,0.12602136380125653,rbf,-1,True,0.0003015355281210108,,,,,,,,,,,,,,,,,,,,
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+15,no_encoding,no_coalescense,,most_frequent,none,,,,,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00025844019785412086,rbf,305,,,,,,,,,,,,,,,,,,liblinear_svr,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,18080.177016227895,False,0.0017155220175954669,True,1,squared_epsilon_insensitive,7.035440846539455e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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+36,one_hot_encoding,no_coalescense,,most_frequent,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,mse,None,0.1686847993740384,None,0.0,1,2,0.0,,,,,,,,,,,
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+42,no_encoding,minority_coalescer,0.0074923422486941615,mean,quantile_transformer,919,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,mse,None,0.7102443468613595,None,0.0,20,4,0.0,,,,,,,,,,,
+45,no_encoding,minority_coalescer,0.00010476041523839592,median,standardize,,,,,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,-0.8964384900187328,,0.015021411517772181,sigmoid,7415,,,,,,,,,,,,,,,,,,ard_regression,,,,,4.544163312418526e-05,0.0003398091634662838,True,6.154987146709e-06,1.2041910604531783e-05,300,21462.056617690938,0.07078995384093524,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+48,one_hot_encoding,no_coalescense,,mean,quantile_transformer,148,uniform,,,select_rates_regression,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.10789818795901182,fwe,f_regression,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,off,0.0002841741346637058,0.024253735132716756,least_squares,255,None,36,1,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+51,one_hot_encoding,no_coalescense,,median,minmax,,,,,select_rates_regression,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.32764403627543237,fdr,f_regression,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,off,0.000153437102327148,0.01698754273672824,least_squares,255,None,47,76,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+52,no_encoding,no_coalescense,,most_frequent,quantile_transformer,1425,uniform,,,feature_agglomeration,,,,,,,,,,,,,,cosine,complete,276,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,off,0.00018459550741867383,0.04757728173371449,least_squares,255,None,16,6,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+57,one_hot_encoding,minority_coalescer,0.00924813449669181,mean,quantile_transformer,987,uniform,,,select_rates_regression,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fwe,f_regression,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,off,2.5833231171101403e-10,0.10682575320034993,least_squares,255,None,31,12,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+59,no_encoding,minority_coalescer,0.010000000000000004,most_frequent,robust_scaler,,,0.7414290235319753,0.2631696868735969,extra_trees_preproc_for_regression,True,mse,None,0.8529069417035176,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,extra_trees,,,,,,,,,,,,,,,,,,,,,True,mse,None,0.9698708516736436,None,0.0,4,2,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+62,one_hot_encoding,minority_coalescer,0.027537836751886195,median,robust_scaler,,,0.9003944797470034,0.2566610073208164,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,5,None,1,2,1.0,10,,,,,,decision_tree,,,,,,,,,,,,,friedman_mse,0.23884067765544847,1.0,None,0.0,11,13,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+66,no_encoding,minority_coalescer,0.006136459298464629,mean,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,extra_trees,,,,,,,,,,,,,,,,,,,,,False,friedman_mse,None,0.9490464657381125,None,0.0,1,6,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+68,one_hot_encoding,minority_coalescer,0.00924813449669181,mean,quantile_transformer,987,uniform,,,select_rates_regression,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fwe,f_regression,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,off,2.5833231171101403e-10,0.10682575320034993,least_squares,255,None,31,12,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+70,no_encoding,no_coalescense,,median,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,mse,None,0.9234161475923298,None,0.0,6,13,0.0,,,,,,,,,,,
diff --git a/autosklearn/metalearning/files/r2_regression_sparse/description.txt b/autosklearn/metalearning/files/r2_regression_sparse/description.txt
index 1260180cea..0768854d6d 100644
--- a/autosklearn/metalearning/files/r2_regression_sparse/description.txt
+++ b/autosklearn/metalearning/files/r2_regression_sparse/description.txt
@@ -40,7 +40,7 @@ features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfFeature
 features_stochastic: 
 default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
 
-algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24
+algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73
 algorithms_stochastic: 
 performance_measures: r2
 performance_type: solution_quality
diff --git a/autosklearn/metalearning/files/r2_regression_sparse/feature_costs.arff b/autosklearn/metalearning/files/r2_regression_sparse/feature_costs.arff
index 0e17e20d22..2c1c95a875 100644
--- a/autosklearn/metalearning/files/r2_regression_sparse/feature_costs.arff
+++ b/autosklearn/metalearning/files/r2_regression_sparse/feature_costs.arff
@@ -39,160 +39,28 @@
 @ATTRIBUTE SkewnessSTD NUMERIC
 
 @DATA
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-75148,1.0,1e-05,1e-05,2e-05,2e-05,0.00129,0.00115,1e-05,0.00012,1e-05,4e-05,0.0,2e-05,2e-05,4e-05,5e-05,2e-05,1e-05,4e-05,1e-05,0.0004,1e-05,0.0,9e-05,0.00021,9e-05,0.00036,7e-05,7e-05,8e-05,0.00015,0.00041,6e-05,8e-05,8e-05,0.00019
-75103,1.0,1e-05,1e-05,1e-05,1e-05,0.01302,0.00819,3e-05,0.00234,3e-05,0.00255,0.0,2e-05,2e-05,6e-05,0.00012,5e-05,1e-05,0.00011,1e-05,0.0005,1e-05,0.0,9e-05,0.00029,0.00011,0.00054,8e-05,0.00011,0.00011,0.00024,0.00066,7e-05,0.00015,0.00011,0.00032
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-75157,1.0,1e-05,1e-05,1e-05,1e-05,0.00125,0.00106,1e-05,0.00015,2e-05,7e-05,0.0,2e-05,2e-05,5e-05,6e-05,2e-05,1e-05,4e-05,1e-05,0.00048,2e-05,0.0,0.0001,0.00026,0.0001,0.00039,6e-05,7e-05,8e-05,0.00019,0.00043,6e-05,9e-05,8e-05,0.0002
-75092,1.0,1e-05,1e-05,1e-05,1e-05,0.00099,0.00072,1e-05,0.00018,1e-05,0.00011,0.0,2e-05,2e-05,4e-05,6e-05,2e-05,1e-05,5e-05,1e-05,0.00058,1e-05,0.0,0.00014,0.00034,9e-05,0.00041,6e-05,8e-05,9e-05,0.00017,0.00045,7e-05,0.0001,9e-05,0.00019
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diff --git a/autosklearn/metalearning/files/r2_regression_sparse/feature_runstatus.arff b/autosklearn/metalearning/files/r2_regression_sparse/feature_runstatus.arff
index 0034ad2bd6..0166848554 100644
--- a/autosklearn/metalearning/files/r2_regression_sparse/feature_runstatus.arff
+++ b/autosklearn/metalearning/files/r2_regression_sparse/feature_runstatus.arff
@@ -39,160 +39,28 @@
 @ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other}
 
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diff --git a/autosklearn/metalearning/files/r2_regression_sparse/feature_values.arff b/autosklearn/metalearning/files/r2_regression_sparse/feature_values.arff
index a60400e3ce..ee955516b5 100644
--- a/autosklearn/metalearning/files/r2_regression_sparse/feature_values.arff
+++ b/autosklearn/metalearning/files/r2_regression_sparse/feature_values.arff
@@ -2,24 +2,17 @@
 
 @ATTRIBUTE instance_id STRING
 @ATTRIBUTE repetition NUMERIC
-@ATTRIBUTE ClassEntropy NUMERIC
-@ATTRIBUTE ClassProbabilityMax NUMERIC
-@ATTRIBUTE ClassProbabilityMean NUMERIC
-@ATTRIBUTE ClassProbabilityMin NUMERIC
-@ATTRIBUTE ClassProbabilitySTD NUMERIC
 @ATTRIBUTE DatasetRatio NUMERIC
 @ATTRIBUTE InverseDatasetRatio NUMERIC
 @ATTRIBUTE KurtosisMax NUMERIC
 @ATTRIBUTE KurtosisMean NUMERIC
 @ATTRIBUTE KurtosisMin NUMERIC
 @ATTRIBUTE KurtosisSTD NUMERIC
-@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC
 @ATTRIBUTE LogDatasetRatio NUMERIC
 @ATTRIBUTE LogInverseDatasetRatio NUMERIC
 @ATTRIBUTE LogNumberOfFeatures NUMERIC
 @ATTRIBUTE LogNumberOfInstances NUMERIC
 @ATTRIBUTE NumberOfCategoricalFeatures NUMERIC
-@ATTRIBUTE NumberOfClasses NUMERIC
 @ATTRIBUTE NumberOfFeatures NUMERIC
 @ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC
 @ATTRIBUTE NumberOfInstances NUMERIC
@@ -42,160 +35,28 @@
 @ATTRIBUTE SymbolsSum NUMERIC
 
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+4835,1.0,0.002145922746781116,466.0,0.8862950801849365,0.45250606536865234,-0.651503324508667,0.5315639913043118,-6.144185634125646,6.144185634125646,1.791759469228055,7.935945103353701,0.0,6.0,0.0,2796.0,0.0,0.0,6.0,0.0,0.0,0.0,0.0,0.0,0.5836177468299866,0.19801302642251054,-0.7145333290100098,0.45392537178033815,0.0,0.0,0.0,0.0,0.0
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+4885,1.0,0.00027254640102477445,3669.1,-1.188598230219335,-1.1964564993963673,-1.2026724815368652,0.003664290259953897,-8.2077016793668,8.2077016793668,2.302585092994046,10.510286772360844,0.0,10.0,0.0,36691.0,0.0,0.0,10.0,0.0,0.0,0.0,0.0,0.0,0.018026534467935562,0.0004419666947796941,-0.011050965636968613,0.007399082491716184,0.0,0.0,0.0,0.0,0.0
+4892,1.0,0.0028486163863266412,351.04761904761904,576.329345703125,81.51044419461924,0.9639408588409424,139.3485299654942,-5.860921881066291,5.860921881066291,3.044522437723423,8.905444318789714,0.0,21.0,0.0,7372.0,0.0,0.0,21.0,0.0,0.0,0.0,0.0,0.0,20.96137046813965,5.398353062924885,-0.7831974029541016,4.943832847158382,0.0,0.0,0.0,0.0,0.0
+4893,1.0,0.0007802974884174591,1281.5625,6719.606795643044,437.862979925658,-1.0920182685895878,1622.03517764269,-7.155835315586378,7.155835315586378,2.772588722239781,9.92842403782616,0.0,16.0,0.0,20505.0,0.0,0.0,16.0,0.0,0.0,0.0,0.0,0.0,70.76852416992188,5.9781048079021275,-5.867455959320068,16.97724081800652,0.0,0.0,0.0,0.0,0.0
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diff --git a/autosklearn/metalearning/files/recall_binary.classification_dense/algorithm_runs.arff b/autosklearn/metalearning/files/recall_binary.classification_dense/algorithm_runs.arff
index 88c7bbfadc..f64886d029 100644
--- a/autosklearn/metalearning/files/recall_binary.classification_dense/algorithm_runs.arff
+++ b/autosklearn/metalearning/files/recall_binary.classification_dense/algorithm_runs.arff
@@ -7,130 +7,135 @@
 @ATTRIBUTE runstatus {ok, timeout, memout, not_applicable, crash, other}
 
 @DATA
-75119,1.0,1,0.008264462809917328,ok
-75212,1.0,2,0.21658986175115202,ok
-252,1.0,3,?,not_applicable
-246,1.0,4,?,not_applicable
-75178,1.0,5,?,not_applicable
-75239,1.0,6,0.0,ok
-75171,1.0,7,0.15085884988797615,ok
-75233,1.0,8,0.03620873269435565,ok
-248,1.0,9,?,not_applicable
-75231,1.0,10,?,not_applicable
-2123,1.0,11,?,not_applicable
-75196,1.0,12,0.0,ok
-75188,1.0,13,?,not_applicable
-75092,1.0,14,0.019230769230769273,ok
-75248,1.0,15,0.0,ok
-75126,1.0,16,0.02460850111856827,ok
-75234,1.0,17,0.016393442622950838,ok
-75150,1.0,18,0.138728323699422,ok
-258,1.0,19,?,not_applicable
-75168,1.0,20,?,not_applicable
-75235,1.0,21,?,not_applicable
-75159,1.0,22,0.1785714285714286,ok
-244,1.0,23,?,not_applicable
-75221,1.0,24,?,not_applicable
-75219,1.0,25,0.05558045719408333,ok
-75202,1.0,26,?,not_applicable
-3043,1.0,27,0.060975609756097615,ok
-75205,1.0,28,?,not_applicable
-75174,1.0,29,0.13559322033898302,ok
-288,1.0,30,?,not_applicable
-75250,1.0,31,?,not_applicable
-275,1.0,32,?,not_applicable
-75142,1.0,33,0.06396808982124391,ok
-75213,1.0,34,0.08139534883720934,ok
-75099,1.0,35,0.2038834951456311,ok
-75243,1.0,36,?,not_applicable
-75184,1.0,37,0.21055684454756385,ok
-75222,1.0,38,0.0714285714285714,ok
-75175,1.0,39,0.13841201716738194,ok
-233,1.0,40,0.0020408163265306367,ok
-75161,1.0,41,0.0625275856995734,ok
-75176,1.0,42,0.01466352448284891,ok
-75090,1.0,43,?,not_applicable
-75114,1.0,44,0.015037593984962405,ok
-260,1.0,45,?,not_applicable
-236,1.0,46,?,not_applicable
-75141,1.0,47,0.038532110091743066,ok
-75107,1.0,48,0.3553054662379421,ok
-262,1.0,49,?,not_applicable
-75146,1.0,50,0.09509433962264147,ok
-75189,1.0,51,0.0166136443973135,ok
-2350,1.0,52,0.3952563751028242,ok
-253,1.0,53,?,not_applicable
-2122,1.0,54,?,not_applicable
-75110,1.0,55,?,not_applicable
-75249,1.0,56,0.010204081632653073,ok
-75108,1.0,57,0.0,ok
-242,1.0,58,?,not_applicable
-75117,1.0,59,0.042735042735042694,ok
-75191,1.0,60,0.1738918060813286,ok
-75226,1.0,61,0.0,ok
-261,1.0,62,0.23232323232323238,ok
-75236,1.0,63,?,not_applicable
-75095,1.0,64,0.048192771084337394,ok
-75148,1.0,65,0.1083172147001934,ok
-75093,1.0,66,0.2784431137724551,ok
-75223,1.0,67,?,not_applicable
-75244,1.0,68,0.07179487179487176,ok
-75109,1.0,69,?,not_applicable
-75197,1.0,70,?,not_applicable
-75127,1.0,71,0.37921695094605834,ok
-75143,1.0,72,0.007936507936507908,ok
-75153,1.0,73,0.08563134978229314,ok
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-75215,1.0,75,0.01603498542274051,ok
-75195,1.0,76,0.00025025025025027237,ok
-75207,1.0,77,?,not_applicable
-266,1.0,78,?,not_applicable
-75225,1.0,79,0.125,ok
-75166,1.0,80,0.08666164280331579,ok
-75100,1.0,81,0.2857142857142857,ok
-75169,1.0,82,?,not_applicable
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-273,1.0,84,0.05254237288135588,ok
-75121,1.0,85,0.0,ok
-75098,1.0,86,?,not_applicable
-75115,1.0,87,0.010683760683760646,ok
-75116,1.0,88,0.007025761124121788,ok
-75185,1.0,89,0.1368015414258189,ok
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-75157,1.0,91,0.5691823899371069,ok
-75113,1.0,92,0.0,ok
-75134,1.0,93,?,not_applicable
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-75182,1.0,96,0.16078790655061836,ok
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-75125,1.0,100,0.012048192771084376,ok
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-75103,1.0,102,0.0,ok
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-75139,1.0,105,0.012232415902140636,ok
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-75177,1.0,117,0.060975609756097615,ok
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+75177,1.0,425,0.060975609756097615,ok
+75092,1.0,38,0.019230769230769273,ok
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+75233,1.0,102,0.028221512247071368,ok
+75182,1.0,26,0.15116811726981216,ok
+253,1.0,27,?,not_applicable
+75157,1.0,87,0.3113207547169812,ok
+75187,1.0,267,0.018425460636515956,ok
+75124,1.0,240,0.1920529801324503,ok
+75090,1.0,32,?,not_applicable
+75222,1.0,183,0.0714285714285714,ok
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+75185,1.0,325,0.1329479768786127,ok
+2123,1.0,39,?,not_applicable
+75150,1.0,41,0.138728323699422,ok
+75143,1.0,43,0.0009920634920634885,ok
+75196,1.0,44,0.0,ok
+75188,1.0,49,?,not_applicable
+75248,1.0,57,0.0,ok
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+75113,1.0,59,0.0,ok
+75126,1.0,61,0.01118568232662187,ok
+288,1.0,64,?,not_applicable
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+75168,1.0,77,?,not_applicable
+75148,1.0,276,0.09671179883945846,ok
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+75159,1.0,229,0.1071428571428571,ok
+75146,1.0,88,0.09169811320754717,ok
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+75141,1.0,90,0.003669724770642202,ok
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+75219,1.0,213,0.023307933662034985,ok
+75202,1.0,96,?,not_applicable
+3043,1.0,99,0.08536585365853655,ok
+75205,1.0,103,?,not_applicable
+75174,1.0,109,0.12230874942739345,ok
+75250,1.0,111,?,not_applicable
+75179,1.0,453,0.17639751552795035,ok
+275,1.0,115,?,not_applicable
+242,1.0,116,?,not_applicable
+75207,1.0,117,?,not_applicable
+75142,1.0,121,0.06426355443935594,ok
+75099,1.0,125,0.11650485436893199,ok
+75243,1.0,126,?,not_applicable
+75175,1.0,259,0.13590844062947072,ok
+233,1.0,134,0.0020408163265306367,ok
+75161,1.0,139,0.058408121229954424,ok
+75176,1.0,328,0.01335428122545168,ok
+262,1.0,146,?,not_applicable
+75129,1.0,147,0.15384615384615385,ok
+261,1.0,148,0.2727272727272727,ok
+75114,1.0,153,0.007518796992481258,ok
+75093,1.0,231,0.3338323353293413,ok
+260,1.0,158,?,not_applicable
+236,1.0,160,?,not_applicable
+254,1.0,166,0.0,ok
+75107,1.0,170,0.2821543408360129,ok
+75181,1.0,175,?,not_applicable
+75189,1.0,187,0.015934587263492728,ok
+75163,1.0,354,0.07427536231884058,ok
+2350,1.0,191,0.4111598574170551,ok
+2122,1.0,195,?,not_applicable
+75110,1.0,198,?,not_applicable
+75213,1.0,394,0.011627906976744207,ok
+75095,1.0,284,0.012048192771084376,ok
+75108,1.0,207,0.0,ok
+75117,1.0,211,0.0064102564102563875,ok
+75191,1.0,217,0.1774941995359629,ok
+75226,1.0,219,0.0,ok
+75244,1.0,241,0.05641025641025643,ok
+75236,1.0,223,?,not_applicable
+75169,1.0,227,?,not_applicable
+75116,1.0,228,0.004683840749414525,ok
+75223,1.0,235,?,not_applicable
+75109,1.0,242,?,not_applicable
+75197,1.0,245,?,not_applicable
+75237,1.0,365,0.0002960423808039403,ok
+248,1.0,250,?,not_applicable
+2119,1.0,254,?,not_applicable
+75127,1.0,256,0.37264115290620037,ok
+75153,1.0,263,0.0870827285921626,ok
+75195,1.0,274,0.00037537537537535304,ok
+266,1.0,277,?,not_applicable
+75225,1.0,332,0.04166666666666663,ok
+75100,1.0,446,0.2857142857142857,ok
+75132,1.0,296,0.2324414715719063,ok
+75210,1.0,298,0.0,ok
+273,1.0,302,0.05254237288135588,ok
+75133,1.0,414,0.2272727272727273,ok
+75121,1.0,304,0.0,ok
+75098,1.0,310,?,not_applicable
+75115,1.0,315,0.0,ok
+75217,1.0,337,?,not_applicable
+75134,1.0,341,?,not_applicable
+75096,1.0,346,?,not_applicable
+75203,1.0,350,?,not_applicable
+75123,1.0,360,?,not_applicable
+75125,1.0,370,0.0072289156626506035,ok
+2120,1.0,371,?,not_applicable
+75232,1.0,377,0.10084033613445376,ok
+75103,1.0,380,0.0,ok
+75230,1.0,386,?,not_applicable
+75240,1.0,398,0.0,ok
+75198,1.0,404,?,not_applicable
+75201,1.0,407,?,not_applicable
+75105,1.0,418,0.2642140468227425,ok
+75154,1.0,420,?,not_applicable
+2117,1.0,427,0.053580206455841384,ok
+75156,1.0,430,0.16913946587537088,ok
+75097,1.0,434,0.0,ok
+75101,1.0,438,0.24362907031618686,ok
+75172,1.0,443,?,not_applicable
+75106,1.0,451,0.07815126050420174,ok
+75120,1.0,458,0.002044989775051076,ok
+75193,1.0,461,?,not_applicable
diff --git a/autosklearn/metalearning/files/recall_binary.classification_dense/configurations.csv b/autosklearn/metalearning/files/recall_binary.classification_dense/configurations.csv
index 75473418b8..1fd9ea6eb0 100644
--- a/autosklearn/metalearning/files/recall_binary.classification_dense/configurations.csv
+++ b/autosklearn/metalearning/files/recall_binary.classification_dense/configurations.csv
@@ -1,128 +1,133 @@
 idx,balancing:strategy,classifier:__choice__,classifier:adaboost:algorithm,classifier:adaboost:learning_rate,classifier:adaboost:max_depth,classifier:adaboost:n_estimators,classifier:bernoulli_nb:alpha,classifier:bernoulli_nb:fit_prior,classifier:decision_tree:criterion,classifier:decision_tree:max_depth_factor,classifier:decision_tree:max_features,classifier:decision_tree:max_leaf_nodes,classifier:decision_tree:min_impurity_decrease,classifier:decision_tree:min_samples_leaf,classifier:decision_tree:min_samples_split,classifier:decision_tree:min_weight_fraction_leaf,classifier:extra_trees:bootstrap,classifier:extra_trees:criterion,classifier:extra_trees:max_depth,classifier:extra_trees:max_features,classifier:extra_trees:max_leaf_nodes,classifier:extra_trees:min_impurity_decrease,classifier:extra_trees:min_samples_leaf,classifier:extra_trees:min_samples_split,classifier:extra_trees:min_weight_fraction_leaf,classifier:gradient_boosting:early_stop,classifier:gradient_boosting:l2_regularization,classifier:gradient_boosting:learning_rate,classifier:gradient_boosting:loss,classifier:gradient_boosting:max_bins,classifier:gradient_boosting:max_depth,classifier:gradient_boosting:max_leaf_nodes,classifier:gradient_boosting:min_samples_leaf,classifier:gradient_boosting:n_iter_no_change,classifier:gradient_boosting:scoring,classifier:gradient_boosting:tol,classifier:gradient_boosting:validation_fraction,classifier:k_nearest_neighbors:n_neighbors,classifier:k_nearest_neighbors:p,classifier:k_nearest_neighbors:weights,classifier:lda:n_components,classifier:lda:shrinkage,classifier:lda:shrinkage_factor,classifier:lda:tol,classifier:liblinear_svc:C,classifier:liblinear_svc:dual,classifier:liblinear_svc:fit_intercept,classifier:liblinear_svc:intercept_scaling,classifier:liblinear_svc:loss,classifier:liblinear_svc:multi_class,classifier:liblinear_svc:penalty,classifier:liblinear_svc:tol,classifier:libsvm_svc:C,classifier:libsvm_svc:coef0,classifier:libsvm_svc:degree,classifier:libsvm_svc:gamma,classifier:libsvm_svc:kernel,classifier:libsvm_svc:max_iter,classifier:libsvm_svc:shrinking,classifier:libsvm_svc:tol,classifier:multinomial_nb:alpha,classifier:multinomial_nb:fit_prior,classifier:passive_aggressive:C,classifier:passive_aggressive:average,classifier:passive_aggressive:fit_intercept,classifier:passive_aggressive:loss,classifier:passive_aggressive:tol,classifier:qda:reg_param,classifier:random_forest:bootstrap,classifier:random_forest:criterion,classifier:random_forest:max_depth,classifier:random_forest:max_features,classifier:random_forest:max_leaf_nodes,classifier:random_forest:min_impurity_decrease,classifier:random_forest:min_samples_leaf,classifier:random_forest:min_samples_split,classifier:random_forest:min_weight_fraction_leaf,classifier:sgd:alpha,classifier:sgd:average,classifier:sgd:epsilon,classifier:sgd:eta0,classifier:sgd:fit_intercept,classifier:sgd:l1_ratio,classifier:sgd:learning_rate,classifier:sgd:loss,classifier:sgd:penalty,classifier:sgd:power_t,classifier:sgd:tol,data_preprocessing:categorical_transformer:categorical_encoding:__choice__,data_preprocessing:categorical_transformer:category_coalescence:__choice__,data_preprocessing:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction,data_preprocessing:numerical_transformer:imputation:strategy,data_preprocessing:numerical_transformer:rescaling:__choice__,data_preprocessing:numerical_transformer:rescaling:quantile_transformer:n_quantiles,data_preprocessing:numerical_transformer:rescaling:quantile_transformer:output_distribution,data_preprocessing:numerical_transformer:rescaling:robust_scaler:q_max,data_preprocessing:numerical_transformer:rescaling:robust_scaler:q_min,feature_preprocessor:__choice__,feature_preprocessor:extra_trees_preproc_for_classification:bootstrap,feature_preprocessor:extra_trees_preproc_for_classification:criterion,feature_preprocessor:extra_trees_preproc_for_classification:max_depth,feature_preprocessor:extra_trees_preproc_for_classification:max_features,feature_preprocessor:extra_trees_preproc_for_classification:max_leaf_nodes,feature_preprocessor:extra_trees_preproc_for_classification:min_impurity_decrease,feature_preprocessor:extra_trees_preproc_for_classification:min_samples_leaf,feature_preprocessor:extra_trees_preproc_for_classification:min_samples_split,feature_preprocessor:extra_trees_preproc_for_classification:min_weight_fraction_leaf,feature_preprocessor:extra_trees_preproc_for_classification:n_estimators,feature_preprocessor:fast_ica:algorithm,feature_preprocessor:fast_ica:fun,feature_preprocessor:fast_ica:n_components,feature_preprocessor:fast_ica:whiten,feature_preprocessor:feature_agglomeration:affinity,feature_preprocessor:feature_agglomeration:linkage,feature_preprocessor:feature_agglomeration:n_clusters,feature_preprocessor:feature_agglomeration:pooling_func,feature_preprocessor:kernel_pca:coef0,feature_preprocessor:kernel_pca:degree,feature_preprocessor:kernel_pca:gamma,feature_preprocessor:kernel_pca:kernel,feature_preprocessor:kernel_pca:n_components,feature_preprocessor:kitchen_sinks:gamma,feature_preprocessor:kitchen_sinks:n_components,feature_preprocessor:liblinear_svc_preprocessor:C,feature_preprocessor:liblinear_svc_preprocessor:dual,feature_preprocessor:liblinear_svc_preprocessor:fit_intercept,feature_preprocessor:liblinear_svc_preprocessor:intercept_scaling,feature_preprocessor:liblinear_svc_preprocessor:loss,feature_preprocessor:liblinear_svc_preprocessor:multi_class,feature_preprocessor:liblinear_svc_preprocessor:penalty,feature_preprocessor:liblinear_svc_preprocessor:tol,feature_preprocessor:nystroem_sampler:coef0,feature_preprocessor:nystroem_sampler:degree,feature_preprocessor:nystroem_sampler:gamma,feature_preprocessor:nystroem_sampler:kernel,feature_preprocessor:nystroem_sampler:n_components,feature_preprocessor:pca:keep_variance,feature_preprocessor:pca:whiten,feature_preprocessor:polynomial:degree,feature_preprocessor:polynomial:include_bias,feature_preprocessor:polynomial:interaction_only,feature_preprocessor:random_trees_embedding:bootstrap,feature_preprocessor:random_trees_embedding:max_depth,feature_preprocessor:random_trees_embedding:max_leaf_nodes,feature_preprocessor:random_trees_embedding:min_samples_leaf,feature_preprocessor:random_trees_embedding:min_samples_split,feature_preprocessor:random_trees_embedding:min_weight_fraction_leaf,feature_preprocessor:random_trees_embedding:n_estimators,feature_preprocessor:select_percentile_classification:percentile,feature_preprocessor:select_percentile_classification:score_func,feature_preprocessor:select_rates_classification:alpha,feature_preprocessor:select_rates_classification:mode,feature_preprocessor:select_rates_classification:score_func
-1,weighting,adaboost,SAMME,0.015984291290723266,7,184,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010000000000000004,median,standardize,,,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.9333242689646983,False,,,,,,,,,,,,,,,
-2,none,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.9525269450493825,None,0.0,4,20,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,standardize,,,,,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-3,none,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,188,None,,0.09976330267940166,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,none,,,,,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,-0.05439674489937307,2,0.051865468486041774,poly,727,,,,,,,,,,,,,,,,,
-4,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,23916.78290516781,False,True,1,squared_hinge,ovr,l2,0.0006655607726658531,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.008808715242823137,median,none,,,,,kernel_pca,,,,,,,,,,,,,,,,,,,0.2633153190620261,2,0.004770332714291669,poly,1483,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-5,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,2,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010091246322497477,median,none,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,95.37976833605768,mutual_info,,,
-6,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.933900673392165,None,0.0,7,4,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,minmax,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-7,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,0.9455840414651977,0.035051597802663065,auto,255,None,6,39,20,loss,1e-07,0.03762410007700571,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,robust_scaler,,,0.8083718526277422,0.24604827304064597,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,1377.6677844231401,False,True,1,squared_hinge,ovr,l1,0.0019190146350085739,,,,,,,,,,,,,,,,,,,,,,
-8,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5238814331135578,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,median,none,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,28,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-9,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1292.7448076998514,0.004489932344506231,2,1.3088036376373702,poly,-1,False,1.7943262139196537e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,minmax,,,,,feature_agglomeration,,,,,,,,,,,,,,,cosine,average,386,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-10,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.5244313211416313,None,0.0,1,2,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.454300236339658,most_frequent,robust_scaler,,,0.7858830918128734,0.2705174720960696,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-11,weighting,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0006294780694492238,most_frequent,standardize,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.063715750591364,fdr,chi2
-12,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.46057831591617715,False,True,hinge,0.04557857428827514,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00027457445401600137,median,standardize,,,,,extra_trees_preproc_for_classification,True,gini,None,0.48190346970486964,None,0.0,17,18,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-13,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.25657910619102237,None,0.0,6,6,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.021086303324492208,most_frequent,quantile_transformer,1247,uniform,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.16021572815319468,fpr,chi2
-14,weighting,decision_tree,,,,,,,gini,0.3438792358428926,1.0,None,0.0,11,5,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.13337505737577984,mean,quantile_transformer,431,normal,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8.71801503052686,f_classif,,,
-15,weighting,decision_tree,,,,,,,entropy,0.5508420416465765,1.0,None,0.0,4,15,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0030731686230236793,most_frequent,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,False,,,,,,,,,,,,
-16,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7947177310891914,None,0.0,13,3,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,mean,robust_scaler,,,0.7681430822715739,0.23411421138225857,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,16.747604337291705,f_classif,,,
-17,none,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.1769332447629957,mean,minmax,,,,,fast_ica,,,,,,,,,,,deflation,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-18,none,decision_tree,,,,,,,entropy,0.9175027108465073,1.0,None,0.0,18,13,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0005144268441601888,most_frequent,none,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.36269138268682594,fdr,chi2
-19,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,199,None,,0.003239674765949011,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,robust_scaler,,,0.7228447795482008,0.25,kernel_pca,,,,,,,,,,,,,,,,,,,0.09440148939740256,3,0.0008114095405154861,poly,1996,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-20,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.6039096182414764,None,0.0,2,20,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.1939609823704447,most_frequent,robust_scaler,,,0.7331327008874781,0.29747488162877694,extra_trees_preproc_for_classification,True,entropy,None,0.33401603690569853,None,0.0,4,4,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-21,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,2.5882079595129472e-05,0.36125906161027654,auto,255,None,341,119,16,loss,1e-07,0.2924550184445239,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.02338478303505337,most_frequent,robust_scaler,,,0.7866731278723753,0.29543475006822734,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,68,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-22,weighting,adaboost,SAMME.R,0.2127290016566289,1,57,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.012688742338096554,median,quantile_transformer,1048,uniform,,,feature_agglomeration,,,,,,,,,,,,,,,euclidean,average,24,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-23,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7725.740943204492,,,3.122840783722913,rbf,-1,True,1.4116065061807418e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.14280178218510184,most_frequent,none,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,27,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-24,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.21794354428393548,None,0.0,2,16,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0025451910134387575,median,quantile_transformer,1477,uniform,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
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-26,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5514127836113225,None,0.0,4,20,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.010750756228608195,most_frequent,robust_scaler,,,0.7471420685032172,0.2916152663822578,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.22557315057515023,fwe,chi2
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-46,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,92.66144306787051,,,0.04654639302091653,rbf,-1,True,0.0022231544280610005,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.005194838694455963,mean,none,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,15,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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-75,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,1.2430504242164082e-09,0.03359568307446533,auto,255,None,1609,29,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,standardize,,,,,feature_agglomeration,,,,,,,,,,,,,,,cosine,complete,40,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-76,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7.491039171224502,True,True,squared_hinge,2.8058138048327884e-05,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,minmax,,,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.7754372809893839,False,True,1,squared_hinge,ovr,l1,3.9643594596766314e-05,,,,,,,,,,,,,,,,,,,,,,
-77,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.34936025988397124,None,0.0,2,13,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00018601309647406987,median,robust_scaler,,,0.8634309348843472,0.26976989465367607,extra_trees_preproc_for_classification,False,entropy,None,0.48025175288975036,None,0.0,6,17,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-78,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,2.925599188129995e-05,0.12560285030651847,auto,255,None,300,193,17,loss,1e-07,0.039442997915546514,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00299400754997757,mean,robust_scaler,,,0.7093292916146515,0.009366983030047793,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-79,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.7272159618385164,None,0.0,20,11,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,robust_scaler,,,0.8136908047125674,0.2289527142816946,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,31.719931153297054,False,True,1,squared_hinge,ovr,l1,0.004749757426411777,,,,,,,,,,,,,,,,,,,,,,
-80,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1661.2274747409913,,,0.025821208852387936,rbf,-1,False,0.0017011435104921824,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,none,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,306,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-81,weighting,adaboost,SAMME,0.08979115391186342,1,74,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,minmax,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4922034012869809,fpr,f_classif
-82,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,21.85207494001697,0.18872999589786676,4,1.1863646021868761,poly,-1,False,0.0021338371901294443,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0034080852834770217,mean,normalize,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.11326005109886934,fdr,f_classif
-83,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.167624869438968e-05,True,True,hinge,0.004633596202702719,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.20347933384609232,most_frequent,none,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,42.644434279190115,f_classif,,,
-84,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,0.0020641735997058964,0.12733757758861702,auto,255,None,10,150,17,loss,1e-07,0.03652115085030125,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,quantile_transformer,1116,normal,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,
-85,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.9499894634982698,None,0.0,20,15,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.01161399161402058,mean,normalize,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,42.76696665279698,f_classif,,,
-86,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,1,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.019797877010737826,mean,normalize,,,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.8239678741128165,False,,,,,,,,,,,,,,,
-87,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.46057831591617715,False,True,hinge,0.04557857428827514,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00027457445401600137,median,standardize,,,,,extra_trees_preproc_for_classification,True,gini,None,0.48190346970486964,None,0.0,17,18,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-88,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,38,auto,,0.00019398431171606024,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010000000000000004,median,quantile_transformer,1440,normal,,,extra_trees_preproc_for_classification,True,gini,None,0.5126639956845847,None,0.0,16,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-89,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5722917944959481,None,0.0,1,14,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,
-90,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.26356754187780895,None,0.0,4,3,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010966178401758774,mean,robust_scaler,,,0.7817043861798886,0.24371720112407158,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
-91,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,1.158218682314328e-10,0.14188487415573833,auto,255,None,37,16,14,loss,1e-07,0.10836823453723118,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010615307864551744,most_frequent,quantile_transformer,1074,uniform,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.42256340858805774,fpr,f_classif
-92,weighting,bernoulli_nb,,,,,0.033296341861861305,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.16969921146466255,median,none,,,,,feature_agglomeration,,,,,,,,,,,,,,,euclidean,average,152,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-93,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.22879311080499845,None,0.0,8,4,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.014320022547615783,median,minmax,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.238630703440461,mutual_info,,,
-94,weighting,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,2,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0073667222552306465,median,robust_scaler,,,0.701595034693991,0.29514640404971515,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6066051105210388,False,,,,,,,,,,,,,,,
-95,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.107967527639527e-05,True,True,hinge,0.00448294745544775,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,quantile_transformer,953,normal,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,35.00114345405331,chi2,,,
-96,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.457928416564218,None,0.0,12,2,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,
-97,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,1.1522414705935405e-10,0.6132800183073315,auto,255,None,1607,67,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010096911249562407,mean,robust_scaler,,,0.7663493326967942,0.136094217337434,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-98,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1228.3897711257332,0.6862670093933454,5,1.113863025113022,poly,-1,True,1.9271116749907707e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.018161834200128672,mean,normalize,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.06278549919408385,fpr,chi2
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-100,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.825844582229201,None,0.0,12,9,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0008422508222149647,median,robust_scaler,,,0.8658803880857064,0.2528423686293341,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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-102,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.7426893291639965,None,0.0,4,19,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010127721455862085,median,quantile_transformer,1108,uniform,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,263,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-103,none,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,128,manual,0.8574560771111307,0.010886139381599514,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.24115007944216788,most_frequent,normalize,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,62.26514170991452,mutual_info,,,
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-107,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,11.046854203258679,,,0.07809571976769347,rbf,-1,False,0.00036193159909053734,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.006445557635410205,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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-112,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.02021502857757827,True,,0.055848937053280076,True,,constant,log,l2,,0.00016356162584832537,no_encoding,no_coalescense,,median,quantile_transformer,27,normal,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.11873149622606233,fwe,chi2
-113,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,9.029473608568458e-09,0.15779990576964315,auto,255,None,37,71,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.018093177195644097,mean,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,False,,,,,,,,,,,,
-114,weighting,bernoulli_nb,,,,,1.0,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,minmax,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,75.62461900097365,chi2,,,
-115,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0442027823406075,True,True,hinge,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.23384446728715488,median,quantile_transformer,941,normal,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,29.854940094889486,f_classif,,,
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-118,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.7759471079076188,None,0.0,2,11,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,mean,standardize,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4672496335705658,fwe,chi2
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-121,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.4210978663047845,None,0.0,5,6,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.006365988550833196,most_frequent,robust_scaler,,,0.8348891074186082,0.00974961635730845,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0695919708890599,fpr,f_classif
-122,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.922871100218626,False,True,squared_hinge,0.09957180737451943,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0016563186856411274,median,quantile_transformer,850,uniform,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.09123695282772847,fpr,chi2
-123,weighting,decision_tree,,,,,,,entropy,0.04461432844342683,1.0,None,0.0,9,17,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0011300271286478988,mean,minmax,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.43863282379418345,,mutual_info_classif
-124,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,26268.34065263221,-0.4665726751360928,,0.0008965575925931543,sigmoid,-1,False,0.00012545045512265426,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,normalize,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.15949044845847538,fpr,f_classif
-125,none,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,113,None,,0.011729739852259162,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.04145014437265771,mean,standardize,,,,,feature_agglomeration,,,,,,,,,,,,,,,euclidean,complete,121,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-126,weighting,adaboost,SAMME.R,1.696588612003168,9,455,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,minmax,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,82.18726095926662,chi2,,,
-127,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.6466927589470762,None,0.0,11,2,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.009672465311659264,most_frequent,standardize,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,369,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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+5,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,0.0001273994050901372,0.3110265003850914,auto,255,None,36,2,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,quantile_transformer,1473,uniform,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.461377888294646,fdr,f_classif
+8,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,114,manual,0.11778631707605758,0.0029112721339713465,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.015582195257240751,median,normalize,,,,,kernel_pca,,,,,,,,,,,,,,,,,,,0.42909623253327767,3,0.25407612000343777,poly,1689,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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+19,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.637214230331827,None,0.0,7,9,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.006685669209833547,most_frequent,standardize,,,,,extra_trees_preproc_for_classification,False,entropy,None,0.4756243915942922,None,0.0,1,13,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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+32,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,101,auto,,1.553086088086431e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.01137059177001425,most_frequent,normalize,,,,,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,-0.021621751597165284,4,0.7468597264810062,poly,5078,,,,,,,,,,,,,,,,,
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+38,weighting,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.5692252382787814,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.02203186625946887,most_frequent,quantile_transformer,1252,normal,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.9983883858340796,True,,,,,,,,,,,,,,,
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+41,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.22436763006793925,None,0.0,12,18,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,standardize,,,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,184.87016739193817,False,True,1,squared_hinge,ovr,l1,9.229405386285091e-05,,,,,,,,,,,,,,,,,,,,,,
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+380,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2677516008848513,True,True,hinge,0.05752557410901496,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0010152857217605206,median,quantile_transformer,1000,uniform,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,51.60974796845029,chi2,,,
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+398,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.9130183368656493,None,0.0,17,19,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.15470275906639006,mean,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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+414,weighting,bernoulli_nb,,,,,50.321502324016244,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.08806499894972263,mean,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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+420,weighting,adaboost,SAMME.R,0.05920795019389743,8,313,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,standardize,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,104,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+425,weighting,decision_tree,,,,,,,entropy,1.4732927621306418,1.0,None,0.0,10,3,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,standardize,,,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,4276.070150555713,False,True,1,squared_hinge,ovr,l1,0.00011385383984576719,,,,,,,,,,,,,,,,,,,,,,
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+430,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5321814610661173,None,0.0,1,3,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,standardize,,,,,extra_trees_preproc_for_classification,True,gini,None,0.5,None,0.0,1,15,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+434,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.28088485314208955,None,0.0,17,16,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.003177530868474569,mean,robust_scaler,,,0.8605669594097658,0.2400110842123775,extra_trees_preproc_for_classification,True,entropy,None,0.32485209130278936,None,0.0,6,10,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+438,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,4.191944635182623e-08,0.06714665420388714,auto,255,None,58,148,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,robust_scaler,,,0.752406584741681,0.2820517493354769,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+443,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.6269076084367177,None,0.0,1,7,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.12943250859943486,most_frequent,standardize,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,62.84526554323969,chi2,,,
+446,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,240.3897817006787,False,True,1,squared_hinge,ovr,l2,0.0007238885641977672,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.015628377472530642,most_frequent,standardize,,,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.9365905387498032,False,,,,,,,,,,,,,,,
+451,weighting,decision_tree,,,,,,,entropy,0.49431715004621385,1.0,None,0.0,1,3,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.00484236454749161,median,robust_scaler,,,0.7619367546642338,0.25,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,58.39951618572543,mutual_info,,,
+453,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.1393173890820956,False,True,1,squared_hinge,ovr,l2,8.283470008521272e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.015152352014390857,mean,normalize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,False,,,,,,,,,,,,
+458,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.21304949388806604,None,0.0,4,8,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0037735029866735902,mean,standardize,,,,,extra_trees_preproc_for_classification,True,gini,None,0.6912995807198827,None,0.0,13,20,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+461,none,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.9796199105973851,None,0.0,2,13,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.00845950033601661,mean,robust_scaler,,,0.8275228560550734,0.23814615163106348,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
diff --git a/autosklearn/metalearning/files/recall_binary.classification_dense/description.txt b/autosklearn/metalearning/files/recall_binary.classification_dense/description.txt
index 9e3b829678..3501332898 100644
--- a/autosklearn/metalearning/files/recall_binary.classification_dense/description.txt
+++ b/autosklearn/metalearning/files/recall_binary.classification_dense/description.txt
@@ -1,8 +1,9 @@
 features_cutoff_time: 3600
 features_cutoff_memory: 3072
-number_of_feature_steps: 35
+number_of_feature_steps: 42
 feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances
 feature_step LogNumberOfInstances: LogNumberOfInstances
+feature_step NumberOfClasses: NumberOfClasses
 feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures
 feature_step LogNumberOfFeatures: LogNumberOfFeatures
 feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues
@@ -20,6 +21,11 @@ feature_step DatasetRatio: DatasetRatio, LogDatasetRatio
 feature_step LogDatasetRatio: LogDatasetRatio
 feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio
 feature_step LogInverseDatasetRatio: LogInverseDatasetRatio
+feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD
+feature_step ClassProbabilityMin: ClassProbabilityMin
+feature_step ClassProbabilityMax: ClassProbabilityMax
+feature_step ClassProbabilityMean: ClassProbabilityMean
+feature_step ClassProbabilitySTD: ClassProbabilitySTD
 feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum
 feature_step SymbolsMin: SymbolsMin
 feature_step SymbolsMax: SymbolsMax
@@ -36,11 +42,12 @@ feature_step SkewnessMin: SkewnessMin
 feature_step SkewnessMax: SkewnessMax
 feature_step SkewnessMean: SkewnessMean
 feature_step SkewnessSTD: SkewnessSTD
-features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
+feature_step ClassEntropy: ClassEntropy
+features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy
 features_stochastic: 
-default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
+default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy
 
-algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127
+algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235,236,237,238,239,240,241,242,243,244,245,246,247,248,249,250,251,252,253,254,255,256,257,258,259,260,261,262,263,264,265,266,267,268,269,270,271,272,273,274,275,276,277,278,279,280,281,282,283,284,285,286,287,288,289,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,312,313,314,315,316,317,318,319,320,321,322,323,324,325,326,327,328,329,330,331,332,333,334,335,336,337,338,339,340,341,342,343,344,345,346,347,348,349,350,351,352,353,354,355,356,357,358,359,360,361,362,363,364,365,366,367,368,369,370,371,372,373,374,375,376,377,378,379,380,381,382,383,384,385,386,387,388,389,390,391,392,393,394,395,396,397,398,399,400,401,402,403,404,405,406,407,408,409,410,411,412,413,414,415,416,417,418,419,420,421,422,423,424,425,426,427,428,429,430,431,432,433,434,435,436,437,438,439,440,441,442,443,444,445,446,447,448,449,450,451,452,453,454,455,456,457,458,459,460,461,462,463,464
 algorithms_stochastic: 
 performance_measures: recall
 performance_type: solution_quality
diff --git a/autosklearn/metalearning/files/recall_binary.classification_dense/feature_costs.arff b/autosklearn/metalearning/files/recall_binary.classification_dense/feature_costs.arff
index 0e17e20d22..8512fd9275 100644
--- a/autosklearn/metalearning/files/recall_binary.classification_dense/feature_costs.arff
+++ b/autosklearn/metalearning/files/recall_binary.classification_dense/feature_costs.arff
@@ -4,6 +4,7 @@
 @ATTRIBUTE repetition NUMERIC
 @ATTRIBUTE NumberOfInstances NUMERIC
 @ATTRIBUTE LogNumberOfInstances NUMERIC
+@ATTRIBUTE NumberOfClasses NUMERIC
 @ATTRIBUTE NumberOfFeatures NUMERIC
 @ATTRIBUTE LogNumberOfFeatures NUMERIC
 @ATTRIBUTE MissingValues NUMERIC
@@ -21,6 +22,11 @@
 @ATTRIBUTE LogDatasetRatio NUMERIC
 @ATTRIBUTE InverseDatasetRatio NUMERIC
 @ATTRIBUTE LogInverseDatasetRatio NUMERIC
+@ATTRIBUTE ClassOccurences NUMERIC
+@ATTRIBUTE ClassProbabilityMin NUMERIC
+@ATTRIBUTE ClassProbabilityMax NUMERIC
+@ATTRIBUTE ClassProbabilityMean NUMERIC
+@ATTRIBUTE ClassProbabilitySTD NUMERIC
 @ATTRIBUTE NumSymbols NUMERIC
 @ATTRIBUTE SymbolsMin NUMERIC
 @ATTRIBUTE SymbolsMax NUMERIC
@@ -37,162 +43,138 @@
 @ATTRIBUTE SkewnessMax NUMERIC
 @ATTRIBUTE SkewnessMean NUMERIC
 @ATTRIBUTE SkewnessSTD NUMERIC
+@ATTRIBUTE ClassEntropy NUMERIC
 
 @DATA
-75123,1.0,1e-05,1e-05,1e-05,1e-05,0.00253,0.00221,1e-05,0.00024,3e-05,0.00011,0.0,2e-05,2e-05,5e-05,8e-05,2e-05,1e-05,7e-05,1e-05,0.00072,1e-05,1e-05,0.00019,0.00041,0.00011,0.00061,0.00012,9e-05,0.00015,0.00025,0.00067,0.00014,0.00011,0.00016,0.00027
-75090,1.0,1e-05,1e-05,2e-05,2e-05,0.00815,0.00368,7e-05,0.00228,9e-05,0.00235,0.0,5e-05,5e-05,0.0001,0.00013,0.00013,1e-05,0.00021,2e-05,0.00064,0.0,0.0,0.00014,0.00038,0.00011,0.00066,0.00011,0.00014,0.00014,0.00026,0.00066,0.00011,0.00013,0.00015,0.00027
-2123,1.0,1e-05,1e-05,1e-05,1e-05,0.00097,0.00079,1e-05,0.00013,1e-05,7e-05,0.0,2e-05,2e-05,5e-05,8e-05,2e-05,1e-05,5e-05,1e-05,0.0007,1e-05,0.0,0.00017,0.00042,0.0001,0.00052,6e-05,8e-05,0.00012,0.00026,0.00049,6e-05,0.00011,9e-05,0.00023
-248,1.0,1e-05,1e-05,1e-05,1e-05,0.00088,0.00077,1e-05,0.0001,1e-05,4e-05,0.0,1e-05,1e-05,4e-05,5e-05,2e-05,1e-05,4e-05,1e-05,0.00041,1e-05,0.0,9e-05,0.00022,9e-05,0.00046,6e-05,8e-05,9e-05,0.00023,0.00049,7e-05,0.0001,0.0001,0.00021
-75101,1.0,1e-05,1e-05,1e-05,1e-05,0.0418,0.03546,1e-05,0.00389,1e-05,0.00248,0.0,2e-05,2e-05,4e-05,6e-05,2e-05,1e-05,5e-05,2e-05,0.00045,1e-05,0.0,0.00011,0.00023,0.0001,0.00041,6e-05,8e-05,0.0001,0.00018,0.00047,6e-05,0.0001,8e-05,0.00023
-75191,1.0,1e-05,1e-05,3e-05,1e-05,0.53436,0.40777,2e-05,0.09655,3e-05,0.0301,1e-05,2e-05,3e-05,5e-05,0.00011,7e-05,1e-05,0.00011,3e-05,0.00084,5e-05,7e-05,0.00017,0.00033,0.00023,0.00088,0.00012,0.00017,0.00032,0.00026,0.00068,9e-05,0.00012,0.00013,0.00035
-75187,1.0,1e-05,1e-05,2e-05,1e-05,0.00548,0.00473,1e-05,0.0005,2e-05,0.00029,1e-05,2e-05,2e-05,7e-05,9e-05,3e-05,1e-05,6e-05,1e-05,0.00062,1e-05,1e-05,0.00014,0.00033,0.00013,0.00038,7e-05,7e-05,8e-05,0.00016,0.00047,6e-05,9e-05,9e-05,0.00023
-266,1.0,1e-05,1e-05,1e-05,1e-05,0.0018,0.00146,1e-05,0.00023,2e-05,0.00015,2e-05,2e-05,2e-05,6e-05,8e-05,3e-05,1e-05,6e-05,1e-05,0.00059,1e-05,1e-05,0.00014,0.00031,0.00013,0.0004,5e-05,7e-05,8e-05,0.0002,0.00044,8e-05,8e-05,8e-05,0.0002
-253,1.0,1e-05,1e-05,1e-05,1e-05,0.00082,0.00063,1e-05,0.00016,2e-05,5e-05,0.0,1e-05,1e-05,4e-05,5e-05,2e-05,1e-05,5e-05,2e-05,0.00046,1e-05,0.0,9e-05,0.00024,0.00012,0.00044,6e-05,7e-05,8e-05,0.00023,0.00043,6e-05,0.0001,8e-05,0.00019
-75217,1.0,1e-05,1e-05,2e-05,1e-05,0.00196,0.00158,1e-05,0.00026,2e-05,0.00016,1e-05,3e-05,3e-05,7e-05,0.0001,3e-05,1e-05,7e-05,2e-05,0.00089,1e-05,1e-05,0.00022,0.00053,0.00013,0.00074,9e-05,0.0001,0.00019,0.00036,0.00065,0.00013,0.0001,0.00011,0.00031
-75176,1.0,1e-05,1e-05,2e-05,1e-05,0.01406,0.01285,1e-05,0.00094,2e-05,0.00031,1e-05,3e-05,3e-05,6e-05,9e-05,3e-05,1e-05,7e-05,2e-05,0.00069,1e-05,1e-05,0.00017,0.00035,0.00015,0.00085,0.00018,0.00011,0.0002,0.00036,0.0011,0.00022,0.00023,0.00023,0.00042
-2117,1.0,1e-05,1e-05,1e-05,1e-05,0.01735,0.0154,1e-05,0.00141,1e-05,0.00057,0.0,1e-05,1e-05,3e-05,6e-05,2e-05,1e-05,5e-05,1e-05,0.0004,1e-05,0.0,8e-05,0.00021,0.0001,0.00065,0.00012,0.00011,0.00015,0.00026,0.00055,7e-05,0.00014,9e-05,0.00025
-75148,1.0,1e-05,1e-05,2e-05,2e-05,0.00129,0.00115,1e-05,0.00012,1e-05,4e-05,0.0,2e-05,2e-05,4e-05,5e-05,2e-05,1e-05,4e-05,1e-05,0.0004,1e-05,0.0,9e-05,0.00021,9e-05,0.00036,7e-05,7e-05,8e-05,0.00015,0.00041,6e-05,8e-05,8e-05,0.00019
-75103,1.0,1e-05,1e-05,1e-05,1e-05,0.01302,0.00819,3e-05,0.00234,3e-05,0.00255,0.0,2e-05,2e-05,6e-05,0.00012,5e-05,1e-05,0.00011,1e-05,0.0005,1e-05,0.0,9e-05,0.00029,0.00011,0.00054,8e-05,0.00011,0.00011,0.00024,0.00066,7e-05,0.00015,0.00011,0.00032
-75168,1.0,1e-05,0.0,1e-05,1e-05,0.02353,0.00601,0.00016,0.01754,0.00018,0.00031,0.0,0.00013,0.00013,0.00027,0.0003,0.00032,1e-05,0.00042,1e-05,0.0004,1e-05,0.0,8e-05,0.00021,0.0001,0.0011,0.00022,0.00025,0.00026,0.00037,0.00124,0.00022,0.00025,0.00028,0.00049
-75157,1.0,1e-05,1e-05,1e-05,1e-05,0.00125,0.00106,1e-05,0.00015,2e-05,7e-05,0.0,2e-05,2e-05,5e-05,6e-05,2e-05,1e-05,4e-05,1e-05,0.00048,2e-05,0.0,0.0001,0.00026,0.0001,0.00039,6e-05,7e-05,8e-05,0.00019,0.00043,6e-05,9e-05,8e-05,0.0002
-75092,1.0,1e-05,1e-05,1e-05,1e-05,0.00099,0.00072,1e-05,0.00018,1e-05,0.00011,0.0,2e-05,2e-05,4e-05,6e-05,2e-05,1e-05,5e-05,1e-05,0.00058,1e-05,0.0,0.00014,0.00034,9e-05,0.00041,6e-05,8e-05,9e-05,0.00017,0.00045,7e-05,0.0001,9e-05,0.00019
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-75227,1.0,1e-05,1e-05,2e-05,1e-05,0.00416,0.00373,1e-05,0.00038,2e-05,9e-05,1e-05,2e-05,2e-05,6e-05,0.0001,3e-05,1e-05,7e-05,2e-05,0.00063,1e-05,1e-05,0.00015,0.00034,0.00013,0.00037,6e-05,8e-05,8e-05,0.00016,0.00043,6e-05,9e-05,9e-05,0.0002
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-75132,1.0,1e-05,0.0,2e-05,1e-05,0.45642,0.41356,1e-05,0.03273,1e-05,0.01015,0.0,1e-05,1e-05,3e-05,6e-05,2e-05,1e-05,6e-05,2e-05,0.00049,1e-05,0.0,0.00012,0.00023,0.00013,0.00046,7e-05,8e-05,8e-05,0.00023,0.00053,6e-05,0.0001,9e-05,0.00028
-75212,1.0,1e-05,1e-05,1e-05,1e-05,0.0008,0.00059,1e-05,0.00015,1e-05,9e-05,0.0,2e-05,1e-05,3e-05,5e-05,2e-05,1e-05,4e-05,1e-05,0.00037,0.0,0.0,8e-05,0.00019,9e-05,0.00055,8e-05,0.00012,0.00011,0.00024,0.00047,6e-05,0.0001,9e-05,0.00022
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+75234,1.0,1e-05,1e-05,0.00015,1e-05,1e-05,0.0058,0.00534,1e-05,0.00033,1e-05,0.00016,0.0,2e-05,2e-05,7e-05,0.00204,3e-05,1e-05,5e-05,1e-05,0.00024,3e-05,2e-05,0.0001,9e-05,0.00043,0.0,0.0,0.00012,0.00024,6e-05,0.00156,6e-05,7e-05,0.00121,0.00021,0.00054,6e-05,7e-05,9e-05,0.00032,0.00501
+75235,1.0,1e-05,1e-05,0.0003,2e-05,1e-05,0.00461,0.00385,2e-05,0.00055,2e-05,0.00026,1e-05,3e-05,3e-05,0.0001,0.00011,3e-05,1e-05,5e-05,2e-05,0.00026,3e-05,1e-05,8e-05,0.00014,0.00075,1e-05,1e-05,0.0002,0.00043,0.00011,0.00127,0.00017,0.0002,0.00029,0.00061,0.0008,0.00015,0.00012,0.00016,0.00038,0.00242
+75236,1.0,1e-05,1e-05,0.0001,1e-05,1e-05,0.00218,0.00118,2e-05,0.00065,3e-05,0.00041,1e-05,3e-05,3e-05,9e-05,9e-05,4e-05,1e-05,7e-05,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.0006,1e-05,1e-05,0.00021,0.00032,6e-05,0.00043,7e-05,7e-05,9e-05,0.00019,0.00062,7e-05,8e-05,0.0001,0.00037,0.00048
+75237,1.0,2e-05,1e-05,0.00785,2e-05,1e-05,0.31031,0.27854,2e-05,0.01161,3e-05,0.02022,1e-05,4e-05,3e-05,9e-05,0.00024,6e-05,2e-05,9e-05,2e-05,0.00062,7e-05,3e-05,0.00017,0.00035,0.0008,1e-05,1e-05,0.00028,0.00043,8e-05,0.00043,8e-05,7e-05,9e-05,0.0002,0.00044,6e-05,8e-05,9e-05,0.00021,0.14028
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+75249,1.0,1e-05,1e-05,0.00011,1e-05,1e-05,0.00192,0.00153,1e-05,0.00025,1e-05,0.00017,1e-05,2e-05,2e-05,5e-05,7e-05,2e-05,1e-05,5e-05,3e-05,0.00014,2e-05,1e-05,3e-05,8e-05,0.00029,1e-05,3e-05,6e-05,0.00012,7e-05,0.00041,6e-05,8e-05,0.0001,0.00017,0.00043,6e-05,7e-05,9e-05,0.00019,0.00092
+75250,1.0,1e-05,1e-05,0.00375,1e-05,1e-05,0.04994,0.047,1e-05,0.00247,1e-05,0.00049,1e-05,2e-05,2e-05,6e-05,7e-05,2e-05,1e-05,3e-05,1e-05,0.00022,4e-05,1e-05,4e-05,0.00012,0.00041,0.0,1e-05,0.00011,0.00023,6e-05,0.00076,0.00012,0.00012,0.00019,0.00032,0.00086,0.00012,0.00013,0.00018,0.00043,0.04178
diff --git a/autosklearn/metalearning/files/recall_binary.classification_dense/feature_runstatus.arff b/autosklearn/metalearning/files/recall_binary.classification_dense/feature_runstatus.arff
index 0034ad2bd6..5fff2dfed3 100644
--- a/autosklearn/metalearning/files/recall_binary.classification_dense/feature_runstatus.arff
+++ b/autosklearn/metalearning/files/recall_binary.classification_dense/feature_runstatus.arff
@@ -4,6 +4,7 @@
 @ATTRIBUTE repetition NUMERIC
 @ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other}
@@ -21,6 +22,11 @@
 @ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other}
@@ -37,162 +43,138 @@
 @ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other}
 
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diff --git a/autosklearn/metalearning/files/recall_binary.classification_dense/feature_values.arff b/autosklearn/metalearning/files/recall_binary.classification_dense/feature_values.arff
index a60400e3ce..736dc64321 100644
--- a/autosklearn/metalearning/files/recall_binary.classification_dense/feature_values.arff
+++ b/autosklearn/metalearning/files/recall_binary.classification_dense/feature_values.arff
@@ -13,7 +13,6 @@
 @ATTRIBUTE KurtosisMean NUMERIC
 @ATTRIBUTE KurtosisMin NUMERIC
 @ATTRIBUTE KurtosisSTD NUMERIC
-@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC
 @ATTRIBUTE LogDatasetRatio NUMERIC
 @ATTRIBUTE LogInverseDatasetRatio NUMERIC
 @ATTRIBUTE LogNumberOfFeatures NUMERIC
@@ -42,160 +41,135 @@
 @ATTRIBUTE SymbolsSum NUMERIC
 
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diff --git a/autosklearn/metalearning/files/recall_binary.classification_sparse/algorithm_runs.arff b/autosklearn/metalearning/files/recall_binary.classification_sparse/algorithm_runs.arff
index 23386d1ec5..7dee887737 100644
--- a/autosklearn/metalearning/files/recall_binary.classification_sparse/algorithm_runs.arff
+++ b/autosklearn/metalearning/files/recall_binary.classification_sparse/algorithm_runs.arff
@@ -7,95 +7,133 @@
 @ATTRIBUTE runstatus {ok, timeout, memout, not_applicable, crash, other}
 
 @DATA
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-75171,1.0,4,0.16280806572068707,ok
-248,1.0,5,?,not_applicable
-75231,1.0,6,?,not_applicable
-75196,1.0,7,0.0,ok
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-75248,1.0,9,0.0,ok
-75126,1.0,10,0.04250559284116329,ok
-75234,1.0,11,0.050000000000000044,ok
-75150,1.0,12,0.138728323699422,ok
-258,1.0,13,?,not_applicable
-75168,1.0,14,?,not_applicable
-75235,1.0,15,?,not_applicable
-244,1.0,16,?,not_applicable
-75221,1.0,17,?,not_applicable
-75219,1.0,18,0.05558045719408333,ok
-75202,1.0,19,?,not_applicable
-3043,1.0,20,0.060975609756097615,ok
-75205,1.0,21,?,not_applicable
-75174,1.0,22,0.1667430142006413,ok
-275,1.0,23,?,not_applicable
-75213,1.0,24,0.08139534883720934,ok
-75099,1.0,25,0.2038834951456311,ok
-75184,1.0,26,0.2511600928074246,ok
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-233,1.0,28,0.0020408163265306367,ok
-75114,1.0,29,0.01754385964912286,ok
-236,1.0,30,?,not_applicable
-75141,1.0,31,0.047706422018348627,ok
-75107,1.0,32,0.3553054662379421,ok
-262,1.0,33,?,not_applicable
-75146,1.0,34,0.12113207547169813,ok
-75189,1.0,35,0.0166136443973135,ok
-2350,1.0,36,0.3952563751028242,ok
-75249,1.0,37,0.010204081632653073,ok
-242,1.0,38,?,not_applicable
-75117,1.0,39,0.06837606837606836,ok
-75191,1.0,40,0.1738918060813286,ok
-261,1.0,41,0.2727272727272727,ok
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-75093,1.0,44,0.2784431137724551,ok
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-75197,1.0,47,?,not_applicable
-75127,1.0,48,0.37921695094605834,ok
-75143,1.0,49,0.007936507936507908,ok
-75153,1.0,50,0.08563134978229314,ok
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-75195,1.0,53,0.0011261261261261701,ok
-75207,1.0,54,?,not_applicable
-75225,1.0,55,0.125,ok
-75166,1.0,56,0.16503391107761867,ok
-75100,1.0,57,0.2857142857142857,ok
-75169,1.0,58,?,not_applicable
-75121,1.0,59,0.0,ok
-75098,1.0,60,?,not_applicable
-75115,1.0,61,0.010683760683760646,ok
-75116,1.0,62,0.021077283372365363,ok
-75185,1.0,63,0.1368015414258189,ok
-2119,1.0,64,?,not_applicable
-75157,1.0,65,0.7169811320754718,ok
-75113,1.0,66,0.01016949152542368,ok
-75203,1.0,67,?,not_applicable
-75182,1.0,68,0.16857535501603294,ok
-251,1.0,69,?,not_applicable
-75123,1.0,70,?,not_applicable
-75125,1.0,71,0.012048192771084376,ok
-75232,1.0,72,0.19327731092436973,ok
-75103,1.0,73,0.01967213114754096,ok
-75192,1.0,74,0.5366614664586584,ok
-75230,1.0,75,?,not_applicable
-75139,1.0,76,0.021406727828746197,ok
-75227,1.0,77,0.1724137931034483,ok
-2120,1.0,78,?,not_applicable
-75124,1.0,79,0.14569536423841056,ok
-75240,1.0,80,0.0,ok
-75198,1.0,81,?,not_applicable
-75201,1.0,82,?,not_applicable
-75133,1.0,83,0.2727272727272727,ok
-75154,1.0,84,?,not_applicable
-75177,1.0,85,0.14634146341463417,ok
-2117,1.0,86,0.16868753072259546,ok
-75156,1.0,87,0.17359050445103863,ok
-75097,1.0,88,0.009909733124018882,ok
-75172,1.0,89,?,not_applicable
-75106,1.0,90,0.3689075630252101,ok
-75187,1.0,91,0.020938023450586263,ok
-75120,1.0,92,0.014314928425357865,ok
+75192,1.0,308,0.4290171606864275,ok
+75119,1.0,3,0.0,ok
+75212,1.0,58,0.2304147465437788,ok
+246,1.0,6,?,not_applicable
+252,1.0,7,?,not_applicable
+75178,1.0,8,?,not_applicable
+75177,1.0,340,0.060975609756097615,ok
+75092,1.0,31,0.038461538461538436,ok
+75239,1.0,13,0.0,ok
+75215,1.0,15,0.01603498542274051,ok
+75171,1.0,16,0.15907393577296491,ok
+75227,1.0,151,0.12068965517241381,ok
+75233,1.0,83,0.028221512247071368,ok
+75182,1.0,22,0.15116811726981216,ok
+253,1.0,23,?,not_applicable
+75157,1.0,70,0.3113207547169812,ok
+75124,1.0,194,0.23178807947019864,ok
+75222,1.0,144,0.1428571428571429,ok
+75231,1.0,28,?,not_applicable
+75185,1.0,260,0.1368015414258189,ok
+2123,1.0,32,?,not_applicable
+75150,1.0,33,0.138728323699422,ok
+75143,1.0,34,0.001984126984126977,ok
+75196,1.0,35,0.0,ok
+75188,1.0,40,?,not_applicable
+75248,1.0,48,0.0,ok
+75249,1.0,49,0.020408163265306145,ok
+75113,1.0,50,0.0,ok
+75126,1.0,51,0.01342281879194629,ok
+251,1.0,53,?,not_applicable
+75184,1.0,122,0.16125290023201855,ok
+75234,1.0,56,0.02622950819672132,ok
+258,1.0,60,?,not_applicable
+75166,1.0,229,0.05275056518462695,ok
+75168,1.0,63,?,not_applicable
+75148,1.0,183,0.11025145067698261,ok
+75235,1.0,67,?,not_applicable
+75159,1.0,186,0.1071428571428571,ok
+244,1.0,71,?,not_applicable
+75141,1.0,131,0.044036697247706424,ok
+75221,1.0,74,?,not_applicable
+75219,1.0,171,0.029134917077543676,ok
+75202,1.0,77,?,not_applicable
+3043,1.0,80,0.08536585365853655,ok
+75205,1.0,84,?,not_applicable
+75174,1.0,88,0.12230874942739345,ok
+288,1.0,89,?,not_applicable
+75250,1.0,90,?,not_applicable
+75179,1.0,363,0.17639751552795035,ok
+275,1.0,92,?,not_applicable
+75207,1.0,93,?,not_applicable
+75142,1.0,95,0.06500221598463585,ok
+75099,1.0,98,0.11650485436893199,ok
+75243,1.0,99,?,not_applicable
+75175,1.0,103,0.13841201716738194,ok
+233,1.0,107,0.0020408163265306367,ok
+75161,1.0,109,0.06341032808592029,ok
+75176,1.0,110,0.014139827179890041,ok
+262,1.0,113,?,not_applicable
+75129,1.0,114,0.21153846153846156,ok
+261,1.0,115,0.2727272727272727,ok
+75090,1.0,116,?,not_applicable
+75114,1.0,120,0.010025062656641603,ok
+75093,1.0,187,0.3338323353293413,ok
+260,1.0,124,?,not_applicable
+236,1.0,126,?,not_applicable
+254,1.0,132,0.0,ok
+75107,1.0,135,0.2821543408360129,ok
+75139,1.0,313,0.017737003058103995,ok
+75146,1.0,140,0.09396226415094344,ok
+75189,1.0,145,0.014883444028948256,ok
+75163,1.0,150,0.07699275362318836,ok
+2350,1.0,155,0.4004661365505895,ok
+2122,1.0,156,?,not_applicable
+75110,1.0,159,?,not_applicable
+75213,1.0,162,0.10465116279069764,ok
+75095,1.0,230,0.012048192771084376,ok
+75108,1.0,167,0.0,ok
+75117,1.0,169,0.0064102564102563875,ok
+75191,1.0,175,0.1774941995359629,ok
+75226,1.0,177,0.0017998560115191076,ok
+75244,1.0,195,0.08205128205128209,ok
+75236,1.0,181,?,not_applicable
+75169,1.0,184,?,not_applicable
+75116,1.0,185,0.004683840749414525,ok
+75223,1.0,189,?,not_applicable
+75109,1.0,196,?,not_applicable
+75197,1.0,199,?,not_applicable
+248,1.0,203,?,not_applicable
+2119,1.0,206,?,not_applicable
+75127,1.0,208,0.37264115290620037,ok
+75153,1.0,215,0.0870827285921626,ok
+75173,1.0,217,0.12250000000000005,ok
+75187,1.0,218,0.018425460636515956,ok
+75195,1.0,223,0.0011261261261261701,ok
+75225,1.0,266,0.04166666666666663,ok
+75100,1.0,356,0.5714285714285714,ok
+75132,1.0,237,0.2324414715719063,ok
+75210,1.0,239,0.0,ok
+273,1.0,241,0.06271186440677967,ok
+75133,1.0,333,0.2272727272727273,ok
+75121,1.0,244,0.0,ok
+75098,1.0,248,?,not_applicable
+75115,1.0,253,0.0,ok
+266,1.0,258,?,not_applicable
+75134,1.0,274,?,not_applicable
+75096,1.0,277,?,not_applicable
+75203,1.0,280,?,not_applicable
+75123,1.0,288,?,not_applicable
+75237,1.0,292,0.0002960423808039403,ok
+75125,1.0,293,0.009638554216867434,ok
+2120,1.0,297,?,not_applicable
+75232,1.0,303,0.18487394957983194,ok
+75103,1.0,306,0.0,ok
+242,1.0,307,?,not_applicable
+75230,1.0,312,?,not_applicable
+75240,1.0,320,0.0,ok
+75198,1.0,325,?,not_applicable
+75201,1.0,328,?,not_applicable
+75112,1.0,331,0.23957856161245994,ok
+75105,1.0,336,0.2642140468227425,ok
+75154,1.0,338,?,not_applicable
+2117,1.0,343,0.05661150253973457,ok
+75156,1.0,345,0.16913946587537088,ok
+75097,1.0,349,0.0,ok
+75101,1.0,352,0.24994100991033508,ok
+75172,1.0,353,?,not_applicable
+75106,1.0,361,0.3294117647058824,ok
+75120,1.0,367,0.002044989775051076,ok
+75193,1.0,369,?,not_applicable
diff --git a/autosklearn/metalearning/files/recall_binary.classification_sparse/configurations.csv b/autosklearn/metalearning/files/recall_binary.classification_sparse/configurations.csv
index 5a080b6a63..8435e51ddf 100644
--- a/autosklearn/metalearning/files/recall_binary.classification_sparse/configurations.csv
+++ b/autosklearn/metalearning/files/recall_binary.classification_sparse/configurations.csv
@@ -1,93 +1,131 @@
 idx,balancing:strategy,classifier:__choice__,classifier:adaboost:algorithm,classifier:adaboost:learning_rate,classifier:adaboost:max_depth,classifier:adaboost:n_estimators,classifier:bernoulli_nb:alpha,classifier:bernoulli_nb:fit_prior,classifier:decision_tree:criterion,classifier:decision_tree:max_depth_factor,classifier:decision_tree:max_features,classifier:decision_tree:max_leaf_nodes,classifier:decision_tree:min_impurity_decrease,classifier:decision_tree:min_samples_leaf,classifier:decision_tree:min_samples_split,classifier:decision_tree:min_weight_fraction_leaf,classifier:extra_trees:bootstrap,classifier:extra_trees:criterion,classifier:extra_trees:max_depth,classifier:extra_trees:max_features,classifier:extra_trees:max_leaf_nodes,classifier:extra_trees:min_impurity_decrease,classifier:extra_trees:min_samples_leaf,classifier:extra_trees:min_samples_split,classifier:extra_trees:min_weight_fraction_leaf,classifier:gradient_boosting:early_stop,classifier:gradient_boosting:l2_regularization,classifier:gradient_boosting:learning_rate,classifier:gradient_boosting:loss,classifier:gradient_boosting:max_bins,classifier:gradient_boosting:max_depth,classifier:gradient_boosting:max_leaf_nodes,classifier:gradient_boosting:min_samples_leaf,classifier:gradient_boosting:n_iter_no_change,classifier:gradient_boosting:scoring,classifier:gradient_boosting:tol,classifier:gradient_boosting:validation_fraction,classifier:k_nearest_neighbors:n_neighbors,classifier:k_nearest_neighbors:p,classifier:k_nearest_neighbors:weights,classifier:lda:n_components,classifier:lda:shrinkage,classifier:lda:shrinkage_factor,classifier:lda:tol,classifier:liblinear_svc:C,classifier:liblinear_svc:dual,classifier:liblinear_svc:fit_intercept,classifier:liblinear_svc:intercept_scaling,classifier:liblinear_svc:loss,classifier:liblinear_svc:multi_class,classifier:liblinear_svc:penalty,classifier:liblinear_svc:tol,classifier:libsvm_svc:C,classifier:libsvm_svc:coef0,classifier:libsvm_svc:degree,classifier:libsvm_svc:gamma,classifier:libsvm_svc:kernel,classifier:libsvm_svc:max_iter,classifier:libsvm_svc:shrinking,classifier:libsvm_svc:tol,classifier:multinomial_nb:alpha,classifier:multinomial_nb:fit_prior,classifier:passive_aggressive:C,classifier:passive_aggressive:average,classifier:passive_aggressive:fit_intercept,classifier:passive_aggressive:loss,classifier:passive_aggressive:tol,classifier:qda:reg_param,classifier:random_forest:bootstrap,classifier:random_forest:criterion,classifier:random_forest:max_depth,classifier:random_forest:max_features,classifier:random_forest:max_leaf_nodes,classifier:random_forest:min_impurity_decrease,classifier:random_forest:min_samples_leaf,classifier:random_forest:min_samples_split,classifier:random_forest:min_weight_fraction_leaf,classifier:sgd:alpha,classifier:sgd:average,classifier:sgd:epsilon,classifier:sgd:eta0,classifier:sgd:fit_intercept,classifier:sgd:l1_ratio,classifier:sgd:learning_rate,classifier:sgd:loss,classifier:sgd:penalty,classifier:sgd:power_t,classifier:sgd:tol,data_preprocessing:categorical_transformer:categorical_encoding:__choice__,data_preprocessing:categorical_transformer:category_coalescence:__choice__,data_preprocessing:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction,data_preprocessing:numerical_transformer:imputation:strategy,data_preprocessing:numerical_transformer:rescaling:__choice__,data_preprocessing:numerical_transformer:rescaling:quantile_transformer:n_quantiles,data_preprocessing:numerical_transformer:rescaling:quantile_transformer:output_distribution,data_preprocessing:numerical_transformer:rescaling:robust_scaler:q_max,data_preprocessing:numerical_transformer:rescaling:robust_scaler:q_min,feature_preprocessor:__choice__,feature_preprocessor:extra_trees_preproc_for_classification:bootstrap,feature_preprocessor:extra_trees_preproc_for_classification:criterion,feature_preprocessor:extra_trees_preproc_for_classification:max_depth,feature_preprocessor:extra_trees_preproc_for_classification:max_features,feature_preprocessor:extra_trees_preproc_for_classification:max_leaf_nodes,feature_preprocessor:extra_trees_preproc_for_classification:min_impurity_decrease,feature_preprocessor:extra_trees_preproc_for_classification:min_samples_leaf,feature_preprocessor:extra_trees_preproc_for_classification:min_samples_split,feature_preprocessor:extra_trees_preproc_for_classification:min_weight_fraction_leaf,feature_preprocessor:extra_trees_preproc_for_classification:n_estimators,feature_preprocessor:kernel_pca:coef0,feature_preprocessor:kernel_pca:degree,feature_preprocessor:kernel_pca:gamma,feature_preprocessor:kernel_pca:kernel,feature_preprocessor:kernel_pca:n_components,feature_preprocessor:kitchen_sinks:gamma,feature_preprocessor:kitchen_sinks:n_components,feature_preprocessor:liblinear_svc_preprocessor:C,feature_preprocessor:liblinear_svc_preprocessor:dual,feature_preprocessor:liblinear_svc_preprocessor:fit_intercept,feature_preprocessor:liblinear_svc_preprocessor:intercept_scaling,feature_preprocessor:liblinear_svc_preprocessor:loss,feature_preprocessor:liblinear_svc_preprocessor:multi_class,feature_preprocessor:liblinear_svc_preprocessor:penalty,feature_preprocessor:liblinear_svc_preprocessor:tol,feature_preprocessor:nystroem_sampler:coef0,feature_preprocessor:nystroem_sampler:degree,feature_preprocessor:nystroem_sampler:gamma,feature_preprocessor:nystroem_sampler:kernel,feature_preprocessor:nystroem_sampler:n_components,feature_preprocessor:polynomial:degree,feature_preprocessor:polynomial:include_bias,feature_preprocessor:polynomial:interaction_only,feature_preprocessor:random_trees_embedding:bootstrap,feature_preprocessor:random_trees_embedding:max_depth,feature_preprocessor:random_trees_embedding:max_leaf_nodes,feature_preprocessor:random_trees_embedding:min_samples_leaf,feature_preprocessor:random_trees_embedding:min_samples_split,feature_preprocessor:random_trees_embedding:min_weight_fraction_leaf,feature_preprocessor:random_trees_embedding:n_estimators,feature_preprocessor:select_percentile_classification:percentile,feature_preprocessor:select_percentile_classification:score_func,feature_preprocessor:select_rates_classification:alpha,feature_preprocessor:select_rates_classification:mode,feature_preprocessor:select_rates_classification:score_func,feature_preprocessor:truncatedSVD:target_dim
-1,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.3163020577549344,None,0.0,15,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.01322981552715978,most_frequent,robust_scaler,,,0.9746667009556722,0.1974876581478311,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-2,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,23916.78290516781,False,True,1,squared_hinge,ovr,l2,0.0006655607726658531,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.008808715242823137,median,none,,,,,kernel_pca,,,,,,,,,,,0.2633153190620261,2,0.004770332714291669,poly,1483,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-3,weighting,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,88,2,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.254263872179305,median,normalize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-4,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,170.37509684994612,0.6734219115470694,3,0.08998608839429428,poly,-1,True,0.024147312348267988,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,none,,,,,extra_trees_preproc_for_classification,False,entropy,None,0.9449154369109399,None,0.0,1,14,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-5,none,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00011009762574324903,True,,0.031641726094190054,True,,invscaling,hinge,l2,0.2208821470267501,0.007895311091089785,one_hot_encoding,minority_coalescer,0.13807502937372682,mean,quantile_transformer,1340,normal,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,,
-6,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.5244313211416313,None,0.0,1,2,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.454300236339658,most_frequent,robust_scaler,,,0.7858830918128734,0.2705174720960696,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-7,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.46057831591617715,False,True,hinge,0.04557857428827514,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00027457445401600137,median,standardize,,,,,extra_trees_preproc_for_classification,True,gini,None,0.48190346970486964,None,0.0,17,18,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-8,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.25657910619102237,None,0.0,6,6,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.021086303324492208,most_frequent,quantile_transformer,1247,uniform,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.16021572815319468,fpr,chi2,
-9,weighting,decision_tree,,,,,,,entropy,0.5508420416465765,1.0,None,0.0,4,15,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0030731686230236793,most_frequent,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,False,,,,,,,,,,,,,
-10,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.8253905225980761,None,0.0,18,20,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.003725255016700434,mean,robust_scaler,,,0.7701705977765594,0.2396520157991559,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.3579038776690903,False,True,1,squared_hinge,ovr,l1,4.994232534734023e-05,,,,,,,,,,,,,,,,,,,,,
-11,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.06263739023781334,None,0.0,18,8,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.02206379295100344,most_frequent,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,,
-12,none,decision_tree,,,,,,,entropy,0.9175027108465073,1.0,None,0.0,18,13,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0005144268441601888,most_frequent,none,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.36269138268682594,fdr,chi2,
-13,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,199,None,,0.003239674765949011,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,robust_scaler,,,0.7228447795482008,0.25,kernel_pca,,,,,,,,,,,0.09440148939740256,3,0.0008114095405154861,poly,1996,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-14,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.6039096182414764,None,0.0,2,20,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.1939609823704447,most_frequent,robust_scaler,,,0.7331327008874781,0.29747488162877694,extra_trees_preproc_for_classification,True,entropy,None,0.33401603690569853,None,0.0,4,4,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-15,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7865652301288171,None,0.0,1,4,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,median,robust_scaler,,,0.8623635641193496,0.13298297278730625,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-16,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.905401147837377,-0.32832122529048147,3,1.001896309040499,poly,-1,False,5.1523484258030785e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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-18,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5010952533165973,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.005838375247601407,mean,normalize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,,
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-82,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.02021502857757827,True,,0.055848937053280076,True,,constant,log,l2,,0.00016356162584832537,no_encoding,no_coalescense,,median,quantile_transformer,27,normal,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.11873149622606233,fwe,chi2,
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diff --git a/autosklearn/metalearning/files/recall_binary.classification_sparse/description.txt b/autosklearn/metalearning/files/recall_binary.classification_sparse/description.txt
index 3bd77ae1f9..4847474fbc 100644
--- a/autosklearn/metalearning/files/recall_binary.classification_sparse/description.txt
+++ b/autosklearn/metalearning/files/recall_binary.classification_sparse/description.txt
@@ -1,8 +1,9 @@
 features_cutoff_time: 3600
 features_cutoff_memory: 3072
-number_of_feature_steps: 35
+number_of_feature_steps: 42
 feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances
 feature_step LogNumberOfInstances: LogNumberOfInstances
+feature_step NumberOfClasses: NumberOfClasses
 feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures
 feature_step LogNumberOfFeatures: LogNumberOfFeatures
 feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues
@@ -20,6 +21,11 @@ feature_step DatasetRatio: DatasetRatio, LogDatasetRatio
 feature_step LogDatasetRatio: LogDatasetRatio
 feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio
 feature_step LogInverseDatasetRatio: LogInverseDatasetRatio
+feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD
+feature_step ClassProbabilityMin: ClassProbabilityMin
+feature_step ClassProbabilityMax: ClassProbabilityMax
+feature_step ClassProbabilityMean: ClassProbabilityMean
+feature_step ClassProbabilitySTD: ClassProbabilitySTD
 feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum
 feature_step SymbolsMin: SymbolsMin
 feature_step SymbolsMax: SymbolsMax
@@ -36,11 +42,12 @@ feature_step SkewnessMin: SkewnessMin
 feature_step SkewnessMax: SkewnessMax
 feature_step SkewnessMean: SkewnessMean
 feature_step SkewnessSTD: SkewnessSTD
-features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
+feature_step ClassEntropy: ClassEntropy
+features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy
 features_stochastic: 
-default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
+default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy
 
-algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92
+algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235,236,237,238,239,240,241,242,243,244,245,246,247,248,249,250,251,252,253,254,255,256,257,258,259,260,261,262,263,264,265,266,267,268,269,270,271,272,273,274,275,276,277,278,279,280,281,282,283,284,285,286,287,288,289,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,312,313,314,315,316,317,318,319,320,321,322,323,324,325,326,327,328,329,330,331,332,333,334,335,336,337,338,339,340,341,342,343,344,345,346,347,348,349,350,351,352,353,354,355,356,357,358,359,360,361,362,363,364,365,366,367,368,369,370,371,372
 algorithms_stochastic: 
 performance_measures: recall
 performance_type: solution_quality
diff --git a/autosklearn/metalearning/files/recall_binary.classification_sparse/feature_costs.arff b/autosklearn/metalearning/files/recall_binary.classification_sparse/feature_costs.arff
index 0e17e20d22..8512fd9275 100644
--- a/autosklearn/metalearning/files/recall_binary.classification_sparse/feature_costs.arff
+++ b/autosklearn/metalearning/files/recall_binary.classification_sparse/feature_costs.arff
@@ -4,6 +4,7 @@
 @ATTRIBUTE repetition NUMERIC
 @ATTRIBUTE NumberOfInstances NUMERIC
 @ATTRIBUTE LogNumberOfInstances NUMERIC
+@ATTRIBUTE NumberOfClasses NUMERIC
 @ATTRIBUTE NumberOfFeatures NUMERIC
 @ATTRIBUTE LogNumberOfFeatures NUMERIC
 @ATTRIBUTE MissingValues NUMERIC
@@ -21,6 +22,11 @@
 @ATTRIBUTE LogDatasetRatio NUMERIC
 @ATTRIBUTE InverseDatasetRatio NUMERIC
 @ATTRIBUTE LogInverseDatasetRatio NUMERIC
+@ATTRIBUTE ClassOccurences NUMERIC
+@ATTRIBUTE ClassProbabilityMin NUMERIC
+@ATTRIBUTE ClassProbabilityMax NUMERIC
+@ATTRIBUTE ClassProbabilityMean NUMERIC
+@ATTRIBUTE ClassProbabilitySTD NUMERIC
 @ATTRIBUTE NumSymbols NUMERIC
 @ATTRIBUTE SymbolsMin NUMERIC
 @ATTRIBUTE SymbolsMax NUMERIC
@@ -37,162 +43,138 @@
 @ATTRIBUTE SkewnessMax NUMERIC
 @ATTRIBUTE SkewnessMean NUMERIC
 @ATTRIBUTE SkewnessSTD NUMERIC
+@ATTRIBUTE ClassEntropy NUMERIC
 
 @DATA
-75123,1.0,1e-05,1e-05,1e-05,1e-05,0.00253,0.00221,1e-05,0.00024,3e-05,0.00011,0.0,2e-05,2e-05,5e-05,8e-05,2e-05,1e-05,7e-05,1e-05,0.00072,1e-05,1e-05,0.00019,0.00041,0.00011,0.00061,0.00012,9e-05,0.00015,0.00025,0.00067,0.00014,0.00011,0.00016,0.00027
-75090,1.0,1e-05,1e-05,2e-05,2e-05,0.00815,0.00368,7e-05,0.00228,9e-05,0.00235,0.0,5e-05,5e-05,0.0001,0.00013,0.00013,1e-05,0.00021,2e-05,0.00064,0.0,0.0,0.00014,0.00038,0.00011,0.00066,0.00011,0.00014,0.00014,0.00026,0.00066,0.00011,0.00013,0.00015,0.00027
-2123,1.0,1e-05,1e-05,1e-05,1e-05,0.00097,0.00079,1e-05,0.00013,1e-05,7e-05,0.0,2e-05,2e-05,5e-05,8e-05,2e-05,1e-05,5e-05,1e-05,0.0007,1e-05,0.0,0.00017,0.00042,0.0001,0.00052,6e-05,8e-05,0.00012,0.00026,0.00049,6e-05,0.00011,9e-05,0.00023
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-75101,1.0,1e-05,1e-05,1e-05,1e-05,0.0418,0.03546,1e-05,0.00389,1e-05,0.00248,0.0,2e-05,2e-05,4e-05,6e-05,2e-05,1e-05,5e-05,2e-05,0.00045,1e-05,0.0,0.00011,0.00023,0.0001,0.00041,6e-05,8e-05,0.0001,0.00018,0.00047,6e-05,0.0001,8e-05,0.00023
-75191,1.0,1e-05,1e-05,3e-05,1e-05,0.53436,0.40777,2e-05,0.09655,3e-05,0.0301,1e-05,2e-05,3e-05,5e-05,0.00011,7e-05,1e-05,0.00011,3e-05,0.00084,5e-05,7e-05,0.00017,0.00033,0.00023,0.00088,0.00012,0.00017,0.00032,0.00026,0.00068,9e-05,0.00012,0.00013,0.00035
-75187,1.0,1e-05,1e-05,2e-05,1e-05,0.00548,0.00473,1e-05,0.0005,2e-05,0.00029,1e-05,2e-05,2e-05,7e-05,9e-05,3e-05,1e-05,6e-05,1e-05,0.00062,1e-05,1e-05,0.00014,0.00033,0.00013,0.00038,7e-05,7e-05,8e-05,0.00016,0.00047,6e-05,9e-05,9e-05,0.00023
-266,1.0,1e-05,1e-05,1e-05,1e-05,0.0018,0.00146,1e-05,0.00023,2e-05,0.00015,2e-05,2e-05,2e-05,6e-05,8e-05,3e-05,1e-05,6e-05,1e-05,0.00059,1e-05,1e-05,0.00014,0.00031,0.00013,0.0004,5e-05,7e-05,8e-05,0.0002,0.00044,8e-05,8e-05,8e-05,0.0002
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-75217,1.0,1e-05,1e-05,2e-05,1e-05,0.00196,0.00158,1e-05,0.00026,2e-05,0.00016,1e-05,3e-05,3e-05,7e-05,0.0001,3e-05,1e-05,7e-05,2e-05,0.00089,1e-05,1e-05,0.00022,0.00053,0.00013,0.00074,9e-05,0.0001,0.00019,0.00036,0.00065,0.00013,0.0001,0.00011,0.00031
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+75230,1.0,1e-05,0.0,9e-05,1e-05,1e-05,0.00087,0.0006,1e-05,0.00018,1e-05,0.00011,0.0,2e-05,2e-05,4e-05,6e-05,2e-05,1e-05,3e-05,1e-05,0.00015,2e-05,1e-05,4e-05,8e-05,0.00038,0.0,1e-05,0.0001,0.00021,5e-05,0.00037,5e-05,6e-05,8e-05,0.00018,0.00033,5e-05,6e-05,7e-05,0.00015,0.00048
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+75234,1.0,1e-05,1e-05,0.00015,1e-05,1e-05,0.0058,0.00534,1e-05,0.00033,1e-05,0.00016,0.0,2e-05,2e-05,7e-05,0.00204,3e-05,1e-05,5e-05,1e-05,0.00024,3e-05,2e-05,0.0001,9e-05,0.00043,0.0,0.0,0.00012,0.00024,6e-05,0.00156,6e-05,7e-05,0.00121,0.00021,0.00054,6e-05,7e-05,9e-05,0.00032,0.00501
+75235,1.0,1e-05,1e-05,0.0003,2e-05,1e-05,0.00461,0.00385,2e-05,0.00055,2e-05,0.00026,1e-05,3e-05,3e-05,0.0001,0.00011,3e-05,1e-05,5e-05,2e-05,0.00026,3e-05,1e-05,8e-05,0.00014,0.00075,1e-05,1e-05,0.0002,0.00043,0.00011,0.00127,0.00017,0.0002,0.00029,0.00061,0.0008,0.00015,0.00012,0.00016,0.00038,0.00242
+75236,1.0,1e-05,1e-05,0.0001,1e-05,1e-05,0.00218,0.00118,2e-05,0.00065,3e-05,0.00041,1e-05,3e-05,3e-05,9e-05,9e-05,4e-05,1e-05,7e-05,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.0006,1e-05,1e-05,0.00021,0.00032,6e-05,0.00043,7e-05,7e-05,9e-05,0.00019,0.00062,7e-05,8e-05,0.0001,0.00037,0.00048
+75237,1.0,2e-05,1e-05,0.00785,2e-05,1e-05,0.31031,0.27854,2e-05,0.01161,3e-05,0.02022,1e-05,4e-05,3e-05,9e-05,0.00024,6e-05,2e-05,9e-05,2e-05,0.00062,7e-05,3e-05,0.00017,0.00035,0.0008,1e-05,1e-05,0.00028,0.00043,8e-05,0.00043,8e-05,7e-05,9e-05,0.0002,0.00044,6e-05,8e-05,9e-05,0.00021,0.14028
+75239,1.0,1e-05,1e-05,7e-05,1e-05,1e-05,0.00105,0.00084,1e-05,0.00015,1e-05,8e-05,1e-05,2e-05,5e-05,4e-05,8e-05,2e-05,1e-05,3e-05,1e-05,0.00016,2e-05,1e-05,5e-05,9e-05,0.00049,0.0,0.0,0.00012,0.00026,0.00011,0.00046,0.00013,8e-05,8e-05,0.00018,0.00044,6e-05,9e-05,0.00011,0.00018,0.00065
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+75243,1.0,1e-05,1e-05,0.00029,1e-05,1e-05,0.00461,0.00415,1e-05,0.00035,1e-05,0.00015,0.0,2e-05,2e-05,5e-05,7e-05,2e-05,1e-05,4e-05,1e-05,0.00018,2e-05,1e-05,4e-05,0.00011,0.00022,1e-05,1e-05,5e-05,0.00011,5e-05,0.0004,6e-05,6e-05,8e-05,0.0002,0.00039,6e-05,6e-05,8e-05,0.00019,0.00265
+75244,1.0,3e-05,2e-05,0.0004,3e-05,2e-05,0.0173,0.01298,4e-05,0.00267,4e-05,0.00174,1e-05,7e-05,9e-05,0.00024,0.00027,8e-05,2e-05,0.00013,3e-05,0.00045,7e-05,2e-05,0.00011,0.00025,0.00077,6e-05,2e-05,0.00021,0.00034,0.00013,0.00077,0.00014,0.00013,0.00026,0.00023,0.00044,7e-05,8e-05,0.0001,0.00019,0.00649
+75248,1.0,1e-05,1e-05,0.00017,1e-05,1e-05,0.00416,0.00305,1e-05,0.0006,2e-05,0.00054,1e-05,2e-05,2e-05,5e-05,7e-05,2e-05,1e-05,4e-05,1e-05,0.00017,2e-05,1e-05,4e-05,0.0001,0.00029,5e-05,1e-05,6e-05,0.00012,6e-05,0.00081,0.00011,0.00015,0.00016,0.00038,0.0005,7e-05,8e-05,0.00012,0.00024,0.00184
+75249,1.0,1e-05,1e-05,0.00011,1e-05,1e-05,0.00192,0.00153,1e-05,0.00025,1e-05,0.00017,1e-05,2e-05,2e-05,5e-05,7e-05,2e-05,1e-05,5e-05,3e-05,0.00014,2e-05,1e-05,3e-05,8e-05,0.00029,1e-05,3e-05,6e-05,0.00012,7e-05,0.00041,6e-05,8e-05,0.0001,0.00017,0.00043,6e-05,7e-05,9e-05,0.00019,0.00092
+75250,1.0,1e-05,1e-05,0.00375,1e-05,1e-05,0.04994,0.047,1e-05,0.00247,1e-05,0.00049,1e-05,2e-05,2e-05,6e-05,7e-05,2e-05,1e-05,3e-05,1e-05,0.00022,4e-05,1e-05,4e-05,0.00012,0.00041,0.0,1e-05,0.00011,0.00023,6e-05,0.00076,0.00012,0.00012,0.00019,0.00032,0.00086,0.00012,0.00013,0.00018,0.00043,0.04178
diff --git a/autosklearn/metalearning/files/recall_binary.classification_sparse/feature_runstatus.arff b/autosklearn/metalearning/files/recall_binary.classification_sparse/feature_runstatus.arff
index 0034ad2bd6..5fff2dfed3 100644
--- a/autosklearn/metalearning/files/recall_binary.classification_sparse/feature_runstatus.arff
+++ b/autosklearn/metalearning/files/recall_binary.classification_sparse/feature_runstatus.arff
@@ -4,6 +4,7 @@
 @ATTRIBUTE repetition NUMERIC
 @ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other}
@@ -21,6 +22,11 @@
 @ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other}
@@ -37,162 +43,138 @@
 @ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other}
 
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diff --git a/autosklearn/metalearning/files/recall_binary.classification_sparse/feature_values.arff b/autosklearn/metalearning/files/recall_binary.classification_sparse/feature_values.arff
index a60400e3ce..736dc64321 100644
--- a/autosklearn/metalearning/files/recall_binary.classification_sparse/feature_values.arff
+++ b/autosklearn/metalearning/files/recall_binary.classification_sparse/feature_values.arff
@@ -13,7 +13,6 @@
 @ATTRIBUTE KurtosisMean NUMERIC
 @ATTRIBUTE KurtosisMin NUMERIC
 @ATTRIBUTE KurtosisSTD NUMERIC
-@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC
 @ATTRIBUTE LogDatasetRatio NUMERIC
 @ATTRIBUTE LogInverseDatasetRatio NUMERIC
 @ATTRIBUTE LogNumberOfFeatures NUMERIC
@@ -42,160 +41,135 @@
 @ATTRIBUTE SymbolsSum NUMERIC
 
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diff --git a/autosklearn/metalearning/files/recall_macro_binary.classification_dense/algorithm_runs.arff b/autosklearn/metalearning/files/recall_macro_binary.classification_dense/algorithm_runs.arff
index c64daef00a..af74b42460 100644
--- a/autosklearn/metalearning/files/recall_macro_binary.classification_dense/algorithm_runs.arff
+++ b/autosklearn/metalearning/files/recall_macro_binary.classification_dense/algorithm_runs.arff
@@ -7,130 +7,135 @@
 @ATTRIBUTE runstatus {ok, timeout, memout, not_applicable, crash, other}
 
 @DATA
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diff --git a/autosklearn/metalearning/files/recall_macro_binary.classification_dense/configurations.csv b/autosklearn/metalearning/files/recall_macro_binary.classification_dense/configurations.csv
index d1a17e1911..70df24a6c1 100644
--- a/autosklearn/metalearning/files/recall_macro_binary.classification_dense/configurations.csv
+++ b/autosklearn/metalearning/files/recall_macro_binary.classification_dense/configurations.csv
@@ -1,128 +1,133 @@
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-1,none,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,standardize,,,,,extra_trees_preproc_for_classification,False,entropy,None,0.8629651625821806,None,0.0,10,10,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-2,none,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.9525269450493825,None,0.0,4,20,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,standardize,,,,,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-3,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,101,auto,,1.1004704874013712e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.009420818612537619,most_frequent,normalize,,,,,kitchen_sinks,,,,,,,,,,,,,,,,,,,,,,,,0.1495664841849168,2334,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-4,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,23916.78290516781,False,True,1,squared_hinge,ovr,l2,0.0006655607726658531,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.008808715242823137,median,none,,,,,kernel_pca,,,,,,,,,,,,,,,,,,,0.2633153190620261,2,0.004770332714291669,poly,1483,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-5,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,2,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,none,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,94.23876232993393,mutual_info,,,
-6,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.933900673392165,None,0.0,7,4,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,minmax,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-7,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.3700222208089159,None,0.0,14,19,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,minmax,,,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,1.0533068843554318,False,True,1,squared_hinge,ovr,l1,0.00618591414610778,,,,,,,,,,,,,,,,,,,,,,
-8,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.1867279289062114,0.40158855342983024,3,1.6874166311129042,poly,-1,True,0.01266646080615196,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.008174660655642629,mean,minmax,,,,,feature_agglomeration,,,,,,,,,,,,,,,euclidean,complete,291,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-9,none,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13,None,,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,none,,,,,feature_agglomeration,,,,,,,,,,,,,,,euclidean,average,74,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-10,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.5244313211416313,None,0.0,1,2,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.454300236339658,most_frequent,robust_scaler,,,0.7858830918128734,0.2705174720960696,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-11,weighting,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.5052761282928767,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,mean,robust_scaler,,,0.7218709194459672,0.23494110317607264,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,62.97598319666472,chi2,,,
-12,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.541827838541001,True,True,squared_hinge,0.0001129851063838815,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,standardize,,,,,extra_trees_preproc_for_classification,True,entropy,None,0.5533459595240017,None,0.0,18,4,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-13,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.3652103526812861,None,0.0,6,8,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,quantile_transformer,79,uniform,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.17357047764133562,fwe,chi2
-14,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6185424163558029,False,True,1,squared_hinge,ovr,l2,4.385767395120581e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010397452590017174,median,quantile_transformer,961,normal,,,feature_agglomeration,,,,,,,,,,,,,,,cosine,average,25,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-15,weighting,decision_tree,,,,,,,entropy,0.5508420416465765,1.0,None,0.0,4,15,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0030731686230236793,most_frequent,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,False,,,,,,,,,,,,
-16,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.8528870159900765,None,0.0,19,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,most_frequent,robust_scaler,,,0.7906395775179057,0.22010064838466728,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.000393619499124,mutual_info,,,
-17,none,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.1769332447629957,mean,minmax,,,,,fast_ica,,,,,,,,,,,deflation,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-18,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.07518858116896743,None,0.0,7,10,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0027133920607872518,mean,robust_scaler,,,0.7873452530915046,0.16215139072179952,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-19,none,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,200,None,,0.0017457216340699586,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,none,,,,,kernel_pca,,,,,,,,,,,,,,,,,,,0.0,4,0.0007494648939847078,poly,1996,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-20,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.6448542484717253,None,0.0,3,20,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.3306639470207569,most_frequent,robust_scaler,,,0.7248166700836332,0.2755431234957578,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-21,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.8054434875626825,None,0.0,1,20,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,median,standardize,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4241048505416849,fpr,f_classif
-22,weighting,adaboost,SAMME,0.2353770068926199,1,62,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.007159634821655197,most_frequent,quantile_transformer,1616,normal,,,feature_agglomeration,,,,,,,,,,,,,,,cosine,average,151,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-23,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7734.521233796252,,,2.6048661611376747,rbf,-1,True,4.68717607435371e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,none,,,,,feature_agglomeration,,,,,,,,,,,,,,,euclidean,average,25,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-24,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.21794354428393548,None,0.0,2,16,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0025451910134387575,median,quantile_transformer,1477,uniform,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
-25,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5010952533165973,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.005838375247601407,mean,normalize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,
-26,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5185406559495093,None,0.0,3,17,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.010000000000000004,median,normalize,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.13046020673147232,fpr,chi2
-27,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.9663114854040722,None,0.0,11,12,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.26932389682904634,median,quantile_transformer,23,uniform,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l1,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,
-28,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5607168729746175,None,0.0,2,3,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.009810589644392154,most_frequent,quantile_transformer,1132,normal,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.06211089008167909,fdr,chi2
-29,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.992156991246829,None,0.0,19,3,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.007016879673679419,median,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,
-30,none,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.9728532763078892,None,0.0,13,9,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,none,,,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6651071190864879,False,,,,,,,,,,,,,,,
-31,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9561075301148197,None,0.0,6,2,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,
-32,none,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,173,None,,1.3919208969870906e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,minmax,,,,,kernel_pca,,,,,,,,,,,,,,,,,,,0.1258530558153379,2,0.008499663955936211,poly,499,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-33,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,1.153931482107762e-10,0.011666190464009397,auto,255,None,29,190,18,loss,1e-07,0.147868307124396,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,
-34,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,22194.242946902177,0.45210849669021247,,0.0005669713702171556,sigmoid,-1,False,0.03542142266081451,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,quantile_transformer,291,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-35,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.8189454407806317,None,0.0,10,10,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,robust_scaler,,,0.7192027795318285,0.24912062393096632,fast_ica,,,,,,,,,,,parallel,logcosh,100,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-36,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,3.6797768538445263e-09,0.05511004217375807,auto,255,None,72,62,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,robust_scaler,,,0.7241481576763391,0.0501033899901458,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,36,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-37,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.7521969592385672,None,0.0,9,5,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.020402264943872365,median,robust_scaler,,,0.737227213630259,0.20752747663727789,fast_ica,,,,,,,,,,,parallel,exp,312,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-38,none,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.563056219822946,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.32793677336996485,most_frequent,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-39,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,1.6343482806592892e-09,0.19537524572679268,auto,255,None,32,29,13,loss,1e-07,0.05061738224302791,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-40,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7088164344533493,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,mean,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,
-41,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,1.943186107984505e-07,0.08852455702036988,auto,255,None,5,72,19,loss,1e-07,0.039837460762271175,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.01591265419367627,median,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,
-42,weighting,adaboost,SAMME,0.5210651042094924,10,401,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,standardize,,,,,feature_agglomeration,,,,,,,,,,,,,,,euclidean,complete,89,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-43,none,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0686374607394679,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,minmax,,,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.8490260132946401,False,,,,,,,,,,,,,,,
-44,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.6931271368243409,None,0.0,11,10,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.362940004438799,most_frequent,robust_scaler,,,0.9177587796305063,0.17084038458597867,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,30.491514144623952,mutual_info,,,
-45,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.2052035060269155,None,0.0,20,16,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,none,,,,,feature_agglomeration,,,,,,,,,,,,,,,cosine,complete,22,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-46,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,92.66144306787051,,,0.04654639302091653,rbf,-1,True,0.0022231544280610005,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.005194838694455963,mean,none,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,15,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-47,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2681.998807317996,-0.9558745731731211,,0.0011299948421773457,sigmoid,-1,False,1.5292507354478086e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-48,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.4467384469668449,None,0.0,19,19,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.00033708405018786203,median,quantile_transformer,711,normal,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,7.744829113696808,False,True,1,squared_hinge,ovr,l1,0.0001869446320464273,,,,,,,,,,,,,,,,,,,,,,
-49,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,26.502259985828186,,,9.475662388405154e-05,rbf,-1,False,0.00019690635968217866,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-50,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,2.5360196477156387e-07,0.011018756408558218,auto,255,None,40,122,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.00011749164080669445,most_frequent,quantile_transformer,757,uniform,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,5.097834697156853,False,True,1,squared_hinge,ovr,l1,0.053877834919477424,,,,,,,,,,,,,,,,,,,,,,
-51,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5255991794296913,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-52,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.34697723213923637,None,0.0,15,18,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.1923594385988973,mean,normalize,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,25.77499302945827,chi2,,,
-53,weighting,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.7776312540090575,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0005253715056715056,mean,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,False,,,,,,,,,,,,
-54,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,1.5661823810486637e-10,0.08996863554015262,auto,255,None,156,89,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.009331521286159971,median,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,
-55,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.6109468076475573,0.8748383232154038,4,0.41333070422336576,poly,-1,False,3.591268778393482e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.011804925819789203,median,minmax,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-56,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.7610308078756552,None,0.0,1,2,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.010000000000000004,mean,robust_scaler,,,0.9205545465103644,0.20272761060743316,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-57,weighting,adaboost,SAMME.R,0.03127273017917347,10,496,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,standardize,,,,,fast_ica,,,,,,,,,,,deflation,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-58,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,151,None,,0.0003283777607180646,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.2510527431514227,mean,normalize,,,,,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,-0.96093182232312,,0.05762510964607661,sigmoid,2632,,,,,,,,,,,,,,,,,
-59,none,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.4702538502800974e-07,True,,,True,0.14999999999999974,optimal,perceptron,elasticnet,,0.0011469929926494495,no_encoding,no_coalescense,,mean,quantile_transformer,1782,normal,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.9897679383028408,True,,,,,,,,,,,,,,,
-60,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.6453555269772124,None,0.0,1,4,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,normalize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-61,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,940.9437776449834,,,1.5839338084625942,rbf,-1,False,0.023072781592565438,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,none,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,77.25763685070707,mutual_info,,,
-62,weighting,extra_trees,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,13,18,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0001574994528707389,most_frequent,standardize,,,,,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,3,None,19,19,1.0,77,,,,,
-63,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.34534888916828577,0.02513634776687268,2,1.0733091880109409,poly,-1,True,0.0006974263047918307,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,quantile_transformer,1000,normal,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,77.99934218022861,mutual_info,,,
-64,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,29.55731828912983,False,True,1,squared_hinge,ovr,l2,0.0011332486951954968,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,
-65,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,1.1387795876755026e-06,0.12245605190343567,auto,255,None,58,7,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,robust_scaler,,,0.79305915648885,0.1841487406876042,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-66,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.8425560317492612,None,0.0,8,15,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.015276988502366128,median,none,,,,,fast_ica,,,,,,,,,,,parallel,cube,17,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-67,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,0.2410465573252999,0.022973469655856883,auto,255,None,272,8,3,loss,1e-07,0.0321932494461218,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.00807996498005836,most_frequent,none,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,65,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-68,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,925.3053743248586,False,True,1,squared_hinge,ovr,l2,0.02764481198395717,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.3850718018366796,most_frequent,quantile_transformer,786,uniform,,,feature_agglomeration,,,,,,,,,,,,,,,cosine,average,156,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-69,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7573078019937971,None,0.0,2,5,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,robust_scaler,,,0.7265312295801113,0.28332515882236425,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.09660564590146253,fpr,f_classif
-70,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5345540246011571,None,0.0,8,4,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.007441222223802043,most_frequent,normalize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-71,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.41019363453549706,None,0.0,15,2,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.0003507389224306018,median,none,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.20614098085343638,fdr,chi2
-72,none,adaboost,SAMME,0.1280458968526125,7,125,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,robust_scaler,,,0.7156460334356771,0.2981902862926971,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,65.16171885308417,chi2,,,
-73,none,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9931779675565967,None,0.0,18,17,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,robust_scaler,,,0.75,0.2945917010472818,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,
-74,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.08646118576686276,None,0.0,4,9,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,most_frequent,quantile_transformer,959,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-75,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,1.2430504242164082e-09,0.03359568307446533,auto,255,None,1609,29,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,standardize,,,,,feature_agglomeration,,,,,,,,,,,,,,,cosine,complete,40,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-76,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7.491039171224502,True,True,squared_hinge,2.8058138048327884e-05,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,minmax,,,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.7754372809893839,False,True,1,squared_hinge,ovr,l1,3.9643594596766314e-05,,,,,,,,,,,,,,,,,,,,,,
-77,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.34936025988397124,None,0.0,2,13,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00018601309647406987,median,robust_scaler,,,0.8634309348843472,0.26976989465367607,extra_trees_preproc_for_classification,False,entropy,None,0.48025175288975036,None,0.0,6,17,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-78,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,0.011547155223520492,0.13665554140837183,auto,255,None,13,5,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.1824584492028449,median,minmax,,,,,feature_agglomeration,,,,,,,,,,,,,,,euclidean,average,261,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-79,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.7308100742740112,None,0.0,18,19,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.014637629296743653,most_frequent,robust_scaler,,,0.7724670628916575,0.2233445340332948,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,29.07440444752882,False,True,1,squared_hinge,ovr,l1,0.00040479645225962257,,,,,,,,,,,,,,,,,,,,,,
-80,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1661.2274747409913,,,0.025821208852387936,rbf,-1,False,0.0017011435104921824,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,none,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,306,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-81,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0016941428709087083,True,,,True,,optimal,squared_hinge,l1,,5.124265599423001e-05,no_encoding,no_coalescense,,most_frequent,quantile_transformer,461,uniform,,,extra_trees_preproc_for_classification,True,gini,None,0.31371431180257103,None,0.0,5,17,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-82,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,21.85207494001697,0.18872999589786676,4,1.1863646021868761,poly,-1,False,0.0021338371901294443,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0034080852834770217,mean,normalize,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.11326005109886934,fdr,f_classif
-83,weighting,adaboost,SAMME,0.38341570193537416,9,250,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,minmax,,,,,feature_agglomeration,,,,,,,,,,,,,,,euclidean,average,13,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-84,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,0.037849720944694,0.1268815770019783,auto,255,None,11,99,11,loss,1e-07,0.10654523627313527,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,
-85,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.593728171200453,None,0.0,7,20,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.02830941310359363,mean,normalize,,,,,extra_trees_preproc_for_classification,False,entropy,None,0.7389446586281172,None,0.0,20,11,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-86,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,1,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.019797877010737826,mean,normalize,,,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.8239678741128165,False,,,,,,,,,,,,,,,
-87,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.46057831591617715,False,True,hinge,0.04557857428827514,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00027457445401600137,median,standardize,,,,,extra_trees_preproc_for_classification,True,gini,None,0.48190346970486964,None,0.0,17,18,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-88,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.46057831591617715,False,True,hinge,0.04557857428827514,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00027457445401600137,median,standardize,,,,,extra_trees_preproc_for_classification,True,gini,None,0.48190346970486964,None,0.0,17,18,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-89,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5722917944959481,None,0.0,1,14,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,
-90,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.26356754187780895,None,0.0,4,3,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010966178401758774,mean,robust_scaler,,,0.7817043861798886,0.24371720112407158,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
-91,weighting,adaboost,SAMME.R,0.13547552520095074,4,62,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.011851011202809002,most_frequent,standardize,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4888776875314595,fpr,chi2
-92,weighting,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.011656655127925412,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.48080598210703135,mean,none,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,205,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-93,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.6931271368243409,None,0.0,11,10,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.362940004438799,most_frequent,robust_scaler,,,0.9177587796305063,0.17084038458597867,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,30.491514144623952,mutual_info,,,
-94,weighting,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,2,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,none,,,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6342574285315175,False,,,,,,,,,,,,,,,
-95,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.107967527639527e-05,True,True,hinge,0.00448294745544775,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,quantile_transformer,953,normal,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,35.00114345405331,chi2,,,
-96,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.457928416564218,None,0.0,12,2,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,
-97,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,1.1522414705935405e-10,0.6132800183073315,auto,255,None,1607,67,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010096911249562407,mean,robust_scaler,,,0.7663493326967942,0.136094217337434,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-98,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2746221958279041,False,True,1,squared_hinge,ovr,l2,6.546077664669006e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,quantile_transformer,1054,normal,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,
-99,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.479405900046494,,,0.013340778632154543,rbf,-1,False,6.913586060890114e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,robust_scaler,,,0.9953232616818098,0.06450577886132274,fast_ica,,,,,,,,,,,deflation,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-100,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.9815182201168626,None,0.0,15,19,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,robust_scaler,,,0.8878662977383482,0.25666364929970126,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,44.15748219826322,mutual_info,,,
-101,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0559127099090374,True,True,hinge,1.4799425163790862e-05,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00029267075188836047,mean,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,False,,,,,,,,,,,,
-102,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.7426893291639965,None,0.0,4,19,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010127721455862085,median,quantile_transformer,1108,uniform,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,263,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-103,none,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,128,manual,0.8574560771111307,0.010886139381599514,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.24115007944216788,most_frequent,normalize,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,62.26514170991452,mutual_info,,,
-104,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.6282663389679584,None,0.0,1,3,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,median,minmax,,,,,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-105,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,1.430083734915855e-09,0.08779621406981941,auto,255,None,17,20,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.29749837779115257,mean,robust_scaler,,,0.7236445277814435,0.25,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
-106,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.3294588572341214,None,0.0,1,3,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
-107,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,11.046854203258679,,,0.07809571976769347,rbf,-1,False,0.00036193159909053734,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.006445557635410205,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-108,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.6728827282646637,None,0.0,18,8,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,robust_scaler,,,0.8512985751386942,0.14092824896182807,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,
-109,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.18060139887649052,None,0.0,10,12,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.010000000000000004,most_frequent,robust_scaler,,,0.9298612094255864,0.1263597946722308,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
-110,none,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.563056219822946,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.32793677336996485,most_frequent,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-111,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6427543483503397,True,True,hinge,0.00045318775930916944,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.009338484332306904,median,quantile_transformer,174,normal,,,kernel_pca,,,,,,,,,,,,,,,,,,,,,,cosine,1848,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-112,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.04981080944617379,True,,0.07309386939525211,True,,constant,log,l2,,0.0006721243213777075,no_encoding,no_coalescense,,mean,quantile_transformer,574,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-113,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,9.029473608568458e-09,0.15779990576964315,auto,255,None,37,71,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.018093177195644097,mean,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,False,,,,,,,,,,,,
-114,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0025185447074997196,True,True,hinge,3.168531728129776e-05,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.05667297499772673,mean,quantile_transformer,796,uniform,,,feature_agglomeration,,,,,,,,,,,,,,,euclidean,average,315,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-115,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.129143822880219,True,True,hinge,0.00012612093569084375,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0016222546082911887,most_frequent,quantile_transformer,1309,uniform,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8.816417526015055,f_classif,,,
-116,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.4864284879550203,None,0.0,1,4,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.008665854799630503,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-117,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.8851946632713493,None,0.0,9,20,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.006095662608922393,most_frequent,quantile_transformer,1002,normal,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.10486328449423876,fpr,f_classif
-118,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.6922752267318475,True,True,squared_hinge,0.06299408576730353,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,quantile_transformer,1742,uniform,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,107,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-119,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.44727252219812963,None,0.0,2,10,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,standardize,,,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l1,8.17533189014765e-05,,,,,,,,,,,,,,,,,,,,,,
-120,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7408631116596028,None,0.0,19,2,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,mean,none,,,,,extra_trees_preproc_for_classification,True,entropy,None,0.8873056409960391,None,0.0,11,18,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-121,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.4210978663047845,None,0.0,5,6,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.006365988550833196,most_frequent,robust_scaler,,,0.8348891074186082,0.00974961635730845,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0695919708890599,fpr,f_classif
-122,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.3341112040803983,False,True,hinge,0.003045682798589668,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.005225537488332702,median,quantile_transformer,1225,uniform,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.10310163232668391,fpr,chi2
-123,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.20612848067724507,False,True,1,squared_hinge,ovr,l2,0.00017832980955957004,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0007502816173508168,median,quantile_transformer,1083,uniform,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.02820386332530596,fwe,f_classif
-124,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1561.1002600303584,,,0.0024048551704194507,rbf,-1,False,0.0001936214667496851,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,minmax,,,,,fast_ica,,,,,,,,,,,parallel,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-125,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.087313242780732,0.0636709914987994,3,0.004481917125024585,poly,-1,False,0.01848880618941367,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.002136015035740733,mean,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-126,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.03204653439894958,True,True,squared_hinge,0.00013006393401112688,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.013506575694382031,most_frequent,normalize,,,,,feature_agglomeration,,,,,,,,,,,,,,,euclidean,ward,356,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-127,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.6466927589470762,None,0.0,11,2,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.009672465311659264,most_frequent,standardize,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,369,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+1,none,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1514.059969351301,False,True,1,squared_hinge,ovr,l2,0.002448814309966599,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,standardize,,,,,extra_trees_preproc_for_classification,True,entropy,None,0.8221481751476923,None,0.0,2,3,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+4,none,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,quantile_transformer,111,uniform,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4748887602127273,fdr,f_classif
+6,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.7283683762613216,None,0.0,12,20,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,median,standardize,,,,,fast_ica,,,,,,,,,,,deflation,exp,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+8,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,114,manual,0.11778631707605758,0.0029112721339713465,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.015582195257240751,median,normalize,,,,,kernel_pca,,,,,,,,,,,,,,,,,,,0.42909623253327767,3,0.25407612000343777,poly,1689,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+11,weighting,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,2,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.3865962574104293,mean,normalize,,,,,fast_ica,,,,,,,,,,,deflation,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+15,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.21772092350860472,None,0.0,14,17,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.0020488212891693275,median,standardize,,,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,33.74257606530722,False,True,1,squared_hinge,ovr,l1,8.740452966205111e-05,,,,,,,,,,,,,,,,,,,,,,
+20,none,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.9866058571747695,None,0.0,16,9,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.1680676743605393,most_frequent,robust_scaler,,,0.8840072797399463,0.08535534311393947,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,False,,,,,,,,,,,,
+23,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,0.00022373024203887573,0.027149343206135476,auto,255,None,49,13,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.00777614320416325,most_frequent,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,
+26,weighting,adaboost,SAMME.R,0.01561270559347964,4,417,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,quantile_transformer,1617,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+28,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,1.0168466808008568e-09,0.21361657232950118,auto,255,None,463,4,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,robust_scaler,,,0.7965217268158734,0.2446550610908456,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,
+32,none,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,174,manual,0.21501892897746921,2.0347110826262054e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.013754893527625522,most_frequent,normalize,,,,,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,-0.019221513778605592,3,0.10190948951339239,poly,2521,,,,,,,,,,,,,,,,,
+35,none,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9.608707084865884,False,True,1,squared_hinge,ovr,l2,0.008086910150491696,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.020901889243957545,median,minmax,,,,,kitchen_sinks,,,,,,,,,,,,,,,,,,,,,,,,0.5694399784333516,3230,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+38,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9025597189684735,None,0.0,18,8,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.007536467725572231,most_frequent,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
+40,none,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.563056219822946,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.32793677336996485,most_frequent,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+41,none,extra_trees,,,,,,,,,,,,,,,True,gini,None,0.5033866291997137,None,0.0,2,14,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,minmax,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+44,none,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l2,2.858526169098402e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0007827505640681123,median,standardize,,,,,extra_trees_preproc_for_classification,False,entropy,None,0.3443310459451119,None,0.0,3,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+48,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.36750008697181424,None,0.0,20,16,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,median,quantile_transformer,753,normal,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,
+51,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.3862436135511355e-05,False,,0.01139834246571156,True,,invscaling,log,l1,0.5,0.00025858980190005947,no_encoding,no_coalescense,,most_frequent,robust_scaler,,,0.7614693069989072,0.2515867504414451,extra_trees_preproc_for_classification,False,gini,None,0.6006906188943036,None,0.0,2,4,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+57,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.24407113189971e-05,True,,,True,,optimal,hinge,l2,,0.018411607920920666,one_hot_encoding,minority_coalescer,0.006594668208397739,mean,standardize,,,,,extra_trees_preproc_for_classification,True,entropy,None,0.20369732810864838,None,0.0,16,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+58,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5214197490559657,None,0.0,1,2,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.00826182798588248,mean,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,
+59,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.15115687152536414,True,True,hinge,0.009954631623104506,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0013410356664535208,most_frequent,quantile_transformer,1293,uniform,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+62,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.7848258054012829,None,0.0,18,15,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,standardize,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,44.11368738669913,chi2,,,
+65,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,1.2822815440468518e-10,0.12922700208645135,auto,255,None,31,44,13,loss,1e-07,0.08789566421608311,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.012230423571314038,mean,robust_scaler,,,0.71734690066056,0.20283418288016852,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,
+66,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,236.02119240292217,False,True,1,squared_hinge,ovr,l2,0.0017427914676337187,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.1020917547461355,mean,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,False,,,,,,,,,,,,
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+446,weighting,bernoulli_nb,,,,,2.493274808167669,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.16300567981899747,mean,standardize,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,17.02935019079249,mutual_info,,,
+447,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.32235597434544716,None,0.0,2,18,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.30368818748392196,median,robust_scaler,,,0.7176393529809699,0.19299987830067816,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.16283145936255025,fwe,chi2
+451,weighting,adaboost,SAMME,0.28074307158760897,3,166,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0030293384621179554,mean,quantile_transformer,965,uniform,,,feature_agglomeration,,,,,,,,,,,,,,,cosine,complete,66,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+453,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,162.34906836693145,,,2.470518094983172,rbf,-1,False,1.5366632541084908e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.33912551828528803,median,none,,,,,fast_ica,,,,,,,,,,,deflation,logcosh,412,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+459,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.745748188733041e-05,True,True,hinge,2.181659959258924e-05,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.13921206193066876,most_frequent,quantile_transformer,60,normal,,,extra_trees_preproc_for_classification,False,entropy,None,0.7619669647366906,None,0.0,3,4,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+461,weighting,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.9815588983620268,None,0.0,2,14,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010000000000000004,mean,robust_scaler,,,0.7617017260745782,0.2386998843971201,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
diff --git a/autosklearn/metalearning/files/recall_macro_binary.classification_dense/description.txt b/autosklearn/metalearning/files/recall_macro_binary.classification_dense/description.txt
index 5e071543d8..a1c0eb6664 100644
--- a/autosklearn/metalearning/files/recall_macro_binary.classification_dense/description.txt
+++ b/autosklearn/metalearning/files/recall_macro_binary.classification_dense/description.txt
@@ -1,8 +1,9 @@
 features_cutoff_time: 3600
 features_cutoff_memory: 3072
-number_of_feature_steps: 35
+number_of_feature_steps: 42
 feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances
 feature_step LogNumberOfInstances: LogNumberOfInstances
+feature_step NumberOfClasses: NumberOfClasses
 feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures
 feature_step LogNumberOfFeatures: LogNumberOfFeatures
 feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues
@@ -20,6 +21,11 @@ feature_step DatasetRatio: DatasetRatio, LogDatasetRatio
 feature_step LogDatasetRatio: LogDatasetRatio
 feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio
 feature_step LogInverseDatasetRatio: LogInverseDatasetRatio
+feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD
+feature_step ClassProbabilityMin: ClassProbabilityMin
+feature_step ClassProbabilityMax: ClassProbabilityMax
+feature_step ClassProbabilityMean: ClassProbabilityMean
+feature_step ClassProbabilitySTD: ClassProbabilitySTD
 feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum
 feature_step SymbolsMin: SymbolsMin
 feature_step SymbolsMax: SymbolsMax
@@ -36,11 +42,12 @@ feature_step SkewnessMin: SkewnessMin
 feature_step SkewnessMax: SkewnessMax
 feature_step SkewnessMean: SkewnessMean
 feature_step SkewnessSTD: SkewnessSTD
-features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
+feature_step ClassEntropy: ClassEntropy
+features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy
 features_stochastic: 
-default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
+default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy
 
-algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127
+algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235,236,237,238,239,240,241,242,243,244,245,246,247,248,249,250,251,252,253,254,255,256,257,258,259,260,261,262,263,264,265,266,267,268,269,270,271,272,273,274,275,276,277,278,279,280,281,282,283,284,285,286,287,288,289,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,312,313,314,315,316,317,318,319,320,321,322,323,324,325,326,327,328,329,330,331,332,333,334,335,336,337,338,339,340,341,342,343,344,345,346,347,348,349,350,351,352,353,354,355,356,357,358,359,360,361,362,363,364,365,366,367,368,369,370,371,372,373,374,375,376,377,378,379,380,381,382,383,384,385,386,387,388,389,390,391,392,393,394,395,396,397,398,399,400,401,402,403,404,405,406,407,408,409,410,411,412,413,414,415,416,417,418,419,420,421,422,423,424,425,426,427,428,429,430,431,432,433,434,435,436,437,438,439,440,441,442,443,444,445,446,447,448,449,450,451,452,453,454,455,456,457,458,459,460,461,462,463,464
 algorithms_stochastic: 
 performance_measures: recall_macro
 performance_type: solution_quality
diff --git a/autosklearn/metalearning/files/recall_macro_binary.classification_dense/feature_costs.arff b/autosklearn/metalearning/files/recall_macro_binary.classification_dense/feature_costs.arff
index 0e17e20d22..8512fd9275 100644
--- a/autosklearn/metalearning/files/recall_macro_binary.classification_dense/feature_costs.arff
+++ b/autosklearn/metalearning/files/recall_macro_binary.classification_dense/feature_costs.arff
@@ -4,6 +4,7 @@
 @ATTRIBUTE repetition NUMERIC
 @ATTRIBUTE NumberOfInstances NUMERIC
 @ATTRIBUTE LogNumberOfInstances NUMERIC
+@ATTRIBUTE NumberOfClasses NUMERIC
 @ATTRIBUTE NumberOfFeatures NUMERIC
 @ATTRIBUTE LogNumberOfFeatures NUMERIC
 @ATTRIBUTE MissingValues NUMERIC
@@ -21,6 +22,11 @@
 @ATTRIBUTE LogDatasetRatio NUMERIC
 @ATTRIBUTE InverseDatasetRatio NUMERIC
 @ATTRIBUTE LogInverseDatasetRatio NUMERIC
+@ATTRIBUTE ClassOccurences NUMERIC
+@ATTRIBUTE ClassProbabilityMin NUMERIC
+@ATTRIBUTE ClassProbabilityMax NUMERIC
+@ATTRIBUTE ClassProbabilityMean NUMERIC
+@ATTRIBUTE ClassProbabilitySTD NUMERIC
 @ATTRIBUTE NumSymbols NUMERIC
 @ATTRIBUTE SymbolsMin NUMERIC
 @ATTRIBUTE SymbolsMax NUMERIC
@@ -37,162 +43,138 @@
 @ATTRIBUTE SkewnessMax NUMERIC
 @ATTRIBUTE SkewnessMean NUMERIC
 @ATTRIBUTE SkewnessSTD NUMERIC
+@ATTRIBUTE ClassEntropy NUMERIC
 
 @DATA
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diff --git a/autosklearn/metalearning/files/recall_macro_binary.classification_dense/feature_runstatus.arff b/autosklearn/metalearning/files/recall_macro_binary.classification_dense/feature_runstatus.arff
index 0034ad2bd6..5fff2dfed3 100644
--- a/autosklearn/metalearning/files/recall_macro_binary.classification_dense/feature_runstatus.arff
+++ b/autosklearn/metalearning/files/recall_macro_binary.classification_dense/feature_runstatus.arff
@@ -4,6 +4,7 @@
 @ATTRIBUTE repetition NUMERIC
 @ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other}
@@ -21,6 +22,11 @@
 @ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other}
@@ -37,162 +43,138 @@
 @ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other}
 
 @DATA
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diff --git a/autosklearn/metalearning/files/recall_macro_binary.classification_dense/feature_values.arff b/autosklearn/metalearning/files/recall_macro_binary.classification_dense/feature_values.arff
index a60400e3ce..736dc64321 100644
--- a/autosklearn/metalearning/files/recall_macro_binary.classification_dense/feature_values.arff
+++ b/autosklearn/metalearning/files/recall_macro_binary.classification_dense/feature_values.arff
@@ -13,7 +13,6 @@
 @ATTRIBUTE KurtosisMean NUMERIC
 @ATTRIBUTE KurtosisMin NUMERIC
 @ATTRIBUTE KurtosisSTD NUMERIC
-@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC
 @ATTRIBUTE LogDatasetRatio NUMERIC
 @ATTRIBUTE LogInverseDatasetRatio NUMERIC
 @ATTRIBUTE LogNumberOfFeatures NUMERIC
@@ -42,160 +41,135 @@
 @ATTRIBUTE SymbolsSum NUMERIC
 
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diff --git a/autosklearn/metalearning/files/recall_macro_binary.classification_sparse/algorithm_runs.arff b/autosklearn/metalearning/files/recall_macro_binary.classification_sparse/algorithm_runs.arff
index aee25ff689..73b0d1dc62 100644
--- a/autosklearn/metalearning/files/recall_macro_binary.classification_sparse/algorithm_runs.arff
+++ b/autosklearn/metalearning/files/recall_macro_binary.classification_sparse/algorithm_runs.arff
@@ -7,95 +7,133 @@
 @ATTRIBUTE runstatus {ok, timeout, memout, not_applicable, crash, other}
 
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diff --git a/autosklearn/metalearning/files/recall_macro_binary.classification_sparse/configurations.csv b/autosklearn/metalearning/files/recall_macro_binary.classification_sparse/configurations.csv
index b81aa5389d..f2f818169e 100644
--- a/autosklearn/metalearning/files/recall_macro_binary.classification_sparse/configurations.csv
+++ b/autosklearn/metalearning/files/recall_macro_binary.classification_sparse/configurations.csv
@@ -1,93 +1,131 @@
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-91,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.087313242780732,0.0636709914987994,3,0.004481917125024585,poly,-1,False,0.01848880618941367,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.002136015035740733,mean,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-92,weighting,adaboost,SAMME,0.028177459966491795,1,484,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,quantile_transformer,875,normal,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,1.502807354196727,False,True,1,squared_hinge,ovr,l1,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,
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+287,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.3850609070926397,None,0.0,1,2,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.013992309764275707,median,normalize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,,
+289,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.818514683854827,None,0.0,15,20,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.4928285185491301,most_frequent,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,,
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+296,weighting,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,1,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.10025594399125329,median,none,,,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.22421266641512966,False,True,1,squared_hinge,ovr,l1,0.0036601423446597503,,,,,,,,,,,,,,,,,,,,,
+303,none,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.15407723980990137,False,True,1,squared_hinge,ovr,l2,0.000145662551000115,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.047373081734019086,median,standardize,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,63.64551651621554,chi2,,,,
+306,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2677516008848513,True,True,hinge,0.05752557410901496,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0010152857217605206,median,quantile_transformer,1000,uniform,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,51.60974796845029,chi2,,,,
+307,none,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,249,None,,0.0005516162390755682,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010000000000000004,mean,quantile_transformer,1350,uniform,,,kernel_pca,,,,,,,,,,,0.162692683181769,3,1.0,poly,487,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+312,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,60.6761602925822,,,0.038224035513907226,rbf,-1,False,0.0018214343969776353,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.421025538999516,median,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+313,weighting,adaboost,SAMME,1.2423888874490505,5,284,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.01582563952987033,most_frequent,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+316,weighting,adaboost,SAMME,1.2615483379313783,8,190,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.00017789451703497617,mean,robust_scaler,,,0.7345811776810758,0.054715769043996304,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+318,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.843098530645432,None,0.0,1,4,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,mean,quantile_transformer,930,normal,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,,
+321,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.5864398404787432,None,0.0,1,14,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0070663750850608895,most_frequent,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,,
+326,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0001329242404968632,False,True,squared_hinge,0.000416219546803132,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.007568009633214092,median,quantile_transformer,1126,normal,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,29.06367291960815,chi2,,,,
+329,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0005200565197711045,True,,0.010000000000000014,True,,constant,squared_hinge,l2,,0.00021764230789274295,one_hot_encoding,minority_coalescer,0.2691388203358419,mean,normalize,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,64.14926024499454,chi2,,,,
+331,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.5,None,0.0,1,5,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.15244281596303316,mean,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+333,weighting,bernoulli_nb,,,,,0.030158213146175936,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,quantile_transformer,1820,uniform,,,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,9,None,11,2,1.0,96,,,,,,
+336,weighting,adaboost,SAMME,0.1313487280585511,3,189,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,robust_scaler,,,0.8008090310131288,0.2935856792824304,extra_trees_preproc_for_classification,False,entropy,None,0.7722400064297721,None,0.0,5,4,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+339,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.36374112587320206,None,0.0,2,10,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010870084126959173,most_frequent,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,,
+340,weighting,decision_tree,,,,,,,entropy,1.4732927621306418,1.0,None,0.0,10,3,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,standardize,,,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,4276.070150555713,False,True,1,squared_hinge,ovr,l1,0.00011385383984576719,,,,,,,,,,,,,,,,,,,,,
+344,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.9039555800017817e-05,False,True,squared_hinge,0.0011536981768375681,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,quantile_transformer,1122,uniform,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,,
+345,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5321814610661173,None,0.0,1,3,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,standardize,,,,,extra_trees_preproc_for_classification,True,gini,None,0.5,None,0.0,1,15,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+351,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.34649900620486696,None,0.0,10,20,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,mean,quantile_transformer,1135,normal,,,extra_trees_preproc_for_classification,True,gini,None,0.9038598364290225,None,0.0,16,18,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+352,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5988524108452234,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,mean,robust_scaler,,,0.75,0.26961884796358077,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+355,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,68.60833575985336,,,0.03905068757955671,rbf,-1,False,0.02846559747114773,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.04775815990561371,mean,standardize,,,,,extra_trees_preproc_for_classification,False,entropy,None,0.45727987956076366,None,0.0,13,11,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+356,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.01400651528258823,True,True,hinge,1.929818740575615e-05,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,normalize,,,,,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,5,None,3,19,1.0,78,,,,,,
+357,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.32235597434544716,None,0.0,2,18,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.30368818748392196,median,robust_scaler,,,0.7176393529809699,0.19299987830067816,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.16283145936255025,fwe,chi2,
+361,weighting,adaboost,SAMME,0.2786884011943926,3,263,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,quantile_transformer,1958,uniform,,,extra_trees_preproc_for_classification,False,entropy,None,0.2099259239256946,None,0.0,19,18,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+363,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.1393173890820956,False,True,1,squared_hinge,ovr,l2,8.283470008521272e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.015152352014390857,mean,normalize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,False,,,,,,,,,,,,,
+368,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.745748188733041e-05,True,True,hinge,2.181659959258924e-05,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.13921206193066876,most_frequent,quantile_transformer,60,normal,,,extra_trees_preproc_for_classification,False,entropy,None,0.7619669647366906,None,0.0,3,4,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+369,weighting,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.9815588983620268,None,0.0,2,14,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010000000000000004,mean,robust_scaler,,,0.7617017260745782,0.2386998843971201,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
diff --git a/autosklearn/metalearning/files/recall_macro_binary.classification_sparse/description.txt b/autosklearn/metalearning/files/recall_macro_binary.classification_sparse/description.txt
index 7f9e34d4e9..5301c73dc3 100644
--- a/autosklearn/metalearning/files/recall_macro_binary.classification_sparse/description.txt
+++ b/autosklearn/metalearning/files/recall_macro_binary.classification_sparse/description.txt
@@ -1,8 +1,9 @@
 features_cutoff_time: 3600
 features_cutoff_memory: 3072
-number_of_feature_steps: 35
+number_of_feature_steps: 42
 feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances
 feature_step LogNumberOfInstances: LogNumberOfInstances
+feature_step NumberOfClasses: NumberOfClasses
 feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures
 feature_step LogNumberOfFeatures: LogNumberOfFeatures
 feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues
@@ -20,6 +21,11 @@ feature_step DatasetRatio: DatasetRatio, LogDatasetRatio
 feature_step LogDatasetRatio: LogDatasetRatio
 feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio
 feature_step LogInverseDatasetRatio: LogInverseDatasetRatio
+feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD
+feature_step ClassProbabilityMin: ClassProbabilityMin
+feature_step ClassProbabilityMax: ClassProbabilityMax
+feature_step ClassProbabilityMean: ClassProbabilityMean
+feature_step ClassProbabilitySTD: ClassProbabilitySTD
 feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum
 feature_step SymbolsMin: SymbolsMin
 feature_step SymbolsMax: SymbolsMax
@@ -36,11 +42,12 @@ feature_step SkewnessMin: SkewnessMin
 feature_step SkewnessMax: SkewnessMax
 feature_step SkewnessMean: SkewnessMean
 feature_step SkewnessSTD: SkewnessSTD
-features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
+feature_step ClassEntropy: ClassEntropy
+features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy
 features_stochastic: 
-default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
+default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy
 
-algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92
+algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235,236,237,238,239,240,241,242,243,244,245,246,247,248,249,250,251,252,253,254,255,256,257,258,259,260,261,262,263,264,265,266,267,268,269,270,271,272,273,274,275,276,277,278,279,280,281,282,283,284,285,286,287,288,289,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,312,313,314,315,316,317,318,319,320,321,322,323,324,325,326,327,328,329,330,331,332,333,334,335,336,337,338,339,340,341,342,343,344,345,346,347,348,349,350,351,352,353,354,355,356,357,358,359,360,361,362,363,364,365,366,367,368,369,370,371,372
 algorithms_stochastic: 
 performance_measures: recall_macro
 performance_type: solution_quality
diff --git a/autosklearn/metalearning/files/recall_macro_binary.classification_sparse/feature_costs.arff b/autosklearn/metalearning/files/recall_macro_binary.classification_sparse/feature_costs.arff
index 0e17e20d22..8512fd9275 100644
--- a/autosklearn/metalearning/files/recall_macro_binary.classification_sparse/feature_costs.arff
+++ b/autosklearn/metalearning/files/recall_macro_binary.classification_sparse/feature_costs.arff
@@ -4,6 +4,7 @@
 @ATTRIBUTE repetition NUMERIC
 @ATTRIBUTE NumberOfInstances NUMERIC
 @ATTRIBUTE LogNumberOfInstances NUMERIC
+@ATTRIBUTE NumberOfClasses NUMERIC
 @ATTRIBUTE NumberOfFeatures NUMERIC
 @ATTRIBUTE LogNumberOfFeatures NUMERIC
 @ATTRIBUTE MissingValues NUMERIC
@@ -21,6 +22,11 @@
 @ATTRIBUTE LogDatasetRatio NUMERIC
 @ATTRIBUTE InverseDatasetRatio NUMERIC
 @ATTRIBUTE LogInverseDatasetRatio NUMERIC
+@ATTRIBUTE ClassOccurences NUMERIC
+@ATTRIBUTE ClassProbabilityMin NUMERIC
+@ATTRIBUTE ClassProbabilityMax NUMERIC
+@ATTRIBUTE ClassProbabilityMean NUMERIC
+@ATTRIBUTE ClassProbabilitySTD NUMERIC
 @ATTRIBUTE NumSymbols NUMERIC
 @ATTRIBUTE SymbolsMin NUMERIC
 @ATTRIBUTE SymbolsMax NUMERIC
@@ -37,162 +43,138 @@
 @ATTRIBUTE SkewnessMax NUMERIC
 @ATTRIBUTE SkewnessMean NUMERIC
 @ATTRIBUTE SkewnessSTD NUMERIC
+@ATTRIBUTE ClassEntropy NUMERIC
 
 @DATA
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+75234,1.0,1e-05,1e-05,0.00015,1e-05,1e-05,0.0058,0.00534,1e-05,0.00033,1e-05,0.00016,0.0,2e-05,2e-05,7e-05,0.00204,3e-05,1e-05,5e-05,1e-05,0.00024,3e-05,2e-05,0.0001,9e-05,0.00043,0.0,0.0,0.00012,0.00024,6e-05,0.00156,6e-05,7e-05,0.00121,0.00021,0.00054,6e-05,7e-05,9e-05,0.00032,0.00501
+75235,1.0,1e-05,1e-05,0.0003,2e-05,1e-05,0.00461,0.00385,2e-05,0.00055,2e-05,0.00026,1e-05,3e-05,3e-05,0.0001,0.00011,3e-05,1e-05,5e-05,2e-05,0.00026,3e-05,1e-05,8e-05,0.00014,0.00075,1e-05,1e-05,0.0002,0.00043,0.00011,0.00127,0.00017,0.0002,0.00029,0.00061,0.0008,0.00015,0.00012,0.00016,0.00038,0.00242
+75236,1.0,1e-05,1e-05,0.0001,1e-05,1e-05,0.00218,0.00118,2e-05,0.00065,3e-05,0.00041,1e-05,3e-05,3e-05,9e-05,9e-05,4e-05,1e-05,7e-05,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.0006,1e-05,1e-05,0.00021,0.00032,6e-05,0.00043,7e-05,7e-05,9e-05,0.00019,0.00062,7e-05,8e-05,0.0001,0.00037,0.00048
+75237,1.0,2e-05,1e-05,0.00785,2e-05,1e-05,0.31031,0.27854,2e-05,0.01161,3e-05,0.02022,1e-05,4e-05,3e-05,9e-05,0.00024,6e-05,2e-05,9e-05,2e-05,0.00062,7e-05,3e-05,0.00017,0.00035,0.0008,1e-05,1e-05,0.00028,0.00043,8e-05,0.00043,8e-05,7e-05,9e-05,0.0002,0.00044,6e-05,8e-05,9e-05,0.00021,0.14028
+75239,1.0,1e-05,1e-05,7e-05,1e-05,1e-05,0.00105,0.00084,1e-05,0.00015,1e-05,8e-05,1e-05,2e-05,5e-05,4e-05,8e-05,2e-05,1e-05,3e-05,1e-05,0.00016,2e-05,1e-05,5e-05,9e-05,0.00049,0.0,0.0,0.00012,0.00026,0.00011,0.00046,0.00013,8e-05,8e-05,0.00018,0.00044,6e-05,9e-05,0.00011,0.00018,0.00065
+75240,1.0,2e-05,1e-05,0.00036,5e-05,4e-05,0.00845,0.00644,6e-05,0.00122,4e-05,0.00091,1e-05,4e-05,4e-05,9e-05,0.00013,5e-05,2e-05,9e-05,5e-05,0.00031,3e-05,1e-05,7e-05,0.0002,0.00073,3e-05,2e-05,0.00019,0.0003,0.00018,0.00055,9e-05,9e-05,0.00011,0.00025,0.0005,7e-05,8e-05,0.00015,0.0002,0.00365
+75243,1.0,1e-05,1e-05,0.00029,1e-05,1e-05,0.00461,0.00415,1e-05,0.00035,1e-05,0.00015,0.0,2e-05,2e-05,5e-05,7e-05,2e-05,1e-05,4e-05,1e-05,0.00018,2e-05,1e-05,4e-05,0.00011,0.00022,1e-05,1e-05,5e-05,0.00011,5e-05,0.0004,6e-05,6e-05,8e-05,0.0002,0.00039,6e-05,6e-05,8e-05,0.00019,0.00265
+75244,1.0,3e-05,2e-05,0.0004,3e-05,2e-05,0.0173,0.01298,4e-05,0.00267,4e-05,0.00174,1e-05,7e-05,9e-05,0.00024,0.00027,8e-05,2e-05,0.00013,3e-05,0.00045,7e-05,2e-05,0.00011,0.00025,0.00077,6e-05,2e-05,0.00021,0.00034,0.00013,0.00077,0.00014,0.00013,0.00026,0.00023,0.00044,7e-05,8e-05,0.0001,0.00019,0.00649
+75248,1.0,1e-05,1e-05,0.00017,1e-05,1e-05,0.00416,0.00305,1e-05,0.0006,2e-05,0.00054,1e-05,2e-05,2e-05,5e-05,7e-05,2e-05,1e-05,4e-05,1e-05,0.00017,2e-05,1e-05,4e-05,0.0001,0.00029,5e-05,1e-05,6e-05,0.00012,6e-05,0.00081,0.00011,0.00015,0.00016,0.00038,0.0005,7e-05,8e-05,0.00012,0.00024,0.00184
+75249,1.0,1e-05,1e-05,0.00011,1e-05,1e-05,0.00192,0.00153,1e-05,0.00025,1e-05,0.00017,1e-05,2e-05,2e-05,5e-05,7e-05,2e-05,1e-05,5e-05,3e-05,0.00014,2e-05,1e-05,3e-05,8e-05,0.00029,1e-05,3e-05,6e-05,0.00012,7e-05,0.00041,6e-05,8e-05,0.0001,0.00017,0.00043,6e-05,7e-05,9e-05,0.00019,0.00092
+75250,1.0,1e-05,1e-05,0.00375,1e-05,1e-05,0.04994,0.047,1e-05,0.00247,1e-05,0.00049,1e-05,2e-05,2e-05,6e-05,7e-05,2e-05,1e-05,3e-05,1e-05,0.00022,4e-05,1e-05,4e-05,0.00012,0.00041,0.0,1e-05,0.00011,0.00023,6e-05,0.00076,0.00012,0.00012,0.00019,0.00032,0.00086,0.00012,0.00013,0.00018,0.00043,0.04178
diff --git a/autosklearn/metalearning/files/recall_macro_binary.classification_sparse/feature_runstatus.arff b/autosklearn/metalearning/files/recall_macro_binary.classification_sparse/feature_runstatus.arff
index 0034ad2bd6..5fff2dfed3 100644
--- a/autosklearn/metalearning/files/recall_macro_binary.classification_sparse/feature_runstatus.arff
+++ b/autosklearn/metalearning/files/recall_macro_binary.classification_sparse/feature_runstatus.arff
@@ -4,6 +4,7 @@
 @ATTRIBUTE repetition NUMERIC
 @ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other}
@@ -21,6 +22,11 @@
 @ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other}
@@ -37,162 +43,138 @@
 @ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other}
 
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diff --git a/autosklearn/metalearning/files/recall_macro_binary.classification_sparse/feature_values.arff b/autosklearn/metalearning/files/recall_macro_binary.classification_sparse/feature_values.arff
index a60400e3ce..736dc64321 100644
--- a/autosklearn/metalearning/files/recall_macro_binary.classification_sparse/feature_values.arff
+++ b/autosklearn/metalearning/files/recall_macro_binary.classification_sparse/feature_values.arff
@@ -13,7 +13,6 @@
 @ATTRIBUTE KurtosisMean NUMERIC
 @ATTRIBUTE KurtosisMin NUMERIC
 @ATTRIBUTE KurtosisSTD NUMERIC
-@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC
 @ATTRIBUTE LogDatasetRatio NUMERIC
 @ATTRIBUTE LogInverseDatasetRatio NUMERIC
 @ATTRIBUTE LogNumberOfFeatures NUMERIC
@@ -42,160 +41,135 @@
 @ATTRIBUTE SymbolsSum NUMERIC
 
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diff --git a/autosklearn/metalearning/files/recall_macro_multiclass.classification_dense/algorithm_runs.arff b/autosklearn/metalearning/files/recall_macro_multiclass.classification_dense/algorithm_runs.arff
index c64daef00a..af74b42460 100644
--- a/autosklearn/metalearning/files/recall_macro_multiclass.classification_dense/algorithm_runs.arff
+++ b/autosklearn/metalearning/files/recall_macro_multiclass.classification_dense/algorithm_runs.arff
@@ -7,130 +7,135 @@
 @ATTRIBUTE runstatus {ok, timeout, memout, not_applicable, crash, other}
 
 @DATA
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diff --git a/autosklearn/metalearning/files/recall_macro_multiclass.classification_dense/configurations.csv b/autosklearn/metalearning/files/recall_macro_multiclass.classification_dense/configurations.csv
index d1a17e1911..70df24a6c1 100644
--- a/autosklearn/metalearning/files/recall_macro_multiclass.classification_dense/configurations.csv
+++ b/autosklearn/metalearning/files/recall_macro_multiclass.classification_dense/configurations.csv
@@ -1,128 +1,133 @@
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-22,weighting,adaboost,SAMME,0.2353770068926199,1,62,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.007159634821655197,most_frequent,quantile_transformer,1616,normal,,,feature_agglomeration,,,,,,,,,,,,,,,cosine,average,151,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-23,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7734.521233796252,,,2.6048661611376747,rbf,-1,True,4.68717607435371e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,none,,,,,feature_agglomeration,,,,,,,,,,,,,,,euclidean,average,25,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-24,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.21794354428393548,None,0.0,2,16,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0025451910134387575,median,quantile_transformer,1477,uniform,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
-25,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5010952533165973,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.005838375247601407,mean,normalize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,
-26,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5185406559495093,None,0.0,3,17,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.010000000000000004,median,normalize,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.13046020673147232,fpr,chi2
-27,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.9663114854040722,None,0.0,11,12,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.26932389682904634,median,quantile_transformer,23,uniform,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l1,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,
-28,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5607168729746175,None,0.0,2,3,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.009810589644392154,most_frequent,quantile_transformer,1132,normal,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.06211089008167909,fdr,chi2
-29,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.992156991246829,None,0.0,19,3,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.007016879673679419,median,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,
-30,none,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.9728532763078892,None,0.0,13,9,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,none,,,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6651071190864879,False,,,,,,,,,,,,,,,
-31,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9561075301148197,None,0.0,6,2,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,
-32,none,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,173,None,,1.3919208969870906e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,minmax,,,,,kernel_pca,,,,,,,,,,,,,,,,,,,0.1258530558153379,2,0.008499663955936211,poly,499,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-33,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,1.153931482107762e-10,0.011666190464009397,auto,255,None,29,190,18,loss,1e-07,0.147868307124396,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,
-34,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,22194.242946902177,0.45210849669021247,,0.0005669713702171556,sigmoid,-1,False,0.03542142266081451,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,quantile_transformer,291,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-35,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.8189454407806317,None,0.0,10,10,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,robust_scaler,,,0.7192027795318285,0.24912062393096632,fast_ica,,,,,,,,,,,parallel,logcosh,100,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-36,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,3.6797768538445263e-09,0.05511004217375807,auto,255,None,72,62,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,robust_scaler,,,0.7241481576763391,0.0501033899901458,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,36,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-37,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.7521969592385672,None,0.0,9,5,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.020402264943872365,median,robust_scaler,,,0.737227213630259,0.20752747663727789,fast_ica,,,,,,,,,,,parallel,exp,312,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-38,none,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.563056219822946,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.32793677336996485,most_frequent,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-39,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,1.6343482806592892e-09,0.19537524572679268,auto,255,None,32,29,13,loss,1e-07,0.05061738224302791,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-40,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7088164344533493,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,mean,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,
-41,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,1.943186107984505e-07,0.08852455702036988,auto,255,None,5,72,19,loss,1e-07,0.039837460762271175,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.01591265419367627,median,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,
-42,weighting,adaboost,SAMME,0.5210651042094924,10,401,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,standardize,,,,,feature_agglomeration,,,,,,,,,,,,,,,euclidean,complete,89,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-43,none,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0686374607394679,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,minmax,,,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.8490260132946401,False,,,,,,,,,,,,,,,
-44,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.6931271368243409,None,0.0,11,10,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.362940004438799,most_frequent,robust_scaler,,,0.9177587796305063,0.17084038458597867,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,30.491514144623952,mutual_info,,,
-45,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.2052035060269155,None,0.0,20,16,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,none,,,,,feature_agglomeration,,,,,,,,,,,,,,,cosine,complete,22,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-46,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,92.66144306787051,,,0.04654639302091653,rbf,-1,True,0.0022231544280610005,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.005194838694455963,mean,none,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,15,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-47,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2681.998807317996,-0.9558745731731211,,0.0011299948421773457,sigmoid,-1,False,1.5292507354478086e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-48,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.4467384469668449,None,0.0,19,19,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.00033708405018786203,median,quantile_transformer,711,normal,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,7.744829113696808,False,True,1,squared_hinge,ovr,l1,0.0001869446320464273,,,,,,,,,,,,,,,,,,,,,,
-49,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,26.502259985828186,,,9.475662388405154e-05,rbf,-1,False,0.00019690635968217866,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-50,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,2.5360196477156387e-07,0.011018756408558218,auto,255,None,40,122,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.00011749164080669445,most_frequent,quantile_transformer,757,uniform,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,5.097834697156853,False,True,1,squared_hinge,ovr,l1,0.053877834919477424,,,,,,,,,,,,,,,,,,,,,,
-51,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5255991794296913,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-52,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.34697723213923637,None,0.0,15,18,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.1923594385988973,mean,normalize,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,25.77499302945827,chi2,,,
-53,weighting,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.7776312540090575,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0005253715056715056,mean,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,False,,,,,,,,,,,,
-54,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,1.5661823810486637e-10,0.08996863554015262,auto,255,None,156,89,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.009331521286159971,median,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,
-55,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.6109468076475573,0.8748383232154038,4,0.41333070422336576,poly,-1,False,3.591268778393482e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.011804925819789203,median,minmax,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-56,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.7610308078756552,None,0.0,1,2,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.010000000000000004,mean,robust_scaler,,,0.9205545465103644,0.20272761060743316,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-57,weighting,adaboost,SAMME.R,0.03127273017917347,10,496,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,standardize,,,,,fast_ica,,,,,,,,,,,deflation,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-58,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,151,None,,0.0003283777607180646,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.2510527431514227,mean,normalize,,,,,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,-0.96093182232312,,0.05762510964607661,sigmoid,2632,,,,,,,,,,,,,,,,,
-59,none,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.4702538502800974e-07,True,,,True,0.14999999999999974,optimal,perceptron,elasticnet,,0.0011469929926494495,no_encoding,no_coalescense,,mean,quantile_transformer,1782,normal,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.9897679383028408,True,,,,,,,,,,,,,,,
-60,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.6453555269772124,None,0.0,1,4,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,normalize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-61,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,940.9437776449834,,,1.5839338084625942,rbf,-1,False,0.023072781592565438,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,none,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,77.25763685070707,mutual_info,,,
-62,weighting,extra_trees,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,13,18,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0001574994528707389,most_frequent,standardize,,,,,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,3,None,19,19,1.0,77,,,,,
-63,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.34534888916828577,0.02513634776687268,2,1.0733091880109409,poly,-1,True,0.0006974263047918307,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,quantile_transformer,1000,normal,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,77.99934218022861,mutual_info,,,
-64,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,29.55731828912983,False,True,1,squared_hinge,ovr,l2,0.0011332486951954968,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,
-65,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,1.1387795876755026e-06,0.12245605190343567,auto,255,None,58,7,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,robust_scaler,,,0.79305915648885,0.1841487406876042,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-66,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.8425560317492612,None,0.0,8,15,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.015276988502366128,median,none,,,,,fast_ica,,,,,,,,,,,parallel,cube,17,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-67,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,0.2410465573252999,0.022973469655856883,auto,255,None,272,8,3,loss,1e-07,0.0321932494461218,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.00807996498005836,most_frequent,none,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,65,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-68,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,925.3053743248586,False,True,1,squared_hinge,ovr,l2,0.02764481198395717,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.3850718018366796,most_frequent,quantile_transformer,786,uniform,,,feature_agglomeration,,,,,,,,,,,,,,,cosine,average,156,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-69,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7573078019937971,None,0.0,2,5,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,robust_scaler,,,0.7265312295801113,0.28332515882236425,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.09660564590146253,fpr,f_classif
-70,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5345540246011571,None,0.0,8,4,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.007441222223802043,most_frequent,normalize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-71,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.41019363453549706,None,0.0,15,2,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.0003507389224306018,median,none,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.20614098085343638,fdr,chi2
-72,none,adaboost,SAMME,0.1280458968526125,7,125,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,robust_scaler,,,0.7156460334356771,0.2981902862926971,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,65.16171885308417,chi2,,,
-73,none,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9931779675565967,None,0.0,18,17,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,robust_scaler,,,0.75,0.2945917010472818,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,
-74,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.08646118576686276,None,0.0,4,9,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,most_frequent,quantile_transformer,959,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-75,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,1.2430504242164082e-09,0.03359568307446533,auto,255,None,1609,29,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,standardize,,,,,feature_agglomeration,,,,,,,,,,,,,,,cosine,complete,40,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-76,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7.491039171224502,True,True,squared_hinge,2.8058138048327884e-05,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,minmax,,,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.7754372809893839,False,True,1,squared_hinge,ovr,l1,3.9643594596766314e-05,,,,,,,,,,,,,,,,,,,,,,
-77,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.34936025988397124,None,0.0,2,13,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00018601309647406987,median,robust_scaler,,,0.8634309348843472,0.26976989465367607,extra_trees_preproc_for_classification,False,entropy,None,0.48025175288975036,None,0.0,6,17,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-78,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,0.011547155223520492,0.13665554140837183,auto,255,None,13,5,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.1824584492028449,median,minmax,,,,,feature_agglomeration,,,,,,,,,,,,,,,euclidean,average,261,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-79,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.7308100742740112,None,0.0,18,19,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.014637629296743653,most_frequent,robust_scaler,,,0.7724670628916575,0.2233445340332948,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,29.07440444752882,False,True,1,squared_hinge,ovr,l1,0.00040479645225962257,,,,,,,,,,,,,,,,,,,,,,
-80,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1661.2274747409913,,,0.025821208852387936,rbf,-1,False,0.0017011435104921824,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,none,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,306,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-81,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0016941428709087083,True,,,True,,optimal,squared_hinge,l1,,5.124265599423001e-05,no_encoding,no_coalescense,,most_frequent,quantile_transformer,461,uniform,,,extra_trees_preproc_for_classification,True,gini,None,0.31371431180257103,None,0.0,5,17,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-82,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,21.85207494001697,0.18872999589786676,4,1.1863646021868761,poly,-1,False,0.0021338371901294443,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0034080852834770217,mean,normalize,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.11326005109886934,fdr,f_classif
-83,weighting,adaboost,SAMME,0.38341570193537416,9,250,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,minmax,,,,,feature_agglomeration,,,,,,,,,,,,,,,euclidean,average,13,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-84,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,0.037849720944694,0.1268815770019783,auto,255,None,11,99,11,loss,1e-07,0.10654523627313527,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,
-85,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.593728171200453,None,0.0,7,20,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.02830941310359363,mean,normalize,,,,,extra_trees_preproc_for_classification,False,entropy,None,0.7389446586281172,None,0.0,20,11,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-86,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,1,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.019797877010737826,mean,normalize,,,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.8239678741128165,False,,,,,,,,,,,,,,,
-87,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.46057831591617715,False,True,hinge,0.04557857428827514,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00027457445401600137,median,standardize,,,,,extra_trees_preproc_for_classification,True,gini,None,0.48190346970486964,None,0.0,17,18,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-88,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.46057831591617715,False,True,hinge,0.04557857428827514,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00027457445401600137,median,standardize,,,,,extra_trees_preproc_for_classification,True,gini,None,0.48190346970486964,None,0.0,17,18,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-89,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5722917944959481,None,0.0,1,14,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,
-90,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.26356754187780895,None,0.0,4,3,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010966178401758774,mean,robust_scaler,,,0.7817043861798886,0.24371720112407158,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
-91,weighting,adaboost,SAMME.R,0.13547552520095074,4,62,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.011851011202809002,most_frequent,standardize,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4888776875314595,fpr,chi2
-92,weighting,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.011656655127925412,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.48080598210703135,mean,none,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,205,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-93,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.6931271368243409,None,0.0,11,10,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.362940004438799,most_frequent,robust_scaler,,,0.9177587796305063,0.17084038458597867,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,30.491514144623952,mutual_info,,,
-94,weighting,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,2,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,none,,,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6342574285315175,False,,,,,,,,,,,,,,,
-95,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.107967527639527e-05,True,True,hinge,0.00448294745544775,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,quantile_transformer,953,normal,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,35.00114345405331,chi2,,,
-96,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.457928416564218,None,0.0,12,2,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,
-97,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,1.1522414705935405e-10,0.6132800183073315,auto,255,None,1607,67,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010096911249562407,mean,robust_scaler,,,0.7663493326967942,0.136094217337434,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-98,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2746221958279041,False,True,1,squared_hinge,ovr,l2,6.546077664669006e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,quantile_transformer,1054,normal,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,
-99,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.479405900046494,,,0.013340778632154543,rbf,-1,False,6.913586060890114e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,robust_scaler,,,0.9953232616818098,0.06450577886132274,fast_ica,,,,,,,,,,,deflation,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-100,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.9815182201168626,None,0.0,15,19,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,robust_scaler,,,0.8878662977383482,0.25666364929970126,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,44.15748219826322,mutual_info,,,
-101,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0559127099090374,True,True,hinge,1.4799425163790862e-05,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00029267075188836047,mean,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,False,,,,,,,,,,,,
-102,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.7426893291639965,None,0.0,4,19,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010127721455862085,median,quantile_transformer,1108,uniform,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,263,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-103,none,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,128,manual,0.8574560771111307,0.010886139381599514,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.24115007944216788,most_frequent,normalize,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,62.26514170991452,mutual_info,,,
-104,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.6282663389679584,None,0.0,1,3,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,median,minmax,,,,,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-105,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,1.430083734915855e-09,0.08779621406981941,auto,255,None,17,20,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.29749837779115257,mean,robust_scaler,,,0.7236445277814435,0.25,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
-106,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.3294588572341214,None,0.0,1,3,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
-107,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,11.046854203258679,,,0.07809571976769347,rbf,-1,False,0.00036193159909053734,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.006445557635410205,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-108,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.6728827282646637,None,0.0,18,8,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,robust_scaler,,,0.8512985751386942,0.14092824896182807,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,
-109,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.18060139887649052,None,0.0,10,12,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.010000000000000004,most_frequent,robust_scaler,,,0.9298612094255864,0.1263597946722308,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
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-111,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6427543483503397,True,True,hinge,0.00045318775930916944,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.009338484332306904,median,quantile_transformer,174,normal,,,kernel_pca,,,,,,,,,,,,,,,,,,,,,,cosine,1848,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-112,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.04981080944617379,True,,0.07309386939525211,True,,constant,log,l2,,0.0006721243213777075,no_encoding,no_coalescense,,mean,quantile_transformer,574,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-113,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,9.029473608568458e-09,0.15779990576964315,auto,255,None,37,71,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.018093177195644097,mean,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,False,,,,,,,,,,,,
-114,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0025185447074997196,True,True,hinge,3.168531728129776e-05,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.05667297499772673,mean,quantile_transformer,796,uniform,,,feature_agglomeration,,,,,,,,,,,,,,,euclidean,average,315,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-115,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.129143822880219,True,True,hinge,0.00012612093569084375,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0016222546082911887,most_frequent,quantile_transformer,1309,uniform,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8.816417526015055,f_classif,,,
-116,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.4864284879550203,None,0.0,1,4,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.008665854799630503,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-117,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.8851946632713493,None,0.0,9,20,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.006095662608922393,most_frequent,quantile_transformer,1002,normal,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.10486328449423876,fpr,f_classif
-118,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.6922752267318475,True,True,squared_hinge,0.06299408576730353,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,quantile_transformer,1742,uniform,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,107,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-119,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.44727252219812963,None,0.0,2,10,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,standardize,,,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l1,8.17533189014765e-05,,,,,,,,,,,,,,,,,,,,,,
-120,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7408631116596028,None,0.0,19,2,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,mean,none,,,,,extra_trees_preproc_for_classification,True,entropy,None,0.8873056409960391,None,0.0,11,18,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-121,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.4210978663047845,None,0.0,5,6,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.006365988550833196,most_frequent,robust_scaler,,,0.8348891074186082,0.00974961635730845,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0695919708890599,fpr,f_classif
-122,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.3341112040803983,False,True,hinge,0.003045682798589668,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.005225537488332702,median,quantile_transformer,1225,uniform,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.10310163232668391,fpr,chi2
-123,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.20612848067724507,False,True,1,squared_hinge,ovr,l2,0.00017832980955957004,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0007502816173508168,median,quantile_transformer,1083,uniform,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.02820386332530596,fwe,f_classif
-124,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1561.1002600303584,,,0.0024048551704194507,rbf,-1,False,0.0001936214667496851,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,minmax,,,,,fast_ica,,,,,,,,,,,parallel,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-125,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.087313242780732,0.0636709914987994,3,0.004481917125024585,poly,-1,False,0.01848880618941367,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.002136015035740733,mean,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-126,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.03204653439894958,True,True,squared_hinge,0.00013006393401112688,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.013506575694382031,most_frequent,normalize,,,,,feature_agglomeration,,,,,,,,,,,,,,,euclidean,ward,356,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-127,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.6466927589470762,None,0.0,11,2,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.009672465311659264,most_frequent,standardize,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,369,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+1,none,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1514.059969351301,False,True,1,squared_hinge,ovr,l2,0.002448814309966599,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,standardize,,,,,extra_trees_preproc_for_classification,True,entropy,None,0.8221481751476923,None,0.0,2,3,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+4,none,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,quantile_transformer,111,uniform,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4748887602127273,fdr,f_classif
+6,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.7283683762613216,None,0.0,12,20,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,median,standardize,,,,,fast_ica,,,,,,,,,,,deflation,exp,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+8,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,114,manual,0.11778631707605758,0.0029112721339713465,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.015582195257240751,median,normalize,,,,,kernel_pca,,,,,,,,,,,,,,,,,,,0.42909623253327767,3,0.25407612000343777,poly,1689,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+11,weighting,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,2,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.3865962574104293,mean,normalize,,,,,fast_ica,,,,,,,,,,,deflation,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+15,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.21772092350860472,None,0.0,14,17,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.0020488212891693275,median,standardize,,,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,33.74257606530722,False,True,1,squared_hinge,ovr,l1,8.740452966205111e-05,,,,,,,,,,,,,,,,,,,,,,
+20,none,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.9866058571747695,None,0.0,16,9,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.1680676743605393,most_frequent,robust_scaler,,,0.8840072797399463,0.08535534311393947,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,False,,,,,,,,,,,,
+23,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,0.00022373024203887573,0.027149343206135476,auto,255,None,49,13,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.00777614320416325,most_frequent,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,
+26,weighting,adaboost,SAMME.R,0.01561270559347964,4,417,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,quantile_transformer,1617,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+28,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,1.0168466808008568e-09,0.21361657232950118,auto,255,None,463,4,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,robust_scaler,,,0.7965217268158734,0.2446550610908456,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,
+32,none,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,174,manual,0.21501892897746921,2.0347110826262054e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.013754893527625522,most_frequent,normalize,,,,,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,-0.019221513778605592,3,0.10190948951339239,poly,2521,,,,,,,,,,,,,,,,,
+35,none,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9.608707084865884,False,True,1,squared_hinge,ovr,l2,0.008086910150491696,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.020901889243957545,median,minmax,,,,,kitchen_sinks,,,,,,,,,,,,,,,,,,,,,,,,0.5694399784333516,3230,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+38,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9025597189684735,None,0.0,18,8,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.007536467725572231,most_frequent,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
+40,none,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.563056219822946,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.32793677336996485,most_frequent,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+41,none,extra_trees,,,,,,,,,,,,,,,True,gini,None,0.5033866291997137,None,0.0,2,14,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,minmax,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+44,none,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l2,2.858526169098402e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0007827505640681123,median,standardize,,,,,extra_trees_preproc_for_classification,False,entropy,None,0.3443310459451119,None,0.0,3,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+48,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.36750008697181424,None,0.0,20,16,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,median,quantile_transformer,753,normal,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,
+51,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.3862436135511355e-05,False,,0.01139834246571156,True,,invscaling,log,l1,0.5,0.00025858980190005947,no_encoding,no_coalescense,,most_frequent,robust_scaler,,,0.7614693069989072,0.2515867504414451,extra_trees_preproc_for_classification,False,gini,None,0.6006906188943036,None,0.0,2,4,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+57,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.24407113189971e-05,True,,,True,,optimal,hinge,l2,,0.018411607920920666,one_hot_encoding,minority_coalescer,0.006594668208397739,mean,standardize,,,,,extra_trees_preproc_for_classification,True,entropy,None,0.20369732810864838,None,0.0,16,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+58,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5214197490559657,None,0.0,1,2,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.00826182798588248,mean,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,
+59,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.15115687152536414,True,True,hinge,0.009954631623104506,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0013410356664535208,most_frequent,quantile_transformer,1293,uniform,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+62,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.7848258054012829,None,0.0,18,15,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,standardize,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,44.11368738669913,chi2,,,
+65,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,1.2822815440468518e-10,0.12922700208645135,auto,255,None,31,44,13,loss,1e-07,0.08789566421608311,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.012230423571314038,mean,robust_scaler,,,0.71734690066056,0.20283418288016852,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,
+66,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,236.02119240292217,False,True,1,squared_hinge,ovr,l2,0.0017427914676337187,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.1020917547461355,mean,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,False,,,,,,,,,,,,
+68,weighting,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,minmax,,,,,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+75,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13.284077389890939,-0.2621334760621076,5,0.9060908941898536,poly,-1,False,0.06323853917206591,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0011743021592446861,most_frequent,normalize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,
+77,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.2309302197434343e-05,False,,0.006503403719704897,True,,constant,log,l1,,1.531583361000749e-05,one_hot_encoding,no_coalescense,,most_frequent,robust_scaler,,,0.7530789965606406,0.2430090971909385,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,cosine,1693,,,,,,,,,,,,,,,,,
+82,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,3.79982070767762e-10,0.10307173798074214,auto,255,None,30,25,6,loss,1e-07,0.09705336338563596,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.004889186641013006,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+87,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.07992258780660727,True,True,squared_hinge,0.022067056607853346,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,robust_scaler,,,0.7602075850543897,0.15799780977101488,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,95.09568612626826,chi2,,,
+94,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5079964624349133,None,0.0,7,5,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.37073794659062626,mean,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,
+97,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.4879444812369075,None,0.0,5,3,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.008704186680582812,mean,normalize,,,,,extra_trees_preproc_for_classification,False,entropy,None,0.6428064165338061,None,0.0,2,17,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+99,weighting,adaboost,SAMME,0.17983222024694556,4,357,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0008898781197407956,mean,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+105,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.268699692198443e-05,True,,4.1225573912733725e-06,True,,constant,log,l2,,4.729985534114764e-05,no_encoding,no_coalescense,,median,quantile_transformer,246,normal,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,20.73090458774975,chi2,,,
+109,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.05953492795583007,True,,1.2244794102245382e-07,True,,constant,perceptron,l2,,0.006468632691846376,no_encoding,no_coalescense,,most_frequent,quantile_transformer,424,uniform,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,False,,,,,,,,,,,,
+110,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0021814768111094564,False,True,hinge,8.726644999887521e-05,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,none,,,,,kitchen_sinks,,,,,,,,,,,,,,,,,,,,,,,,0.0010909910768059797,970,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+113,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9208911584010107,None,0.0,4,14,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,False,,,,,,,,,,,,
+115,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.463780563279276,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.011474025594258192,mean,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,
+116,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,236,None,,0.08905291537442941,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,normalize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,
+121,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.08836825866738698,None,0.0,5,19,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0003943043347109662,median,robust_scaler,,,0.7564808439539721,0.23469821956116474,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2206955933275169,fdr,f_classif
+125,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.6719618199439291,None,0.0,7,6,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0011674787451616529,most_frequent,none,,,,,fast_ica,,,,,,,,,,,parallel,logcosh,43,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+126,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,1.4547955251450584e-08,0.03465981099276111,auto,255,None,10,4,7,loss,1e-07,0.12579700249284784,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.001559260617305279,most_frequent,normalize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,False,,,,,,,,,,,,
+128,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,,,0.07737077475438128,rbf,-1,True,0.0010000000000000002,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.096644842091904,most_frequent,standardize,,,,,feature_agglomeration,,,,,,,,,,,,,,,euclidean,average,333,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+136,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,4.136413423055797e-09,0.13801185065309887,auto,255,None,186,1,15,loss,1e-07,0.042836514118739795,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,none,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,372,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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+380,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2677516008848513,True,True,hinge,0.05752557410901496,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0010152857217605206,median,quantile_transformer,1000,uniform,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,51.60974796845029,chi2,,,
+385,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.8280243811834952,None,0.0,9,14,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.08913760502443781,median,none,,,,,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+388,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,53.67009896349757,,,0.03687753298495132,rbf,-1,False,0.028791286245157307,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.047859755696856916,mean,standardize,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,70,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+392,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,1.8250137234576287e-09,0.01865408018981487,auto,255,None,27,5,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0752156562901201,median,quantile_transformer,1281,normal,,,feature_agglomeration,,,,,,,,,,,,,,,cosine,complete,75,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+394,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0025736560290786094,False,True,squared_hinge,0.0006196872282311157,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.042446796272035237,most_frequent,quantile_transformer,773,normal,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,89.84127958910369,f_classif,,,
+399,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.5864398404787432,None,0.0,1,14,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0070663750850608895,most_frequent,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
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+414,weighting,bernoulli_nb,,,,,0.030158213146175936,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,quantile_transformer,1820,uniform,,,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,9,None,11,2,1.0,96,,,,,
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+419,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.985686293012224,,,0.007514476113632565,rbf,-1,True,0.0007610347922656268,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.006285936847328083,most_frequent,robust_scaler,,,0.7904917229582098,0.18967899969665603,feature_agglomeration,,,,,,,,,,,,,,,cosine,complete,354,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+422,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.36374112587320206,None,0.0,2,10,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010870084126959173,most_frequent,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,
+425,weighting,decision_tree,,,,,,,entropy,1.4732927621306418,1.0,None,0.0,10,3,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,standardize,,,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,4276.070150555713,False,True,1,squared_hinge,ovr,l1,0.00011385383984576719,,,,,,,,,,,,,,,,,,,,,,
+429,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.9039555800017817e-05,False,True,squared_hinge,0.0011536981768375681,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,quantile_transformer,1122,uniform,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,
+430,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5321814610661173,None,0.0,1,3,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,standardize,,,,,extra_trees_preproc_for_classification,True,gini,None,0.5,None,0.0,1,15,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+437,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.34649900620486696,None,0.0,10,20,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,mean,quantile_transformer,1135,normal,,,extra_trees_preproc_for_classification,True,gini,None,0.9038598364290225,None,0.0,16,18,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+438,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,6.090138036399552e-10,0.017305568433437588,auto,255,None,202,188,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,robust_scaler,,,0.7526196011313747,0.1563616005702636,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+445,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,68.60833575985336,,,0.03905068757955671,rbf,-1,False,0.02846559747114773,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.04775815990561371,mean,standardize,,,,,extra_trees_preproc_for_classification,False,entropy,None,0.45727987956076366,None,0.0,13,11,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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+451,weighting,adaboost,SAMME,0.28074307158760897,3,166,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0030293384621179554,mean,quantile_transformer,965,uniform,,,feature_agglomeration,,,,,,,,,,,,,,,cosine,complete,66,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+453,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,162.34906836693145,,,2.470518094983172,rbf,-1,False,1.5366632541084908e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.33912551828528803,median,none,,,,,fast_ica,,,,,,,,,,,deflation,logcosh,412,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+459,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.745748188733041e-05,True,True,hinge,2.181659959258924e-05,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.13921206193066876,most_frequent,quantile_transformer,60,normal,,,extra_trees_preproc_for_classification,False,entropy,None,0.7619669647366906,None,0.0,3,4,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+461,weighting,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.9815588983620268,None,0.0,2,14,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010000000000000004,mean,robust_scaler,,,0.7617017260745782,0.2386998843971201,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
diff --git a/autosklearn/metalearning/files/recall_macro_multiclass.classification_dense/description.txt b/autosklearn/metalearning/files/recall_macro_multiclass.classification_dense/description.txt
index 5e071543d8..a1c0eb6664 100644
--- a/autosklearn/metalearning/files/recall_macro_multiclass.classification_dense/description.txt
+++ b/autosklearn/metalearning/files/recall_macro_multiclass.classification_dense/description.txt
@@ -1,8 +1,9 @@
 features_cutoff_time: 3600
 features_cutoff_memory: 3072
-number_of_feature_steps: 35
+number_of_feature_steps: 42
 feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances
 feature_step LogNumberOfInstances: LogNumberOfInstances
+feature_step NumberOfClasses: NumberOfClasses
 feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures
 feature_step LogNumberOfFeatures: LogNumberOfFeatures
 feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues
@@ -20,6 +21,11 @@ feature_step DatasetRatio: DatasetRatio, LogDatasetRatio
 feature_step LogDatasetRatio: LogDatasetRatio
 feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio
 feature_step LogInverseDatasetRatio: LogInverseDatasetRatio
+feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD
+feature_step ClassProbabilityMin: ClassProbabilityMin
+feature_step ClassProbabilityMax: ClassProbabilityMax
+feature_step ClassProbabilityMean: ClassProbabilityMean
+feature_step ClassProbabilitySTD: ClassProbabilitySTD
 feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum
 feature_step SymbolsMin: SymbolsMin
 feature_step SymbolsMax: SymbolsMax
@@ -36,11 +42,12 @@ feature_step SkewnessMin: SkewnessMin
 feature_step SkewnessMax: SkewnessMax
 feature_step SkewnessMean: SkewnessMean
 feature_step SkewnessSTD: SkewnessSTD
-features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
+feature_step ClassEntropy: ClassEntropy
+features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy
 features_stochastic: 
-default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
+default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy
 
-algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127
+algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235,236,237,238,239,240,241,242,243,244,245,246,247,248,249,250,251,252,253,254,255,256,257,258,259,260,261,262,263,264,265,266,267,268,269,270,271,272,273,274,275,276,277,278,279,280,281,282,283,284,285,286,287,288,289,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,312,313,314,315,316,317,318,319,320,321,322,323,324,325,326,327,328,329,330,331,332,333,334,335,336,337,338,339,340,341,342,343,344,345,346,347,348,349,350,351,352,353,354,355,356,357,358,359,360,361,362,363,364,365,366,367,368,369,370,371,372,373,374,375,376,377,378,379,380,381,382,383,384,385,386,387,388,389,390,391,392,393,394,395,396,397,398,399,400,401,402,403,404,405,406,407,408,409,410,411,412,413,414,415,416,417,418,419,420,421,422,423,424,425,426,427,428,429,430,431,432,433,434,435,436,437,438,439,440,441,442,443,444,445,446,447,448,449,450,451,452,453,454,455,456,457,458,459,460,461,462,463,464
 algorithms_stochastic: 
 performance_measures: recall_macro
 performance_type: solution_quality
diff --git a/autosklearn/metalearning/files/recall_macro_multiclass.classification_dense/feature_costs.arff b/autosklearn/metalearning/files/recall_macro_multiclass.classification_dense/feature_costs.arff
index 0e17e20d22..8512fd9275 100644
--- a/autosklearn/metalearning/files/recall_macro_multiclass.classification_dense/feature_costs.arff
+++ b/autosklearn/metalearning/files/recall_macro_multiclass.classification_dense/feature_costs.arff
@@ -4,6 +4,7 @@
 @ATTRIBUTE repetition NUMERIC
 @ATTRIBUTE NumberOfInstances NUMERIC
 @ATTRIBUTE LogNumberOfInstances NUMERIC
+@ATTRIBUTE NumberOfClasses NUMERIC
 @ATTRIBUTE NumberOfFeatures NUMERIC
 @ATTRIBUTE LogNumberOfFeatures NUMERIC
 @ATTRIBUTE MissingValues NUMERIC
@@ -21,6 +22,11 @@
 @ATTRIBUTE LogDatasetRatio NUMERIC
 @ATTRIBUTE InverseDatasetRatio NUMERIC
 @ATTRIBUTE LogInverseDatasetRatio NUMERIC
+@ATTRIBUTE ClassOccurences NUMERIC
+@ATTRIBUTE ClassProbabilityMin NUMERIC
+@ATTRIBUTE ClassProbabilityMax NUMERIC
+@ATTRIBUTE ClassProbabilityMean NUMERIC
+@ATTRIBUTE ClassProbabilitySTD NUMERIC
 @ATTRIBUTE NumSymbols NUMERIC
 @ATTRIBUTE SymbolsMin NUMERIC
 @ATTRIBUTE SymbolsMax NUMERIC
@@ -37,162 +43,138 @@
 @ATTRIBUTE SkewnessMax NUMERIC
 @ATTRIBUTE SkewnessMean NUMERIC
 @ATTRIBUTE SkewnessSTD NUMERIC
+@ATTRIBUTE ClassEntropy NUMERIC
 
 @DATA
-75123,1.0,1e-05,1e-05,1e-05,1e-05,0.00253,0.00221,1e-05,0.00024,3e-05,0.00011,0.0,2e-05,2e-05,5e-05,8e-05,2e-05,1e-05,7e-05,1e-05,0.00072,1e-05,1e-05,0.00019,0.00041,0.00011,0.00061,0.00012,9e-05,0.00015,0.00025,0.00067,0.00014,0.00011,0.00016,0.00027
-75090,1.0,1e-05,1e-05,2e-05,2e-05,0.00815,0.00368,7e-05,0.00228,9e-05,0.00235,0.0,5e-05,5e-05,0.0001,0.00013,0.00013,1e-05,0.00021,2e-05,0.00064,0.0,0.0,0.00014,0.00038,0.00011,0.00066,0.00011,0.00014,0.00014,0.00026,0.00066,0.00011,0.00013,0.00015,0.00027
-2123,1.0,1e-05,1e-05,1e-05,1e-05,0.00097,0.00079,1e-05,0.00013,1e-05,7e-05,0.0,2e-05,2e-05,5e-05,8e-05,2e-05,1e-05,5e-05,1e-05,0.0007,1e-05,0.0,0.00017,0.00042,0.0001,0.00052,6e-05,8e-05,0.00012,0.00026,0.00049,6e-05,0.00011,9e-05,0.00023
-248,1.0,1e-05,1e-05,1e-05,1e-05,0.00088,0.00077,1e-05,0.0001,1e-05,4e-05,0.0,1e-05,1e-05,4e-05,5e-05,2e-05,1e-05,4e-05,1e-05,0.00041,1e-05,0.0,9e-05,0.00022,9e-05,0.00046,6e-05,8e-05,9e-05,0.00023,0.00049,7e-05,0.0001,0.0001,0.00021
-75101,1.0,1e-05,1e-05,1e-05,1e-05,0.0418,0.03546,1e-05,0.00389,1e-05,0.00248,0.0,2e-05,2e-05,4e-05,6e-05,2e-05,1e-05,5e-05,2e-05,0.00045,1e-05,0.0,0.00011,0.00023,0.0001,0.00041,6e-05,8e-05,0.0001,0.00018,0.00047,6e-05,0.0001,8e-05,0.00023
-75191,1.0,1e-05,1e-05,3e-05,1e-05,0.53436,0.40777,2e-05,0.09655,3e-05,0.0301,1e-05,2e-05,3e-05,5e-05,0.00011,7e-05,1e-05,0.00011,3e-05,0.00084,5e-05,7e-05,0.00017,0.00033,0.00023,0.00088,0.00012,0.00017,0.00032,0.00026,0.00068,9e-05,0.00012,0.00013,0.00035
-75187,1.0,1e-05,1e-05,2e-05,1e-05,0.00548,0.00473,1e-05,0.0005,2e-05,0.00029,1e-05,2e-05,2e-05,7e-05,9e-05,3e-05,1e-05,6e-05,1e-05,0.00062,1e-05,1e-05,0.00014,0.00033,0.00013,0.00038,7e-05,7e-05,8e-05,0.00016,0.00047,6e-05,9e-05,9e-05,0.00023
-266,1.0,1e-05,1e-05,1e-05,1e-05,0.0018,0.00146,1e-05,0.00023,2e-05,0.00015,2e-05,2e-05,2e-05,6e-05,8e-05,3e-05,1e-05,6e-05,1e-05,0.00059,1e-05,1e-05,0.00014,0.00031,0.00013,0.0004,5e-05,7e-05,8e-05,0.0002,0.00044,8e-05,8e-05,8e-05,0.0002
-253,1.0,1e-05,1e-05,1e-05,1e-05,0.00082,0.00063,1e-05,0.00016,2e-05,5e-05,0.0,1e-05,1e-05,4e-05,5e-05,2e-05,1e-05,5e-05,2e-05,0.00046,1e-05,0.0,9e-05,0.00024,0.00012,0.00044,6e-05,7e-05,8e-05,0.00023,0.00043,6e-05,0.0001,8e-05,0.00019
-75217,1.0,1e-05,1e-05,2e-05,1e-05,0.00196,0.00158,1e-05,0.00026,2e-05,0.00016,1e-05,3e-05,3e-05,7e-05,0.0001,3e-05,1e-05,7e-05,2e-05,0.00089,1e-05,1e-05,0.00022,0.00053,0.00013,0.00074,9e-05,0.0001,0.00019,0.00036,0.00065,0.00013,0.0001,0.00011,0.00031
-75176,1.0,1e-05,1e-05,2e-05,1e-05,0.01406,0.01285,1e-05,0.00094,2e-05,0.00031,1e-05,3e-05,3e-05,6e-05,9e-05,3e-05,1e-05,7e-05,2e-05,0.00069,1e-05,1e-05,0.00017,0.00035,0.00015,0.00085,0.00018,0.00011,0.0002,0.00036,0.0011,0.00022,0.00023,0.00023,0.00042
-2117,1.0,1e-05,1e-05,1e-05,1e-05,0.01735,0.0154,1e-05,0.00141,1e-05,0.00057,0.0,1e-05,1e-05,3e-05,6e-05,2e-05,1e-05,5e-05,1e-05,0.0004,1e-05,0.0,8e-05,0.00021,0.0001,0.00065,0.00012,0.00011,0.00015,0.00026,0.00055,7e-05,0.00014,9e-05,0.00025
-75148,1.0,1e-05,1e-05,2e-05,2e-05,0.00129,0.00115,1e-05,0.00012,1e-05,4e-05,0.0,2e-05,2e-05,4e-05,5e-05,2e-05,1e-05,4e-05,1e-05,0.0004,1e-05,0.0,9e-05,0.00021,9e-05,0.00036,7e-05,7e-05,8e-05,0.00015,0.00041,6e-05,8e-05,8e-05,0.00019
-75103,1.0,1e-05,1e-05,1e-05,1e-05,0.01302,0.00819,3e-05,0.00234,3e-05,0.00255,0.0,2e-05,2e-05,6e-05,0.00012,5e-05,1e-05,0.00011,1e-05,0.0005,1e-05,0.0,9e-05,0.00029,0.00011,0.00054,8e-05,0.00011,0.00011,0.00024,0.00066,7e-05,0.00015,0.00011,0.00032
-75168,1.0,1e-05,0.0,1e-05,1e-05,0.02353,0.00601,0.00016,0.01754,0.00018,0.00031,0.0,0.00013,0.00013,0.00027,0.0003,0.00032,1e-05,0.00042,1e-05,0.0004,1e-05,0.0,8e-05,0.00021,0.0001,0.0011,0.00022,0.00025,0.00026,0.00037,0.00124,0.00022,0.00025,0.00028,0.00049
-75157,1.0,1e-05,1e-05,1e-05,1e-05,0.00125,0.00106,1e-05,0.00015,2e-05,7e-05,0.0,2e-05,2e-05,5e-05,6e-05,2e-05,1e-05,4e-05,1e-05,0.00048,2e-05,0.0,0.0001,0.00026,0.0001,0.00039,6e-05,7e-05,8e-05,0.00019,0.00043,6e-05,9e-05,8e-05,0.0002
-75092,1.0,1e-05,1e-05,1e-05,1e-05,0.00099,0.00072,1e-05,0.00018,1e-05,0.00011,0.0,2e-05,2e-05,4e-05,6e-05,2e-05,1e-05,5e-05,1e-05,0.00058,1e-05,0.0,0.00014,0.00034,9e-05,0.00041,6e-05,8e-05,9e-05,0.00017,0.00045,7e-05,0.0001,9e-05,0.00019
-75213,1.0,1e-05,1e-05,1e-05,1e-05,0.00096,0.00079,1e-05,0.00013,1e-05,7e-05,1e-05,2e-05,2e-05,5e-05,7e-05,2e-05,1e-05,6e-05,1e-05,0.00056,1e-05,1e-05,0.00013,0.00029,0.00012,0.00056,6e-05,8e-05,9e-05,0.00033,0.00063,8e-05,0.00014,0.00013,0.00029
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-75227,1.0,1e-05,1e-05,2e-05,1e-05,0.00416,0.00373,1e-05,0.00038,2e-05,9e-05,1e-05,2e-05,2e-05,6e-05,0.0001,3e-05,1e-05,7e-05,2e-05,0.00063,1e-05,1e-05,0.00015,0.00034,0.00013,0.00037,6e-05,8e-05,8e-05,0.00016,0.00043,6e-05,9e-05,9e-05,0.0002
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-75192,1.0,1e-05,1e-05,2e-05,1e-05,0.00188,0.00165,1e-05,0.00016,1e-05,9e-05,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,1e-05,4e-05,1e-05,0.00039,1e-05,0.0,9e-05,0.00021,9e-05,0.00048,5e-05,8e-05,0.0001,0.00025,0.00045,6e-05,0.0001,0.0001,0.0002
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-75132,1.0,1e-05,0.0,2e-05,1e-05,0.45642,0.41356,1e-05,0.03273,1e-05,0.01015,0.0,1e-05,1e-05,3e-05,6e-05,2e-05,1e-05,6e-05,2e-05,0.00049,1e-05,0.0,0.00012,0.00023,0.00013,0.00046,7e-05,8e-05,8e-05,0.00023,0.00053,6e-05,0.0001,9e-05,0.00028
-75212,1.0,1e-05,1e-05,1e-05,1e-05,0.0008,0.00059,1e-05,0.00015,1e-05,9e-05,0.0,2e-05,1e-05,3e-05,5e-05,2e-05,1e-05,4e-05,1e-05,0.00037,0.0,0.0,8e-05,0.00019,9e-05,0.00055,8e-05,0.00012,0.00011,0.00024,0.00047,6e-05,0.0001,9e-05,0.00022
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+75236,1.0,1e-05,1e-05,0.0001,1e-05,1e-05,0.00218,0.00118,2e-05,0.00065,3e-05,0.00041,1e-05,3e-05,3e-05,9e-05,9e-05,4e-05,1e-05,7e-05,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.0006,1e-05,1e-05,0.00021,0.00032,6e-05,0.00043,7e-05,7e-05,9e-05,0.00019,0.00062,7e-05,8e-05,0.0001,0.00037,0.00048
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+75249,1.0,1e-05,1e-05,0.00011,1e-05,1e-05,0.00192,0.00153,1e-05,0.00025,1e-05,0.00017,1e-05,2e-05,2e-05,5e-05,7e-05,2e-05,1e-05,5e-05,3e-05,0.00014,2e-05,1e-05,3e-05,8e-05,0.00029,1e-05,3e-05,6e-05,0.00012,7e-05,0.00041,6e-05,8e-05,0.0001,0.00017,0.00043,6e-05,7e-05,9e-05,0.00019,0.00092
+75250,1.0,1e-05,1e-05,0.00375,1e-05,1e-05,0.04994,0.047,1e-05,0.00247,1e-05,0.00049,1e-05,2e-05,2e-05,6e-05,7e-05,2e-05,1e-05,3e-05,1e-05,0.00022,4e-05,1e-05,4e-05,0.00012,0.00041,0.0,1e-05,0.00011,0.00023,6e-05,0.00076,0.00012,0.00012,0.00019,0.00032,0.00086,0.00012,0.00013,0.00018,0.00043,0.04178
diff --git a/autosklearn/metalearning/files/recall_macro_multiclass.classification_dense/feature_runstatus.arff b/autosklearn/metalearning/files/recall_macro_multiclass.classification_dense/feature_runstatus.arff
index 0034ad2bd6..5fff2dfed3 100644
--- a/autosklearn/metalearning/files/recall_macro_multiclass.classification_dense/feature_runstatus.arff
+++ b/autosklearn/metalearning/files/recall_macro_multiclass.classification_dense/feature_runstatus.arff
@@ -4,6 +4,7 @@
 @ATTRIBUTE repetition NUMERIC
 @ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other}
@@ -21,6 +22,11 @@
 @ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other}
@@ -37,162 +43,138 @@
 @ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other}
 
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diff --git a/autosklearn/metalearning/files/recall_macro_multiclass.classification_dense/feature_values.arff b/autosklearn/metalearning/files/recall_macro_multiclass.classification_dense/feature_values.arff
index a60400e3ce..736dc64321 100644
--- a/autosklearn/metalearning/files/recall_macro_multiclass.classification_dense/feature_values.arff
+++ b/autosklearn/metalearning/files/recall_macro_multiclass.classification_dense/feature_values.arff
@@ -13,7 +13,6 @@
 @ATTRIBUTE KurtosisMean NUMERIC
 @ATTRIBUTE KurtosisMin NUMERIC
 @ATTRIBUTE KurtosisSTD NUMERIC
-@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC
 @ATTRIBUTE LogDatasetRatio NUMERIC
 @ATTRIBUTE LogInverseDatasetRatio NUMERIC
 @ATTRIBUTE LogNumberOfFeatures NUMERIC
@@ -42,160 +41,135 @@
 @ATTRIBUTE SymbolsSum NUMERIC
 
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diff --git a/autosklearn/metalearning/files/recall_macro_multiclass.classification_sparse/algorithm_runs.arff b/autosklearn/metalearning/files/recall_macro_multiclass.classification_sparse/algorithm_runs.arff
index aee25ff689..73b0d1dc62 100644
--- a/autosklearn/metalearning/files/recall_macro_multiclass.classification_sparse/algorithm_runs.arff
+++ b/autosklearn/metalearning/files/recall_macro_multiclass.classification_sparse/algorithm_runs.arff
@@ -7,95 +7,133 @@
 @ATTRIBUTE runstatus {ok, timeout, memout, not_applicable, crash, other}
 
 @DATA
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diff --git a/autosklearn/metalearning/files/recall_macro_multiclass.classification_sparse/configurations.csv b/autosklearn/metalearning/files/recall_macro_multiclass.classification_sparse/configurations.csv
index b81aa5389d..f2f818169e 100644
--- a/autosklearn/metalearning/files/recall_macro_multiclass.classification_sparse/configurations.csv
+++ b/autosklearn/metalearning/files/recall_macro_multiclass.classification_sparse/configurations.csv
@@ -1,93 +1,131 @@
 idx,balancing:strategy,classifier:__choice__,classifier:adaboost:algorithm,classifier:adaboost:learning_rate,classifier:adaboost:max_depth,classifier:adaboost:n_estimators,classifier:bernoulli_nb:alpha,classifier:bernoulli_nb:fit_prior,classifier:decision_tree:criterion,classifier:decision_tree:max_depth_factor,classifier:decision_tree:max_features,classifier:decision_tree:max_leaf_nodes,classifier:decision_tree:min_impurity_decrease,classifier:decision_tree:min_samples_leaf,classifier:decision_tree:min_samples_split,classifier:decision_tree:min_weight_fraction_leaf,classifier:extra_trees:bootstrap,classifier:extra_trees:criterion,classifier:extra_trees:max_depth,classifier:extra_trees:max_features,classifier:extra_trees:max_leaf_nodes,classifier:extra_trees:min_impurity_decrease,classifier:extra_trees:min_samples_leaf,classifier:extra_trees:min_samples_split,classifier:extra_trees:min_weight_fraction_leaf,classifier:gradient_boosting:early_stop,classifier:gradient_boosting:l2_regularization,classifier:gradient_boosting:learning_rate,classifier:gradient_boosting:loss,classifier:gradient_boosting:max_bins,classifier:gradient_boosting:max_depth,classifier:gradient_boosting:max_leaf_nodes,classifier:gradient_boosting:min_samples_leaf,classifier:gradient_boosting:n_iter_no_change,classifier:gradient_boosting:scoring,classifier:gradient_boosting:tol,classifier:gradient_boosting:validation_fraction,classifier:k_nearest_neighbors:n_neighbors,classifier:k_nearest_neighbors:p,classifier:k_nearest_neighbors:weights,classifier:lda:n_components,classifier:lda:shrinkage,classifier:lda:shrinkage_factor,classifier:lda:tol,classifier:liblinear_svc:C,classifier:liblinear_svc:dual,classifier:liblinear_svc:fit_intercept,classifier:liblinear_svc:intercept_scaling,classifier:liblinear_svc:loss,classifier:liblinear_svc:multi_class,classifier:liblinear_svc:penalty,classifier:liblinear_svc:tol,classifier:libsvm_svc:C,classifier:libsvm_svc:coef0,classifier:libsvm_svc:degree,classifier:libsvm_svc:gamma,classifier:libsvm_svc:kernel,classifier:libsvm_svc:max_iter,classifier:libsvm_svc:shrinking,classifier:libsvm_svc:tol,classifier:multinomial_nb:alpha,classifier:multinomial_nb:fit_prior,classifier:passive_aggressive:C,classifier:passive_aggressive:average,classifier:passive_aggressive:fit_intercept,classifier:passive_aggressive:loss,classifier:passive_aggressive:tol,classifier:qda:reg_param,classifier:random_forest:bootstrap,classifier:random_forest:criterion,classifier:random_forest:max_depth,classifier:random_forest:max_features,classifier:random_forest:max_leaf_nodes,classifier:random_forest:min_impurity_decrease,classifier:random_forest:min_samples_leaf,classifier:random_forest:min_samples_split,classifier:random_forest:min_weight_fraction_leaf,classifier:sgd:alpha,classifier:sgd:average,classifier:sgd:epsilon,classifier:sgd:eta0,classifier:sgd:fit_intercept,classifier:sgd:l1_ratio,classifier:sgd:learning_rate,classifier:sgd:loss,classifier:sgd:penalty,classifier:sgd:power_t,classifier:sgd:tol,data_preprocessing:categorical_transformer:categorical_encoding:__choice__,data_preprocessing:categorical_transformer:category_coalescence:__choice__,data_preprocessing:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction,data_preprocessing:numerical_transformer:imputation:strategy,data_preprocessing:numerical_transformer:rescaling:__choice__,data_preprocessing:numerical_transformer:rescaling:quantile_transformer:n_quantiles,data_preprocessing:numerical_transformer:rescaling:quantile_transformer:output_distribution,data_preprocessing:numerical_transformer:rescaling:robust_scaler:q_max,data_preprocessing:numerical_transformer:rescaling:robust_scaler:q_min,feature_preprocessor:__choice__,feature_preprocessor:extra_trees_preproc_for_classification:bootstrap,feature_preprocessor:extra_trees_preproc_for_classification:criterion,feature_preprocessor:extra_trees_preproc_for_classification:max_depth,feature_preprocessor:extra_trees_preproc_for_classification:max_features,feature_preprocessor:extra_trees_preproc_for_classification:max_leaf_nodes,feature_preprocessor:extra_trees_preproc_for_classification:min_impurity_decrease,feature_preprocessor:extra_trees_preproc_for_classification:min_samples_leaf,feature_preprocessor:extra_trees_preproc_for_classification:min_samples_split,feature_preprocessor:extra_trees_preproc_for_classification:min_weight_fraction_leaf,feature_preprocessor:extra_trees_preproc_for_classification:n_estimators,feature_preprocessor:kernel_pca:coef0,feature_preprocessor:kernel_pca:degree,feature_preprocessor:kernel_pca:gamma,feature_preprocessor:kernel_pca:kernel,feature_preprocessor:kernel_pca:n_components,feature_preprocessor:kitchen_sinks:gamma,feature_preprocessor:kitchen_sinks:n_components,feature_preprocessor:liblinear_svc_preprocessor:C,feature_preprocessor:liblinear_svc_preprocessor:dual,feature_preprocessor:liblinear_svc_preprocessor:fit_intercept,feature_preprocessor:liblinear_svc_preprocess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-1,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.3163020577549344,None,0.0,15,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.01322981552715978,most_frequent,robust_scaler,,,0.9746667009556722,0.1974876581478311,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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-15,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7865652301288171,None,0.0,1,4,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,median,robust_scaler,,,0.8623635641193496,0.13298297278730625,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-16,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.905401147837377,-0.32832122529048147,3,1.001896309040499,poly,-1,False,5.1523484258030785e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-17,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.21794354428393548,None,0.0,2,16,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0025451910134387575,median,quantile_transformer,1477,uniform,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,,
-18,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5010952533165973,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.005838375247601407,mean,normalize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,,
-19,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5185406559495093,None,0.0,3,17,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.010000000000000004,median,normalize,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.13046020673147232,fpr,chi2,
-20,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.9663114854040722,None,0.0,11,12,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.26932389682904634,median,quantile_transformer,23,uniform,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l1,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,
-21,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5607168729746175,None,0.0,2,3,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.009810589644392154,most_frequent,quantile_transformer,1132,normal,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.06211089008167909,fdr,chi2,
-22,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9333237919291875,None,0.0,8,3,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,,
-23,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,173,None,,3.188383865493971e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,quantile_transformer,880,normal,,,kernel_pca,,,,,,,,,,,,,0.008499663955936211,rbf,450,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-24,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,22194.242946902177,0.45210849669021247,,0.0005669713702171556,sigmoid,-1,False,0.03542142266081451,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,quantile_transformer,291,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-25,weighting,extra_trees,,,,,,,,,,,,,,,True,gini,None,0.8424814442101096,None,0.0,17,16,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0012963479651174736,most_frequent,robust_scaler,,,0.7721224171367294,0.019641775400724187,extra_trees_preproc_for_classification,True,entropy,None,0.5260601497769445,None,0.0,2,12,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-26,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7422641456052917,None,0.0,8,15,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.005561351266169067,most_frequent,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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-29,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,10,1,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0022606253047811435,median,quantile_transformer,1247,normal,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,25.327793585266683,chi2,,,,
-30,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,24.786439230569382,,,0.03411197494635044,rbf,-1,True,0.003003351499585181,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0007994838629325874,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-31,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2681.998807317996,-0.9558745731731211,,0.0011299948421773457,sigmoid,-1,False,1.5292507354478086e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-32,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.4467384469668449,None,0.0,19,19,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.00033708405018786203,median,quantile_transformer,711,normal,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,7.744829113696808,False,True,1,squared_hinge,ovr,l1,0.0001869446320464273,,,,,,,,,,,,,,,,,,,,,
-33,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,26.502259985828186,,,9.475662388405154e-05,rbf,-1,False,0.00019690635968217866,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-34,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.339600708038189,False,True,1,squared_hinge,ovr,l2,9.572641158475304e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,quantile_transformer,1193,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-35,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5255991794296913,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-36,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.34697723213923637,None,0.0,15,18,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.1923594385988973,mean,normalize,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,25.77499302945827,chi2,,,,
-37,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.7610308078756552,None,0.0,1,2,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.010000000000000004,mean,robust_scaler,,,0.9205545465103644,0.20272761060743316,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-38,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,249.12499213127998,,,0.0005283374995918348,rbf,-1,False,0.0016932707039261864,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.48910068367909115,most_frequent,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-39,none,bernoulli_nb,,,,,0.6524090653939782,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.005164169126808402,most_frequent,quantile_transformer,16,normal,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.033082145443631535,False,True,1,squared_hinge,ovr,l1,0.002615020898325393,,,,,,,,,,,,,,,,,,,,,
-40,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.6453555269772124,None,0.0,1,4,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,normalize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-41,weighting,extra_trees,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,13,18,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0001574994528707389,most_frequent,standardize,,,,,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,3,None,19,19,1.0,77,,,,,,
-42,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5138705053438934,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.14411802159506462,most_frequent,quantile_transformer,857,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-43,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,29.55731828912983,False,True,1,squared_hinge,ovr,l2,0.0011332486951954968,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,,
-44,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.37801720673725603,True,True,squared_hinge,0.0001255132393635067,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.025993178344125793,most_frequent,standardize,,,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,1.4421737618429475,False,True,1,squared_hinge,ovr,l1,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,
-45,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.10581257327556097,None,0.0,7,12,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.004219392676189224,median,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,,
-46,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.4188764793561401,None,0.0,5,5,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.010589444903605501,mean,robust_scaler,,,0.9796997860990453,0.27262832749694405,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-47,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5345540246011571,None,0.0,8,4,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.007441222223802043,most_frequent,normalize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-48,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.41019363453549706,None,0.0,15,2,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.0003507389224306018,median,none,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.20614098085343638,fdr,chi2,
-49,none,adaboost,SAMME,0.1280458968526125,7,125,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,robust_scaler,,,0.7156460334356771,0.2981902862926971,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,65.16171885308417,chi2,,,,
-50,none,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9931779675565967,None,0.0,18,17,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,robust_scaler,,,0.75,0.2945917010472818,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,,
-51,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.08646118576686276,None,0.0,4,9,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,most_frequent,quantile_transformer,959,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-52,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.6271213423927893,None,0.0,1,4,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.009403509495453675,mean,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,,
-53,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8.85547546146731,True,True,squared_hinge,4.0111304608748804e-05,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-54,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.34936025988397124,None,0.0,2,13,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00018601309647406987,median,robust_scaler,,,0.8634309348843472,0.26976989465367607,extra_trees_preproc_for_classification,False,entropy,None,0.48025175288975036,None,0.0,6,17,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-55,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.7308100742740112,None,0.0,18,19,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.014637629296743653,most_frequent,robust_scaler,,,0.7724670628916575,0.2233445340332948,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,29.07440444752882,False,True,1,squared_hinge,ovr,l1,0.00040479645225962257,,,,,,,,,,,,,,,,,,,,,
-56,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.014911374426433241,None,0.0,6,2,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,,
-57,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0016941428709087083,True,,,True,,optimal,squared_hinge,l1,,5.124265599423001e-05,no_encoding,no_coalescense,,most_frequent,quantile_transformer,461,uniform,,,extra_trees_preproc_for_classification,True,gini,None,0.31371431180257103,None,0.0,5,17,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-58,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.7591396576940418,-0.02447104417007273,2,0.7325180366342191,poly,-1,True,0.00045922794962860523,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,standardize,,,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,2.057731506094293,False,True,1,squared_hinge,ovr,l1,0.03575998247131604,,,,,,,,,,,,,,,,,,,,,
-59,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.593728171200453,None,0.0,7,20,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.02830941310359363,mean,normalize,,,,,extra_trees_preproc_for_classification,False,entropy,None,0.7389446586281172,None,0.0,20,11,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-60,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5542793596955801,None,0.0,1,3,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,most_frequent,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-61,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.46057831591617715,False,True,hinge,0.04557857428827514,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00027457445401600137,median,standardize,,,,,extra_trees_preproc_for_classification,True,gini,None,0.48190346970486964,None,0.0,17,18,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-62,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.46057831591617715,False,True,hinge,0.04557857428827514,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00027457445401600137,median,standardize,,,,,extra_trees_preproc_for_classification,True,gini,None,0.48190346970486964,None,0.0,17,18,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-63,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.8494314157539654,None,0.0,3,19,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,mean,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,,
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+357,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.32235597434544716,None,0.0,2,18,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.30368818748392196,median,robust_scaler,,,0.7176393529809699,0.19299987830067816,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.16283145936255025,fwe,chi2,
+361,weighting,adaboost,SAMME,0.2786884011943926,3,263,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,quantile_transformer,1958,uniform,,,extra_trees_preproc_for_classification,False,entropy,None,0.2099259239256946,None,0.0,19,18,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+363,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.1393173890820956,False,True,1,squared_hinge,ovr,l2,8.283470008521272e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.015152352014390857,mean,normalize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,False,,,,,,,,,,,,,
+368,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.745748188733041e-05,True,True,hinge,2.181659959258924e-05,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.13921206193066876,most_frequent,quantile_transformer,60,normal,,,extra_trees_preproc_for_classification,False,entropy,None,0.7619669647366906,None,0.0,3,4,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+369,weighting,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.9815588983620268,None,0.0,2,14,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010000000000000004,mean,robust_scaler,,,0.7617017260745782,0.2386998843971201,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
diff --git a/autosklearn/metalearning/files/recall_macro_multiclass.classification_sparse/description.txt b/autosklearn/metalearning/files/recall_macro_multiclass.classification_sparse/description.txt
index 7f9e34d4e9..5301c73dc3 100644
--- a/autosklearn/metalearning/files/recall_macro_multiclass.classification_sparse/description.txt
+++ b/autosklearn/metalearning/files/recall_macro_multiclass.classification_sparse/description.txt
@@ -1,8 +1,9 @@
 features_cutoff_time: 3600
 features_cutoff_memory: 3072
-number_of_feature_steps: 35
+number_of_feature_steps: 42
 feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances
 feature_step LogNumberOfInstances: LogNumberOfInstances
+feature_step NumberOfClasses: NumberOfClasses
 feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures
 feature_step LogNumberOfFeatures: LogNumberOfFeatures
 feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues
@@ -20,6 +21,11 @@ feature_step DatasetRatio: DatasetRatio, LogDatasetRatio
 feature_step LogDatasetRatio: LogDatasetRatio
 feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio
 feature_step LogInverseDatasetRatio: LogInverseDatasetRatio
+feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD
+feature_step ClassProbabilityMin: ClassProbabilityMin
+feature_step ClassProbabilityMax: ClassProbabilityMax
+feature_step ClassProbabilityMean: ClassProbabilityMean
+feature_step ClassProbabilitySTD: ClassProbabilitySTD
 feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum
 feature_step SymbolsMin: SymbolsMin
 feature_step SymbolsMax: SymbolsMax
@@ -36,11 +42,12 @@ feature_step SkewnessMin: SkewnessMin
 feature_step SkewnessMax: SkewnessMax
 feature_step SkewnessMean: SkewnessMean
 feature_step SkewnessSTD: SkewnessSTD
-features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
+feature_step ClassEntropy: ClassEntropy
+features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy
 features_stochastic: 
-default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
+default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy
 
-algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92
+algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235,236,237,238,239,240,241,242,243,244,245,246,247,248,249,250,251,252,253,254,255,256,257,258,259,260,261,262,263,264,265,266,267,268,269,270,271,272,273,274,275,276,277,278,279,280,281,282,283,284,285,286,287,288,289,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,312,313,314,315,316,317,318,319,320,321,322,323,324,325,326,327,328,329,330,331,332,333,334,335,336,337,338,339,340,341,342,343,344,345,346,347,348,349,350,351,352,353,354,355,356,357,358,359,360,361,362,363,364,365,366,367,368,369,370,371,372
 algorithms_stochastic: 
 performance_measures: recall_macro
 performance_type: solution_quality
diff --git a/autosklearn/metalearning/files/recall_macro_multiclass.classification_sparse/feature_costs.arff b/autosklearn/metalearning/files/recall_macro_multiclass.classification_sparse/feature_costs.arff
index 0e17e20d22..8512fd9275 100644
--- a/autosklearn/metalearning/files/recall_macro_multiclass.classification_sparse/feature_costs.arff
+++ b/autosklearn/metalearning/files/recall_macro_multiclass.classification_sparse/feature_costs.arff
@@ -4,6 +4,7 @@
 @ATTRIBUTE repetition NUMERIC
 @ATTRIBUTE NumberOfInstances NUMERIC
 @ATTRIBUTE LogNumberOfInstances NUMERIC
+@ATTRIBUTE NumberOfClasses NUMERIC
 @ATTRIBUTE NumberOfFeatures NUMERIC
 @ATTRIBUTE LogNumberOfFeatures NUMERIC
 @ATTRIBUTE MissingValues NUMERIC
@@ -21,6 +22,11 @@
 @ATTRIBUTE LogDatasetRatio NUMERIC
 @ATTRIBUTE InverseDatasetRatio NUMERIC
 @ATTRIBUTE LogInverseDatasetRatio NUMERIC
+@ATTRIBUTE ClassOccurences NUMERIC
+@ATTRIBUTE ClassProbabilityMin NUMERIC
+@ATTRIBUTE ClassProbabilityMax NUMERIC
+@ATTRIBUTE ClassProbabilityMean NUMERIC
+@ATTRIBUTE ClassProbabilitySTD NUMERIC
 @ATTRIBUTE NumSymbols NUMERIC
 @ATTRIBUTE SymbolsMin NUMERIC
 @ATTRIBUTE SymbolsMax NUMERIC
@@ -37,162 +43,138 @@
 @ATTRIBUTE SkewnessMax NUMERIC
 @ATTRIBUTE SkewnessMean NUMERIC
 @ATTRIBUTE SkewnessSTD NUMERIC
+@ATTRIBUTE ClassEntropy NUMERIC
 
 @DATA
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diff --git a/autosklearn/metalearning/files/recall_macro_multiclass.classification_sparse/feature_runstatus.arff b/autosklearn/metalearning/files/recall_macro_multiclass.classification_sparse/feature_runstatus.arff
index 0034ad2bd6..5fff2dfed3 100644
--- a/autosklearn/metalearning/files/recall_macro_multiclass.classification_sparse/feature_runstatus.arff
+++ b/autosklearn/metalearning/files/recall_macro_multiclass.classification_sparse/feature_runstatus.arff
@@ -4,6 +4,7 @@
 @ATTRIBUTE repetition NUMERIC
 @ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other}
@@ -21,6 +22,11 @@
 @ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other}
@@ -37,162 +43,138 @@
 @ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other}
 
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diff --git a/autosklearn/metalearning/files/recall_macro_multiclass.classification_sparse/feature_values.arff b/autosklearn/metalearning/files/recall_macro_multiclass.classification_sparse/feature_values.arff
index a60400e3ce..736dc64321 100644
--- a/autosklearn/metalearning/files/recall_macro_multiclass.classification_sparse/feature_values.arff
+++ b/autosklearn/metalearning/files/recall_macro_multiclass.classification_sparse/feature_values.arff
@@ -13,7 +13,6 @@
 @ATTRIBUTE KurtosisMean NUMERIC
 @ATTRIBUTE KurtosisMin NUMERIC
 @ATTRIBUTE KurtosisSTD NUMERIC
-@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC
 @ATTRIBUTE LogDatasetRatio NUMERIC
 @ATTRIBUTE LogInverseDatasetRatio NUMERIC
 @ATTRIBUTE LogNumberOfFeatures NUMERIC
@@ -42,160 +41,135 @@
 @ATTRIBUTE SymbolsSum NUMERIC
 
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diff --git a/autosklearn/metalearning/files/recall_micro_binary.classification_dense/algorithm_runs.arff b/autosklearn/metalearning/files/recall_micro_binary.classification_dense/algorithm_runs.arff
index 3c7cbd3895..f4de6858fa 100644
--- a/autosklearn/metalearning/files/recall_micro_binary.classification_dense/algorithm_runs.arff
+++ b/autosklearn/metalearning/files/recall_micro_binary.classification_dense/algorithm_runs.arff
@@ -7,130 +7,135 @@
 @ATTRIBUTE runstatus {ok, timeout, memout, not_applicable, crash, other}
 
 @DATA
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diff --git a/autosklearn/metalearning/files/recall_micro_binary.classification_dense/configurations.csv b/autosklearn/metalearning/files/recall_micro_binary.classification_dense/configurations.csv
index 14ccf74d94..85e767fd63 100644
--- a/autosklearn/metalearning/files/recall_micro_binary.classification_dense/configurations.csv
+++ b/autosklearn/metalearning/files/recall_micro_binary.classification_dense/configurations.csv
@@ -1,128 +1,133 @@
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-43,none,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0686374607394679,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,minmax,,,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.8490260132946401,False,,,,,,,,,,,,,,,
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-46,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,92.66144306787051,,,0.04654639302091653,rbf,-1,True,0.0022231544280610005,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.005194838694455963,mean,none,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,15,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-47,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2681.998807317996,-0.9558745731731211,,0.0011299948421773457,sigmoid,-1,False,1.5292507354478086e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-48,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.8095522998548572,None,0.0,17,10,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.031940595071539304,mean,quantile_transformer,1563,normal,,,extra_trees_preproc_for_classification,False,entropy,None,0.9330145598603196,None,0.0,18,10,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-49,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,26.502259985828186,,,9.475662388405154e-05,rbf,-1,False,0.00019690635968217866,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-50,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,2.5360196477156387e-07,0.011018756408558218,auto,255,None,40,122,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.00011749164080669445,most_frequent,quantile_transformer,757,uniform,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,5.097834697156853,False,True,1,squared_hinge,ovr,l1,0.053877834919477424,,,,,,,,,,,,,,,,,,,,,,
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-54,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,1.488333818193668e-10,0.09526676471267805,auto,255,None,258,87,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,normalize,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.3561953264350478,fpr,f_classif
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-58,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,151,None,,0.0003283777607180646,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.2510527431514227,mean,normalize,,,,,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,-0.96093182232312,,0.05762510964607661,sigmoid,2632,,,,,,,,,,,,,,,,,
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-64,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,29.55731828912983,False,True,1,squared_hinge,ovr,l2,0.0011332486951954968,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,
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-69,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7573078019937971,None,0.0,2,5,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,robust_scaler,,,0.7265312295801113,0.28332515882236425,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.09660564590146253,fpr,f_classif
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-72,none,adaboost,SAMME,0.1280458968526125,7,125,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,robust_scaler,,,0.7156460334356771,0.2981902862926971,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,65.16171885308417,chi2,,,
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-76,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7.491039171224502,True,True,squared_hinge,2.8058138048327884e-05,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,minmax,,,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.7754372809893839,False,True,1,squared_hinge,ovr,l1,3.9643594596766314e-05,,,,,,,,,,,,,,,,,,,,,,
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-78,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,0.011547155223520492,0.13665554140837183,auto,255,None,13,5,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.1824584492028449,median,minmax,,,,,feature_agglomeration,,,,,,,,,,,,,,,euclidean,average,261,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-79,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.933900673392165,None,0.0,7,4,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,minmax,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-80,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1661.2274747409913,,,0.025821208852387936,rbf,-1,False,0.0017011435104921824,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,none,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,306,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-81,none,adaboost,SAMME,0.011233995624432622,9,477,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.018370622484682127,mean,standardize,,,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6039710338898471,False,,,,,,,,,,,,,,,
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-84,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,0.037849720944694,0.1268815770019783,auto,255,None,11,99,11,loss,1e-07,0.10654523627313527,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,
-85,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.593728171200453,None,0.0,7,20,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.02830941310359363,mean,normalize,,,,,extra_trees_preproc_for_classification,False,entropy,None,0.7389446586281172,None,0.0,20,11,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-86,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,1,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.019797877010737826,mean,normalize,,,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.8239678741128165,False,,,,,,,,,,,,,,,
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-89,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5722917944959481,None,0.0,1,14,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,
-90,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.26356754187780895,None,0.0,4,3,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010966178401758774,mean,robust_scaler,,,0.7817043861798886,0.24371720112407158,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
-91,weighting,adaboost,SAMME.R,0.13547552520095074,4,62,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.011851011202809002,most_frequent,standardize,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4888776875314595,fpr,chi2
-92,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,80.0028902468169,False,True,1,squared_hinge,ovr,l2,0.0024481999829574722,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.15047970487774687,mean,quantile_transformer,1130,normal,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,1.1912646635544712,False,True,1,squared_hinge,ovr,l1,0.00014314728486450857,,,,,,,,,,,,,,,,,,,,,,
-93,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.22879311080499845,None,0.0,8,4,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.014320022547615783,median,minmax,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.238630703440461,mutual_info,,,
-94,weighting,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,2,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,none,,,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6342574285315175,False,,,,,,,,,,,,,,,
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-96,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.14148638702297855,None,0.0,2,6,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.0038499650338378643,most_frequent,quantile_transformer,1759,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-97,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,1.1522414705935405e-10,0.6132800183073315,auto,255,None,1607,67,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010096911249562407,mean,robust_scaler,,,0.7663493326967942,0.136094217337434,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-98,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2746221958279041,False,True,1,squared_hinge,ovr,l2,6.546077664669006e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,quantile_transformer,1054,normal,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,
-99,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.479405900046494,,,0.013340778632154543,rbf,-1,False,6.913586060890114e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,robust_scaler,,,0.9953232616818098,0.06450577886132274,fast_ica,,,,,,,,,,,deflation,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-100,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.825844582229201,None,0.0,12,9,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0008422508222149647,median,robust_scaler,,,0.8658803880857064,0.2528423686293341,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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-102,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.7033210835501171,None,0.0,3,19,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,robust_scaler,,,0.7699195245692886,0.29258311638476237,feature_agglomeration,,,,,,,,,,,,,,,euclidean,ward,143,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-103,none,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,128,manual,0.8574560771111307,0.010886139381599514,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.24115007944216788,most_frequent,normalize,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,62.26514170991452,mutual_info,,,
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-105,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,1.430083734915855e-09,0.08779621406981941,auto,255,None,17,20,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.29749837779115257,mean,robust_scaler,,,0.7236445277814435,0.25,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
-106,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.3294588572341214,None,0.0,1,3,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
-107,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,39.22980757485479,,,0.13597389869623427,rbf,-1,False,0.00011639060052687874,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.006288306814260224,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-108,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.7922837297504705,False,True,1,squared_hinge,ovr,l2,0.0005476597326342352,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.012674631437254894,most_frequent,none,,,,,extra_trees_preproc_for_classification,False,entropy,None,0.23145146971319638,None,0.0,7,20,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-109,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.18060139887649052,None,0.0,10,12,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.010000000000000004,most_frequent,robust_scaler,,,0.9298612094255864,0.1263597946722308,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
-110,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.4111348710613201,None,0.0,16,8,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.01224348527546641,most_frequent,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,
-111,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6427543483503397,True,True,hinge,0.00045318775930916944,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.009338484332306904,median,quantile_transformer,174,normal,,,kernel_pca,,,,,,,,,,,,,,,,,,,,,,cosine,1848,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-112,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.04981080944617379,True,,0.07309386939525211,True,,constant,log,l2,,0.0006721243213777075,no_encoding,no_coalescense,,mean,quantile_transformer,574,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-113,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,9.029473608568458e-09,0.15779990576964315,auto,255,None,37,71,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.018093177195644097,mean,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,False,,,,,,,,,,,,
-114,none,adaboost,SAMME,0.011233995624432622,9,477,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.018370622484682127,mean,standardize,,,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6039710338898471,False,,,,,,,,,,,,,,,
-115,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l2,2.9468189962573666e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.014870368357163507,most_frequent,quantile_transformer,1000,uniform,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,83.43414935094258,f_classif,,,
-116,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.4864284879550203,None,0.0,1,4,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.008665854799630503,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-117,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.43699642289829416,None,0.0,6,20,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,quantile_transformer,1604,uniform,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-118,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.6922752267318475,True,True,squared_hinge,0.06299408576730353,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,quantile_transformer,1742,uniform,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,107,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-119,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.44727252219812963,None,0.0,2,10,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,standardize,,,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l1,8.17533189014765e-05,,,,,,,,,,,,,,,,,,,,,,
-120,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.46057831591617715,False,True,hinge,0.04557857428827514,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00027457445401600137,median,standardize,,,,,extra_trees_preproc_for_classification,True,gini,None,0.48190346970486964,None,0.0,17,18,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-121,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.4210978663047845,None,0.0,5,6,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.006365988550833196,most_frequent,robust_scaler,,,0.8348891074186082,0.00974961635730845,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0695919708890599,fpr,f_classif
-122,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4424195769042466,False,True,hinge,0.02490432909709204,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,mean,quantile_transformer,1000,uniform,,,kernel_pca,,,,,,,,,,,,,,,,,,,,,,cosine,769,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-123,weighting,decision_tree,,,,,,,entropy,1.0435267397863155,1.0,None,0.0,15,3,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.36913659113454333,most_frequent,normalize,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4947786362821451,fpr,f_classif
-124,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1561.1002600303584,,,0.0024048551704194507,rbf,-1,False,0.0001936214667496851,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,minmax,,,,,fast_ica,,,,,,,,,,,parallel,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-125,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.087313242780732,0.0636709914987994,3,0.004481917125024585,poly,-1,False,0.01848880618941367,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.002136015035740733,mean,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-126,weighting,adaboost,SAMME.R,1.696588612003168,9,455,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,minmax,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,82.18726095926662,chi2,,,
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+1,none,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1514.059969351301,False,True,1,squared_hinge,ovr,l2,0.002448814309966599,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,standardize,,,,,extra_trees_preproc_for_classification,True,entropy,None,0.8221481751476923,None,0.0,2,3,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+3,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5707458022222313,None,0.0,3,12,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,minmax,,,,,extra_trees_preproc_for_classification,False,gini,None,0.5651056128131684,None,0.0,9,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+6,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.7283683762613216,None,0.0,12,20,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,median,standardize,,,,,fast_ica,,,,,,,,,,,deflation,exp,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+8,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,114,manual,0.11778631707605758,0.0029112721339713465,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.015582195257240751,median,normalize,,,,,kernel_pca,,,,,,,,,,,,,,,,,,,0.42909623253327767,3,0.25407612000343777,poly,1689,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+11,weighting,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,2,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.3865962574104293,mean,normalize,,,,,fast_ica,,,,,,,,,,,deflation,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+13,weighting,adaboost,SAMME,0.17983222024694556,4,357,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0008898781197407956,mean,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+15,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.21772092350860472,None,0.0,14,17,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.0020488212891693275,median,standardize,,,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,33.74257606530722,False,True,1,squared_hinge,ovr,l1,8.740452966205111e-05,,,,,,,,,,,,,,,,,,,,,,
+18,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,2.4243922131268786e-05,0.07570328162448307,auto,255,None,71,42,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,robust_scaler,,,0.8898729094898769,0.12138152991327475,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,94.96086286583851,chi2,,,
+20,none,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.9866058571747695,None,0.0,16,9,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.1680676743605393,most_frequent,robust_scaler,,,0.8840072797399463,0.08535534311393947,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,False,,,,,,,,,,,,
+23,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,0.00022373024203887573,0.027149343206135476,auto,255,None,49,13,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.00777614320416325,most_frequent,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,
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+379,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,1.2940289605295536e-09,0.15591343717459372,auto,255,None,7,59,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0005549095909238961,mean,quantile_transformer,297,uniform,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,58.10560700979111,chi2,,,
+388,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,53.67009896349757,,,0.03687753298495132,rbf,-1,False,0.028791286245157307,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.047859755696856916,mean,standardize,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,70,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+392,none,adaboost,SAMME,0.7387448781351068,8,450,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0473229636375385,median,standardize,,,,,feature_agglomeration,,,,,,,,,,,,,,,euclidean,ward,344,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+399,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.5864398404787432,None,0.0,1,14,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0070663750850608895,most_frequent,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
+403,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1866.9172613016979,,,0.012606336762631641,rbf,-1,True,0.000504574634300575,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,
+405,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0001329242404968632,False,True,squared_hinge,0.000416219546803132,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.007568009633214092,median,quantile_transformer,1126,normal,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,29.06367291960815,chi2,,,
+408,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.6642356723981304,False,True,1,squared_hinge,ovr,l2,7.912577426155621e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.08464913801800422,most_frequent,normalize,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,53.35800942135187,chi2,,,
+413,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,7.194851113809193e-10,0.013882834218943657,auto,255,None,18,2,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0005124238915083231,mean,quantile_transformer,948,normal,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,60.45885911036194,f_classif,,,
+415,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,0.00037966471646911845,0.016422945536911454,auto,255,None,10,56,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.002419439132687246,median,quantile_transformer,828,uniform,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+422,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.36374112587320206,None,0.0,2,10,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010870084126959173,most_frequent,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,
+424,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,2.344883102482138e-07,0.0328548789988159,auto,255,None,42,24,16,loss,1e-07,0.13015466346727866,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,most_frequent,robust_scaler,,,0.7010898774332888,0.2534280821273264,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,
+428,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,3.738096432152635e-10,0.04468500715913894,auto,255,None,31,156,20,loss,1e-07,0.10279783687118692,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010000000000000004,most_frequent,quantile_transformer,1132,normal,,,feature_agglomeration,,,,,,,,,,,,,,,euclidean,ward,43,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+430,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5321814610661173,None,0.0,1,3,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,standardize,,,,,extra_trees_preproc_for_classification,True,gini,None,0.5,None,0.0,1,15,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+435,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.44903177399085925,None,0.0,2,17,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,mean,robust_scaler,,,0.7812408516672696,0.12791233577400205,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,
+438,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,6.090138036399552e-10,0.017305568433437588,auto,255,None,202,188,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,robust_scaler,,,0.7526196011313747,0.1563616005702636,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+445,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,68.60833575985336,,,0.03905068757955671,rbf,-1,False,0.02846559747114773,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.04775815990561371,mean,standardize,,,,,extra_trees_preproc_for_classification,False,entropy,None,0.45727987956076366,None,0.0,13,11,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+447,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.3431705351883592,None,0.0,4,16,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.09444471061261189,most_frequent,normalize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+450,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,7.837701348094735e-09,0.05315388879642303,auto,255,None,67,2,10,loss,1e-07,0.11655723047843726,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.001857226018763921,median,none,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.014648191862400293,fdr,f_classif
+458,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,5.581658271288433e-10,0.06171988234311011,auto,255,None,25,6,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.014859405637537319,most_frequent,robust_scaler,,,0.7983900845251328,0.2545046978458648,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,1.380085213170737,False,True,1,squared_hinge,ovr,l1,0.0061450732015354784,,,,,,,,,,,,,,,,,,,,,,
+462,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6,1,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.017471096998391875,median,none,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,67.7801145492284,f_classif,,,
diff --git a/autosklearn/metalearning/files/recall_micro_binary.classification_dense/description.txt b/autosklearn/metalearning/files/recall_micro_binary.classification_dense/description.txt
index 1250bf200f..1a8cc71707 100644
--- a/autosklearn/metalearning/files/recall_micro_binary.classification_dense/description.txt
+++ b/autosklearn/metalearning/files/recall_micro_binary.classification_dense/description.txt
@@ -1,8 +1,9 @@
 features_cutoff_time: 3600
 features_cutoff_memory: 3072
-number_of_feature_steps: 35
+number_of_feature_steps: 42
 feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances
 feature_step LogNumberOfInstances: LogNumberOfInstances
+feature_step NumberOfClasses: NumberOfClasses
 feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures
 feature_step LogNumberOfFeatures: LogNumberOfFeatures
 feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues
@@ -20,6 +21,11 @@ feature_step DatasetRatio: DatasetRatio, LogDatasetRatio
 feature_step LogDatasetRatio: LogDatasetRatio
 feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio
 feature_step LogInverseDatasetRatio: LogInverseDatasetRatio
+feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD
+feature_step ClassProbabilityMin: ClassProbabilityMin
+feature_step ClassProbabilityMax: ClassProbabilityMax
+feature_step ClassProbabilityMean: ClassProbabilityMean
+feature_step ClassProbabilitySTD: ClassProbabilitySTD
 feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum
 feature_step SymbolsMin: SymbolsMin
 feature_step SymbolsMax: SymbolsMax
@@ -36,11 +42,12 @@ feature_step SkewnessMin: SkewnessMin
 feature_step SkewnessMax: SkewnessMax
 feature_step SkewnessMean: SkewnessMean
 feature_step SkewnessSTD: SkewnessSTD
-features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
+feature_step ClassEntropy: ClassEntropy
+features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy
 features_stochastic: 
-default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
+default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy
 
-algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127
+algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235,236,237,238,239,240,241,242,243,244,245,246,247,248,249,250,251,252,253,254,255,256,257,258,259,260,261,262,263,264,265,266,267,268,269,270,271,272,273,274,275,276,277,278,279,280,281,282,283,284,285,286,287,288,289,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,312,313,314,315,316,317,318,319,320,321,322,323,324,325,326,327,328,329,330,331,332,333,334,335,336,337,338,339,340,341,342,343,344,345,346,347,348,349,350,351,352,353,354,355,356,357,358,359,360,361,362,363,364,365,366,367,368,369,370,371,372,373,374,375,376,377,378,379,380,381,382,383,384,385,386,387,388,389,390,391,392,393,394,395,396,397,398,399,400,401,402,403,404,405,406,407,408,409,410,411,412,413,414,415,416,417,418,419,420,421,422,423,424,425,426,427,428,429,430,431,432,433,434,435,436,437,438,439,440,441,442,443,444,445,446,447,448,449,450,451,452,453,454,455,456,457,458,459,460,461,462,463,464
 algorithms_stochastic: 
 performance_measures: recall_micro
 performance_type: solution_quality
diff --git a/autosklearn/metalearning/files/recall_micro_binary.classification_dense/feature_costs.arff b/autosklearn/metalearning/files/recall_micro_binary.classification_dense/feature_costs.arff
index 0e17e20d22..8512fd9275 100644
--- a/autosklearn/metalearning/files/recall_micro_binary.classification_dense/feature_costs.arff
+++ b/autosklearn/metalearning/files/recall_micro_binary.classification_dense/feature_costs.arff
@@ -4,6 +4,7 @@
 @ATTRIBUTE repetition NUMERIC
 @ATTRIBUTE NumberOfInstances NUMERIC
 @ATTRIBUTE LogNumberOfInstances NUMERIC
+@ATTRIBUTE NumberOfClasses NUMERIC
 @ATTRIBUTE NumberOfFeatures NUMERIC
 @ATTRIBUTE LogNumberOfFeatures NUMERIC
 @ATTRIBUTE MissingValues NUMERIC
@@ -21,6 +22,11 @@
 @ATTRIBUTE LogDatasetRatio NUMERIC
 @ATTRIBUTE InverseDatasetRatio NUMERIC
 @ATTRIBUTE LogInverseDatasetRatio NUMERIC
+@ATTRIBUTE ClassOccurences NUMERIC
+@ATTRIBUTE ClassProbabilityMin NUMERIC
+@ATTRIBUTE ClassProbabilityMax NUMERIC
+@ATTRIBUTE ClassProbabilityMean NUMERIC
+@ATTRIBUTE ClassProbabilitySTD NUMERIC
 @ATTRIBUTE NumSymbols NUMERIC
 @ATTRIBUTE SymbolsMin NUMERIC
 @ATTRIBUTE SymbolsMax NUMERIC
@@ -37,162 +43,138 @@
 @ATTRIBUTE SkewnessMax NUMERIC
 @ATTRIBUTE SkewnessMean NUMERIC
 @ATTRIBUTE SkewnessSTD NUMERIC
+@ATTRIBUTE ClassEntropy NUMERIC
 
 @DATA
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-75101,1.0,1e-05,1e-05,1e-05,1e-05,0.0418,0.03546,1e-05,0.00389,1e-05,0.00248,0.0,2e-05,2e-05,4e-05,6e-05,2e-05,1e-05,5e-05,2e-05,0.00045,1e-05,0.0,0.00011,0.00023,0.0001,0.00041,6e-05,8e-05,0.0001,0.00018,0.00047,6e-05,0.0001,8e-05,0.00023
-75191,1.0,1e-05,1e-05,3e-05,1e-05,0.53436,0.40777,2e-05,0.09655,3e-05,0.0301,1e-05,2e-05,3e-05,5e-05,0.00011,7e-05,1e-05,0.00011,3e-05,0.00084,5e-05,7e-05,0.00017,0.00033,0.00023,0.00088,0.00012,0.00017,0.00032,0.00026,0.00068,9e-05,0.00012,0.00013,0.00035
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+75240,1.0,2e-05,1e-05,0.00036,5e-05,4e-05,0.00845,0.00644,6e-05,0.00122,4e-05,0.00091,1e-05,4e-05,4e-05,9e-05,0.00013,5e-05,2e-05,9e-05,5e-05,0.00031,3e-05,1e-05,7e-05,0.0002,0.00073,3e-05,2e-05,0.00019,0.0003,0.00018,0.00055,9e-05,9e-05,0.00011,0.00025,0.0005,7e-05,8e-05,0.00015,0.0002,0.00365
+75243,1.0,1e-05,1e-05,0.00029,1e-05,1e-05,0.00461,0.00415,1e-05,0.00035,1e-05,0.00015,0.0,2e-05,2e-05,5e-05,7e-05,2e-05,1e-05,4e-05,1e-05,0.00018,2e-05,1e-05,4e-05,0.00011,0.00022,1e-05,1e-05,5e-05,0.00011,5e-05,0.0004,6e-05,6e-05,8e-05,0.0002,0.00039,6e-05,6e-05,8e-05,0.00019,0.00265
+75244,1.0,3e-05,2e-05,0.0004,3e-05,2e-05,0.0173,0.01298,4e-05,0.00267,4e-05,0.00174,1e-05,7e-05,9e-05,0.00024,0.00027,8e-05,2e-05,0.00013,3e-05,0.00045,7e-05,2e-05,0.00011,0.00025,0.00077,6e-05,2e-05,0.00021,0.00034,0.00013,0.00077,0.00014,0.00013,0.00026,0.00023,0.00044,7e-05,8e-05,0.0001,0.00019,0.00649
+75248,1.0,1e-05,1e-05,0.00017,1e-05,1e-05,0.00416,0.00305,1e-05,0.0006,2e-05,0.00054,1e-05,2e-05,2e-05,5e-05,7e-05,2e-05,1e-05,4e-05,1e-05,0.00017,2e-05,1e-05,4e-05,0.0001,0.00029,5e-05,1e-05,6e-05,0.00012,6e-05,0.00081,0.00011,0.00015,0.00016,0.00038,0.0005,7e-05,8e-05,0.00012,0.00024,0.00184
+75249,1.0,1e-05,1e-05,0.00011,1e-05,1e-05,0.00192,0.00153,1e-05,0.00025,1e-05,0.00017,1e-05,2e-05,2e-05,5e-05,7e-05,2e-05,1e-05,5e-05,3e-05,0.00014,2e-05,1e-05,3e-05,8e-05,0.00029,1e-05,3e-05,6e-05,0.00012,7e-05,0.00041,6e-05,8e-05,0.0001,0.00017,0.00043,6e-05,7e-05,9e-05,0.00019,0.00092
+75250,1.0,1e-05,1e-05,0.00375,1e-05,1e-05,0.04994,0.047,1e-05,0.00247,1e-05,0.00049,1e-05,2e-05,2e-05,6e-05,7e-05,2e-05,1e-05,3e-05,1e-05,0.00022,4e-05,1e-05,4e-05,0.00012,0.00041,0.0,1e-05,0.00011,0.00023,6e-05,0.00076,0.00012,0.00012,0.00019,0.00032,0.00086,0.00012,0.00013,0.00018,0.00043,0.04178
diff --git a/autosklearn/metalearning/files/recall_micro_binary.classification_dense/feature_runstatus.arff b/autosklearn/metalearning/files/recall_micro_binary.classification_dense/feature_runstatus.arff
index 0034ad2bd6..5fff2dfed3 100644
--- a/autosklearn/metalearning/files/recall_micro_binary.classification_dense/feature_runstatus.arff
+++ b/autosklearn/metalearning/files/recall_micro_binary.classification_dense/feature_runstatus.arff
@@ -4,6 +4,7 @@
 @ATTRIBUTE repetition NUMERIC
 @ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other}
@@ -21,6 +22,11 @@
 @ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other}
@@ -37,162 +43,138 @@
 @ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other}
 
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diff --git a/autosklearn/metalearning/files/recall_micro_binary.classification_dense/feature_values.arff b/autosklearn/metalearning/files/recall_micro_binary.classification_dense/feature_values.arff
index a60400e3ce..736dc64321 100644
--- a/autosklearn/metalearning/files/recall_micro_binary.classification_dense/feature_values.arff
+++ b/autosklearn/metalearning/files/recall_micro_binary.classification_dense/feature_values.arff
@@ -13,7 +13,6 @@
 @ATTRIBUTE KurtosisMean NUMERIC
 @ATTRIBUTE KurtosisMin NUMERIC
 @ATTRIBUTE KurtosisSTD NUMERIC
-@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC
 @ATTRIBUTE LogDatasetRatio NUMERIC
 @ATTRIBUTE LogInverseDatasetRatio NUMERIC
 @ATTRIBUTE LogNumberOfFeatures NUMERIC
@@ -42,160 +41,135 @@
 @ATTRIBUTE SymbolsSum NUMERIC
 
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diff --git a/autosklearn/metalearning/files/recall_micro_binary.classification_sparse/algorithm_runs.arff b/autosklearn/metalearning/files/recall_micro_binary.classification_sparse/algorithm_runs.arff
index a4e95a3447..b935e142cb 100644
--- a/autosklearn/metalearning/files/recall_micro_binary.classification_sparse/algorithm_runs.arff
+++ b/autosklearn/metalearning/files/recall_micro_binary.classification_sparse/algorithm_runs.arff
@@ -7,95 +7,133 @@
 @ATTRIBUTE runstatus {ok, timeout, memout, not_applicable, crash, other}
 
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diff --git a/autosklearn/metalearning/files/recall_micro_binary.classification_sparse/configurations.csv b/autosklearn/metalearning/files/recall_micro_binary.classification_sparse/configurations.csv
index 75662496d2..36fd991e13 100644
--- a/autosklearn/metalearning/files/recall_micro_binary.classification_sparse/configurations.csv
+++ b/autosklearn/metalearning/files/recall_micro_binary.classification_sparse/configurations.csv
@@ -1,93 +1,131 @@
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-89,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4424195769042466,False,True,hinge,0.02490432909709204,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,mean,quantile_transformer,1000,uniform,,,kernel_pca,,,,,,,,,,,,,,cosine,769,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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+278,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5878222472475889,None,0.0,6,15,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,robust_scaler,,,0.7419183699029103,0.22028507583137247,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.21256588660504683,fdr,chi2,
+282,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.002017899394165588,False,,0.0005911937660372945,True,0.17801822098174286,constant,log,elasticnet,,0.00041682529601986016,no_encoding,no_coalescense,,median,quantile_transformer,1335,normal,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0642825062494559,fpr,chi2,
+284,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5609113236355627,None,0.0,2,2,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.47917908217902894,mean,quantile_transformer,673,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+286,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,33.440072270881174,-0.22811742088286835,3,0.05010299776905627,poly,-1,False,2.275627187537573e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,normalize,,,,,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,2,None,5,14,1.0,52,,,,,,
+287,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.3850609070926397,None,0.0,1,2,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.013992309764275707,median,normalize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,,
+289,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.818514683854827,None,0.0,15,20,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.4928285185491301,most_frequent,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,,
+292,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.0479982468066316,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,quantile_transformer,1097,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+293,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7040496889211508,None,0.0,17,19,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.016450784709133564,mean,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+300,none,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.7010137052424128,None,0.0,7,10,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.2761813291805052,most_frequent,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,,
+306,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2677516008848513,True,True,hinge,0.05752557410901496,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0010152857217605206,median,quantile_transformer,1000,uniform,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,51.60974796845029,chi2,,,,
+307,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,33,None,,0.006687546229372618,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.06231343712708962,mean,none,,,,,kernel_pca,,,,,,,,,,,-0.7000777299811654,3,0.008780219231661734,poly,498,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+312,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,60.6761602925822,,,0.038224035513907226,rbf,-1,False,0.0018214343969776353,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.421025538999516,median,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+313,weighting,adaboost,SAMME,1.2423888874490505,5,284,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.01582563952987033,most_frequent,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+316,weighting,adaboost,SAMME,1.2615483379313783,8,190,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.00017789451703497617,mean,robust_scaler,,,0.7345811776810758,0.054715769043996304,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+317,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.4572679693563934,None,0.0,1,11,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,mean,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,,
+318,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.843098530645432,None,0.0,1,4,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,mean,quantile_transformer,930,normal,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,,
+321,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.5864398404787432,None,0.0,1,14,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0070663750850608895,most_frequent,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,,
+326,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0001329242404968632,False,True,squared_hinge,0.000416219546803132,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.007568009633214092,median,quantile_transformer,1126,normal,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,29.06367291960815,chi2,,,,
+329,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.6642356723981304,False,True,1,squared_hinge,ovr,l2,7.912577426155621e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.08464913801800422,most_frequent,normalize,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,53.35800942135187,chi2,,,,
+331,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.5,None,0.0,1,5,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.15244281596303316,mean,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+332,none,adaboost,SAMME,0.22805500898886877,8,172,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.019649593891454008,mean,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+334,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7516795829719571,None,0.0,14,7,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0010013681780844724,mean,none,,,,,extra_trees_preproc_for_classification,True,entropy,None,0.6123323873969666,None,0.0,17,7,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+339,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.36374112587320206,None,0.0,2,10,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010870084126959173,most_frequent,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,,
+343,none,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9062834897256731,None,0.0,20,9,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.005187379382274718,mean,quantile_transformer,349,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+345,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5321814610661173,None,0.0,1,3,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,standardize,,,,,extra_trees_preproc_for_classification,True,gini,None,0.5,None,0.0,1,15,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+350,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.44903177399085925,None,0.0,2,17,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,mean,robust_scaler,,,0.7812408516672696,0.12791233577400205,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,,
+352,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5988524108452234,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,mean,robust_scaler,,,0.75,0.26961884796358077,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+355,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,68.60833575985336,,,0.03905068757955671,rbf,-1,False,0.02846559747114773,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.04775815990561371,mean,standardize,,,,,extra_trees_preproc_for_classification,False,entropy,None,0.45727987956076366,None,0.0,13,11,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+357,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.3431705351883592,None,0.0,4,16,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.09444471061261189,most_frequent,normalize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+359,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.9282407375709691,None,0.0,16,3,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.015995805068365877,mean,standardize,,,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.10042232633120375,False,True,1,squared_hinge,ovr,l1,0.004451009526629745,,,,,,,,,,,,,,,,,,,,,
+367,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.938372431886818e-05,True,True,hinge,0.0016143408148969647,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00024949978971758427,median,quantile_transformer,754,uniform,,,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,-0.22339839702350717,2,0.5718982387915436,poly,5260,,,,,,,,,,,,,,,,
+371,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4,1,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.01020427226149318,median,none,,,,,extra_trees_preproc_for_classification,True,entropy,None,0.3663460889081009,None,0.0,2,7,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
diff --git a/autosklearn/metalearning/files/recall_micro_binary.classification_sparse/description.txt b/autosklearn/metalearning/files/recall_micro_binary.classification_sparse/description.txt
index fb41d56060..1dffcd8886 100644
--- a/autosklearn/metalearning/files/recall_micro_binary.classification_sparse/description.txt
+++ b/autosklearn/metalearning/files/recall_micro_binary.classification_sparse/description.txt
@@ -1,8 +1,9 @@
 features_cutoff_time: 3600
 features_cutoff_memory: 3072
-number_of_feature_steps: 35
+number_of_feature_steps: 42
 feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances
 feature_step LogNumberOfInstances: LogNumberOfInstances
+feature_step NumberOfClasses: NumberOfClasses
 feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures
 feature_step LogNumberOfFeatures: LogNumberOfFeatures
 feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues
@@ -20,6 +21,11 @@ feature_step DatasetRatio: DatasetRatio, LogDatasetRatio
 feature_step LogDatasetRatio: LogDatasetRatio
 feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio
 feature_step LogInverseDatasetRatio: LogInverseDatasetRatio
+feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD
+feature_step ClassProbabilityMin: ClassProbabilityMin
+feature_step ClassProbabilityMax: ClassProbabilityMax
+feature_step ClassProbabilityMean: ClassProbabilityMean
+feature_step ClassProbabilitySTD: ClassProbabilitySTD
 feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum
 feature_step SymbolsMin: SymbolsMin
 feature_step SymbolsMax: SymbolsMax
@@ -36,11 +42,12 @@ feature_step SkewnessMin: SkewnessMin
 feature_step SkewnessMax: SkewnessMax
 feature_step SkewnessMean: SkewnessMean
 feature_step SkewnessSTD: SkewnessSTD
-features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
+feature_step ClassEntropy: ClassEntropy
+features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy
 features_stochastic: 
-default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
+default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy
 
-algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92
+algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235,236,237,238,239,240,241,242,243,244,245,246,247,248,249,250,251,252,253,254,255,256,257,258,259,260,261,262,263,264,265,266,267,268,269,270,271,272,273,274,275,276,277,278,279,280,281,282,283,284,285,286,287,288,289,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,312,313,314,315,316,317,318,319,320,321,322,323,324,325,326,327,328,329,330,331,332,333,334,335,336,337,338,339,340,341,342,343,344,345,346,347,348,349,350,351,352,353,354,355,356,357,358,359,360,361,362,363,364,365,366,367,368,369,370,371,372
 algorithms_stochastic: 
 performance_measures: recall_micro
 performance_type: solution_quality
diff --git a/autosklearn/metalearning/files/recall_micro_binary.classification_sparse/feature_costs.arff b/autosklearn/metalearning/files/recall_micro_binary.classification_sparse/feature_costs.arff
index 0e17e20d22..8512fd9275 100644
--- a/autosklearn/metalearning/files/recall_micro_binary.classification_sparse/feature_costs.arff
+++ b/autosklearn/metalearning/files/recall_micro_binary.classification_sparse/feature_costs.arff
@@ -4,6 +4,7 @@
 @ATTRIBUTE repetition NUMERIC
 @ATTRIBUTE NumberOfInstances NUMERIC
 @ATTRIBUTE LogNumberOfInstances NUMERIC
+@ATTRIBUTE NumberOfClasses NUMERIC
 @ATTRIBUTE NumberOfFeatures NUMERIC
 @ATTRIBUTE LogNumberOfFeatures NUMERIC
 @ATTRIBUTE MissingValues NUMERIC
@@ -21,6 +22,11 @@
 @ATTRIBUTE LogDatasetRatio NUMERIC
 @ATTRIBUTE InverseDatasetRatio NUMERIC
 @ATTRIBUTE LogInverseDatasetRatio NUMERIC
+@ATTRIBUTE ClassOccurences NUMERIC
+@ATTRIBUTE ClassProbabilityMin NUMERIC
+@ATTRIBUTE ClassProbabilityMax NUMERIC
+@ATTRIBUTE ClassProbabilityMean NUMERIC
+@ATTRIBUTE ClassProbabilitySTD NUMERIC
 @ATTRIBUTE NumSymbols NUMERIC
 @ATTRIBUTE SymbolsMin NUMERIC
 @ATTRIBUTE SymbolsMax NUMERIC
@@ -37,162 +43,138 @@
 @ATTRIBUTE SkewnessMax NUMERIC
 @ATTRIBUTE SkewnessMean NUMERIC
 @ATTRIBUTE SkewnessSTD NUMERIC
+@ATTRIBUTE ClassEntropy NUMERIC
 
 @DATA
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+75234,1.0,1e-05,1e-05,0.00015,1e-05,1e-05,0.0058,0.00534,1e-05,0.00033,1e-05,0.00016,0.0,2e-05,2e-05,7e-05,0.00204,3e-05,1e-05,5e-05,1e-05,0.00024,3e-05,2e-05,0.0001,9e-05,0.00043,0.0,0.0,0.00012,0.00024,6e-05,0.00156,6e-05,7e-05,0.00121,0.00021,0.00054,6e-05,7e-05,9e-05,0.00032,0.00501
+75235,1.0,1e-05,1e-05,0.0003,2e-05,1e-05,0.00461,0.00385,2e-05,0.00055,2e-05,0.00026,1e-05,3e-05,3e-05,0.0001,0.00011,3e-05,1e-05,5e-05,2e-05,0.00026,3e-05,1e-05,8e-05,0.00014,0.00075,1e-05,1e-05,0.0002,0.00043,0.00011,0.00127,0.00017,0.0002,0.00029,0.00061,0.0008,0.00015,0.00012,0.00016,0.00038,0.00242
+75236,1.0,1e-05,1e-05,0.0001,1e-05,1e-05,0.00218,0.00118,2e-05,0.00065,3e-05,0.00041,1e-05,3e-05,3e-05,9e-05,9e-05,4e-05,1e-05,7e-05,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.0006,1e-05,1e-05,0.00021,0.00032,6e-05,0.00043,7e-05,7e-05,9e-05,0.00019,0.00062,7e-05,8e-05,0.0001,0.00037,0.00048
+75237,1.0,2e-05,1e-05,0.00785,2e-05,1e-05,0.31031,0.27854,2e-05,0.01161,3e-05,0.02022,1e-05,4e-05,3e-05,9e-05,0.00024,6e-05,2e-05,9e-05,2e-05,0.00062,7e-05,3e-05,0.00017,0.00035,0.0008,1e-05,1e-05,0.00028,0.00043,8e-05,0.00043,8e-05,7e-05,9e-05,0.0002,0.00044,6e-05,8e-05,9e-05,0.00021,0.14028
+75239,1.0,1e-05,1e-05,7e-05,1e-05,1e-05,0.00105,0.00084,1e-05,0.00015,1e-05,8e-05,1e-05,2e-05,5e-05,4e-05,8e-05,2e-05,1e-05,3e-05,1e-05,0.00016,2e-05,1e-05,5e-05,9e-05,0.00049,0.0,0.0,0.00012,0.00026,0.00011,0.00046,0.00013,8e-05,8e-05,0.00018,0.00044,6e-05,9e-05,0.00011,0.00018,0.00065
+75240,1.0,2e-05,1e-05,0.00036,5e-05,4e-05,0.00845,0.00644,6e-05,0.00122,4e-05,0.00091,1e-05,4e-05,4e-05,9e-05,0.00013,5e-05,2e-05,9e-05,5e-05,0.00031,3e-05,1e-05,7e-05,0.0002,0.00073,3e-05,2e-05,0.00019,0.0003,0.00018,0.00055,9e-05,9e-05,0.00011,0.00025,0.0005,7e-05,8e-05,0.00015,0.0002,0.00365
+75243,1.0,1e-05,1e-05,0.00029,1e-05,1e-05,0.00461,0.00415,1e-05,0.00035,1e-05,0.00015,0.0,2e-05,2e-05,5e-05,7e-05,2e-05,1e-05,4e-05,1e-05,0.00018,2e-05,1e-05,4e-05,0.00011,0.00022,1e-05,1e-05,5e-05,0.00011,5e-05,0.0004,6e-05,6e-05,8e-05,0.0002,0.00039,6e-05,6e-05,8e-05,0.00019,0.00265
+75244,1.0,3e-05,2e-05,0.0004,3e-05,2e-05,0.0173,0.01298,4e-05,0.00267,4e-05,0.00174,1e-05,7e-05,9e-05,0.00024,0.00027,8e-05,2e-05,0.00013,3e-05,0.00045,7e-05,2e-05,0.00011,0.00025,0.00077,6e-05,2e-05,0.00021,0.00034,0.00013,0.00077,0.00014,0.00013,0.00026,0.00023,0.00044,7e-05,8e-05,0.0001,0.00019,0.00649
+75248,1.0,1e-05,1e-05,0.00017,1e-05,1e-05,0.00416,0.00305,1e-05,0.0006,2e-05,0.00054,1e-05,2e-05,2e-05,5e-05,7e-05,2e-05,1e-05,4e-05,1e-05,0.00017,2e-05,1e-05,4e-05,0.0001,0.00029,5e-05,1e-05,6e-05,0.00012,6e-05,0.00081,0.00011,0.00015,0.00016,0.00038,0.0005,7e-05,8e-05,0.00012,0.00024,0.00184
+75249,1.0,1e-05,1e-05,0.00011,1e-05,1e-05,0.00192,0.00153,1e-05,0.00025,1e-05,0.00017,1e-05,2e-05,2e-05,5e-05,7e-05,2e-05,1e-05,5e-05,3e-05,0.00014,2e-05,1e-05,3e-05,8e-05,0.00029,1e-05,3e-05,6e-05,0.00012,7e-05,0.00041,6e-05,8e-05,0.0001,0.00017,0.00043,6e-05,7e-05,9e-05,0.00019,0.00092
+75250,1.0,1e-05,1e-05,0.00375,1e-05,1e-05,0.04994,0.047,1e-05,0.00247,1e-05,0.00049,1e-05,2e-05,2e-05,6e-05,7e-05,2e-05,1e-05,3e-05,1e-05,0.00022,4e-05,1e-05,4e-05,0.00012,0.00041,0.0,1e-05,0.00011,0.00023,6e-05,0.00076,0.00012,0.00012,0.00019,0.00032,0.00086,0.00012,0.00013,0.00018,0.00043,0.04178
diff --git a/autosklearn/metalearning/files/recall_micro_binary.classification_sparse/feature_runstatus.arff b/autosklearn/metalearning/files/recall_micro_binary.classification_sparse/feature_runstatus.arff
index 0034ad2bd6..5fff2dfed3 100644
--- a/autosklearn/metalearning/files/recall_micro_binary.classification_sparse/feature_runstatus.arff
+++ b/autosklearn/metalearning/files/recall_micro_binary.classification_sparse/feature_runstatus.arff
@@ -4,6 +4,7 @@
 @ATTRIBUTE repetition NUMERIC
 @ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other}
@@ -21,6 +22,11 @@
 @ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other}
@@ -37,162 +43,138 @@
 @ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other}
 
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diff --git a/autosklearn/metalearning/files/recall_micro_binary.classification_sparse/feature_values.arff b/autosklearn/metalearning/files/recall_micro_binary.classification_sparse/feature_values.arff
index a60400e3ce..736dc64321 100644
--- a/autosklearn/metalearning/files/recall_micro_binary.classification_sparse/feature_values.arff
+++ b/autosklearn/metalearning/files/recall_micro_binary.classification_sparse/feature_values.arff
@@ -13,7 +13,6 @@
 @ATTRIBUTE KurtosisMean NUMERIC
 @ATTRIBUTE KurtosisMin NUMERIC
 @ATTRIBUTE KurtosisSTD NUMERIC
-@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC
 @ATTRIBUTE LogDatasetRatio NUMERIC
 @ATTRIBUTE LogInverseDatasetRatio NUMERIC
 @ATTRIBUTE LogNumberOfFeatures NUMERIC
@@ -42,160 +41,135 @@
 @ATTRIBUTE SymbolsSum NUMERIC
 
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diff --git a/autosklearn/metalearning/files/recall_micro_multiclass.classification_dense/algorithm_runs.arff b/autosklearn/metalearning/files/recall_micro_multiclass.classification_dense/algorithm_runs.arff
index 3c7cbd3895..f4de6858fa 100644
--- a/autosklearn/metalearning/files/recall_micro_multiclass.classification_dense/algorithm_runs.arff
+++ b/autosklearn/metalearning/files/recall_micro_multiclass.classification_dense/algorithm_runs.arff
@@ -7,130 +7,135 @@
 @ATTRIBUTE runstatus {ok, timeout, memout, not_applicable, crash, other}
 
 @DATA
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diff --git a/autosklearn/metalearning/files/recall_micro_multiclass.classification_dense/configurations.csv b/autosklearn/metalearning/files/recall_micro_multiclass.classification_dense/configurations.csv
index 14ccf74d94..85e767fd63 100644
--- a/autosklearn/metalearning/files/recall_micro_multiclass.classification_dense/configurations.csv
+++ b/autosklearn/metalearning/files/recall_micro_multiclass.classification_dense/configurations.csv
@@ -1,128 +1,133 @@
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-22,none,adaboost,SAMME,0.011233995624432622,9,477,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.018370622484682127,mean,standardize,,,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6039710338898471,False,,,,,,,,,,,,,,,
-23,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7734.521233796252,,,2.6048661611376747,rbf,-1,True,4.68717607435371e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,none,,,,,feature_agglomeration,,,,,,,,,,,,,,,euclidean,average,25,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-24,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.8367655423352977,None,0.0,1,5,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.01866043949164283,median,standardize,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.3824115704370156,fpr,f_classif
-25,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5010952533165973,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.005838375247601407,mean,normalize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,
-26,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.7297122444166401,False,True,1,squared_hinge,ovr,l2,0.00023416745444541063,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.03941815152197094,most_frequent,normalize,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0973649546833947,fpr,chi2
-27,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.4426097579931897,None,0.0,16,4,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,mean,robust_scaler,,,0.7407323456920554,0.0804756921037867,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,
-28,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5607168729746175,None,0.0,2,3,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.009810589644392154,most_frequent,quantile_transformer,1132,normal,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.06211089008167909,fdr,chi2
-29,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9333237919291875,None,0.0,8,3,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,
-30,none,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.9728532763078892,None,0.0,13,9,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,none,,,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6651071190864879,False,,,,,,,,,,,,,,,
-31,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9561075301148197,None,0.0,6,2,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,
-32,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.557321265077371,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-33,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,1.153931482107762e-10,0.011666190464009397,auto,255,None,29,190,18,loss,1e-07,0.147868307124396,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,
-34,none,adaboost,SAMME,0.6255555220658239,6,446,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.008833097306364968,most_frequent,none,,,,,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,10,None,4,17,1.0,76,,,,,
-35,none,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.563056219822946,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.32793677336996485,most_frequent,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-36,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,3.6797768538445263e-09,0.05511004217375807,auto,255,None,72,62,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,robust_scaler,,,0.7241481576763391,0.0501033899901458,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,36,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-37,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.7521969592385672,None,0.0,9,5,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.020402264943872365,median,robust_scaler,,,0.737227213630259,0.20752747663727789,fast_ica,,,,,,,,,,,parallel,exp,312,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-38,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.887898394612674,None,0.0,10,19,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,robust_scaler,,,0.8350698288864961,0.2347163538066168,extra_trees_preproc_for_classification,False,entropy,None,0.49795108918064074,None,0.0,13,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-39,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,1.6343482806592892e-09,0.19537524572679268,auto,255,None,32,29,13,loss,1e-07,0.05061738224302791,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-40,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7088164344533493,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,mean,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,
-41,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,1.943186107984505e-07,0.08852455702036988,auto,255,None,5,72,19,loss,1e-07,0.039837460762271175,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.01591265419367627,median,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,
-42,weighting,adaboost,SAMME,0.5210651042094924,10,401,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,standardize,,,,,feature_agglomeration,,,,,,,,,,,,,,,euclidean,complete,89,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-43,none,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0686374607394679,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,minmax,,,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.8490260132946401,False,,,,,,,,,,,,,,,
-44,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.6931271368243409,None,0.0,11,10,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.362940004438799,most_frequent,robust_scaler,,,0.9177587796305063,0.17084038458597867,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,30.491514144623952,mutual_info,,,
-45,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.9398869869697849,None,0.0,20,15,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,normalize,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,269,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-46,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,92.66144306787051,,,0.04654639302091653,rbf,-1,True,0.0022231544280610005,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.005194838694455963,mean,none,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,15,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-47,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2681.998807317996,-0.9558745731731211,,0.0011299948421773457,sigmoid,-1,False,1.5292507354478086e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-48,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.8095522998548572,None,0.0,17,10,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.031940595071539304,mean,quantile_transformer,1563,normal,,,extra_trees_preproc_for_classification,False,entropy,None,0.9330145598603196,None,0.0,18,10,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-49,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,26.502259985828186,,,9.475662388405154e-05,rbf,-1,False,0.00019690635968217866,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-50,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,2.5360196477156387e-07,0.011018756408558218,auto,255,None,40,122,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.00011749164080669445,most_frequent,quantile_transformer,757,uniform,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,5.097834697156853,False,True,1,squared_hinge,ovr,l1,0.053877834919477424,,,,,,,,,,,,,,,,,,,,,,
-51,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5255991794296913,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-52,weighting,decision_tree,,,,,,,gini,1.7853447328017091,1.0,None,0.0,13,7,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0001265964225445088,median,normalize,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.26399878867042303,,mutual_info_classif
-53,weighting,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.7776312540090575,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0005253715056715056,mean,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,False,,,,,,,,,,,,
-54,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,1.488333818193668e-10,0.09526676471267805,auto,255,None,258,87,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,normalize,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.3561953264350478,fpr,f_classif
-55,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.9523435078770708,0.8748383232154038,4,0.2195992292488049,poly,-1,True,4.849685143365339e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,minmax,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-56,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.7610308078756552,None,0.0,1,2,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.010000000000000004,mean,robust_scaler,,,0.9205545465103644,0.20272761060743316,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-57,weighting,adaboost,SAMME.R,0.03127273017917347,10,496,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,standardize,,,,,fast_ica,,,,,,,,,,,deflation,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-58,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,151,None,,0.0003283777607180646,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.2510527431514227,mean,normalize,,,,,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,-0.96093182232312,,0.05762510964607661,sigmoid,2632,,,,,,,,,,,,,,,,,
-59,none,bernoulli_nb,,,,,0.6524090653939782,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.005164169126808402,most_frequent,quantile_transformer,16,normal,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.033082145443631535,False,True,1,squared_hinge,ovr,l1,0.002615020898325393,,,,,,,,,,,,,,,,,,,,,,
-60,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.6453555269772124,None,0.0,1,4,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,normalize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-61,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,940.9437776449834,,,1.5839338084625942,rbf,-1,False,0.023072781592565438,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,none,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,77.25763685070707,mutual_info,,,
-62,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,220,auto,,0.0013824567992319003,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00021328932075284388,mean,minmax,,,,,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.039972474835314076,,0.01017810882899714,sigmoid,295,,,,,,,,,,,,,,,,,
-63,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.7326858450533382,0.02513634776687268,2,0.8635510319471909,poly,-1,True,0.0014598272110141993,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.06447266871225255,most_frequent,quantile_transformer,1000,normal,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,79.01961823033078,f_classif,,,
-64,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,29.55731828912983,False,True,1,squared_hinge,ovr,l2,0.0011332486951954968,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,
-65,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,1.1387795876755026e-06,0.12245605190343567,auto,255,None,58,7,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,robust_scaler,,,0.79305915648885,0.1841487406876042,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-66,none,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.563056219822946,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.32793677336996485,most_frequent,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-67,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,0.2638627266631576,0.022360820165005443,auto,255,None,171,6,19,loss,1e-07,0.016209060012924756,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.008763072395677399,mean,standardize,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,78,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-68,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,925.3053743248586,False,True,1,squared_hinge,ovr,l2,0.02764481198395717,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.3850718018366796,most_frequent,quantile_transformer,786,uniform,,,feature_agglomeration,,,,,,,,,,,,,,,cosine,average,156,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-69,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7573078019937971,None,0.0,2,5,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,robust_scaler,,,0.7265312295801113,0.28332515882236425,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.09660564590146253,fpr,f_classif
-70,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.005753626505113041,False,True,squared_hinge,0.00924401505172238,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.3192271696445429,median,quantile_transformer,913,normal,,,kernel_pca,,,,,,,,,,,,,,,,,,,,,,cosine,1963,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-71,none,adaboost,SAMME.R,0.22802213935525836,6,284,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,robust_scaler,,,0.7468645381288924,0.22838821662367073,feature_agglomeration,,,,,,,,,,,,,,,cosine,average,128,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-72,none,adaboost,SAMME,0.1280458968526125,7,125,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,robust_scaler,,,0.7156460334356771,0.2981902862926971,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,65.16171885308417,chi2,,,
-73,none,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9931779675565967,None,0.0,18,17,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,robust_scaler,,,0.75,0.2945917010472818,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,
-74,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.08646118576686276,None,0.0,4,9,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,most_frequent,quantile_transformer,959,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-75,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,1.2430504242164082e-09,0.03359568307446533,auto,255,None,1609,29,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,standardize,,,,,feature_agglomeration,,,,,,,,,,,,,,,cosine,complete,40,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-76,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7.491039171224502,True,True,squared_hinge,2.8058138048327884e-05,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,minmax,,,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.7754372809893839,False,True,1,squared_hinge,ovr,l1,3.9643594596766314e-05,,,,,,,,,,,,,,,,,,,,,,
-77,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.34936025988397124,None,0.0,2,13,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00018601309647406987,median,robust_scaler,,,0.8634309348843472,0.26976989465367607,extra_trees_preproc_for_classification,False,entropy,None,0.48025175288975036,None,0.0,6,17,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-78,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,0.011547155223520492,0.13665554140837183,auto,255,None,13,5,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.1824584492028449,median,minmax,,,,,feature_agglomeration,,,,,,,,,,,,,,,euclidean,average,261,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-79,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.933900673392165,None,0.0,7,4,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,minmax,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-80,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1661.2274747409913,,,0.025821208852387936,rbf,-1,False,0.0017011435104921824,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,none,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,306,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-81,none,adaboost,SAMME,0.011233995624432622,9,477,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.018370622484682127,mean,standardize,,,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6039710338898471,False,,,,,,,,,,,,,,,
-82,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,21.85207494001697,0.18872999589786676,4,1.1863646021868761,poly,-1,False,0.0021338371901294443,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0034080852834770217,mean,normalize,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.11326005109886934,fdr,f_classif
-83,none,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.563056219822946,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.32793677336996485,most_frequent,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-84,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,0.037849720944694,0.1268815770019783,auto,255,None,11,99,11,loss,1e-07,0.10654523627313527,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,
-85,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.593728171200453,None,0.0,7,20,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.02830941310359363,mean,normalize,,,,,extra_trees_preproc_for_classification,False,entropy,None,0.7389446586281172,None,0.0,20,11,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-86,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,1,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.019797877010737826,mean,normalize,,,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.8239678741128165,False,,,,,,,,,,,,,,,
-87,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.46057831591617715,False,True,hinge,0.04557857428827514,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00027457445401600137,median,standardize,,,,,extra_trees_preproc_for_classification,True,gini,None,0.48190346970486964,None,0.0,17,18,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-88,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,38,auto,,0.00019398431171606024,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010000000000000004,median,quantile_transformer,1440,normal,,,extra_trees_preproc_for_classification,True,gini,None,0.5126639956845847,None,0.0,16,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-89,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5722917944959481,None,0.0,1,14,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,
-90,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.26356754187780895,None,0.0,4,3,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010966178401758774,mean,robust_scaler,,,0.7817043861798886,0.24371720112407158,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
-91,weighting,adaboost,SAMME.R,0.13547552520095074,4,62,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.011851011202809002,most_frequent,standardize,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4888776875314595,fpr,chi2
-92,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,80.0028902468169,False,True,1,squared_hinge,ovr,l2,0.0024481999829574722,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.15047970487774687,mean,quantile_transformer,1130,normal,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,1.1912646635544712,False,True,1,squared_hinge,ovr,l1,0.00014314728486450857,,,,,,,,,,,,,,,,,,,,,,
-93,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.22879311080499845,None,0.0,8,4,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.014320022547615783,median,minmax,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.238630703440461,mutual_info,,,
-94,weighting,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,2,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,none,,,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6342574285315175,False,,,,,,,,,,,,,,,
-95,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.107967527639527e-05,True,True,hinge,0.00448294745544775,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,quantile_transformer,953,normal,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,35.00114345405331,chi2,,,
-96,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.14148638702297855,None,0.0,2,6,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.0038499650338378643,most_frequent,quantile_transformer,1759,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-97,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,1.1522414705935405e-10,0.6132800183073315,auto,255,None,1607,67,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010096911249562407,mean,robust_scaler,,,0.7663493326967942,0.136094217337434,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-98,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2746221958279041,False,True,1,squared_hinge,ovr,l2,6.546077664669006e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,quantile_transformer,1054,normal,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,
-99,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.479405900046494,,,0.013340778632154543,rbf,-1,False,6.913586060890114e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,robust_scaler,,,0.9953232616818098,0.06450577886132274,fast_ica,,,,,,,,,,,deflation,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-100,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.825844582229201,None,0.0,12,9,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0008422508222149647,median,robust_scaler,,,0.8658803880857064,0.2528423686293341,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-101,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2574971638664341,True,True,hinge,1.8027411099296446e-05,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.00536396328125103,median,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,
-102,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.7033210835501171,None,0.0,3,19,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,robust_scaler,,,0.7699195245692886,0.29258311638476237,feature_agglomeration,,,,,,,,,,,,,,,euclidean,ward,143,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-103,none,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,128,manual,0.8574560771111307,0.010886139381599514,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.24115007944216788,most_frequent,normalize,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,62.26514170991452,mutual_info,,,
-104,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.6282663389679584,None,0.0,1,3,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,median,minmax,,,,,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-105,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,1.430083734915855e-09,0.08779621406981941,auto,255,None,17,20,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.29749837779115257,mean,robust_scaler,,,0.7236445277814435,0.25,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
-106,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.3294588572341214,None,0.0,1,3,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
-107,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,39.22980757485479,,,0.13597389869623427,rbf,-1,False,0.00011639060052687874,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.006288306814260224,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-108,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.7922837297504705,False,True,1,squared_hinge,ovr,l2,0.0005476597326342352,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.012674631437254894,most_frequent,none,,,,,extra_trees_preproc_for_classification,False,entropy,None,0.23145146971319638,None,0.0,7,20,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-109,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.18060139887649052,None,0.0,10,12,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.010000000000000004,most_frequent,robust_scaler,,,0.9298612094255864,0.1263597946722308,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
-110,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.4111348710613201,None,0.0,16,8,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.01224348527546641,most_frequent,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,
-111,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6427543483503397,True,True,hinge,0.00045318775930916944,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.009338484332306904,median,quantile_transformer,174,normal,,,kernel_pca,,,,,,,,,,,,,,,,,,,,,,cosine,1848,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-112,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.04981080944617379,True,,0.07309386939525211,True,,constant,log,l2,,0.0006721243213777075,no_encoding,no_coalescense,,mean,quantile_transformer,574,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-113,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,9.029473608568458e-09,0.15779990576964315,auto,255,None,37,71,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.018093177195644097,mean,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,False,,,,,,,,,,,,
-114,none,adaboost,SAMME,0.011233995624432622,9,477,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.018370622484682127,mean,standardize,,,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6039710338898471,False,,,,,,,,,,,,,,,
-115,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l2,2.9468189962573666e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.014870368357163507,most_frequent,quantile_transformer,1000,uniform,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,83.43414935094258,f_classif,,,
-116,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.4864284879550203,None,0.0,1,4,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.008665854799630503,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-117,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.43699642289829416,None,0.0,6,20,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,quantile_transformer,1604,uniform,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-118,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.6922752267318475,True,True,squared_hinge,0.06299408576730353,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,quantile_transformer,1742,uniform,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,107,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-119,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.44727252219812963,None,0.0,2,10,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,standardize,,,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l1,8.17533189014765e-05,,,,,,,,,,,,,,,,,,,,,,
-120,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.46057831591617715,False,True,hinge,0.04557857428827514,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00027457445401600137,median,standardize,,,,,extra_trees_preproc_for_classification,True,gini,None,0.48190346970486964,None,0.0,17,18,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-121,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.4210978663047845,None,0.0,5,6,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.006365988550833196,most_frequent,robust_scaler,,,0.8348891074186082,0.00974961635730845,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0695919708890599,fpr,f_classif
-122,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4424195769042466,False,True,hinge,0.02490432909709204,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,mean,quantile_transformer,1000,uniform,,,kernel_pca,,,,,,,,,,,,,,,,,,,,,,cosine,769,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-123,weighting,decision_tree,,,,,,,entropy,1.0435267397863155,1.0,None,0.0,15,3,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.36913659113454333,most_frequent,normalize,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4947786362821451,fpr,f_classif
-124,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1561.1002600303584,,,0.0024048551704194507,rbf,-1,False,0.0001936214667496851,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,minmax,,,,,fast_ica,,,,,,,,,,,parallel,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-125,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.087313242780732,0.0636709914987994,3,0.004481917125024585,poly,-1,False,0.01848880618941367,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.002136015035740733,mean,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-126,weighting,adaboost,SAMME.R,1.696588612003168,9,455,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,minmax,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,82.18726095926662,chi2,,,
-127,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.6466927589470762,None,0.0,11,2,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.009672465311659264,most_frequent,standardize,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,369,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+1,none,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1514.059969351301,False,True,1,squared_hinge,ovr,l2,0.002448814309966599,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,standardize,,,,,extra_trees_preproc_for_classification,True,entropy,None,0.8221481751476923,None,0.0,2,3,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+3,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5707458022222313,None,0.0,3,12,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,minmax,,,,,extra_trees_preproc_for_classification,False,gini,None,0.5651056128131684,None,0.0,9,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+6,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.7283683762613216,None,0.0,12,20,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,median,standardize,,,,,fast_ica,,,,,,,,,,,deflation,exp,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+8,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,114,manual,0.11778631707605758,0.0029112721339713465,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.015582195257240751,median,normalize,,,,,kernel_pca,,,,,,,,,,,,,,,,,,,0.42909623253327767,3,0.25407612000343777,poly,1689,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+11,weighting,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,2,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.3865962574104293,mean,normalize,,,,,fast_ica,,,,,,,,,,,deflation,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+13,weighting,adaboost,SAMME,0.17983222024694556,4,357,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0008898781197407956,mean,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+15,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.21772092350860472,None,0.0,14,17,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.0020488212891693275,median,standardize,,,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,33.74257606530722,False,True,1,squared_hinge,ovr,l1,8.740452966205111e-05,,,,,,,,,,,,,,,,,,,,,,
+18,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,2.4243922131268786e-05,0.07570328162448307,auto,255,None,71,42,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,robust_scaler,,,0.8898729094898769,0.12138152991327475,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,94.96086286583851,chi2,,,
+20,none,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.9866058571747695,None,0.0,16,9,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.1680676743605393,most_frequent,robust_scaler,,,0.8840072797399463,0.08535534311393947,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,False,,,,,,,,,,,,
+23,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,0.00022373024203887573,0.027149343206135476,auto,255,None,49,13,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.00777614320416325,most_frequent,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,
+27,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.41025844265119926,None,0.0,18,15,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,median,quantile_transformer,1911,normal,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,False,,,,,,,,,,,,
+29,none,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.563056219822946,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.32793677336996485,most_frequent,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+31,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,8.757412830636845e-06,0.10095376320667454,auto,255,None,3,2,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.015401766535365336,median,quantile_transformer,647,normal,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,
+32,none,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,174,manual,0.21501892897746921,2.0347110826262054e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.013754893527625522,most_frequent,normalize,,,,,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,-0.019221513778605592,3,0.10190948951339239,poly,2521,,,,,,,,,,,,,,,,,
+35,none,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9.608707084865884,False,True,1,squared_hinge,ovr,l2,0.008086910150491696,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.020901889243957545,median,minmax,,,,,kitchen_sinks,,,,,,,,,,,,,,,,,,,,,,,,0.5694399784333516,3230,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+39,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.6799215694343422,None,0.0,1,5,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.004724310659953132,most_frequent,quantile_transformer,1037,normal,,,fast_ica,,,,,,,,,,,parallel,exp,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+41,none,extra_trees,,,,,,,,,,,,,,,True,gini,None,0.5033866291997137,None,0.0,2,14,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,minmax,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+43,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.9018061166635376,None,0.0,7,9,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,quantile_transformer,1802,normal,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,84.54647012968938,chi2,,,
+44,none,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l2,2.858526169098402e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0007827505640681123,median,standardize,,,,,extra_trees_preproc_for_classification,False,entropy,None,0.3443310459451119,None,0.0,3,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+49,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.049504283446131e-05,False,,0.0004438773852753879,True,2.4110816673432424e-07,constant,log,elasticnet,,0.01321814853186568,one_hot_encoding,minority_coalescer,0.01510416593631535,most_frequent,none,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,66.65285268958851,chi2,,,
+52,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,6.421059381158897e-08,0.11261253191288709,auto,255,None,45,28,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,
+55,none,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.9822179211645446,None,0.0,14,8,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.013966871874533052,most_frequent,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,
+58,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5214197490559657,None,0.0,1,2,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.00826182798588248,mean,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,
+63,none,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,130,manual,0.5446276346297756,0.00846307015171434,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.005736348762561667,median,quantile_transformer,973,uniform,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.08483615095863663,fdr,chi2
+65,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,1.2822815440468518e-10,0.12922700208645135,auto,255,None,31,44,13,loss,1e-07,0.08789566421608311,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.012230423571314038,mean,robust_scaler,,,0.71734690066056,0.20283418288016852,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,
+68,weighting,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,minmax,,,,,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+75,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13.284077389890939,-0.2621334760621076,5,0.9060908941898536,poly,-1,False,0.06323853917206591,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0011743021592446861,most_frequent,normalize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,
+82,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,3.79982070767762e-10,0.10307173798074214,auto,255,None,30,25,6,loss,1e-07,0.09705336338563596,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.004889186641013006,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+84,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.5507560981489436,None,0.0,7,4,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,
+91,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,0.0006322356877253852,0.02155479872417127,auto,255,None,6,15,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
+93,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5012519528536651,None,0.0,1,8,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,mean,standardize,,,,,feature_agglomeration,,,,,,,,,,,,,,,cosine,complete,323,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+96,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5602498483292023,None,0.0,1,3,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.3195602228752535,mean,standardize,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.09782235573464702,fdr,chi2
+99,weighting,adaboost,SAMME,0.17983222024694556,4,357,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0008898781197407956,mean,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+105,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.268699692198443e-05,True,,4.1225573912733725e-06,True,,constant,log,l2,,4.729985534114764e-05,no_encoding,no_coalescense,,median,quantile_transformer,246,normal,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,20.73090458774975,chi2,,,
+106,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,2.047681354714616e-06,0.016073071519249763,auto,255,None,127,60,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.005262156195293648,median,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,
+110,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0021814768111094564,False,True,hinge,8.726644999887521e-05,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,none,,,,,kitchen_sinks,,,,,,,,,,,,,,,,,,,,,,,,0.0010909910768059797,970,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+111,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.4990576176544739,None,0.0,6,6,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
+114,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,83,auto,,0.024875973599406742,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,normalize,,,,,kitchen_sinks,,,,,,,,,,,,,,,,,,,,,,,,0.041059688084044955,7269,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+115,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.463780563279276,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.011474025594258192,mean,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,
+116,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,236,None,,0.08905291537442941,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,normalize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,
+121,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.08836825866738698,None,0.0,5,19,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0003943043347109662,median,robust_scaler,,,0.7564808439539721,0.23469821956116474,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2206955933275169,fdr,f_classif
+124,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.3955965159441456,None,0.0,3,7,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,median,minmax,,,,,extra_trees_preproc_for_classification,False,entropy,None,0.029027785145223306,None,0.0,17,7,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+126,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,1.4547955251450584e-08,0.03465981099276111,auto,255,None,10,4,7,loss,1e-07,0.12579700249284784,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.001559260617305279,most_frequent,normalize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,False,,,,,,,,,,,,
+130,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,9.471664460969401e-09,0.05921990503832894,auto,255,None,1357,22,15,loss,1e-07,0.10690431328779236,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.08690713722954983,most_frequent,robust_scaler,,,0.9305626247167209,0.22989015341001506,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.33284956824843637,fwe,f_classif
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+354,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.47037910442880493,None,0.0,10,3,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.17511596636550678,mean,none,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,87.25836994493127,mutual_info,,,
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+379,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,1.2940289605295536e-09,0.15591343717459372,auto,255,None,7,59,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0005549095909238961,mean,quantile_transformer,297,uniform,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,58.10560700979111,chi2,,,
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+413,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,7.194851113809193e-10,0.013882834218943657,auto,255,None,18,2,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0005124238915083231,mean,quantile_transformer,948,normal,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,60.45885911036194,f_classif,,,
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+422,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.36374112587320206,None,0.0,2,10,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010870084126959173,most_frequent,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,
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+430,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5321814610661173,None,0.0,1,3,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,standardize,,,,,extra_trees_preproc_for_classification,True,gini,None,0.5,None,0.0,1,15,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+435,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.44903177399085925,None,0.0,2,17,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,mean,robust_scaler,,,0.7812408516672696,0.12791233577400205,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,
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+462,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6,1,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.017471096998391875,median,none,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,67.7801145492284,f_classif,,,
diff --git a/autosklearn/metalearning/files/recall_micro_multiclass.classification_dense/description.txt b/autosklearn/metalearning/files/recall_micro_multiclass.classification_dense/description.txt
index 1250bf200f..1a8cc71707 100644
--- a/autosklearn/metalearning/files/recall_micro_multiclass.classification_dense/description.txt
+++ b/autosklearn/metalearning/files/recall_micro_multiclass.classification_dense/description.txt
@@ -1,8 +1,9 @@
 features_cutoff_time: 3600
 features_cutoff_memory: 3072
-number_of_feature_steps: 35
+number_of_feature_steps: 42
 feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances
 feature_step LogNumberOfInstances: LogNumberOfInstances
+feature_step NumberOfClasses: NumberOfClasses
 feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures
 feature_step LogNumberOfFeatures: LogNumberOfFeatures
 feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues
@@ -20,6 +21,11 @@ feature_step DatasetRatio: DatasetRatio, LogDatasetRatio
 feature_step LogDatasetRatio: LogDatasetRatio
 feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio
 feature_step LogInverseDatasetRatio: LogInverseDatasetRatio
+feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD
+feature_step ClassProbabilityMin: ClassProbabilityMin
+feature_step ClassProbabilityMax: ClassProbabilityMax
+feature_step ClassProbabilityMean: ClassProbabilityMean
+feature_step ClassProbabilitySTD: ClassProbabilitySTD
 feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum
 feature_step SymbolsMin: SymbolsMin
 feature_step SymbolsMax: SymbolsMax
@@ -36,11 +42,12 @@ feature_step SkewnessMin: SkewnessMin
 feature_step SkewnessMax: SkewnessMax
 feature_step SkewnessMean: SkewnessMean
 feature_step SkewnessSTD: SkewnessSTD
-features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
+feature_step ClassEntropy: ClassEntropy
+features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy
 features_stochastic: 
-default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
+default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy
 
-algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127
+algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235,236,237,238,239,240,241,242,243,244,245,246,247,248,249,250,251,252,253,254,255,256,257,258,259,260,261,262,263,264,265,266,267,268,269,270,271,272,273,274,275,276,277,278,279,280,281,282,283,284,285,286,287,288,289,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,312,313,314,315,316,317,318,319,320,321,322,323,324,325,326,327,328,329,330,331,332,333,334,335,336,337,338,339,340,341,342,343,344,345,346,347,348,349,350,351,352,353,354,355,356,357,358,359,360,361,362,363,364,365,366,367,368,369,370,371,372,373,374,375,376,377,378,379,380,381,382,383,384,385,386,387,388,389,390,391,392,393,394,395,396,397,398,399,400,401,402,403,404,405,406,407,408,409,410,411,412,413,414,415,416,417,418,419,420,421,422,423,424,425,426,427,428,429,430,431,432,433,434,435,436,437,438,439,440,441,442,443,444,445,446,447,448,449,450,451,452,453,454,455,456,457,458,459,460,461,462,463,464
 algorithms_stochastic: 
 performance_measures: recall_micro
 performance_type: solution_quality
diff --git a/autosklearn/metalearning/files/recall_micro_multiclass.classification_dense/feature_costs.arff b/autosklearn/metalearning/files/recall_micro_multiclass.classification_dense/feature_costs.arff
index 0e17e20d22..8512fd9275 100644
--- a/autosklearn/metalearning/files/recall_micro_multiclass.classification_dense/feature_costs.arff
+++ b/autosklearn/metalearning/files/recall_micro_multiclass.classification_dense/feature_costs.arff
@@ -4,6 +4,7 @@
 @ATTRIBUTE repetition NUMERIC
 @ATTRIBUTE NumberOfInstances NUMERIC
 @ATTRIBUTE LogNumberOfInstances NUMERIC
+@ATTRIBUTE NumberOfClasses NUMERIC
 @ATTRIBUTE NumberOfFeatures NUMERIC
 @ATTRIBUTE LogNumberOfFeatures NUMERIC
 @ATTRIBUTE MissingValues NUMERIC
@@ -21,6 +22,11 @@
 @ATTRIBUTE LogDatasetRatio NUMERIC
 @ATTRIBUTE InverseDatasetRatio NUMERIC
 @ATTRIBUTE LogInverseDatasetRatio NUMERIC
+@ATTRIBUTE ClassOccurences NUMERIC
+@ATTRIBUTE ClassProbabilityMin NUMERIC
+@ATTRIBUTE ClassProbabilityMax NUMERIC
+@ATTRIBUTE ClassProbabilityMean NUMERIC
+@ATTRIBUTE ClassProbabilitySTD NUMERIC
 @ATTRIBUTE NumSymbols NUMERIC
 @ATTRIBUTE SymbolsMin NUMERIC
 @ATTRIBUTE SymbolsMax NUMERIC
@@ -37,162 +43,138 @@
 @ATTRIBUTE SkewnessMax NUMERIC
 @ATTRIBUTE SkewnessMean NUMERIC
 @ATTRIBUTE SkewnessSTD NUMERIC
+@ATTRIBUTE ClassEntropy NUMERIC
 
 @DATA
-75123,1.0,1e-05,1e-05,1e-05,1e-05,0.00253,0.00221,1e-05,0.00024,3e-05,0.00011,0.0,2e-05,2e-05,5e-05,8e-05,2e-05,1e-05,7e-05,1e-05,0.00072,1e-05,1e-05,0.00019,0.00041,0.00011,0.00061,0.00012,9e-05,0.00015,0.00025,0.00067,0.00014,0.00011,0.00016,0.00027
-75090,1.0,1e-05,1e-05,2e-05,2e-05,0.00815,0.00368,7e-05,0.00228,9e-05,0.00235,0.0,5e-05,5e-05,0.0001,0.00013,0.00013,1e-05,0.00021,2e-05,0.00064,0.0,0.0,0.00014,0.00038,0.00011,0.00066,0.00011,0.00014,0.00014,0.00026,0.00066,0.00011,0.00013,0.00015,0.00027
-2123,1.0,1e-05,1e-05,1e-05,1e-05,0.00097,0.00079,1e-05,0.00013,1e-05,7e-05,0.0,2e-05,2e-05,5e-05,8e-05,2e-05,1e-05,5e-05,1e-05,0.0007,1e-05,0.0,0.00017,0.00042,0.0001,0.00052,6e-05,8e-05,0.00012,0.00026,0.00049,6e-05,0.00011,9e-05,0.00023
-248,1.0,1e-05,1e-05,1e-05,1e-05,0.00088,0.00077,1e-05,0.0001,1e-05,4e-05,0.0,1e-05,1e-05,4e-05,5e-05,2e-05,1e-05,4e-05,1e-05,0.00041,1e-05,0.0,9e-05,0.00022,9e-05,0.00046,6e-05,8e-05,9e-05,0.00023,0.00049,7e-05,0.0001,0.0001,0.00021
-75101,1.0,1e-05,1e-05,1e-05,1e-05,0.0418,0.03546,1e-05,0.00389,1e-05,0.00248,0.0,2e-05,2e-05,4e-05,6e-05,2e-05,1e-05,5e-05,2e-05,0.00045,1e-05,0.0,0.00011,0.00023,0.0001,0.00041,6e-05,8e-05,0.0001,0.00018,0.00047,6e-05,0.0001,8e-05,0.00023
-75191,1.0,1e-05,1e-05,3e-05,1e-05,0.53436,0.40777,2e-05,0.09655,3e-05,0.0301,1e-05,2e-05,3e-05,5e-05,0.00011,7e-05,1e-05,0.00011,3e-05,0.00084,5e-05,7e-05,0.00017,0.00033,0.00023,0.00088,0.00012,0.00017,0.00032,0.00026,0.00068,9e-05,0.00012,0.00013,0.00035
-75187,1.0,1e-05,1e-05,2e-05,1e-05,0.00548,0.00473,1e-05,0.0005,2e-05,0.00029,1e-05,2e-05,2e-05,7e-05,9e-05,3e-05,1e-05,6e-05,1e-05,0.00062,1e-05,1e-05,0.00014,0.00033,0.00013,0.00038,7e-05,7e-05,8e-05,0.00016,0.00047,6e-05,9e-05,9e-05,0.00023
-266,1.0,1e-05,1e-05,1e-05,1e-05,0.0018,0.00146,1e-05,0.00023,2e-05,0.00015,2e-05,2e-05,2e-05,6e-05,8e-05,3e-05,1e-05,6e-05,1e-05,0.00059,1e-05,1e-05,0.00014,0.00031,0.00013,0.0004,5e-05,7e-05,8e-05,0.0002,0.00044,8e-05,8e-05,8e-05,0.0002
-253,1.0,1e-05,1e-05,1e-05,1e-05,0.00082,0.00063,1e-05,0.00016,2e-05,5e-05,0.0,1e-05,1e-05,4e-05,5e-05,2e-05,1e-05,5e-05,2e-05,0.00046,1e-05,0.0,9e-05,0.00024,0.00012,0.00044,6e-05,7e-05,8e-05,0.00023,0.00043,6e-05,0.0001,8e-05,0.00019
-75217,1.0,1e-05,1e-05,2e-05,1e-05,0.00196,0.00158,1e-05,0.00026,2e-05,0.00016,1e-05,3e-05,3e-05,7e-05,0.0001,3e-05,1e-05,7e-05,2e-05,0.00089,1e-05,1e-05,0.00022,0.00053,0.00013,0.00074,9e-05,0.0001,0.00019,0.00036,0.00065,0.00013,0.0001,0.00011,0.00031
-75176,1.0,1e-05,1e-05,2e-05,1e-05,0.01406,0.01285,1e-05,0.00094,2e-05,0.00031,1e-05,3e-05,3e-05,6e-05,9e-05,3e-05,1e-05,7e-05,2e-05,0.00069,1e-05,1e-05,0.00017,0.00035,0.00015,0.00085,0.00018,0.00011,0.0002,0.00036,0.0011,0.00022,0.00023,0.00023,0.00042
-2117,1.0,1e-05,1e-05,1e-05,1e-05,0.01735,0.0154,1e-05,0.00141,1e-05,0.00057,0.0,1e-05,1e-05,3e-05,6e-05,2e-05,1e-05,5e-05,1e-05,0.0004,1e-05,0.0,8e-05,0.00021,0.0001,0.00065,0.00012,0.00011,0.00015,0.00026,0.00055,7e-05,0.00014,9e-05,0.00025
-75148,1.0,1e-05,1e-05,2e-05,2e-05,0.00129,0.00115,1e-05,0.00012,1e-05,4e-05,0.0,2e-05,2e-05,4e-05,5e-05,2e-05,1e-05,4e-05,1e-05,0.0004,1e-05,0.0,9e-05,0.00021,9e-05,0.00036,7e-05,7e-05,8e-05,0.00015,0.00041,6e-05,8e-05,8e-05,0.00019
-75103,1.0,1e-05,1e-05,1e-05,1e-05,0.01302,0.00819,3e-05,0.00234,3e-05,0.00255,0.0,2e-05,2e-05,6e-05,0.00012,5e-05,1e-05,0.00011,1e-05,0.0005,1e-05,0.0,9e-05,0.00029,0.00011,0.00054,8e-05,0.00011,0.00011,0.00024,0.00066,7e-05,0.00015,0.00011,0.00032
-75168,1.0,1e-05,0.0,1e-05,1e-05,0.02353,0.00601,0.00016,0.01754,0.00018,0.00031,0.0,0.00013,0.00013,0.00027,0.0003,0.00032,1e-05,0.00042,1e-05,0.0004,1e-05,0.0,8e-05,0.00021,0.0001,0.0011,0.00022,0.00025,0.00026,0.00037,0.00124,0.00022,0.00025,0.00028,0.00049
-75157,1.0,1e-05,1e-05,1e-05,1e-05,0.00125,0.00106,1e-05,0.00015,2e-05,7e-05,0.0,2e-05,2e-05,5e-05,6e-05,2e-05,1e-05,4e-05,1e-05,0.00048,2e-05,0.0,0.0001,0.00026,0.0001,0.00039,6e-05,7e-05,8e-05,0.00019,0.00043,6e-05,9e-05,8e-05,0.0002
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-75132,1.0,1e-05,0.0,2e-05,1e-05,0.45642,0.41356,1e-05,0.03273,1e-05,0.01015,0.0,1e-05,1e-05,3e-05,6e-05,2e-05,1e-05,6e-05,2e-05,0.00049,1e-05,0.0,0.00012,0.00023,0.00013,0.00046,7e-05,8e-05,8e-05,0.00023,0.00053,6e-05,0.0001,9e-05,0.00028
-75212,1.0,1e-05,1e-05,1e-05,1e-05,0.0008,0.00059,1e-05,0.00015,1e-05,9e-05,0.0,2e-05,1e-05,3e-05,5e-05,2e-05,1e-05,4e-05,1e-05,0.00037,0.0,0.0,8e-05,0.00019,9e-05,0.00055,8e-05,0.00012,0.00011,0.00024,0.00047,6e-05,0.0001,9e-05,0.00022
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diff --git a/autosklearn/metalearning/files/recall_micro_multiclass.classification_dense/feature_runstatus.arff b/autosklearn/metalearning/files/recall_micro_multiclass.classification_dense/feature_runstatus.arff
index 0034ad2bd6..5fff2dfed3 100644
--- a/autosklearn/metalearning/files/recall_micro_multiclass.classification_dense/feature_runstatus.arff
+++ b/autosklearn/metalearning/files/recall_micro_multiclass.classification_dense/feature_runstatus.arff
@@ -4,6 +4,7 @@
 @ATTRIBUTE repetition NUMERIC
 @ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other}
@@ -21,6 +22,11 @@
 @ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other}
@@ -37,162 +43,138 @@
 @ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other}
 
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diff --git a/autosklearn/metalearning/files/recall_micro_multiclass.classification_dense/feature_values.arff b/autosklearn/metalearning/files/recall_micro_multiclass.classification_dense/feature_values.arff
index a60400e3ce..736dc64321 100644
--- a/autosklearn/metalearning/files/recall_micro_multiclass.classification_dense/feature_values.arff
+++ b/autosklearn/metalearning/files/recall_micro_multiclass.classification_dense/feature_values.arff
@@ -13,7 +13,6 @@
 @ATTRIBUTE KurtosisMean NUMERIC
 @ATTRIBUTE KurtosisMin NUMERIC
 @ATTRIBUTE KurtosisSTD NUMERIC
-@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC
 @ATTRIBUTE LogDatasetRatio NUMERIC
 @ATTRIBUTE LogInverseDatasetRatio NUMERIC
 @ATTRIBUTE LogNumberOfFeatures NUMERIC
@@ -42,160 +41,135 @@
 @ATTRIBUTE SymbolsSum NUMERIC
 
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diff --git a/autosklearn/metalearning/files/recall_micro_multiclass.classification_sparse/algorithm_runs.arff b/autosklearn/metalearning/files/recall_micro_multiclass.classification_sparse/algorithm_runs.arff
index a4e95a3447..b935e142cb 100644
--- a/autosklearn/metalearning/files/recall_micro_multiclass.classification_sparse/algorithm_runs.arff
+++ b/autosklearn/metalearning/files/recall_micro_multiclass.classification_sparse/algorithm_runs.arff
@@ -7,95 +7,133 @@
 @ATTRIBUTE runstatus {ok, timeout, memout, not_applicable, crash, other}
 
 @DATA
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diff --git a/autosklearn/metalearning/files/recall_micro_multiclass.classification_sparse/configurations.csv b/autosklearn/metalearning/files/recall_micro_multiclass.classification_sparse/configurations.csv
index 75662496d2..36fd991e13 100644
--- a/autosklearn/metalearning/files/recall_micro_multiclass.classification_sparse/configurations.csv
+++ b/autosklearn/metalearning/files/recall_micro_multiclass.classification_sparse/configurations.csv
@@ -1,93 +1,131 @@
 idx,balancing:strategy,classifier:__choice__,classifier:adaboost:algorithm,classifier:adaboost:learning_rate,classifier:adaboost:max_depth,classifier:adaboost:n_estimators,classifier:bernoulli_nb:alpha,classifier:bernoulli_nb:fit_prior,classifier:decision_tree:criterion,classifier:decision_tree:max_depth_factor,classifier:decision_tree:max_features,classifier:decision_tree:max_leaf_nodes,classifier:decision_tree:min_impurity_decrease,classifier:decision_tree:min_samples_leaf,classifier:decision_tree:min_samples_split,classifier:decision_tree:min_weight_fraction_leaf,classifier:extra_trees:bootstrap,classifier:extra_trees:criterion,classifier:extra_trees:max_depth,classifier:extra_trees:max_features,classifier:extra_trees:max_leaf_nodes,classifier:extra_trees:min_impurity_decrease,classifier:extra_trees:min_samples_leaf,classifier:extra_trees:min_samples_split,classifier:extra_trees:min_weight_fraction_leaf,classifier:gradient_boosting:early_stop,classifier:gradient_boosting:l2_regularization,classifier:gradient_boosting:learning_rate,classifier:gradient_boosting:loss,classifier:gradient_boosting:max_bins,classifier:gradient_boosting:max_depth,classifier:gradient_boosting:max_leaf_nodes,classifier:gradient_boosting:min_samples_leaf,classifier:gradient_boosting:n_iter_no_change,classifier:gradient_boosting:scoring,classifier:gradient_boosting:tol,classifier:gradient_boosting:validation_fraction,classifier:k_nearest_neighbors:n_neighbors,classifier:k_nearest_neighbors:p,classifier:k_nearest_neighbors:weights,classifier:lda:n_components,classifier:lda:shrinkage,classifier:lda:shrinkage_factor,classifier:lda:tol,classifier:liblinear_svc:C,classifier:liblinear_svc:dual,classifier:liblinear_svc:fit_intercept,classifier:liblinear_svc:intercept_scaling,classifier:liblinear_svc:loss,classifier:liblinear_svc:multi_class,classifier:liblinear_svc:penalty,classifier:liblinear_svc:tol,classifier:libsvm_svc:C,classifier:libsvm_svc:coef0,classifier:libsvm_svc:degree,classifier:libsvm_svc:gamma,classifier:libsvm_svc:kernel,classifier:libsvm_svc:max_iter,classifier:libsvm_svc:shrinking,classifier:libsvm_svc:tol,classifier:multinomial_nb:alpha,classifier:multinomial_nb:fit_prior,classifier:passive_aggressive:C,classifier:passive_aggressive:average,classifier:passive_aggressive:fit_intercept,classifier:passive_aggressive:loss,classifier:passive_aggressive:tol,classifier:qda:reg_param,classifier:random_forest:bootstrap,classifier:random_forest:criterion,classifier:random_forest:max_depth,classifier:random_forest:max_features,classifier:random_forest:max_leaf_nodes,classifier:random_forest:min_impurity_decrease,classifier:random_forest:min_samples_leaf,classifier:random_forest:min_samples_split,classifier:random_forest:min_weight_fraction_leaf,classifier:sgd:alpha,classifier:sgd:average,classifier:sgd:epsilon,classifier:sgd:eta0,classifier:sgd:fit_intercept,classifier:sgd:l1_ratio,classifier:sgd:learning_rate,classifier:sgd:loss,classifier:sgd:penalty,classifier:sgd:power_t,classifier:sgd:tol,data_preprocessing:categorical_transformer:categorical_encoding:__choice__,data_preprocessing:categorical_transformer:category_coalescence:__choice__,data_preprocessing:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction,data_preprocessing:numerical_transformer:imputation:strategy,data_preprocessing:numerical_transformer:rescaling:__choice__,data_preprocessing:numerical_transformer:rescaling:quantile_transformer:n_quantiles,data_preprocessing:numerical_transformer:rescaling:quantile_transformer:output_distribution,data_preprocessing:numerical_transformer:rescaling:robust_scaler:q_max,data_preprocessing:numerical_transformer:rescaling:robust_scaler:q_min,feature_preprocessor:__choice__,feature_preprocessor:extra_trees_preproc_for_classification:bootstrap,feature_preprocessor:extra_trees_preproc_for_classification:criterion,feature_preprocessor:extra_trees_preproc_for_classification:max_depth,feature_preprocessor:extra_trees_preproc_for_classification:max_features,feature_preprocessor:extra_trees_preproc_for_classification:max_leaf_nodes,feature_preprocessor:extra_trees_preproc_for_classification:min_impurity_decrease,feature_preprocessor:extra_trees_preproc_for_classification:min_samples_leaf,feature_preprocessor:extra_trees_preproc_for_classification:min_samples_split,feature_preprocessor:extra_trees_preproc_for_classification:min_weight_fraction_leaf,feature_preprocessor:extra_trees_preproc_for_classification:n_estimators,feature_preprocessor:kernel_pca:coef0,feature_preprocessor:kernel_pca:degree,feature_preprocessor:kernel_pca:gamma,feature_preprocessor:kernel_pca:kernel,feature_preprocessor:kernel_pca:n_components,feature_preprocessor:kitchen_sinks:gamma,feature_preprocessor:kitchen_sinks:n_components,feature_preprocessor:liblinear_svc_preprocessor:C,feature_preprocessor:liblinear_svc_preprocessor:dual,feature_preprocessor:liblinear_svc_preprocessor:fit_intercept,feature_preprocessor:liblinear_svc_preprocess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-13,none,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,200,None,,0.0017457216340699586,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,none,,,,,kernel_pca,,,,,,,,,,,0.0,4,0.0007494648939847078,poly,1996,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-14,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.6039096182414764,None,0.0,2,20,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.1939609823704447,most_frequent,robust_scaler,,,0.7331327008874781,0.29747488162877694,extra_trees_preproc_for_classification,True,entropy,None,0.33401603690569853,None,0.0,4,4,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-15,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7865652301288171,None,0.0,1,4,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,median,robust_scaler,,,0.8623635641193496,0.13298297278730625,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-16,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.905401147837377,-0.32832122529048147,3,1.001896309040499,poly,-1,False,5.1523484258030785e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-17,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.3304182089730454,None,0.0,1,11,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.015634546384927752,median,quantile_transformer,332,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-18,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5010952533165973,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.005838375247601407,mean,normalize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,,
-19,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.7297122444166401,False,True,1,squared_hinge,ovr,l2,0.00023416745444541063,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.03941815152197094,most_frequent,normalize,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0973649546833947,fpr,chi2,
-20,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.4426097579931897,None,0.0,16,4,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,mean,robust_scaler,,,0.7407323456920554,0.0804756921037867,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,,
-21,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5607168729746175,None,0.0,2,3,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.009810589644392154,most_frequent,quantile_transformer,1132,normal,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.06211089008167909,fdr,chi2,
-22,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9333237919291875,None,0.0,8,3,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,,
-23,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.557321265077371,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-24,none,adaboost,SAMME,0.6255555220658239,6,446,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.008833097306364968,most_frequent,none,,,,,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,10,None,4,17,1.0,76,,,,,,
-25,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.7772914874049878,None,0.0,10,15,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00015285482513309234,mean,robust_scaler,,,0.8373404000074934,0.14303151080827242,extra_trees_preproc_for_classification,True,entropy,None,0.5021713909357703,None,0.0,13,16,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-26,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7422641456052917,None,0.0,8,15,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.005561351266169067,most_frequent,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-27,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.887898394612674,None,0.0,10,19,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,robust_scaler,,,0.8350698288864961,0.2347163538066168,extra_trees_preproc_for_classification,False,entropy,None,0.49795108918064074,None,0.0,13,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-28,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7088164344533493,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,mean,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,,
-29,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,10,1,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0022606253047811435,median,quantile_transformer,1247,normal,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,25.327793585266683,chi2,,,,
-30,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,24.786439230569382,,,0.03411197494635044,rbf,-1,True,0.003003351499585181,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0007994838629325874,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-31,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2681.998807317996,-0.9558745731731211,,0.0011299948421773457,sigmoid,-1,False,1.5292507354478086e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-32,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.8095522998548572,None,0.0,17,10,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.031940595071539304,mean,quantile_transformer,1563,normal,,,extra_trees_preproc_for_classification,False,entropy,None,0.9330145598603196,None,0.0,18,10,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-33,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,26.502259985828186,,,9.475662388405154e-05,rbf,-1,False,0.00019690635968217866,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-34,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.339600708038189,False,True,1,squared_hinge,ovr,l2,9.572641158475304e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,quantile_transformer,1193,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-35,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5255991794296913,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-36,weighting,decision_tree,,,,,,,gini,1.7853447328017091,1.0,None,0.0,13,7,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0001265964225445088,median,normalize,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.26399878867042303,,mutual_info_classif,
-37,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.7610308078756552,None,0.0,1,2,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.010000000000000004,mean,robust_scaler,,,0.9205545465103644,0.20272761060743316,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-38,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,249.12499213127998,,,0.0005283374995918348,rbf,-1,False,0.0016932707039261864,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.48910068367909115,most_frequent,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-39,none,bernoulli_nb,,,,,0.6524090653939782,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.005164169126808402,most_frequent,quantile_transformer,16,normal,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.033082145443631535,False,True,1,squared_hinge,ovr,l1,0.002615020898325393,,,,,,,,,,,,,,,,,,,,,
-40,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.6453555269772124,None,0.0,1,4,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,normalize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-41,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.20911875507660596,None,0.0,12,8,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,median,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,,
-42,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5138705053438934,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.14411802159506462,most_frequent,quantile_transformer,857,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-43,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,29.55731828912983,False,True,1,squared_hinge,ovr,l2,0.0011332486951954968,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,,
-44,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.37801720673725603,True,True,squared_hinge,0.0001255132393635067,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.025993178344125793,most_frequent,standardize,,,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,1.4421737618429475,False,True,1,squared_hinge,ovr,l1,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,
-45,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.10581257327556097,None,0.0,7,12,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.004219392676189224,median,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,,
-46,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.4188764793561401,None,0.0,5,5,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.010589444903605501,mean,robust_scaler,,,0.9796997860990453,0.27262832749694405,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-47,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.005753626505113041,False,True,squared_hinge,0.00924401505172238,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.3192271696445429,median,quantile_transformer,913,normal,,,kernel_pca,,,,,,,,,,,,,,cosine,1963,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-48,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.41019363453549706,None,0.0,15,2,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.0003507389224306018,median,none,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.20614098085343638,fdr,chi2,
-49,none,adaboost,SAMME,0.1280458968526125,7,125,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,robust_scaler,,,0.7156460334356771,0.2981902862926971,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,65.16171885308417,chi2,,,,
-50,none,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9931779675565967,None,0.0,18,17,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,robust_scaler,,,0.75,0.2945917010472818,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,,
-51,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.08646118576686276,None,0.0,4,9,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,most_frequent,quantile_transformer,959,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-52,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.6271213423927893,None,0.0,1,4,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.009403509495453675,mean,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,,
-53,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8.85547546146731,True,True,squared_hinge,4.0111304608748804e-05,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-54,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.34936025988397124,None,0.0,2,13,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00018601309647406987,median,robust_scaler,,,0.8634309348843472,0.26976989465367607,extra_trees_preproc_for_classification,False,entropy,None,0.48025175288975036,None,0.0,6,17,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-55,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.8793175987664956,None,0.0,10,5,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,robust_scaler,,,0.7645748130904642,0.2677156877155214,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,130.79089036625916,False,True,1,squared_hinge,ovr,l1,0.005538878421721295,,,,,,,,,,,,,,,,,,,,,
-56,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.014911374426433241,None,0.0,6,2,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,,
-57,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0016941428709087083,True,,,True,,optimal,squared_hinge,l1,,5.124265599423001e-05,no_encoding,no_coalescense,,most_frequent,quantile_transformer,461,uniform,,,extra_trees_preproc_for_classification,True,gini,None,0.31371431180257103,None,0.0,5,17,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-58,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.7591396576940418,-0.02447104417007273,2,0.7325180366342191,poly,-1,True,0.00045922794962860523,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,standardize,,,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,2.057731506094293,False,True,1,squared_hinge,ovr,l1,0.03575998247131604,,,,,,,,,,,,,,,,,,,,,
-59,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.593728171200453,None,0.0,7,20,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.02830941310359363,mean,normalize,,,,,extra_trees_preproc_for_classification,False,entropy,None,0.7389446586281172,None,0.0,20,11,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-60,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5542793596955801,None,0.0,1,3,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,most_frequent,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-61,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.46057831591617715,False,True,hinge,0.04557857428827514,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00027457445401600137,median,standardize,,,,,extra_trees_preproc_for_classification,True,gini,None,0.48190346970486964,None,0.0,17,18,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-62,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.46057831591617715,False,True,hinge,0.04557857428827514,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00027457445401600137,median,standardize,,,,,extra_trees_preproc_for_classification,True,gini,None,0.48190346970486964,None,0.0,17,18,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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+367,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.938372431886818e-05,True,True,hinge,0.0016143408148969647,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00024949978971758427,median,quantile_transformer,754,uniform,,,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,-0.22339839702350717,2,0.5718982387915436,poly,5260,,,,,,,,,,,,,,,,
+371,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4,1,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.01020427226149318,median,none,,,,,extra_trees_preproc_for_classification,True,entropy,None,0.3663460889081009,None,0.0,2,7,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
diff --git a/autosklearn/metalearning/files/recall_micro_multiclass.classification_sparse/description.txt b/autosklearn/metalearning/files/recall_micro_multiclass.classification_sparse/description.txt
index fb41d56060..1dffcd8886 100644
--- a/autosklearn/metalearning/files/recall_micro_multiclass.classification_sparse/description.txt
+++ b/autosklearn/metalearning/files/recall_micro_multiclass.classification_sparse/description.txt
@@ -1,8 +1,9 @@
 features_cutoff_time: 3600
 features_cutoff_memory: 3072
-number_of_feature_steps: 35
+number_of_feature_steps: 42
 feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances
 feature_step LogNumberOfInstances: LogNumberOfInstances
+feature_step NumberOfClasses: NumberOfClasses
 feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures
 feature_step LogNumberOfFeatures: LogNumberOfFeatures
 feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues
@@ -20,6 +21,11 @@ feature_step DatasetRatio: DatasetRatio, LogDatasetRatio
 feature_step LogDatasetRatio: LogDatasetRatio
 feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio
 feature_step LogInverseDatasetRatio: LogInverseDatasetRatio
+feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD
+feature_step ClassProbabilityMin: ClassProbabilityMin
+feature_step ClassProbabilityMax: ClassProbabilityMax
+feature_step ClassProbabilityMean: ClassProbabilityMean
+feature_step ClassProbabilitySTD: ClassProbabilitySTD
 feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum
 feature_step SymbolsMin: SymbolsMin
 feature_step SymbolsMax: SymbolsMax
@@ -36,11 +42,12 @@ feature_step SkewnessMin: SkewnessMin
 feature_step SkewnessMax: SkewnessMax
 feature_step SkewnessMean: SkewnessMean
 feature_step SkewnessSTD: SkewnessSTD
-features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
+feature_step ClassEntropy: ClassEntropy
+features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy
 features_stochastic: 
-default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
+default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy
 
-algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92
+algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235,236,237,238,239,240,241,242,243,244,245,246,247,248,249,250,251,252,253,254,255,256,257,258,259,260,261,262,263,264,265,266,267,268,269,270,271,272,273,274,275,276,277,278,279,280,281,282,283,284,285,286,287,288,289,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,312,313,314,315,316,317,318,319,320,321,322,323,324,325,326,327,328,329,330,331,332,333,334,335,336,337,338,339,340,341,342,343,344,345,346,347,348,349,350,351,352,353,354,355,356,357,358,359,360,361,362,363,364,365,366,367,368,369,370,371,372
 algorithms_stochastic: 
 performance_measures: recall_micro
 performance_type: solution_quality
diff --git a/autosklearn/metalearning/files/recall_micro_multiclass.classification_sparse/feature_costs.arff b/autosklearn/metalearning/files/recall_micro_multiclass.classification_sparse/feature_costs.arff
index 0e17e20d22..8512fd9275 100644
--- a/autosklearn/metalearning/files/recall_micro_multiclass.classification_sparse/feature_costs.arff
+++ b/autosklearn/metalearning/files/recall_micro_multiclass.classification_sparse/feature_costs.arff
@@ -4,6 +4,7 @@
 @ATTRIBUTE repetition NUMERIC
 @ATTRIBUTE NumberOfInstances NUMERIC
 @ATTRIBUTE LogNumberOfInstances NUMERIC
+@ATTRIBUTE NumberOfClasses NUMERIC
 @ATTRIBUTE NumberOfFeatures NUMERIC
 @ATTRIBUTE LogNumberOfFeatures NUMERIC
 @ATTRIBUTE MissingValues NUMERIC
@@ -21,6 +22,11 @@
 @ATTRIBUTE LogDatasetRatio NUMERIC
 @ATTRIBUTE InverseDatasetRatio NUMERIC
 @ATTRIBUTE LogInverseDatasetRatio NUMERIC
+@ATTRIBUTE ClassOccurences NUMERIC
+@ATTRIBUTE ClassProbabilityMin NUMERIC
+@ATTRIBUTE ClassProbabilityMax NUMERIC
+@ATTRIBUTE ClassProbabilityMean NUMERIC
+@ATTRIBUTE ClassProbabilitySTD NUMERIC
 @ATTRIBUTE NumSymbols NUMERIC
 @ATTRIBUTE SymbolsMin NUMERIC
 @ATTRIBUTE SymbolsMax NUMERIC
@@ -37,162 +43,138 @@
 @ATTRIBUTE SkewnessMax NUMERIC
 @ATTRIBUTE SkewnessMean NUMERIC
 @ATTRIBUTE SkewnessSTD NUMERIC
+@ATTRIBUTE ClassEntropy NUMERIC
 
 @DATA
-75123,1.0,1e-05,1e-05,1e-05,1e-05,0.00253,0.00221,1e-05,0.00024,3e-05,0.00011,0.0,2e-05,2e-05,5e-05,8e-05,2e-05,1e-05,7e-05,1e-05,0.00072,1e-05,1e-05,0.00019,0.00041,0.00011,0.00061,0.00012,9e-05,0.00015,0.00025,0.00067,0.00014,0.00011,0.00016,0.00027
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diff --git a/autosklearn/metalearning/files/recall_micro_multiclass.classification_sparse/feature_runstatus.arff b/autosklearn/metalearning/files/recall_micro_multiclass.classification_sparse/feature_runstatus.arff
index 0034ad2bd6..5fff2dfed3 100644
--- a/autosklearn/metalearning/files/recall_micro_multiclass.classification_sparse/feature_runstatus.arff
+++ b/autosklearn/metalearning/files/recall_micro_multiclass.classification_sparse/feature_runstatus.arff
@@ -4,6 +4,7 @@
 @ATTRIBUTE repetition NUMERIC
 @ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other}
@@ -21,6 +22,11 @@
 @ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other}
@@ -37,162 +43,138 @@
 @ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other}
 
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diff --git a/autosklearn/metalearning/files/recall_micro_multiclass.classification_sparse/feature_values.arff b/autosklearn/metalearning/files/recall_micro_multiclass.classification_sparse/feature_values.arff
index a60400e3ce..736dc64321 100644
--- a/autosklearn/metalearning/files/recall_micro_multiclass.classification_sparse/feature_values.arff
+++ b/autosklearn/metalearning/files/recall_micro_multiclass.classification_sparse/feature_values.arff
@@ -13,7 +13,6 @@
 @ATTRIBUTE KurtosisMean NUMERIC
 @ATTRIBUTE KurtosisMin NUMERIC
 @ATTRIBUTE KurtosisSTD NUMERIC
-@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC
 @ATTRIBUTE LogDatasetRatio NUMERIC
 @ATTRIBUTE LogInverseDatasetRatio NUMERIC
 @ATTRIBUTE LogNumberOfFeatures NUMERIC
@@ -42,160 +41,135 @@
 @ATTRIBUTE SymbolsSum NUMERIC
 
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diff --git a/autosklearn/metalearning/files/recall_multiclass.classification_dense/algorithm_runs.arff b/autosklearn/metalearning/files/recall_multiclass.classification_dense/algorithm_runs.arff
index 88c7bbfadc..f64886d029 100644
--- a/autosklearn/metalearning/files/recall_multiclass.classification_dense/algorithm_runs.arff
+++ b/autosklearn/metalearning/files/recall_multiclass.classification_dense/algorithm_runs.arff
@@ -7,130 +7,135 @@
 @ATTRIBUTE runstatus {ok, timeout, memout, not_applicable, crash, other}
 
 @DATA
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-75212,1.0,2,0.21658986175115202,ok
-252,1.0,3,?,not_applicable
-246,1.0,4,?,not_applicable
-75178,1.0,5,?,not_applicable
-75239,1.0,6,0.0,ok
-75171,1.0,7,0.15085884988797615,ok
-75233,1.0,8,0.03620873269435565,ok
-248,1.0,9,?,not_applicable
-75231,1.0,10,?,not_applicable
-2123,1.0,11,?,not_applicable
-75196,1.0,12,0.0,ok
-75188,1.0,13,?,not_applicable
-75092,1.0,14,0.019230769230769273,ok
-75248,1.0,15,0.0,ok
-75126,1.0,16,0.02460850111856827,ok
-75234,1.0,17,0.016393442622950838,ok
-75150,1.0,18,0.138728323699422,ok
-258,1.0,19,?,not_applicable
-75168,1.0,20,?,not_applicable
-75235,1.0,21,?,not_applicable
-75159,1.0,22,0.1785714285714286,ok
-244,1.0,23,?,not_applicable
-75221,1.0,24,?,not_applicable
-75219,1.0,25,0.05558045719408333,ok
-75202,1.0,26,?,not_applicable
-3043,1.0,27,0.060975609756097615,ok
-75205,1.0,28,?,not_applicable
-75174,1.0,29,0.13559322033898302,ok
-288,1.0,30,?,not_applicable
-75250,1.0,31,?,not_applicable
-275,1.0,32,?,not_applicable
-75142,1.0,33,0.06396808982124391,ok
-75213,1.0,34,0.08139534883720934,ok
-75099,1.0,35,0.2038834951456311,ok
-75243,1.0,36,?,not_applicable
-75184,1.0,37,0.21055684454756385,ok
-75222,1.0,38,0.0714285714285714,ok
-75175,1.0,39,0.13841201716738194,ok
-233,1.0,40,0.0020408163265306367,ok
-75161,1.0,41,0.0625275856995734,ok
-75176,1.0,42,0.01466352448284891,ok
-75090,1.0,43,?,not_applicable
-75114,1.0,44,0.015037593984962405,ok
-260,1.0,45,?,not_applicable
-236,1.0,46,?,not_applicable
-75141,1.0,47,0.038532110091743066,ok
-75107,1.0,48,0.3553054662379421,ok
-262,1.0,49,?,not_applicable
-75146,1.0,50,0.09509433962264147,ok
-75189,1.0,51,0.0166136443973135,ok
-2350,1.0,52,0.3952563751028242,ok
-253,1.0,53,?,not_applicable
-2122,1.0,54,?,not_applicable
-75110,1.0,55,?,not_applicable
-75249,1.0,56,0.010204081632653073,ok
-75108,1.0,57,0.0,ok
-242,1.0,58,?,not_applicable
-75117,1.0,59,0.042735042735042694,ok
-75191,1.0,60,0.1738918060813286,ok
-75226,1.0,61,0.0,ok
-261,1.0,62,0.23232323232323238,ok
-75236,1.0,63,?,not_applicable
-75095,1.0,64,0.048192771084337394,ok
-75148,1.0,65,0.1083172147001934,ok
-75093,1.0,66,0.2784431137724551,ok
-75223,1.0,67,?,not_applicable
-75244,1.0,68,0.07179487179487176,ok
-75109,1.0,69,?,not_applicable
-75197,1.0,70,?,not_applicable
-75127,1.0,71,0.37921695094605834,ok
-75143,1.0,72,0.007936507936507908,ok
-75153,1.0,73,0.08563134978229314,ok
-75173,1.0,74,0.12,ok
-75215,1.0,75,0.01603498542274051,ok
-75195,1.0,76,0.00025025025025027237,ok
-75207,1.0,77,?,not_applicable
-266,1.0,78,?,not_applicable
-75225,1.0,79,0.125,ok
-75166,1.0,80,0.08666164280331579,ok
-75100,1.0,81,0.2857142857142857,ok
-75169,1.0,82,?,not_applicable
-75132,1.0,83,0.24929946669077108,ok
-273,1.0,84,0.05254237288135588,ok
-75121,1.0,85,0.0,ok
-75098,1.0,86,?,not_applicable
-75115,1.0,87,0.010683760683760646,ok
-75116,1.0,88,0.007025761124121788,ok
-75185,1.0,89,0.1368015414258189,ok
-2119,1.0,90,?,not_applicable
-75157,1.0,91,0.5691823899371069,ok
-75113,1.0,92,0.0,ok
-75134,1.0,93,?,not_applicable
-75096,1.0,94,?,not_applicable
-75203,1.0,95,?,not_applicable
-75182,1.0,96,0.16078790655061836,ok
-251,1.0,97,?,not_applicable
-75123,1.0,98,?,not_applicable
-75237,1.0,99,0.00038952944842629567,ok
-75125,1.0,100,0.012048192771084376,ok
-75232,1.0,101,0.10084033613445376,ok
-75103,1.0,102,0.0,ok
-75192,1.0,103,0.5179407176287052,ok
-75230,1.0,104,?,not_applicable
-75139,1.0,105,0.012232415902140636,ok
-75227,1.0,106,0.1724137931034483,ok
-2120,1.0,107,?,not_applicable
-75124,1.0,108,0.14569536423841056,ok
-75240,1.0,109,0.0,ok
-75129,1.0,110,0.17307692307692313,ok
-75198,1.0,111,?,not_applicable
-75201,1.0,112,?,not_applicable
-75112,1.0,113,0.21071919377004122,ok
-75133,1.0,114,0.18181818181818177,ok
-75105,1.0,115,0.2240802675585284,ok
-75154,1.0,116,?,not_applicable
-75177,1.0,117,0.060975609756097615,ok
-2117,1.0,118,0.16868753072259546,ok
-75156,1.0,119,0.17359050445103863,ok
-75097,1.0,120,0.009909733124018882,ok
-75101,1.0,121,0.24457291175082585,ok
-75172,1.0,122,?,not_applicable
-75106,1.0,123,0.07815126050420174,ok
-75179,1.0,124,0.1838509316770186,ok
-75187,1.0,125,0.02010050251256279,ok
-75120,1.0,126,0.006134969325153339,ok
-75193,1.0,127,?,not_applicable
+75192,1.0,382,0.4290171606864275,ok
+75119,1.0,3,0.0,ok
+75139,1.0,5,0.01590214067278284,ok
+75212,1.0,71,0.2304147465437788,ok
+246,1.0,8,?,not_applicable
+252,1.0,9,?,not_applicable
+75178,1.0,10,?,not_applicable
+75177,1.0,425,0.060975609756097615,ok
+75092,1.0,38,0.019230769230769273,ok
+75239,1.0,15,0.0,ok
+75173,1.0,265,0.12,ok
+75215,1.0,359,0.015063168124392567,ok
+75171,1.0,19,0.15907393577296491,ok
+75112,1.0,23,0.2061383417315621,ok
+75227,1.0,190,0.1130268199233716,ok
+75233,1.0,102,0.028221512247071368,ok
+75182,1.0,26,0.15116811726981216,ok
+253,1.0,27,?,not_applicable
+75157,1.0,87,0.3113207547169812,ok
+75187,1.0,267,0.018425460636515956,ok
+75124,1.0,240,0.1920529801324503,ok
+75090,1.0,32,?,not_applicable
+75222,1.0,183,0.0714285714285714,ok
+75231,1.0,34,?,not_applicable
+75185,1.0,325,0.1329479768786127,ok
+2123,1.0,39,?,not_applicable
+75150,1.0,41,0.138728323699422,ok
+75143,1.0,43,0.0009920634920634885,ok
+75196,1.0,44,0.0,ok
+75188,1.0,49,?,not_applicable
+75248,1.0,57,0.0,ok
+75249,1.0,58,0.020408163265306145,ok
+75113,1.0,59,0.0,ok
+75126,1.0,61,0.01118568232662187,ok
+288,1.0,64,?,not_applicable
+251,1.0,65,?,not_applicable
+75184,1.0,66,0.16125290023201855,ok
+75234,1.0,67,0.003278688524590123,ok
+258,1.0,73,?,not_applicable
+75166,1.0,283,0.05275056518462695,ok
+75168,1.0,77,?,not_applicable
+75148,1.0,276,0.09671179883945846,ok
+75235,1.0,81,?,not_applicable
+75159,1.0,229,0.1071428571428571,ok
+75146,1.0,88,0.09169811320754717,ok
+244,1.0,89,?,not_applicable
+75141,1.0,90,0.003669724770642202,ok
+75221,1.0,92,?,not_applicable
+75219,1.0,213,0.023307933662034985,ok
+75202,1.0,96,?,not_applicable
+3043,1.0,99,0.08536585365853655,ok
+75205,1.0,103,?,not_applicable
+75174,1.0,109,0.12230874942739345,ok
+75250,1.0,111,?,not_applicable
+75179,1.0,453,0.17639751552795035,ok
+275,1.0,115,?,not_applicable
+242,1.0,116,?,not_applicable
+75207,1.0,117,?,not_applicable
+75142,1.0,121,0.06426355443935594,ok
+75099,1.0,125,0.11650485436893199,ok
+75243,1.0,126,?,not_applicable
+75175,1.0,259,0.13590844062947072,ok
+233,1.0,134,0.0020408163265306367,ok
+75161,1.0,139,0.058408121229954424,ok
+75176,1.0,328,0.01335428122545168,ok
+262,1.0,146,?,not_applicable
+75129,1.0,147,0.15384615384615385,ok
+261,1.0,148,0.2727272727272727,ok
+75114,1.0,153,0.007518796992481258,ok
+75093,1.0,231,0.3338323353293413,ok
+260,1.0,158,?,not_applicable
+236,1.0,160,?,not_applicable
+254,1.0,166,0.0,ok
+75107,1.0,170,0.2821543408360129,ok
+75181,1.0,175,?,not_applicable
+75189,1.0,187,0.015934587263492728,ok
+75163,1.0,354,0.07427536231884058,ok
+2350,1.0,191,0.4111598574170551,ok
+2122,1.0,195,?,not_applicable
+75110,1.0,198,?,not_applicable
+75213,1.0,394,0.011627906976744207,ok
+75095,1.0,284,0.012048192771084376,ok
+75108,1.0,207,0.0,ok
+75117,1.0,211,0.0064102564102563875,ok
+75191,1.0,217,0.1774941995359629,ok
+75226,1.0,219,0.0,ok
+75244,1.0,241,0.05641025641025643,ok
+75236,1.0,223,?,not_applicable
+75169,1.0,227,?,not_applicable
+75116,1.0,228,0.004683840749414525,ok
+75223,1.0,235,?,not_applicable
+75109,1.0,242,?,not_applicable
+75197,1.0,245,?,not_applicable
+75237,1.0,365,0.0002960423808039403,ok
+248,1.0,250,?,not_applicable
+2119,1.0,254,?,not_applicable
+75127,1.0,256,0.37264115290620037,ok
+75153,1.0,263,0.0870827285921626,ok
+75195,1.0,274,0.00037537537537535304,ok
+266,1.0,277,?,not_applicable
+75225,1.0,332,0.04166666666666663,ok
+75100,1.0,446,0.2857142857142857,ok
+75132,1.0,296,0.2324414715719063,ok
+75210,1.0,298,0.0,ok
+273,1.0,302,0.05254237288135588,ok
+75133,1.0,414,0.2272727272727273,ok
+75121,1.0,304,0.0,ok
+75098,1.0,310,?,not_applicable
+75115,1.0,315,0.0,ok
+75217,1.0,337,?,not_applicable
+75134,1.0,341,?,not_applicable
+75096,1.0,346,?,not_applicable
+75203,1.0,350,?,not_applicable
+75123,1.0,360,?,not_applicable
+75125,1.0,370,0.0072289156626506035,ok
+2120,1.0,371,?,not_applicable
+75232,1.0,377,0.10084033613445376,ok
+75103,1.0,380,0.0,ok
+75230,1.0,386,?,not_applicable
+75240,1.0,398,0.0,ok
+75198,1.0,404,?,not_applicable
+75201,1.0,407,?,not_applicable
+75105,1.0,418,0.2642140468227425,ok
+75154,1.0,420,?,not_applicable
+2117,1.0,427,0.053580206455841384,ok
+75156,1.0,430,0.16913946587537088,ok
+75097,1.0,434,0.0,ok
+75101,1.0,438,0.24362907031618686,ok
+75172,1.0,443,?,not_applicable
+75106,1.0,451,0.07815126050420174,ok
+75120,1.0,458,0.002044989775051076,ok
+75193,1.0,461,?,not_applicable
diff --git a/autosklearn/metalearning/files/recall_multiclass.classification_dense/configurations.csv b/autosklearn/metalearning/files/recall_multiclass.classification_dense/configurations.csv
index 75473418b8..1fd9ea6eb0 100644
--- a/autosklearn/metalearning/files/recall_multiclass.classification_dense/configurations.csv
+++ b/autosklearn/metalearning/files/recall_multiclass.classification_dense/configurations.csv
@@ -1,128 +1,133 @@
 idx,balancing:strategy,classifier:__choice__,classifier:adaboost:algorithm,classifier:adaboost:learning_rate,classifier:adaboost:max_depth,classifier:adaboost:n_estimators,classifier:bernoulli_nb:alpha,classifier:bernoulli_nb:fit_prior,classifier:decision_tree:criterion,classifier:decision_tree:max_depth_factor,classifier:decision_tree:max_features,classifier:decision_tree:max_leaf_nodes,classifier:decision_tree:min_impurity_decrease,classifier:decision_tree:min_samples_leaf,classifier:decision_tree:min_samples_split,classifier:decision_tree:min_weight_fraction_leaf,classifier:extra_trees:bootstrap,classifier:extra_trees:criterion,classifier:extra_trees:max_depth,classifier:extra_trees:max_features,classifier:extra_trees:max_leaf_nodes,classifier:extra_trees:min_impurity_decrease,classifier:extra_trees:min_samples_leaf,classifier:extra_trees:min_samples_split,classifier:extra_trees:min_weight_fraction_leaf,classifier:gradient_boosting:early_stop,classifier:gradient_boosting:l2_regularization,classifier:gradient_boosting:learning_rate,classifier:gradient_boosting:loss,classifier:gradient_boosting:max_bins,classifier:gradient_boosting:max_depth,classifier:gradient_boosting:max_leaf_nodes,classifier:gradient_boosting:min_samples_leaf,classifier:gradient_boosting:n_iter_no_change,classifier:gradient_boosting:scoring,classifier:gradient_boosting:tol,classifier:gradient_boosting:validation_fraction,classifier:k_nearest_neighbors:n_neighbors,classifier:k_nearest_neighbors:p,classifier:k_nearest_neighbors:weights,classifier:lda:n_components,classifier:lda:shrinkage,classifier:lda:shrinkage_factor,classifier:lda:tol,classifier:liblinear_svc:C,classifier:liblinear_svc:dual,classifier:liblinear_svc:fit_intercept,classifier:liblinear_svc:intercept_scaling,classifier:liblinear_svc:loss,classifier:liblinear_svc:multi_class,classifier:liblinear_svc:penalty,classifier:liblinear_svc:tol,classifier:libsvm_svc:C,classifier:libsvm_svc:coef0,classifier:libsvm_svc:degree,classifier:libsvm_svc:gamma,classifier:libsvm_svc:kernel,classifier:libsvm_svc:max_iter,classifier:libsvm_svc:shrinking,classifier:libsvm_svc:tol,classifier:multinomial_nb:alpha,classifier:multinomial_nb:fit_prior,classifier:passive_aggressive:C,classifier:passive_aggressive:average,classifier:passive_aggressive:fit_intercept,classifier:passive_aggressive:loss,classifier:passive_aggressive:tol,classifier:qda:reg_param,classifier:random_forest:bootstrap,classifier:random_forest:criterion,classifier:random_forest:max_depth,classifier:random_forest:max_features,classifier:random_forest:max_leaf_nodes,classifier:random_forest:min_impurity_decrease,classifier:random_forest:min_samples_leaf,classifier:random_forest:min_samples_split,classifier:random_forest:min_weight_fraction_leaf,classifier:sgd:alpha,classifier:sgd:average,classifier:sgd:epsilon,classifier:sgd:eta0,classifier:sgd:fit_intercept,classifier:sgd:l1_ratio,classifier:sgd:learning_rate,classifier:sgd:loss,classifier:sgd:penalty,classifier:sgd:power_t,classifier:sgd:tol,data_preprocessing:categorical_transformer:categorical_encoding:__choice__,data_preprocessing:categorical_transformer:category_coalescence:__choice__,data_preprocessing:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction,data_preprocessing:numerical_transformer:imputation:strategy,data_preprocessing:numerical_transformer:rescaling:__choice__,data_preprocessing:numerical_transformer:rescaling:quantile_transformer:n_quantiles,data_preprocessing:numerical_transformer:rescaling:quantile_transformer:output_distribution,data_preprocessing:numerical_transformer:rescaling:robust_scaler:q_max,data_preprocessing:numerical_transformer:rescaling:robust_scaler:q_min,feature_preprocessor:__choice__,feature_preprocessor:extra_trees_preproc_for_classification:bootstrap,feature_preprocessor:extra_trees_preproc_for_classification:criterion,feature_preprocessor:extra_trees_preproc_for_classification:max_depth,feature_preprocessor:extra_trees_preproc_for_classification:max_features,feature_preprocessor:extra_trees_preproc_for_classification:max_leaf_nodes,feature_preprocessor:extra_trees_preproc_for_classification:min_impurity_decrease,feature_preprocessor:extra_trees_preproc_for_classification:min_samples_leaf,feature_preprocessor:extra_trees_preproc_for_classification:min_samples_split,feature_preprocessor:extra_trees_preproc_for_classification:min_weight_fraction_leaf,feature_preprocessor:extra_trees_preproc_for_classification:n_estimators,feature_preprocessor:fast_ica:algorithm,feature_preprocessor:fast_ica:fun,feature_preprocessor:fast_ica:n_components,feature_preprocessor:fast_ica:whiten,feature_preprocessor:feature_agglomeration:affinity,feature_preprocessor:feature_agglomeration:linkage,feature_preprocessor:feature_agglomeration:n_clusters,feature_preprocessor:feature_agglomeration:pooling_func,feature_preprocessor:kernel_pca:coef0,feature_preprocessor:kernel_pca:degree,feature_preprocessor:kernel_pca:gamma,feature_preprocessor:kernel_pca:kernel,feature_preprocessor:kernel_pca:n_components,feature_preprocessor:kitchen_sinks:gamma,feature_preprocessor:kitchen_sinks:n_components,feature_preprocessor:liblinear_svc_preprocessor:C,feature_preprocessor:liblinear_svc_preprocessor:dual,feature_preprocessor:liblinear_svc_preprocessor:fit_intercept,feature_preprocessor:liblinear_svc_preprocessor:intercept_scaling,feature_preprocessor:liblinear_svc_preprocessor:loss,feature_preprocessor:liblinear_svc_preprocessor:multi_class,feature_preprocessor:liblinear_svc_preprocessor:penalty,feature_preprocessor:liblinear_svc_preprocessor:tol,feature_preprocessor:nystroem_sampler:coef0,feature_preprocessor:nystroem_sampler:degree,feature_preprocessor:nystroem_sampler:gamma,feature_preprocessor:nystroem_sampler:kernel,feature_preprocessor:nystroem_sampler:n_components,feature_preprocessor:pca:keep_variance,feature_preprocessor:pca:whiten,feature_preprocessor:polynomial:degree,feature_preprocessor:polynomial:include_bias,feature_preprocessor:polynomial:interaction_only,feature_preprocessor:random_trees_embedding:bootstrap,feature_preprocessor:random_trees_embedding:max_depth,feature_preprocessor:random_trees_embedding:max_leaf_nodes,feature_preprocessor:random_trees_embedding:min_samples_leaf,feature_preprocessor:random_trees_embedding:min_samples_split,feature_preprocessor:random_trees_embedding:min_weight_fraction_leaf,feature_preprocessor:random_trees_embedding:n_estimators,feature_preprocessor:select_percentile_classification:percentile,feature_preprocessor:select_percentile_classification:score_func,feature_preprocessor:select_rates_classification:alpha,feature_preprocessor:select_rates_classification:mode,feature_preprocessor:select_rates_classification:score_func
-1,weighting,adaboost,SAMME,0.015984291290723266,7,184,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010000000000000004,median,standardize,,,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.9333242689646983,False,,,,,,,,,,,,,,,
-2,none,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.9525269450493825,None,0.0,4,20,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,standardize,,,,,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-3,none,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,188,None,,0.09976330267940166,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,none,,,,,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,-0.05439674489937307,2,0.051865468486041774,poly,727,,,,,,,,,,,,,,,,,
-4,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,23916.78290516781,False,True,1,squared_hinge,ovr,l2,0.0006655607726658531,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.008808715242823137,median,none,,,,,kernel_pca,,,,,,,,,,,,,,,,,,,0.2633153190620261,2,0.004770332714291669,poly,1483,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-5,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,2,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010091246322497477,median,none,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,95.37976833605768,mutual_info,,,
-6,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.933900673392165,None,0.0,7,4,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,minmax,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-7,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,0.9455840414651977,0.035051597802663065,auto,255,None,6,39,20,loss,1e-07,0.03762410007700571,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,robust_scaler,,,0.8083718526277422,0.24604827304064597,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,1377.6677844231401,False,True,1,squared_hinge,ovr,l1,0.0019190146350085739,,,,,,,,,,,,,,,,,,,,,,
-8,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5238814331135578,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,median,none,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,28,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-9,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1292.7448076998514,0.004489932344506231,2,1.3088036376373702,poly,-1,False,1.7943262139196537e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,minmax,,,,,feature_agglomeration,,,,,,,,,,,,,,,cosine,average,386,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-10,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.5244313211416313,None,0.0,1,2,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.454300236339658,most_frequent,robust_scaler,,,0.7858830918128734,0.2705174720960696,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-11,weighting,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0006294780694492238,most_frequent,standardize,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.063715750591364,fdr,chi2
-12,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.46057831591617715,False,True,hinge,0.04557857428827514,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00027457445401600137,median,standardize,,,,,extra_trees_preproc_for_classification,True,gini,None,0.48190346970486964,None,0.0,17,18,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-13,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.25657910619102237,None,0.0,6,6,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.021086303324492208,most_frequent,quantile_transformer,1247,uniform,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.16021572815319468,fpr,chi2
-14,weighting,decision_tree,,,,,,,gini,0.3438792358428926,1.0,None,0.0,11,5,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.13337505737577984,mean,quantile_transformer,431,normal,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8.71801503052686,f_classif,,,
-15,weighting,decision_tree,,,,,,,entropy,0.5508420416465765,1.0,None,0.0,4,15,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0030731686230236793,most_frequent,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,False,,,,,,,,,,,,
-16,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7947177310891914,None,0.0,13,3,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,mean,robust_scaler,,,0.7681430822715739,0.23411421138225857,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,16.747604337291705,f_classif,,,
-17,none,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.1769332447629957,mean,minmax,,,,,fast_ica,,,,,,,,,,,deflation,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-18,none,decision_tree,,,,,,,entropy,0.9175027108465073,1.0,None,0.0,18,13,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0005144268441601888,most_frequent,none,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.36269138268682594,fdr,chi2
-19,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,199,None,,0.003239674765949011,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,robust_scaler,,,0.7228447795482008,0.25,kernel_pca,,,,,,,,,,,,,,,,,,,0.09440148939740256,3,0.0008114095405154861,poly,1996,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-20,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.6039096182414764,None,0.0,2,20,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.1939609823704447,most_frequent,robust_scaler,,,0.7331327008874781,0.29747488162877694,extra_trees_preproc_for_classification,True,entropy,None,0.33401603690569853,None,0.0,4,4,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-21,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,2.5882079595129472e-05,0.36125906161027654,auto,255,None,341,119,16,loss,1e-07,0.2924550184445239,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.02338478303505337,most_frequent,robust_scaler,,,0.7866731278723753,0.29543475006822734,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,68,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-22,weighting,adaboost,SAMME.R,0.2127290016566289,1,57,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.012688742338096554,median,quantile_transformer,1048,uniform,,,feature_agglomeration,,,,,,,,,,,,,,,euclidean,average,24,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-23,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7725.740943204492,,,3.122840783722913,rbf,-1,True,1.4116065061807418e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.14280178218510184,most_frequent,none,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,27,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-24,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.21794354428393548,None,0.0,2,16,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0025451910134387575,median,quantile_transformer,1477,uniform,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
-25,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5010952533165973,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.005838375247601407,mean,normalize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,
-26,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5514127836113225,None,0.0,4,20,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.010750756228608195,most_frequent,robust_scaler,,,0.7471420685032172,0.2916152663822578,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.22557315057515023,fwe,chi2
-27,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.9663114854040722,None,0.0,11,12,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.26932389682904634,median,quantile_transformer,23,uniform,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l1,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,
-28,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.506204385946851,None,0.0,2,8,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.03433007982751011,mean,none,,,,,extra_trees_preproc_for_classification,False,gini,None,0.32676415738837183,None,0.0,5,6,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-29,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.992156991246829,None,0.0,19,3,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.007016879673679419,median,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,
-30,none,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.9728532763078892,None,0.0,13,9,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,none,,,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6651071190864879,False,,,,,,,,,,,,,,,
-31,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9561075301148197,None,0.0,6,2,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,
-32,none,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,184,manual,0.5022041786628263,2.3064966255468173e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,most_frequent,standardize,,,,,kernel_pca,,,,,,,,,,,,,,,,,,,-0.32023224593255817,2,0.09479590655633498,poly,1382,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-33,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.8832772200272849,None,0.0,17,3,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,quantile_transformer,1733,normal,,,feature_agglomeration,,,,,,,,,,,,,,,euclidean,complete,262,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-34,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7443968404417798,None,0.0,19,8,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,robust_scaler,,,0.9191984929613786,0.22205227482707854,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-35,weighting,extra_trees,,,,,,,,,,,,,,,True,gini,None,0.8424814442101096,None,0.0,17,16,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0012963479651174736,most_frequent,robust_scaler,,,0.7721224171367294,0.019641775400724187,extra_trees_preproc_for_classification,True,entropy,None,0.5260601497769445,None,0.0,2,12,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-36,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,0.032732094595234795,0.09918078053098744,auto,255,None,97,54,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.019135985037746533,median,quantile_transformer,1000,uniform,,,feature_agglomeration,,,,,,,,,,,,,,,euclidean,average,37,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-37,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.7521969592385672,None,0.0,9,5,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.020402264943872365,median,robust_scaler,,,0.737227213630259,0.20752747663727789,fast_ica,,,,,,,,,,,parallel,exp,312,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-38,none,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.563056219822946,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.32793677336996485,most_frequent,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-39,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.6778910178089097,None,0.0,4,12,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.00023672357514698225,mean,robust_scaler,,,0.75,0.24363005289850256,feature_agglomeration,,,,,,,,,,,,,,,cosine,average,264,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-40,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7088164344533493,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,mean,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,
-41,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,0.03115574063595768,0.1599560586192091,auto,255,None,9,149,13,loss,1e-07,0.08754949552794389,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,quantile_transformer,700,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-42,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5078950534264713,None,0.0,1,3,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,median,robust_scaler,,,0.7734742961654887,0.26137921473001047,feature_agglomeration,,,,,,,,,,,,,,,euclidean,complete,30,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-43,none,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0686374607394679,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,minmax,,,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.8490260132946401,False,,,,,,,,,,,,,,,
-44,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.6931271368243409,None,0.0,11,10,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.362940004438799,most_frequent,robust_scaler,,,0.9177587796305063,0.17084038458597867,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,30.491514144623952,mutual_info,,,
-45,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.3663040729445102,None,0.0,20,19,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.010000000000000004,median,robust_scaler,,,0.7338553275791654,0.25,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,331,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-46,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,92.66144306787051,,,0.04654639302091653,rbf,-1,True,0.0022231544280610005,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.005194838694455963,mean,none,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,15,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-47,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.5569097234498784,None,0.0,20,14,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.000746996810951261,most_frequent,minmax,,,,,feature_agglomeration,,,,,,,,,,,,,,,cosine,complete,268,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-48,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.4467384469668449,None,0.0,19,19,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.00033708405018786203,median,quantile_transformer,711,normal,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,7.744829113696808,False,True,1,squared_hinge,ovr,l1,0.0001869446320464273,,,,,,,,,,,,,,,,,,,,,,
-49,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,26.502259985828186,,,9.475662388405154e-05,rbf,-1,False,0.00019690635968217866,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-50,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,2.5360196477156387e-07,0.011018756408558218,auto,255,None,40,122,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.00011749164080669445,most_frequent,quantile_transformer,757,uniform,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,5.097834697156853,False,True,1,squared_hinge,ovr,l1,0.053877834919477424,,,,,,,,,,,,,,,,,,,,,,
-51,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5255991794296913,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-52,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,12949.73191522963,0.722395694371404,2,0.00017896201985332012,poly,-1,True,0.0004183743529226739,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0003763319541577531,mean,normalize,,,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,505.9404894398784,False,True,1,squared_hinge,ovr,l1,0.0001920230051228414,,,,,,,,,,,,,,,,,,,,,,
-53,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1252.7997374154156,0.20811431041897355,5,0.0018140593074227453,poly,-1,False,0.03149782358621565,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.001930822730474197,most_frequent,quantile_transformer,233,normal,,,feature_agglomeration,,,,,,,,,,,,,,,euclidean,ward,29,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-54,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,1.5661823810486637e-10,0.08996863554015262,auto,255,None,156,89,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.009331521286159971,median,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,
-55,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.6109468076475573,0.8748383232154038,4,0.41333070422336576,poly,-1,False,3.591268778393482e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.011804925819789203,median,minmax,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-56,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.6081832264766056,None,0.0,3,3,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,mean,robust_scaler,,,0.7313537411224409,0.2393370140768918,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-57,weighting,adaboost,SAMME.R,0.03127273017917347,10,496,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,standardize,,,,,fast_ica,,,,,,,,,,,deflation,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-58,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,151,None,,0.0003283777607180646,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.2510527431514227,mean,normalize,,,,,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,-0.96093182232312,,0.05762510964607661,sigmoid,2632,,,,,,,,,,,,,,,,,
-59,none,adaboost,SAMME,0.011233995624432622,9,477,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.018370622484682127,mean,standardize,,,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6039710338898471,False,,,,,,,,,,,,,,,
-60,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.47543228691314265,None,0.0,1,14,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,normalize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-61,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,940.9437776449834,,,1.5839338084625942,rbf,-1,False,0.023072781592565438,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,none,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,77.25763685070707,mutual_info,,,
-62,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.5706821052852115,None,0.0,17,6,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00020922118900917625,mean,normalize,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.23208700543538066,fdr,f_classif
-63,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.34534888916828577,0.02513634776687268,2,1.0733091880109409,poly,-1,True,0.0006974263047918307,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,quantile_transformer,1000,normal,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,77.99934218022861,mutual_info,,,
-64,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,270.1603671903993,False,True,1,squared_hinge,ovr,l2,0.019241340633844056,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.002839838862758512,median,robust_scaler,,,0.8207422911803299,0.044863396307434945,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,
-65,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,9.295260273107016e-08,0.07298633116789383,auto,255,None,88,4,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.02279687006911295,median,robust_scaler,,,0.7654770265945997,0.2359176097506509,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-66,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,True,True,hinge,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.25280837549938784,mean,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-67,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,0.2638627266631576,0.022360820165005443,auto,255,None,171,6,19,loss,1e-07,0.016209060012924756,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.008763072395677399,mean,standardize,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,78,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-68,none,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.563056219822946,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.32793677336996485,most_frequent,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-69,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.8278163313806293,None,0.0,3,5,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,robust_scaler,,,0.725835793765774,0.2571431961341018,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.07784020380291123,fdr,f_classif
-70,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0023391956112474004,False,True,squared_hinge,0.011792446147470903,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.08295440990272292,most_frequent,quantile_transformer,962,normal,,,kernel_pca,,,,,,,,,,,,,,,,,,,,,,cosine,1778,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-71,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.41019363453549706,None,0.0,15,2,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.0003507389224306018,median,none,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.20614098085343638,fdr,chi2
-72,none,adaboost,SAMME,0.1280458968526125,7,125,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,robust_scaler,,,0.7156460334356771,0.2981902862926971,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,65.16171885308417,chi2,,,
-73,none,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9931779675565967,None,0.0,18,17,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,robust_scaler,,,0.75,0.2945917010472818,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,
-74,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.49682150231562006,None,0.0,14,7,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.0007935018131713797,mean,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,
-75,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,1.2430504242164082e-09,0.03359568307446533,auto,255,None,1609,29,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,standardize,,,,,feature_agglomeration,,,,,,,,,,,,,,,cosine,complete,40,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-76,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7.491039171224502,True,True,squared_hinge,2.8058138048327884e-05,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,minmax,,,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.7754372809893839,False,True,1,squared_hinge,ovr,l1,3.9643594596766314e-05,,,,,,,,,,,,,,,,,,,,,,
-77,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.34936025988397124,None,0.0,2,13,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00018601309647406987,median,robust_scaler,,,0.8634309348843472,0.26976989465367607,extra_trees_preproc_for_classification,False,entropy,None,0.48025175288975036,None,0.0,6,17,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-78,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,2.925599188129995e-05,0.12560285030651847,auto,255,None,300,193,17,loss,1e-07,0.039442997915546514,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00299400754997757,mean,robust_scaler,,,0.7093292916146515,0.009366983030047793,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-79,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.7272159618385164,None,0.0,20,11,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,robust_scaler,,,0.8136908047125674,0.2289527142816946,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,31.719931153297054,False,True,1,squared_hinge,ovr,l1,0.004749757426411777,,,,,,,,,,,,,,,,,,,,,,
-80,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1661.2274747409913,,,0.025821208852387936,rbf,-1,False,0.0017011435104921824,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,none,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,306,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-81,weighting,adaboost,SAMME,0.08979115391186342,1,74,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,minmax,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4922034012869809,fpr,f_classif
-82,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,21.85207494001697,0.18872999589786676,4,1.1863646021868761,poly,-1,False,0.0021338371901294443,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0034080852834770217,mean,normalize,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.11326005109886934,fdr,f_classif
-83,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.167624869438968e-05,True,True,hinge,0.004633596202702719,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.20347933384609232,most_frequent,none,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,42.644434279190115,f_classif,,,
-84,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,0.0020641735997058964,0.12733757758861702,auto,255,None,10,150,17,loss,1e-07,0.03652115085030125,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,quantile_transformer,1116,normal,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,
-85,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.9499894634982698,None,0.0,20,15,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.01161399161402058,mean,normalize,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,42.76696665279698,f_classif,,,
-86,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,1,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.019797877010737826,mean,normalize,,,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.8239678741128165,False,,,,,,,,,,,,,,,
-87,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.46057831591617715,False,True,hinge,0.04557857428827514,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00027457445401600137,median,standardize,,,,,extra_trees_preproc_for_classification,True,gini,None,0.48190346970486964,None,0.0,17,18,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-88,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,38,auto,,0.00019398431171606024,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010000000000000004,median,quantile_transformer,1440,normal,,,extra_trees_preproc_for_classification,True,gini,None,0.5126639956845847,None,0.0,16,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-89,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5722917944959481,None,0.0,1,14,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,
-90,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.26356754187780895,None,0.0,4,3,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010966178401758774,mean,robust_scaler,,,0.7817043861798886,0.24371720112407158,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
-91,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,1.158218682314328e-10,0.14188487415573833,auto,255,None,37,16,14,loss,1e-07,0.10836823453723118,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010615307864551744,most_frequent,quantile_transformer,1074,uniform,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.42256340858805774,fpr,f_classif
-92,weighting,bernoulli_nb,,,,,0.033296341861861305,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.16969921146466255,median,none,,,,,feature_agglomeration,,,,,,,,,,,,,,,euclidean,average,152,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-93,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.22879311080499845,None,0.0,8,4,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.014320022547615783,median,minmax,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.238630703440461,mutual_info,,,
-94,weighting,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,2,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0073667222552306465,median,robust_scaler,,,0.701595034693991,0.29514640404971515,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6066051105210388,False,,,,,,,,,,,,,,,
-95,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.107967527639527e-05,True,True,hinge,0.00448294745544775,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,quantile_transformer,953,normal,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,35.00114345405331,chi2,,,
-96,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.457928416564218,None,0.0,12,2,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,
-97,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,1.1522414705935405e-10,0.6132800183073315,auto,255,None,1607,67,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010096911249562407,mean,robust_scaler,,,0.7663493326967942,0.136094217337434,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-98,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1228.3897711257332,0.6862670093933454,5,1.113863025113022,poly,-1,True,1.9271116749907707e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.018161834200128672,mean,normalize,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.06278549919408385,fpr,chi2
-99,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.479405900046494,,,0.013340778632154543,rbf,-1,False,6.913586060890114e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,robust_scaler,,,0.9953232616818098,0.06450577886132274,fast_ica,,,,,,,,,,,deflation,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-100,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.825844582229201,None,0.0,12,9,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0008422508222149647,median,robust_scaler,,,0.8658803880857064,0.2528423686293341,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-101,none,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.563056219822946,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.32793677336996485,most_frequent,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-102,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.7426893291639965,None,0.0,4,19,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010127721455862085,median,quantile_transformer,1108,uniform,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,263,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-103,none,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,128,manual,0.8574560771111307,0.010886139381599514,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.24115007944216788,most_frequent,normalize,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,62.26514170991452,mutual_info,,,
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-106,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.3294588572341214,None,0.0,1,3,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
-107,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,11.046854203258679,,,0.07809571976769347,rbf,-1,False,0.00036193159909053734,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.006445557635410205,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-108,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4754.922921252711,-0.4031311942980875,,0.00031056594358820036,sigmoid,-1,False,8.311300758492926e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.01584901918868692,most_frequent,normalize,,,,,extra_trees_preproc_for_classification,True,entropy,None,0.1368425617257062,None,0.0,6,5,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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-112,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.02021502857757827,True,,0.055848937053280076,True,,constant,log,l2,,0.00016356162584832537,no_encoding,no_coalescense,,median,quantile_transformer,27,normal,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.11873149622606233,fwe,chi2
-113,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,9.029473608568458e-09,0.15779990576964315,auto,255,None,37,71,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.018093177195644097,mean,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,False,,,,,,,,,,,,
-114,weighting,bernoulli_nb,,,,,1.0,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,minmax,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,75.62461900097365,chi2,,,
-115,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0442027823406075,True,True,hinge,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.23384446728715488,median,quantile_transformer,941,normal,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,29.854940094889486,f_classif,,,
-116,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.4864284879550203,None,0.0,1,4,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.008665854799630503,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-117,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.8851946632713493,None,0.0,9,20,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.006095662608922393,most_frequent,quantile_transformer,1002,normal,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.10486328449423876,fpr,f_classif
-118,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.7759471079076188,None,0.0,2,11,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,mean,standardize,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4672496335705658,fwe,chi2
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-122,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.922871100218626,False,True,squared_hinge,0.09957180737451943,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0016563186856411274,median,quantile_transformer,850,uniform,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.09123695282772847,fpr,chi2
-123,weighting,decision_tree,,,,,,,entropy,0.04461432844342683,1.0,None,0.0,9,17,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0011300271286478988,mean,minmax,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.43863282379418345,,mutual_info_classif
-124,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,26268.34065263221,-0.4665726751360928,,0.0008965575925931543,sigmoid,-1,False,0.00012545045512265426,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,normalize,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.15949044845847538,fpr,f_classif
-125,none,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,113,None,,0.011729739852259162,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.04145014437265771,mean,standardize,,,,,feature_agglomeration,,,,,,,,,,,,,,,euclidean,complete,121,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-126,weighting,adaboost,SAMME.R,1.696588612003168,9,455,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,minmax,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,82.18726095926662,chi2,,,
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+3,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.4342857835717273,None,0.0,7,14,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.009546208289604168,mean,standardize,,,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,3.0334030872930415,False,True,1,squared_hinge,ovr,l1,0.035733772578340574,,,,,,,,,,,,,,,,,,,,,,
+5,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,0.0001273994050901372,0.3110265003850914,auto,255,None,36,2,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,quantile_transformer,1473,uniform,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.461377888294646,fdr,f_classif
+8,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,114,manual,0.11778631707605758,0.0029112721339713465,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.015582195257240751,median,normalize,,,,,kernel_pca,,,,,,,,,,,,,,,,,,,0.42909623253327767,3,0.25407612000343777,poly,1689,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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+41,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.22436763006793925,None,0.0,12,18,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,standardize,,,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,184.87016739193817,False,True,1,squared_hinge,ovr,l1,9.229405386285091e-05,,,,,,,,,,,,,,,,,,,,,,
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+443,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.6269076084367177,None,0.0,1,7,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.12943250859943486,most_frequent,standardize,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,62.84526554323969,chi2,,,
+446,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,240.3897817006787,False,True,1,squared_hinge,ovr,l2,0.0007238885641977672,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.015628377472530642,most_frequent,standardize,,,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.9365905387498032,False,,,,,,,,,,,,,,,
+451,weighting,decision_tree,,,,,,,entropy,0.49431715004621385,1.0,None,0.0,1,3,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.00484236454749161,median,robust_scaler,,,0.7619367546642338,0.25,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,58.39951618572543,mutual_info,,,
+453,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.1393173890820956,False,True,1,squared_hinge,ovr,l2,8.283470008521272e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.015152352014390857,mean,normalize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,False,,,,,,,,,,,,
+458,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.21304949388806604,None,0.0,4,8,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0037735029866735902,mean,standardize,,,,,extra_trees_preproc_for_classification,True,gini,None,0.6912995807198827,None,0.0,13,20,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+461,none,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.9796199105973851,None,0.0,2,13,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.00845950033601661,mean,robust_scaler,,,0.8275228560550734,0.23814615163106348,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
diff --git a/autosklearn/metalearning/files/recall_multiclass.classification_dense/description.txt b/autosklearn/metalearning/files/recall_multiclass.classification_dense/description.txt
index 9e3b829678..3501332898 100644
--- a/autosklearn/metalearning/files/recall_multiclass.classification_dense/description.txt
+++ b/autosklearn/metalearning/files/recall_multiclass.classification_dense/description.txt
@@ -1,8 +1,9 @@
 features_cutoff_time: 3600
 features_cutoff_memory: 3072
-number_of_feature_steps: 35
+number_of_feature_steps: 42
 feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances
 feature_step LogNumberOfInstances: LogNumberOfInstances
+feature_step NumberOfClasses: NumberOfClasses
 feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures
 feature_step LogNumberOfFeatures: LogNumberOfFeatures
 feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues
@@ -20,6 +21,11 @@ feature_step DatasetRatio: DatasetRatio, LogDatasetRatio
 feature_step LogDatasetRatio: LogDatasetRatio
 feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio
 feature_step LogInverseDatasetRatio: LogInverseDatasetRatio
+feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD
+feature_step ClassProbabilityMin: ClassProbabilityMin
+feature_step ClassProbabilityMax: ClassProbabilityMax
+feature_step ClassProbabilityMean: ClassProbabilityMean
+feature_step ClassProbabilitySTD: ClassProbabilitySTD
 feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum
 feature_step SymbolsMin: SymbolsMin
 feature_step SymbolsMax: SymbolsMax
@@ -36,11 +42,12 @@ feature_step SkewnessMin: SkewnessMin
 feature_step SkewnessMax: SkewnessMax
 feature_step SkewnessMean: SkewnessMean
 feature_step SkewnessSTD: SkewnessSTD
-features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
+feature_step ClassEntropy: ClassEntropy
+features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy
 features_stochastic: 
-default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
+default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy
 
-algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127
+algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235,236,237,238,239,240,241,242,243,244,245,246,247,248,249,250,251,252,253,254,255,256,257,258,259,260,261,262,263,264,265,266,267,268,269,270,271,272,273,274,275,276,277,278,279,280,281,282,283,284,285,286,287,288,289,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,312,313,314,315,316,317,318,319,320,321,322,323,324,325,326,327,328,329,330,331,332,333,334,335,336,337,338,339,340,341,342,343,344,345,346,347,348,349,350,351,352,353,354,355,356,357,358,359,360,361,362,363,364,365,366,367,368,369,370,371,372,373,374,375,376,377,378,379,380,381,382,383,384,385,386,387,388,389,390,391,392,393,394,395,396,397,398,399,400,401,402,403,404,405,406,407,408,409,410,411,412,413,414,415,416,417,418,419,420,421,422,423,424,425,426,427,428,429,430,431,432,433,434,435,436,437,438,439,440,441,442,443,444,445,446,447,448,449,450,451,452,453,454,455,456,457,458,459,460,461,462,463,464
 algorithms_stochastic: 
 performance_measures: recall
 performance_type: solution_quality
diff --git a/autosklearn/metalearning/files/recall_multiclass.classification_dense/feature_costs.arff b/autosklearn/metalearning/files/recall_multiclass.classification_dense/feature_costs.arff
index 0e17e20d22..8512fd9275 100644
--- a/autosklearn/metalearning/files/recall_multiclass.classification_dense/feature_costs.arff
+++ b/autosklearn/metalearning/files/recall_multiclass.classification_dense/feature_costs.arff
@@ -4,6 +4,7 @@
 @ATTRIBUTE repetition NUMERIC
 @ATTRIBUTE NumberOfInstances NUMERIC
 @ATTRIBUTE LogNumberOfInstances NUMERIC
+@ATTRIBUTE NumberOfClasses NUMERIC
 @ATTRIBUTE NumberOfFeatures NUMERIC
 @ATTRIBUTE LogNumberOfFeatures NUMERIC
 @ATTRIBUTE MissingValues NUMERIC
@@ -21,6 +22,11 @@
 @ATTRIBUTE LogDatasetRatio NUMERIC
 @ATTRIBUTE InverseDatasetRatio NUMERIC
 @ATTRIBUTE LogInverseDatasetRatio NUMERIC
+@ATTRIBUTE ClassOccurences NUMERIC
+@ATTRIBUTE ClassProbabilityMin NUMERIC
+@ATTRIBUTE ClassProbabilityMax NUMERIC
+@ATTRIBUTE ClassProbabilityMean NUMERIC
+@ATTRIBUTE ClassProbabilitySTD NUMERIC
 @ATTRIBUTE NumSymbols NUMERIC
 @ATTRIBUTE SymbolsMin NUMERIC
 @ATTRIBUTE SymbolsMax NUMERIC
@@ -37,162 +43,138 @@
 @ATTRIBUTE SkewnessMax NUMERIC
 @ATTRIBUTE SkewnessMean NUMERIC
 @ATTRIBUTE SkewnessSTD NUMERIC
+@ATTRIBUTE ClassEntropy NUMERIC
 
 @DATA
-75123,1.0,1e-05,1e-05,1e-05,1e-05,0.00253,0.00221,1e-05,0.00024,3e-05,0.00011,0.0,2e-05,2e-05,5e-05,8e-05,2e-05,1e-05,7e-05,1e-05,0.00072,1e-05,1e-05,0.00019,0.00041,0.00011,0.00061,0.00012,9e-05,0.00015,0.00025,0.00067,0.00014,0.00011,0.00016,0.00027
-75090,1.0,1e-05,1e-05,2e-05,2e-05,0.00815,0.00368,7e-05,0.00228,9e-05,0.00235,0.0,5e-05,5e-05,0.0001,0.00013,0.00013,1e-05,0.00021,2e-05,0.00064,0.0,0.0,0.00014,0.00038,0.00011,0.00066,0.00011,0.00014,0.00014,0.00026,0.00066,0.00011,0.00013,0.00015,0.00027
-2123,1.0,1e-05,1e-05,1e-05,1e-05,0.00097,0.00079,1e-05,0.00013,1e-05,7e-05,0.0,2e-05,2e-05,5e-05,8e-05,2e-05,1e-05,5e-05,1e-05,0.0007,1e-05,0.0,0.00017,0.00042,0.0001,0.00052,6e-05,8e-05,0.00012,0.00026,0.00049,6e-05,0.00011,9e-05,0.00023
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-75101,1.0,1e-05,1e-05,1e-05,1e-05,0.0418,0.03546,1e-05,0.00389,1e-05,0.00248,0.0,2e-05,2e-05,4e-05,6e-05,2e-05,1e-05,5e-05,2e-05,0.00045,1e-05,0.0,0.00011,0.00023,0.0001,0.00041,6e-05,8e-05,0.0001,0.00018,0.00047,6e-05,0.0001,8e-05,0.00023
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-75187,1.0,1e-05,1e-05,2e-05,1e-05,0.00548,0.00473,1e-05,0.0005,2e-05,0.00029,1e-05,2e-05,2e-05,7e-05,9e-05,3e-05,1e-05,6e-05,1e-05,0.00062,1e-05,1e-05,0.00014,0.00033,0.00013,0.00038,7e-05,7e-05,8e-05,0.00016,0.00047,6e-05,9e-05,9e-05,0.00023
-266,1.0,1e-05,1e-05,1e-05,1e-05,0.0018,0.00146,1e-05,0.00023,2e-05,0.00015,2e-05,2e-05,2e-05,6e-05,8e-05,3e-05,1e-05,6e-05,1e-05,0.00059,1e-05,1e-05,0.00014,0.00031,0.00013,0.0004,5e-05,7e-05,8e-05,0.0002,0.00044,8e-05,8e-05,8e-05,0.0002
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-75217,1.0,1e-05,1e-05,2e-05,1e-05,0.00196,0.00158,1e-05,0.00026,2e-05,0.00016,1e-05,3e-05,3e-05,7e-05,0.0001,3e-05,1e-05,7e-05,2e-05,0.00089,1e-05,1e-05,0.00022,0.00053,0.00013,0.00074,9e-05,0.0001,0.00019,0.00036,0.00065,0.00013,0.0001,0.00011,0.00031
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-75092,1.0,1e-05,1e-05,1e-05,1e-05,0.00099,0.00072,1e-05,0.00018,1e-05,0.00011,0.0,2e-05,2e-05,4e-05,6e-05,2e-05,1e-05,5e-05,1e-05,0.00058,1e-05,0.0,0.00014,0.00034,9e-05,0.00041,6e-05,8e-05,9e-05,0.00017,0.00045,7e-05,0.0001,9e-05,0.00019
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diff --git a/autosklearn/metalearning/files/recall_multiclass.classification_dense/feature_runstatus.arff b/autosklearn/metalearning/files/recall_multiclass.classification_dense/feature_runstatus.arff
index 0034ad2bd6..5fff2dfed3 100644
--- a/autosklearn/metalearning/files/recall_multiclass.classification_dense/feature_runstatus.arff
+++ b/autosklearn/metalearning/files/recall_multiclass.classification_dense/feature_runstatus.arff
@@ -4,6 +4,7 @@
 @ATTRIBUTE repetition NUMERIC
 @ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other}
@@ -21,6 +22,11 @@
 @ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other}
@@ -37,162 +43,138 @@
 @ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other}
 
 @DATA
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diff --git a/autosklearn/metalearning/files/recall_multiclass.classification_dense/feature_values.arff b/autosklearn/metalearning/files/recall_multiclass.classification_dense/feature_values.arff
index a60400e3ce..736dc64321 100644
--- a/autosklearn/metalearning/files/recall_multiclass.classification_dense/feature_values.arff
+++ b/autosklearn/metalearning/files/recall_multiclass.classification_dense/feature_values.arff
@@ -13,7 +13,6 @@
 @ATTRIBUTE KurtosisMean NUMERIC
 @ATTRIBUTE KurtosisMin NUMERIC
 @ATTRIBUTE KurtosisSTD NUMERIC
-@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC
 @ATTRIBUTE LogDatasetRatio NUMERIC
 @ATTRIBUTE LogInverseDatasetRatio NUMERIC
 @ATTRIBUTE LogNumberOfFeatures NUMERIC
@@ -42,160 +41,135 @@
 @ATTRIBUTE SymbolsSum NUMERIC
 
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diff --git a/autosklearn/metalearning/files/recall_multiclass.classification_sparse/algorithm_runs.arff b/autosklearn/metalearning/files/recall_multiclass.classification_sparse/algorithm_runs.arff
index 23386d1ec5..7dee887737 100644
--- a/autosklearn/metalearning/files/recall_multiclass.classification_sparse/algorithm_runs.arff
+++ b/autosklearn/metalearning/files/recall_multiclass.classification_sparse/algorithm_runs.arff
@@ -7,95 +7,133 @@
 @ATTRIBUTE runstatus {ok, timeout, memout, not_applicable, crash, other}
 
 @DATA
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+75237,1.0,292,0.0002960423808039403,ok
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+75172,1.0,353,?,not_applicable
+75106,1.0,361,0.3294117647058824,ok
+75120,1.0,367,0.002044989775051076,ok
+75193,1.0,369,?,not_applicable
diff --git a/autosklearn/metalearning/files/recall_multiclass.classification_sparse/configurations.csv b/autosklearn/metalearning/files/recall_multiclass.classification_sparse/configurations.csv
index 5a080b6a63..8435e51ddf 100644
--- a/autosklearn/metalearning/files/recall_multiclass.classification_sparse/configurations.csv
+++ b/autosklearn/metalearning/files/recall_multiclass.classification_sparse/configurations.csv
@@ -1,93 +1,131 @@
 idx,balancing:strategy,classifier:__choice__,classifier:adaboost:algorithm,classifier:adaboost:learning_rate,classifier:adaboost:max_depth,classifier:adaboost:n_estimators,classifier:bernoulli_nb:alpha,classifier:bernoulli_nb:fit_prior,classifier:decision_tree:criterion,classifier:decision_tree:max_depth_factor,classifier:decision_tree:max_features,classifier:decision_tree:max_leaf_nodes,classifier:decision_tree:min_impurity_decrease,classifier:decision_tree:min_samples_leaf,classifier:decision_tree:min_samples_split,classifier:decision_tree:min_weight_fraction_leaf,classifier:extra_trees:bootstrap,classifier:extra_trees:criterion,classifier:extra_trees:max_depth,classifier:extra_trees:max_features,classifier:extra_trees:max_leaf_nodes,classifier:extra_trees:min_impurity_decrease,classifier:extra_trees:min_samples_leaf,classifier:extra_trees:min_samples_split,classifier:extra_trees:min_weight_fraction_leaf,classifier:gradient_boosting:early_stop,classifier:gradient_boosting:l2_regularization,classifier:gradient_boosting:learning_rate,classifier:gradient_boosting:loss,classifier:gradient_boosting:max_bins,classifier:gradient_boosting:max_depth,classifier:gradient_boosting:max_leaf_nodes,classifier:gradient_boosting:min_samples_leaf,classifier:gradient_boosting:n_iter_no_change,classifier:gradient_boosting:scoring,classifier:gradient_boosting:tol,classifier:gradient_boosting:validation_fraction,classifier:k_nearest_neighbors:n_neighbors,classifier:k_nearest_neighbors:p,classifier:k_nearest_neighbors:weights,classifier:lda:n_components,classifier:lda:shrinkage,classifier:lda:shrinkage_factor,classifier:lda:tol,classifier:liblinear_svc:C,classifier:liblinear_svc:dual,classifier:liblinear_svc:fit_intercept,classifier:liblinear_svc:intercept_scaling,classifier:liblinear_svc:loss,classifier:liblinear_svc:multi_class,classifier:liblinear_svc:penalty,classifier:liblinear_svc:tol,classifier:libsvm_svc:C,classifier:libsvm_svc:coef0,classifier:libsvm_svc:degree,classifier:libsvm_svc:gamma,classifier:libsvm_svc:kernel,classifier:libsvm_svc:max_iter,classifier:libsvm_svc:shrinking,classifier:libsvm_svc:tol,classifier:multinomial_nb:alpha,classifier:multinomial_nb:fit_prior,classifier:passive_aggressive:C,classifier:passive_aggressive:average,classifier:passive_aggressive:fit_intercept,classifier:passive_aggressive:loss,classifier:passive_aggressive:tol,classifier:qda:reg_param,classifier:random_forest:bootstrap,classifier:random_forest:criterion,classifier:random_forest:max_depth,classifier:random_forest:max_features,classifier:random_forest:max_leaf_nodes,classifier:random_forest:min_impurity_decrease,classifier:random_forest:min_samples_leaf,classifier:random_forest:min_samples_split,classifier:random_forest:min_weight_fraction_leaf,classifier:sgd:alpha,classifier:sgd:average,classifier:sgd:epsilon,classifier:sgd:eta0,classifier:sgd:fit_intercept,classifier:sgd:l1_ratio,classifier:sgd:learning_rate,classifier:sgd:loss,classifier:sgd:penalty,classifier:sgd:power_t,classifier:sgd:tol,data_preprocessing:categorical_transformer:categorical_encoding:__choice__,data_preprocessing:categorical_transformer:category_coalescence:__choice__,data_preprocessing:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction,data_preprocessing:numerical_transformer:imputation:strategy,data_preprocessing:numerical_transformer:rescaling:__choice__,data_preprocessing:numerical_transformer:rescaling:quantile_transformer:n_quantiles,data_preprocessing:numerical_transformer:rescaling:quantile_transformer:output_distribution,data_preprocessing:numerical_transformer:rescaling:robust_scaler:q_max,data_preprocessing:numerical_transformer:rescaling:robust_scaler:q_min,feature_preprocessor:__choice__,feature_preprocessor:extra_trees_preproc_for_classification:bootstrap,feature_preprocessor:extra_trees_preproc_for_classification:criterion,feature_preprocessor:extra_trees_preproc_for_classification:max_depth,feature_preprocessor:extra_trees_preproc_for_classification:max_features,feature_preprocessor:extra_trees_preproc_for_classification:max_leaf_nodes,feature_preprocessor:extra_trees_preproc_for_classification:min_impurity_decrease,feature_preprocessor:extra_trees_preproc_for_classification:min_samples_leaf,feature_preprocessor:extra_trees_preproc_for_classification:min_samples_split,feature_preprocessor:extra_trees_preproc_for_classification:min_weight_fraction_leaf,feature_preprocessor:extra_trees_preproc_for_classification:n_estimators,feature_preprocessor:kernel_pca:coef0,feature_preprocessor:kernel_pca:degree,feature_preprocessor:kernel_pca:gamma,feature_preprocessor:kernel_pca:kernel,feature_preprocessor:kernel_pca:n_components,feature_preprocessor:kitchen_sinks:gamma,feature_preprocessor:kitchen_sinks:n_components,feature_preprocessor:liblinear_svc_preprocessor:C,feature_preprocessor:liblinear_svc_preprocessor:dual,feature_preprocessor:liblinear_svc_preprocessor:fit_intercept,feature_preprocessor:liblinear_svc_preprocessor:intercept_scaling,feature_preprocessor:liblinear_svc_preprocessor:loss,feature_preprocessor:liblinear_svc_preprocessor:multi_class,feature_preprocessor:liblinear_svc_preprocessor:penalty,feature_preprocessor:liblinear_svc_preprocessor:tol,feature_preprocessor:nystroem_sampler:coef0,feature_preprocessor:nystroem_sampler:degree,feature_preprocessor:nystroem_sampler:gamma,feature_preprocessor:nystroem_sampler:kernel,feature_preprocessor:nystroem_sampler:n_components,feature_preprocessor:polynomial:degree,feature_preprocessor:polynomial:include_bias,feature_preprocessor:polynomial:interaction_only,feature_preprocessor:random_trees_embedding:bootstrap,feature_preprocessor:random_trees_embedding:max_depth,feature_preprocessor:random_trees_embedding:max_leaf_nodes,feature_preprocessor:random_trees_embedding:min_samples_leaf,feature_preprocessor:random_trees_embedding:min_samples_split,feature_preprocessor:random_trees_embedding:min_weight_fraction_leaf,feature_preprocessor:random_trees_embedding:n_estimators,feature_preprocessor:select_percentile_classification:percentile,feature_preprocessor:select_percentile_classification:score_func,feature_preprocessor:select_rates_classification:alpha,feature_preprocessor:select_rates_classification:mode,feature_preprocessor:select_rates_classification:score_func,feature_preprocessor:truncatedSVD:target_dim
-1,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.3163020577549344,None,0.0,15,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.01322981552715978,most_frequent,robust_scaler,,,0.9746667009556722,0.1974876581478311,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-2,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,23916.78290516781,False,True,1,squared_hinge,ovr,l2,0.0006655607726658531,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.008808715242823137,median,none,,,,,kernel_pca,,,,,,,,,,,0.2633153190620261,2,0.004770332714291669,poly,1483,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-3,weighting,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,88,2,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.254263872179305,median,normalize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-4,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,170.37509684994612,0.6734219115470694,3,0.08998608839429428,poly,-1,True,0.024147312348267988,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,none,,,,,extra_trees_preproc_for_classification,False,entropy,None,0.9449154369109399,None,0.0,1,14,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-5,none,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00011009762574324903,True,,0.031641726094190054,True,,invscaling,hinge,l2,0.2208821470267501,0.007895311091089785,one_hot_encoding,minority_coalescer,0.13807502937372682,mean,quantile_transformer,1340,normal,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,,
-6,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.5244313211416313,None,0.0,1,2,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.454300236339658,most_frequent,robust_scaler,,,0.7858830918128734,0.2705174720960696,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-7,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.46057831591617715,False,True,hinge,0.04557857428827514,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00027457445401600137,median,standardize,,,,,extra_trees_preproc_for_classification,True,gini,None,0.48190346970486964,None,0.0,17,18,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-8,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.25657910619102237,None,0.0,6,6,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.021086303324492208,most_frequent,quantile_transformer,1247,uniform,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.16021572815319468,fpr,chi2,
-9,weighting,decision_tree,,,,,,,entropy,0.5508420416465765,1.0,None,0.0,4,15,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0030731686230236793,most_frequent,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,False,,,,,,,,,,,,,
-10,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.8253905225980761,None,0.0,18,20,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.003725255016700434,mean,robust_scaler,,,0.7701705977765594,0.2396520157991559,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.3579038776690903,False,True,1,squared_hinge,ovr,l1,4.994232534734023e-05,,,,,,,,,,,,,,,,,,,,,
-11,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.06263739023781334,None,0.0,18,8,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.02206379295100344,most_frequent,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,,
-12,none,decision_tree,,,,,,,entropy,0.9175027108465073,1.0,None,0.0,18,13,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0005144268441601888,most_frequent,none,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.36269138268682594,fdr,chi2,
-13,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,199,None,,0.003239674765949011,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,robust_scaler,,,0.7228447795482008,0.25,kernel_pca,,,,,,,,,,,0.09440148939740256,3,0.0008114095405154861,poly,1996,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-14,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.6039096182414764,None,0.0,2,20,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.1939609823704447,most_frequent,robust_scaler,,,0.7331327008874781,0.29747488162877694,extra_trees_preproc_for_classification,True,entropy,None,0.33401603690569853,None,0.0,4,4,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-15,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7865652301288171,None,0.0,1,4,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,median,robust_scaler,,,0.8623635641193496,0.13298297278730625,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-16,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.905401147837377,-0.32832122529048147,3,1.001896309040499,poly,-1,False,5.1523484258030785e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-17,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.21794354428393548,None,0.0,2,16,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0025451910134387575,median,quantile_transformer,1477,uniform,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,,
-18,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5010952533165973,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.005838375247601407,mean,normalize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,,
-19,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5514127836113225,None,0.0,4,20,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.010750756228608195,most_frequent,robust_scaler,,,0.7471420685032172,0.2916152663822578,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.22557315057515023,fwe,chi2,
-20,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.9663114854040722,None,0.0,11,12,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.26932389682904634,median,quantile_transformer,23,uniform,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l1,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,
-21,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.506204385946851,None,0.0,2,8,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.03433007982751011,mean,none,,,,,extra_trees_preproc_for_classification,False,gini,None,0.32676415738837183,None,0.0,5,6,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-22,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9333237919291875,None,0.0,8,3,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,,
-23,none,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,184,manual,0.5022041786628263,2.3064966255468173e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,most_frequent,standardize,,,,,kernel_pca,,,,,,,,,,,-0.32023224593255817,2,0.09479590655633498,poly,1382,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-24,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7443968404417798,None,0.0,19,8,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,robust_scaler,,,0.9191984929613786,0.22205227482707854,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-25,weighting,extra_trees,,,,,,,,,,,,,,,True,gini,None,0.8424814442101096,None,0.0,17,16,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0012963479651174736,most_frequent,robust_scaler,,,0.7721224171367294,0.019641775400724187,extra_trees_preproc_for_classification,True,entropy,None,0.5260601497769445,None,0.0,2,12,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-26,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7422641456052917,None,0.0,8,15,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.005561351266169067,most_frequent,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-27,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.756167892711991e-05,False,True,squared_hinge,5.235791933868702e-05,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0001632172987320906,most_frequent,standardize,,,,,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,5,None,19,12,1.0,70,,,,,,
-28,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7088164344533493,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,mean,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,,
-29,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,10,1,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0022606253047811435,median,quantile_transformer,1247,normal,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,25.327793585266683,chi2,,,,
-30,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,45.824472650016915,,,0.04733593018751384,rbf,-1,False,0.0001002563992422035,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.007882457896645694,most_frequent,robust_scaler,,,0.7360778699898849,0.27800774875566153,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-31,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2681.998807317996,-0.9558745731731211,,0.0011299948421773457,sigmoid,-1,False,1.5292507354478086e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-32,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.4467384469668449,None,0.0,19,19,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.00033708405018786203,median,quantile_transformer,711,normal,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,7.744829113696808,False,True,1,squared_hinge,ovr,l1,0.0001869446320464273,,,,,,,,,,,,,,,,,,,,,
-33,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,26.502259985828186,,,9.475662388405154e-05,rbf,-1,False,0.00019690635968217866,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-34,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.339600708038189,False,True,1,squared_hinge,ovr,l2,9.572641158475304e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,quantile_transformer,1193,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-35,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5255991794296913,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-36,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,12949.73191522963,0.722395694371404,2,0.00017896201985332012,poly,-1,True,0.0004183743529226739,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0003763319541577531,mean,normalize,,,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,505.9404894398784,False,True,1,squared_hinge,ovr,l1,0.0001920230051228414,,,,,,,,,,,,,,,,,,,,,
-37,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.6081832264766056,None,0.0,3,3,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,mean,robust_scaler,,,0.7313537411224409,0.2393370140768918,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-38,none,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4994182104906286,False,True,1,squared_hinge,ovr,l2,0.006324920556381277,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.06848370232187935,mean,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-39,none,bernoulli_nb,,,,,0.6524090653939782,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.005164169126808402,most_frequent,quantile_transformer,16,normal,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.033082145443631535,False,True,1,squared_hinge,ovr,l1,0.002615020898325393,,,,,,,,,,,,,,,,,,,,,
-40,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.47543228691314265,None,0.0,1,14,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,normalize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-41,weighting,extra_trees,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,13,18,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0001574994528707389,most_frequent,standardize,,,,,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,3,None,19,19,1.0,77,,,,,,
-42,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.49918755377415064,None,0.0,1,2,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.36992315774620843,mean,robust_scaler,,,0.7410494147895569,0.29895918805929733,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-43,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,270.1603671903993,False,True,1,squared_hinge,ovr,l2,0.019241340633844056,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.002839838862758512,median,robust_scaler,,,0.8207422911803299,0.044863396307434945,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,,
-44,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,True,True,hinge,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.25280837549938784,mean,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-45,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.10581257327556097,None,0.0,7,12,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.004219392676189224,median,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,,
-46,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.4188764793561401,None,0.0,5,5,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.010589444903605501,mean,robust_scaler,,,0.9796997860990453,0.27262832749694405,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-47,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0023391956112474004,False,True,squared_hinge,0.011792446147470903,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.08295440990272292,most_frequent,quantile_transformer,962,normal,,,kernel_pca,,,,,,,,,,,,,,cosine,1778,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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-49,none,adaboost,SAMME,0.1280458968526125,7,125,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,robust_scaler,,,0.7156460334356771,0.2981902862926971,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,65.16171885308417,chi2,,,,
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+320,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.9130183368656493,None,0.0,17,19,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.15470275906639006,mean,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+325,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8.380057363340162,False,True,hinge,0.0020653831067503525,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.05020677662128942,median,quantile_transformer,560,normal,,,kernel_pca,,,,,,,,,,,,,,cosine,891,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+328,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.7292011996224884,True,True,hinge,8.162763086383311e-05,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010337761588362885,median,quantile_transformer,877,uniform,,,kernel_pca,,,,,,,,,,,,,,cosine,1317,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+331,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.5,None,0.0,1,5,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.15244281596303316,mean,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+333,weighting,bernoulli_nb,,,,,50.321502324016244,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.08806499894972263,mean,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+336,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5501.252565062252,False,True,1,squared_hinge,ovr,l2,0.0006688304268563973,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.40704734431738154,most_frequent,standardize,,,,,extra_trees_preproc_for_classification,False,gini,None,0.08936669006295328,None,0.0,16,8,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+338,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.37035966336010345,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.007136588202285817,mean,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+340,weighting,decision_tree,,,,,,,entropy,1.4732927621306418,1.0,None,0.0,10,3,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,standardize,,,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,4276.070150555713,False,True,1,squared_hinge,ovr,l1,0.00011385383984576719,,,,,,,,,,,,,,,,,,,,,
+343,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.7139498722492832,None,0.0,14,5,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0006591123720866668,mean,normalize,,,,,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,6,None,2,4,1.0,87,,,,,,
+345,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5321814610661173,None,0.0,1,3,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,standardize,,,,,extra_trees_preproc_for_classification,True,gini,None,0.5,None,0.0,1,15,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+349,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.28088485314208955,None,0.0,17,16,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.003177530868474569,mean,robust_scaler,,,0.8605669594097658,0.2400110842123775,extra_trees_preproc_for_classification,True,entropy,None,0.32485209130278936,None,0.0,6,10,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+352,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5746384738423225,None,0.0,1,2,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.014830459445672814,mean,robust_scaler,,,0.7814390720260649,0.2588734594830571,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+353,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.6269076084367177,None,0.0,1,7,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.12943250859943486,most_frequent,standardize,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,62.84526554323969,chi2,,,,
+356,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.01400651528258823,True,True,hinge,1.929818740575615e-05,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,normalize,,,,,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,5,None,3,19,1.0,78,,,,,,
+361,weighting,adaboost,SAMME,0.2786884011943926,3,263,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,quantile_transformer,1958,uniform,,,extra_trees_preproc_for_classification,False,entropy,None,0.2099259239256946,None,0.0,19,18,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+363,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.1393173890820956,False,True,1,squared_hinge,ovr,l2,8.283470008521272e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.015152352014390857,mean,normalize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,False,,,,,,,,,,,,,
+367,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.21304949388806604,None,0.0,4,8,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0037735029866735902,mean,standardize,,,,,extra_trees_preproc_for_classification,True,gini,None,0.6912995807198827,None,0.0,13,20,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+369,none,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.9796199105973851,None,0.0,2,13,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.00845950033601661,mean,robust_scaler,,,0.8275228560550734,0.23814615163106348,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
diff --git a/autosklearn/metalearning/files/recall_multiclass.classification_sparse/description.txt b/autosklearn/metalearning/files/recall_multiclass.classification_sparse/description.txt
index 3bd77ae1f9..4847474fbc 100644
--- a/autosklearn/metalearning/files/recall_multiclass.classification_sparse/description.txt
+++ b/autosklearn/metalearning/files/recall_multiclass.classification_sparse/description.txt
@@ -1,8 +1,9 @@
 features_cutoff_time: 3600
 features_cutoff_memory: 3072
-number_of_feature_steps: 35
+number_of_feature_steps: 42
 feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances
 feature_step LogNumberOfInstances: LogNumberOfInstances
+feature_step NumberOfClasses: NumberOfClasses
 feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures
 feature_step LogNumberOfFeatures: LogNumberOfFeatures
 feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues
@@ -20,6 +21,11 @@ feature_step DatasetRatio: DatasetRatio, LogDatasetRatio
 feature_step LogDatasetRatio: LogDatasetRatio
 feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio
 feature_step LogInverseDatasetRatio: LogInverseDatasetRatio
+feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD
+feature_step ClassProbabilityMin: ClassProbabilityMin
+feature_step ClassProbabilityMax: ClassProbabilityMax
+feature_step ClassProbabilityMean: ClassProbabilityMean
+feature_step ClassProbabilitySTD: ClassProbabilitySTD
 feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum
 feature_step SymbolsMin: SymbolsMin
 feature_step SymbolsMax: SymbolsMax
@@ -36,11 +42,12 @@ feature_step SkewnessMin: SkewnessMin
 feature_step SkewnessMax: SkewnessMax
 feature_step SkewnessMean: SkewnessMean
 feature_step SkewnessSTD: SkewnessSTD
-features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
+feature_step ClassEntropy: ClassEntropy
+features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy
 features_stochastic: 
-default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
+default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy
 
-algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92
+algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235,236,237,238,239,240,241,242,243,244,245,246,247,248,249,250,251,252,253,254,255,256,257,258,259,260,261,262,263,264,265,266,267,268,269,270,271,272,273,274,275,276,277,278,279,280,281,282,283,284,285,286,287,288,289,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,312,313,314,315,316,317,318,319,320,321,322,323,324,325,326,327,328,329,330,331,332,333,334,335,336,337,338,339,340,341,342,343,344,345,346,347,348,349,350,351,352,353,354,355,356,357,358,359,360,361,362,363,364,365,366,367,368,369,370,371,372
 algorithms_stochastic: 
 performance_measures: recall
 performance_type: solution_quality
diff --git a/autosklearn/metalearning/files/recall_multiclass.classification_sparse/feature_costs.arff b/autosklearn/metalearning/files/recall_multiclass.classification_sparse/feature_costs.arff
index 0e17e20d22..8512fd9275 100644
--- a/autosklearn/metalearning/files/recall_multiclass.classification_sparse/feature_costs.arff
+++ b/autosklearn/metalearning/files/recall_multiclass.classification_sparse/feature_costs.arff
@@ -4,6 +4,7 @@
 @ATTRIBUTE repetition NUMERIC
 @ATTRIBUTE NumberOfInstances NUMERIC
 @ATTRIBUTE LogNumberOfInstances NUMERIC
+@ATTRIBUTE NumberOfClasses NUMERIC
 @ATTRIBUTE NumberOfFeatures NUMERIC
 @ATTRIBUTE LogNumberOfFeatures NUMERIC
 @ATTRIBUTE MissingValues NUMERIC
@@ -21,6 +22,11 @@
 @ATTRIBUTE LogDatasetRatio NUMERIC
 @ATTRIBUTE InverseDatasetRatio NUMERIC
 @ATTRIBUTE LogInverseDatasetRatio NUMERIC
+@ATTRIBUTE ClassOccurences NUMERIC
+@ATTRIBUTE ClassProbabilityMin NUMERIC
+@ATTRIBUTE ClassProbabilityMax NUMERIC
+@ATTRIBUTE ClassProbabilityMean NUMERIC
+@ATTRIBUTE ClassProbabilitySTD NUMERIC
 @ATTRIBUTE NumSymbols NUMERIC
 @ATTRIBUTE SymbolsMin NUMERIC
 @ATTRIBUTE SymbolsMax NUMERIC
@@ -37,162 +43,138 @@
 @ATTRIBUTE SkewnessMax NUMERIC
 @ATTRIBUTE SkewnessMean NUMERIC
 @ATTRIBUTE SkewnessSTD NUMERIC
+@ATTRIBUTE ClassEntropy NUMERIC
 
 @DATA
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+75244,1.0,3e-05,2e-05,0.0004,3e-05,2e-05,0.0173,0.01298,4e-05,0.00267,4e-05,0.00174,1e-05,7e-05,9e-05,0.00024,0.00027,8e-05,2e-05,0.00013,3e-05,0.00045,7e-05,2e-05,0.00011,0.00025,0.00077,6e-05,2e-05,0.00021,0.00034,0.00013,0.00077,0.00014,0.00013,0.00026,0.00023,0.00044,7e-05,8e-05,0.0001,0.00019,0.00649
+75248,1.0,1e-05,1e-05,0.00017,1e-05,1e-05,0.00416,0.00305,1e-05,0.0006,2e-05,0.00054,1e-05,2e-05,2e-05,5e-05,7e-05,2e-05,1e-05,4e-05,1e-05,0.00017,2e-05,1e-05,4e-05,0.0001,0.00029,5e-05,1e-05,6e-05,0.00012,6e-05,0.00081,0.00011,0.00015,0.00016,0.00038,0.0005,7e-05,8e-05,0.00012,0.00024,0.00184
+75249,1.0,1e-05,1e-05,0.00011,1e-05,1e-05,0.00192,0.00153,1e-05,0.00025,1e-05,0.00017,1e-05,2e-05,2e-05,5e-05,7e-05,2e-05,1e-05,5e-05,3e-05,0.00014,2e-05,1e-05,3e-05,8e-05,0.00029,1e-05,3e-05,6e-05,0.00012,7e-05,0.00041,6e-05,8e-05,0.0001,0.00017,0.00043,6e-05,7e-05,9e-05,0.00019,0.00092
+75250,1.0,1e-05,1e-05,0.00375,1e-05,1e-05,0.04994,0.047,1e-05,0.00247,1e-05,0.00049,1e-05,2e-05,2e-05,6e-05,7e-05,2e-05,1e-05,3e-05,1e-05,0.00022,4e-05,1e-05,4e-05,0.00012,0.00041,0.0,1e-05,0.00011,0.00023,6e-05,0.00076,0.00012,0.00012,0.00019,0.00032,0.00086,0.00012,0.00013,0.00018,0.00043,0.04178
diff --git a/autosklearn/metalearning/files/recall_multiclass.classification_sparse/feature_runstatus.arff b/autosklearn/metalearning/files/recall_multiclass.classification_sparse/feature_runstatus.arff
index 0034ad2bd6..5fff2dfed3 100644
--- a/autosklearn/metalearning/files/recall_multiclass.classification_sparse/feature_runstatus.arff
+++ b/autosklearn/metalearning/files/recall_multiclass.classification_sparse/feature_runstatus.arff
@@ -4,6 +4,7 @@
 @ATTRIBUTE repetition NUMERIC
 @ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other}
@@ -21,6 +22,11 @@
 @ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other}
@@ -37,162 +43,138 @@
 @ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other}
 
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diff --git a/autosklearn/metalearning/files/recall_multiclass.classification_sparse/feature_values.arff b/autosklearn/metalearning/files/recall_multiclass.classification_sparse/feature_values.arff
index a60400e3ce..736dc64321 100644
--- a/autosklearn/metalearning/files/recall_multiclass.classification_sparse/feature_values.arff
+++ b/autosklearn/metalearning/files/recall_multiclass.classification_sparse/feature_values.arff
@@ -13,7 +13,6 @@
 @ATTRIBUTE KurtosisMean NUMERIC
 @ATTRIBUTE KurtosisMin NUMERIC
 @ATTRIBUTE KurtosisSTD NUMERIC
-@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC
 @ATTRIBUTE LogDatasetRatio NUMERIC
 @ATTRIBUTE LogInverseDatasetRatio NUMERIC
 @ATTRIBUTE LogNumberOfFeatures NUMERIC
@@ -42,160 +41,135 @@
 @ATTRIBUTE SymbolsSum NUMERIC
 
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+75236,1.0,3.3208751934243548,0.10486891385767791,0.1,0.09363295880149813,0.003810297743328803,0.2397003745318352,4.171875,22.422517776489258,-0.9225467168815645,-1.9997756481170654,1.9352856443240884,-1.4283655750405777,1.4283655750405777,5.545177444479562,6.97354301952014,0.0,10.0,256.0,0.0,1068.0,0.0,0.0,256.0,0.0,0.0,0.0,0.0,0.0,4.94191312789917,0.8186543448100565,-0.6194983720779419,0.6381679017395948,0.0,0.0,0.0,0.0,0.0
+75237,1.0,0.7378736147990802,0.7918800894091566,0.5,0.20811991059084348,0.29188008940915655,1.8271625991997027e-05,54729.666666666664,-0.5975548261535093,-0.7926032911766772,-0.9140236377716064,0.13929334047064063,-10.910161193614513,10.910161193614513,1.0986122886681098,12.008773482282624,0.0,2.0,3.0,0.0,164189.0,0.0,0.0,3.0,0.0,0.0,0.0,0.0,0.0,-0.039879728108644485,-0.3348021370669206,-0.6223916411399841,0.2378660138627058,0.0,0.0,0.0,0.0,0.0
+75239,1.0,0.9443547030267275,0.6379707916986933,0.5,0.3620292083013067,0.1379707916986933,0.02536510376633359,39.42424242424242,30.96304702758789,5.229958094222913,-1.8830041885375977,9.159290860687747,-3.674380917046025,3.674380917046025,3.4965075614664802,7.170888478512505,0.0,2.0,33.0,0.0,1301.0,0.0,0.0,33.0,0.0,0.0,0.0,0.0,0.0,5.591684341430664,1.5151810998266393,-0.8763390183448792,1.68213778251707,0.0,0.0,0.0,0.0,0.0
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diff --git a/autosklearn/metalearning/files/recall_samples_binary.classification_dense/algorithm_runs.arff b/autosklearn/metalearning/files/recall_samples_binary.classification_dense/algorithm_runs.arff
index 090f0ed822..4176a9b9a8 100644
--- a/autosklearn/metalearning/files/recall_samples_binary.classification_dense/algorithm_runs.arff
+++ b/autosklearn/metalearning/files/recall_samples_binary.classification_dense/algorithm_runs.arff
@@ -7,130 +7,135 @@
 @ATTRIBUTE runstatus {ok, timeout, memout, not_applicable, crash, other}
 
 @DATA
-75119,1.0,1,?,not_applicable
-75212,1.0,2,?,not_applicable
-252,1.0,3,?,not_applicable
-246,1.0,4,?,not_applicable
-75178,1.0,5,?,not_applicable
-75239,1.0,6,?,not_applicable
-75171,1.0,7,?,not_applicable
-75233,1.0,8,?,not_applicable
-248,1.0,9,?,not_applicable
-75231,1.0,10,?,not_applicable
-2123,1.0,11,?,not_applicable
-75196,1.0,12,?,not_applicable
-75188,1.0,13,?,not_applicable
+75192,1.0,1,?,not_applicable
+75119,1.0,2,?,not_applicable
+75139,1.0,5,?,not_applicable
+75212,1.0,6,?,not_applicable
+246,1.0,8,?,not_applicable
+252,1.0,9,?,not_applicable
+75178,1.0,10,?,not_applicable
+75177,1.0,13,?,not_applicable
 75092,1.0,14,?,not_applicable
-75248,1.0,15,?,not_applicable
-75126,1.0,16,?,not_applicable
-75234,1.0,17,?,not_applicable
-75150,1.0,18,?,not_applicable
-258,1.0,19,?,not_applicable
-75168,1.0,20,?,not_applicable
-75235,1.0,21,?,not_applicable
-75159,1.0,22,?,not_applicable
-244,1.0,23,?,not_applicable
-75221,1.0,24,?,not_applicable
-75219,1.0,25,?,not_applicable
-75202,1.0,26,?,not_applicable
-3043,1.0,27,?,not_applicable
-75205,1.0,28,?,not_applicable
-75174,1.0,29,?,not_applicable
-288,1.0,30,?,not_applicable
-75250,1.0,31,?,not_applicable
-275,1.0,32,?,not_applicable
-75142,1.0,33,?,not_applicable
-75213,1.0,34,?,not_applicable
-75099,1.0,35,?,not_applicable
-75243,1.0,36,?,not_applicable
-75184,1.0,37,?,not_applicable
-75222,1.0,38,?,not_applicable
-75175,1.0,39,?,not_applicable
-233,1.0,40,?,not_applicable
-75161,1.0,41,?,not_applicable
-75176,1.0,42,?,not_applicable
-75090,1.0,43,?,not_applicable
-75114,1.0,44,?,not_applicable
-260,1.0,45,?,not_applicable
-236,1.0,46,?,not_applicable
-75141,1.0,47,?,not_applicable
-75107,1.0,48,?,not_applicable
-262,1.0,49,?,not_applicable
-75146,1.0,50,?,not_applicable
-75189,1.0,51,?,not_applicable
-2350,1.0,52,?,not_applicable
-253,1.0,53,?,not_applicable
-2122,1.0,54,?,not_applicable
-75110,1.0,55,?,not_applicable
-75249,1.0,56,?,not_applicable
-75108,1.0,57,?,not_applicable
-242,1.0,58,?,not_applicable
-75117,1.0,59,?,not_applicable
-75191,1.0,60,?,not_applicable
-75226,1.0,61,?,not_applicable
-261,1.0,62,?,not_applicable
-75236,1.0,63,?,not_applicable
-75095,1.0,64,?,not_applicable
-75148,1.0,65,?,not_applicable
-75093,1.0,66,?,not_applicable
-75223,1.0,67,?,not_applicable
-75244,1.0,68,?,not_applicable
-75109,1.0,69,?,not_applicable
-75197,1.0,70,?,not_applicable
-75127,1.0,71,?,not_applicable
-75143,1.0,72,?,not_applicable
-75153,1.0,73,?,not_applicable
-75173,1.0,74,?,not_applicable
-75215,1.0,75,?,not_applicable
-75195,1.0,76,?,not_applicable
-75207,1.0,77,?,not_applicable
-266,1.0,78,?,not_applicable
-75225,1.0,79,?,not_applicable
-75166,1.0,80,?,not_applicable
-75100,1.0,81,?,not_applicable
-75169,1.0,82,?,not_applicable
-75132,1.0,83,?,not_applicable
-273,1.0,84,?,not_applicable
-75121,1.0,85,?,not_applicable
-75098,1.0,86,?,not_applicable
-75115,1.0,87,?,not_applicable
-75116,1.0,88,?,not_applicable
-75185,1.0,89,?,not_applicable
-2119,1.0,90,?,not_applicable
-75157,1.0,91,?,not_applicable
-75113,1.0,92,?,not_applicable
-75134,1.0,93,?,not_applicable
-75096,1.0,94,?,not_applicable
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-75182,1.0,96,?,not_applicable
-251,1.0,97,?,not_applicable
-75123,1.0,98,?,not_applicable
-75237,1.0,99,?,not_applicable
-75125,1.0,100,?,not_applicable
-75232,1.0,101,?,not_applicable
-75103,1.0,102,?,not_applicable
-75192,1.0,103,?,not_applicable
-75230,1.0,104,?,not_applicable
-75139,1.0,105,?,not_applicable
-75227,1.0,106,?,not_applicable
-2120,1.0,107,?,not_applicable
-75124,1.0,108,?,not_applicable
-75240,1.0,109,?,not_applicable
-75129,1.0,110,?,not_applicable
-75198,1.0,111,?,not_applicable
-75201,1.0,112,?,not_applicable
-75112,1.0,113,?,not_applicable
-75133,1.0,114,?,not_applicable
-75105,1.0,115,?,not_applicable
-75154,1.0,116,?,not_applicable
-75177,1.0,117,?,not_applicable
-2117,1.0,118,?,not_applicable
-75156,1.0,119,?,not_applicable
-75097,1.0,120,?,not_applicable
-75101,1.0,121,?,not_applicable
-75172,1.0,122,?,not_applicable
-75106,1.0,123,?,not_applicable
-75179,1.0,124,?,not_applicable
-75187,1.0,125,?,not_applicable
-75120,1.0,126,?,not_applicable
-75193,1.0,127,?,not_applicable
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+75215,1.0,18,?,not_applicable
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+288,1.0,64,?,not_applicable
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+75234,1.0,67,?,not_applicable
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+75202,1.0,96,?,not_applicable
+3043,1.0,98,?,not_applicable
+75205,1.0,103,?,not_applicable
+75174,1.0,106,?,not_applicable
+75250,1.0,111,?,not_applicable
+75179,1.0,114,?,not_applicable
+275,1.0,115,?,not_applicable
+242,1.0,116,?,not_applicable
+75207,1.0,117,?,not_applicable
+75142,1.0,118,?,not_applicable
+75099,1.0,123,?,not_applicable
+75243,1.0,126,?,not_applicable
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+261,1.0,148,?,not_applicable
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+260,1.0,158,?,not_applicable
+236,1.0,160,?,not_applicable
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+75100,1.0,286,?,not_applicable
+75132,1.0,293,?,not_applicable
+75210,1.0,298,?,not_applicable
+273,1.0,300,?,not_applicable
+75133,1.0,303,?,not_applicable
+75121,1.0,304,?,not_applicable
+75098,1.0,310,?,not_applicable
+75115,1.0,315,?,not_applicable
+75217,1.0,337,?,not_applicable
+75134,1.0,341,?,not_applicable
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+75203,1.0,350,?,not_applicable
+75123,1.0,360,?,not_applicable
+75125,1.0,367,?,not_applicable
+2120,1.0,371,?,not_applicable
+75232,1.0,374,?,not_applicable
+75103,1.0,378,?,not_applicable
+75230,1.0,386,?,not_applicable
+75240,1.0,398,?,not_applicable
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+75201,1.0,407,?,not_applicable
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+2117,1.0,426,?,not_applicable
+75156,1.0,430,?,not_applicable
+75097,1.0,434,?,not_applicable
+75101,1.0,438,?,not_applicable
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+75120,1.0,456,?,not_applicable
+75193,1.0,461,?,not_applicable
diff --git a/autosklearn/metalearning/files/recall_samples_binary.classification_dense/configurations.csv b/autosklearn/metalearning/files/recall_samples_binary.classification_dense/configurations.csv
index 8d94f66c45..03d7f03e43 100644
--- a/autosklearn/metalearning/files/recall_samples_binary.classification_dense/configurations.csv
+++ b/autosklearn/metalearning/files/recall_samples_binary.classification_dense/configurations.csv
@@ -1,128 +1,133 @@
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-38,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.756167892711991e-05,False,True,squared_hinge,5.235791933868702e-05,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0001632172987320906,most_frequent,standardize,,,,,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,5,None,19,12,1.0,70,,,,,
-39,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,3.6796787331285204e-07,0.14399102828508656,auto,255,None,18,12,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,quantile_transformer,965,normal,,,feature_agglomeration,,,,,,,,,,,,,,,cosine,average,365,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-40,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7005303455027131,None,0.0,1,5,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.027348815457551995,mean,quantile_transformer,789,uniform,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,
-41,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,1.943186107984505e-07,0.08852455702036988,auto,255,None,5,72,19,loss,1e-07,0.039837460762271175,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.01591265419367627,median,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,
-42,weighting,adaboost,SAMME,0.34205296521106404,10,89,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.02025205191860567,most_frequent,minmax,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-43,none,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0686374607394679,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,minmax,,,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.8490260132946401,False,,,,,,,,,,,,,,,
-44,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,10,1,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0022606253047811435,median,quantile_transformer,1247,normal,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,25.327793585266683,chi2,,,
-45,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.3663040729445102,None,0.0,20,19,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.010000000000000004,median,robust_scaler,,,0.7338553275791654,0.25,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,331,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-46,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,92.66144306787051,,,0.04654639302091653,rbf,-1,True,0.0022231544280610005,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.005194838694455963,mean,none,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,15,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-47,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1550807214391849,True,True,hinge,0.00019875748182257,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.05786688075604592,mean,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,
-48,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.4467384469668449,None,0.0,19,19,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.00033708405018786203,median,quantile_transformer,711,normal,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,7.744829113696808,False,True,1,squared_hinge,ovr,l1,0.0001869446320464273,,,,,,,,,,,,,,,,,,,,,,
-49,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,26.502259985828186,,,9.475662388405154e-05,rbf,-1,False,0.00019690635968217866,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-50,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,2.3520913174804126e-07,0.052918150906133596,auto,255,None,16,185,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.00790855487341428,mean,robust_scaler,,,0.746273035018099,0.21296174657639108,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,1.298440997922375,False,True,1,squared_hinge,ovr,l1,0.0018510248972884416,,,,,,,,,,,,,,,,,,,,,,
-51,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5255991794296913,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-52,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.34697723213923637,None,0.0,15,18,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.1923594385988973,mean,normalize,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,25.77499302945827,chi2,,,
-53,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1252.7997374154156,0.20811431041897355,5,0.0018140593074227453,poly,-1,False,0.03149782358621565,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.001930822730474197,most_frequent,quantile_transformer,233,normal,,,feature_agglomeration,,,,,,,,,,,,,,,euclidean,ward,29,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-54,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,1.5661823810486637e-10,0.08996863554015262,auto,255,None,156,89,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.009331521286159971,median,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,
-55,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.6109468076475573,0.8748383232154038,4,0.41333070422336576,poly,-1,False,3.591268778393482e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.011804925819789203,median,minmax,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-56,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.7388247096873266,None,0.0,1,2,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.02005334514549439,most_frequent,robust_scaler,,,0.9347422252540531,0.2909868620945952,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,
-57,weighting,adaboost,SAMME.R,0.03127273017917347,10,496,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,standardize,,,,,fast_ica,,,,,,,,,,,deflation,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-58,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,151,None,,0.0003283777607180646,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.2510527431514227,mean,normalize,,,,,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,-0.96093182232312,,0.05762510964607661,sigmoid,2632,,,,,,,,,,,,,,,,,
-59,none,bernoulli_nb,,,,,1.9593357170002308,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.005109073482613361,mean,quantile_transformer,10,normal,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.03495379340060141,False,True,1,squared_hinge,ovr,l1,0.0033386410851928994,,,,,,,,,,,,,,,,,,,,,,
-60,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.6453555269772124,None,0.0,1,4,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,normalize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-61,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5363.189119763322,,,0.4294659312024404,rbf,-1,True,0.06935839422940453,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.019860129781307266,most_frequent,none,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,92.1980997621979,mutual_info,,,
-62,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.20911875507660596,None,0.0,12,8,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,median,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,
-63,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.34534888916828577,0.02513634776687268,2,1.0733091880109409,poly,-1,True,0.0006974263047918307,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,quantile_transformer,1000,normal,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,77.99934218022861,mutual_info,,,
-64,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,29.55731828912983,False,True,1,squared_hinge,ovr,l2,0.0011332486951954968,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,
-65,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,1.1387795876755026e-06,0.12245605190343567,auto,255,None,58,7,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,robust_scaler,,,0.79305915648885,0.1841487406876042,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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-67,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,0.2638627266631576,0.022360820165005443,auto,255,None,171,6,19,loss,1e-07,0.016209060012924756,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.008763072395677399,mean,standardize,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,78,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-68,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5664.233390385957,0.3496807683995542,4,0.00017689402501527962,poly,-1,True,0.01756282016660122,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.13312139731266243,median,quantile_transformer,1362,uniform,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,72.39572310756226,mutual_info,,,
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-71,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.4264370333817712,None,0.0,13,3,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,median,minmax,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.14972073341699849,fpr,chi2
-72,none,adaboost,SAMME,0.07025975715693651,5,339,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,median,robust_scaler,,,0.7117466125338451,0.2984609070131515,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.21351971922962878,fdr,chi2
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-75,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,3.044019499065853e-08,0.030966538899129755,auto,255,None,64,50,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.00860619823050038,median,minmax,,,,,feature_agglomeration,,,,,,,,,,,,,,,cosine,average,36,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-76,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7.667945934601232,True,True,squared_hinge,1.5995631297928604e-05,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.004775063434540409,most_frequent,minmax,,,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.28455344994291587,False,True,1,squared_hinge,ovr,l1,6.230255219574715e-05,,,,,,,,,,,,,,,,,,,,,,
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-78,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,2.925599188129995e-05,0.12560285030651847,auto,255,None,300,193,17,loss,1e-07,0.039442997915546514,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00299400754997757,mean,robust_scaler,,,0.7093292916146515,0.009366983030047793,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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-80,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2471.298583654142,,,0.025866749427709803,rbf,-1,False,0.0009481287482516433,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,none,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,323,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-81,weighting,adaboost,SAMME,0.028939256953011736,2,368,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,quantile_transformer,1954,uniform,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,41.04018582009321,mutual_info,,,
-82,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,21.85207494001697,0.18872999589786676,4,1.1863646021868761,poly,-1,False,0.0021338371901294443,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0034080852834770217,mean,normalize,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.11326005109886934,fdr,f_classif
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-84,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,0.023106206735923184,0.12762814679231496,auto,255,None,24,151,16,loss,1e-07,0.08978875361280989,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.007723510861056437,median,quantile_transformer,1217,normal,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,
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-86,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,1,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.019797877010737826,mean,normalize,,,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.8239678741128165,False,,,,,,,,,,,,,,,
-87,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.46057831591617715,False,True,hinge,0.04557857428827514,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00027457445401600137,median,standardize,,,,,extra_trees_preproc_for_classification,True,gini,None,0.48190346970486964,None,0.0,17,18,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-88,none,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,230,auto,,0.0014037334852669801,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,quantile_transformer,848,uniform,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,61.06963527679606,f_classif,,,
-89,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,64,auto,,0.00021332620906594865,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.026032882800756493,median,standardize,,,,,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,-0.9513223792709335,,0.00029881582960753004,sigmoid,543,,,,,,,,,,,,,,,,,
-90,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.26356754187780895,None,0.0,4,3,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010966178401758774,mean,robust_scaler,,,0.7817043861798886,0.24371720112407158,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
-91,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,3.7287807266408084e-10,0.15544439749103187,auto,255,None,28,16,18,loss,1e-07,0.10835808508958217,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0207886310256421,mean,quantile_transformer,1245,normal,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.39383167612281667,fpr,f_classif
-92,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,130.2903387607578,False,True,1,squared_hinge,ovr,l2,0.0020820269897903502,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.08534605541971435,mean,quantile_transformer,613,normal,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,12.936259313956771,False,True,1,squared_hinge,ovr,l1,9.225567050752251e-05,,,,,,,,,,,,,,,,,,,,,,
-93,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.22879311080499845,None,0.0,8,4,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.014320022547615783,median,minmax,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.238630703440461,mutual_info,,,
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-97,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,1.1522414705935405e-10,0.6132800183073315,auto,255,None,1607,67,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010096911249562407,mean,robust_scaler,,,0.7663493326967942,0.136094217337434,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-98,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1228.3897711257332,0.6862670093933454,5,1.113863025113022,poly,-1,True,1.9271116749907707e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.018161834200128672,mean,normalize,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.06278549919408385,fpr,chi2
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-100,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.9815182201168626,None,0.0,15,19,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,robust_scaler,,,0.8878662977383482,0.25666364929970126,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,44.15748219826322,mutual_info,,,
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-102,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.7285597583341324,None,0.0,3,20,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.011700861722450575,most_frequent,robust_scaler,,,0.7911119178158159,0.2999762250828743,feature_agglomeration,,,,,,,,,,,,,,,euclidean,ward,151,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-103,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2897.996028838154,,,0.04621527412391808,rbf,-1,False,0.0017262380676728777,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.07770313303106262,mean,robust_scaler,,,0.7510736797641198,0.23032782852489117,extra_trees_preproc_for_classification,False,gini,None,0.9588902962283966,None,0.0,13,12,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-104,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.6282663389679584,None,0.0,1,3,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,median,minmax,,,,,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-105,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,8.14394583618027e-09,0.1314274861878701,auto,255,None,27,15,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.3598067419611515,mean,robust_scaler,,,0.7494615433236279,0.260649229829574,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
-106,weighting,adaboost,SAMME.R,0.46962472583666215,8,455,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.03708420001141539,most_frequent,robust_scaler,,,0.7364857891944228,0.23021698558282877,feature_agglomeration,,,,,,,,,,,,,,,euclidean,complete,5,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-107,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,11.046854203258679,,,0.07809571976769347,rbf,-1,False,0.00036193159909053734,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.006445557635410205,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-108,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.6728827282646637,None,0.0,18,8,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,robust_scaler,,,0.8512985751386942,0.14092824896182807,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,
-109,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.18060139887649052,None,0.0,10,12,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.010000000000000004,most_frequent,robust_scaler,,,0.9298612094255864,0.1263597946722308,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
-110,none,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.16236660803443026,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,quantile_transformer,377,normal,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2611168895496939,fpr,f_classif
-111,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6427543483503397,True,True,hinge,0.00045318775930916944,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.009338484332306904,median,quantile_transformer,174,normal,,,kernel_pca,,,,,,,,,,,,,,,,,,,,,,cosine,1848,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-112,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.02021502857757827,True,,0.055848937053280076,True,,constant,log,l2,,0.00016356162584832537,no_encoding,no_coalescense,,median,quantile_transformer,27,normal,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.11873149622606233,fwe,chi2
-113,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,9.029473608568458e-09,0.15779990576964315,auto,255,None,37,71,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.018093177195644097,mean,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,False,,,,,,,,,,,,
-114,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8.24807027221455,-0.23458710218209156,,0.0001030034352044511,sigmoid,-1,True,0.0002726309373020207,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,quantile_transformer,1974,normal,,,extra_trees_preproc_for_classification,True,entropy,None,0.9395220389606214,None,0.0,2,20,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-115,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.3747863651224668,True,True,hinge,0.00019106780876479388,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.002442315142014378,median,quantile_transformer,1850,uniform,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.570194774022733,f_classif,,,
-116,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.4864284879550203,None,0.0,1,4,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.008665854799630503,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-117,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.8785519499336585,None,0.0,13,17,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.05156816034494817,most_frequent,quantile_transformer,1243,uniform,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.11082264321263885,fdr,f_classif
-118,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.536709062860521e-07,True,,,True,,optimal,squared_hinge,l1,,1.6934515188687678e-05,one_hot_encoding,no_coalescense,,most_frequent,normalize,,,,,feature_agglomeration,,,,,,,,,,,,,,,cosine,complete,306,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-119,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.6905196223806276,None,0.0,6,5,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,minmax,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-120,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.6154724467600127,None,0.0,19,12,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,mean,minmax,,,,,extra_trees_preproc_for_classification,True,entropy,None,0.7630778936392856,None,0.0,11,20,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-121,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5836006490230695,None,0.0,1,19,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.02127792104027424,most_frequent,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,
-122,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.922871100218626,False,True,squared_hinge,0.09957180737451943,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0016563186856411274,median,quantile_transformer,850,uniform,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.09123695282772847,fpr,chi2
-123,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.06571678518052461,False,True,1,squared_hinge,ovr,l2,3.5551445493853517e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0007406565537349627,median,quantile_transformer,1114,normal,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.13436517316012828,fwe,f_classif
-124,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1561.1002600303584,,,0.0024048551704194507,rbf,-1,False,0.0001936214667496851,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,minmax,,,,,fast_ica,,,,,,,,,,,parallel,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-125,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.087313242780732,0.0636709914987994,3,0.004481917125024585,poly,-1,False,0.01848880618941367,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.002136015035740733,mean,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-126,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.03204653439894958,True,True,squared_hinge,0.00013006393401112688,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.013506575694382031,most_frequent,normalize,,,,,feature_agglomeration,,,,,,,,,,,,,,,euclidean,ward,356,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-127,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.6466927589470762,None,0.0,11,2,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.009672465311659264,most_frequent,standardize,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,369,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+1,none,decision_tree,,,,,,,entropy,1.877536950957045,1.0,None,0.0,10,13,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,quantile_transformer,1275,uniform,,,extra_trees_preproc_for_classification,True,gini,None,0.04910706843318435,None,0.0,12,12,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+2,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.8905944749638223,None,0.0,5,4,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.15463552048633444,most_frequent,robust_scaler,,,0.7545842296967857,0.22898551665984185,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.24780801481682346,fwe,chi2
+5,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,9.83838677007206e-09,0.09269618726619562,auto,255,None,37,16,14,loss,1e-07,0.04208303685387712,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
+6,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,4.267426422904654e-06,0.011555121209024403,auto,255,None,121,34,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010064483887441845,most_frequent,quantile_transformer,1194,uniform,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,
+8,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,114,manual,0.11778631707605758,0.0029112721339713465,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.015582195257240751,median,normalize,,,,,kernel_pca,,,,,,,,,,,,,,,,,,,0.42909623253327767,3,0.25407612000343777,poly,1689,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+9,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.685918004243461,True,True,squared_hinge,1.7784504877380735e-05,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010335606354842895,most_frequent,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,
+10,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.7396493120555564,None,0.0,1,18,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010000000000000004,median,robust_scaler,,,0.8908334215036855,0.08666907249785906,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,
+13,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,0.9907352051102745,0.018115439174701313,auto,255,None,84,5,3,loss,1e-07,0.13697766901072808,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.036524416007602305,mean,robust_scaler,,,0.9824116214519696,0.2911868066464925,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,
+14,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,4.629265598705481e-10,0.03894546521791516,auto,255,None,5,22,18,loss,1e-07,0.0837137336215462,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.008397749525484112,mean,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
+15,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.21772092350860472,None,0.0,14,17,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.0020488212891693275,median,standardize,,,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,33.74257606530722,False,True,1,squared_hinge,ovr,l1,8.740452966205111e-05,,,,,,,,,,,,,,,,,,,,,,
+17,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.6591033863118033,None,0.0,10,10,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,none,,,,,fast_ica,,,,,,,,,,,deflation,exp,1506,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+18,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,1.9110915129429343e-09,0.06380311481632683,auto,255,None,20,62,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,robust_scaler,,,0.7307407504330838,0.2660206566992171,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,
+19,weighting,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,99,2,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010033505607662804,median,none,,,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.069760700053376,False,True,1,squared_hinge,ovr,l1,0.06611177025130774,,,,,,,,,,,,,,,,,,,,,,
+23,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,2.0447842449942555e-09,0.04257026709693251,auto,255,None,47,13,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010000000000000004,median,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,
+24,weighting,adaboost,SAMME.R,0.44886160948796244,9,493,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.031007103864624616,mean,robust_scaler,,,0.7575180367976682,0.26457274298753825,feature_agglomeration,,,,,,,,,,,,,,,cosine,complete,211,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+25,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,0.008942041103505539,0.05229567301622349,auto,255,None,6,24,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.01936143670847451,mean,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,
+26,weighting,adaboost,SAMME.R,0.01561270559347964,4,417,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,quantile_transformer,1617,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+27,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.41025844265119926,None,0.0,18,15,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,median,quantile_transformer,1911,normal,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,False,,,,,,,,,,,,
+29,none,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.9034033923099005,False,True,1,squared_hinge,ovr,l2,1.2159485299684885e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.027586423552745792,most_frequent,none,,,,,kernel_pca,,,,,,,,,,,,,,,,,,,,,0.4034250963681743,rbf,735,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+30,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,490.8047233341048,0.13853827450882505,,0.004044309450174347,sigmoid,-1,False,0.005958796387567885,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.008446690235218424,median,minmax,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,229,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+31,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,3.0538220042587883e-07,0.0704056758356995,auto,255,None,9,3,19,loss,1e-07,0.01927933154167906,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.019804174585488932,median,quantile_transformer,921,normal,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,
+32,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,101,auto,,1.553086088086431e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.01137059177001425,most_frequent,normalize,,,,,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,-0.021621751597165284,4,0.7468597264810062,poly,5078,,,,,,,,,,,,,,,,,
+33,none,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.5769165522851386,None,0.0,1,6,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,none,,,,,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,7,None,9,14,1.0,74,,,,,
+34,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,0.06671977293714751,0.04456236025167353,auto,255,None,3,163,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.4333293305857194,mean,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+37,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0618160212153551,True,True,squared_hinge,1.2370453081062464e-05,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.01448339838697973,mean,standardize,,,,,kitchen_sinks,,,,,,,,,,,,,,,,,,,,,,,,0.018879929475513826,1637,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+39,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.6799215694343422,None,0.0,1,5,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.004724310659953132,most_frequent,quantile_transformer,1037,normal,,,fast_ica,,,,,,,,,,,parallel,exp,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+41,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.6965935192702026,None,0.0,12,16,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,none,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,67.60637688434707,f_classif,,,
+43,none,decision_tree,,,,,,,gini,1.1110863621017264,1.0,None,0.0,16,19,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.00043339385698342055,mean,none,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.20957492283649354,fwe,f_classif
+44,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,2.6159836259985015e-05,0.05532294838858921,auto,255,None,116,4,14,loss,1e-07,0.20422881335187132,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,
+49,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00010123414317660679,False,,0.04011567850168966,True,,constant,log,l1,,0.00014179758438189507,one_hot_encoding,minority_coalescer,0.008920898694379564,mean,robust_scaler,,,0.7712963178784032,0.2702398922671455,kernel_pca,,,,,,,,,,,,,,,,,,,,,,cosine,1090,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+54,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,1.0607494292547588e-08,0.014599960935351135,auto,255,None,5,7,1,loss,1e-07,0.01508309574734627,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
+58,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5547994712622472,None,0.0,1,2,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.008028893322827371,most_frequent,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
+59,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9768.124422997415,False,True,1,squared_hinge,ovr,l2,0.002391133479983495,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,robust_scaler,,,0.7418544320601411,0.258610892841927,fast_ica,,,,,,,,,,,parallel,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+60,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.8757952087292588,None,0.0,3,7,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.00023383575324676886,most_frequent,robust_scaler,,,0.75,0.25885426505910014,feature_agglomeration,,,,,,,,,,,,,,,cosine,average,83,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+64,weighting,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.5927254703947399,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.004193053494360822,median,none,,,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.5779022723643873,False,,,,,,,,,,,,,,,
+65,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,4.427693008576421e-07,0.1480512588831196,auto,255,None,5,46,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.07105836118280727,median,quantile_transformer,875,normal,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,False,,,,,,,,,,,,
+66,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,236.02119240292217,False,True,1,squared_hinge,ovr,l2,0.0017427914676337187,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.1020917547461355,mean,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,False,,,,,,,,,,,,
+67,weighting,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2974150304199443,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0054956766969650565,median,standardize,,,,,feature_agglomeration,,,,,,,,,,,,,,,cosine,average,311,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+73,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,6.794013031623291e-09,0.07399415058344509,auto,255,None,4,112,15,loss,1e-07,0.07001899213382194,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,quantile_transformer,1301,normal,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,
+76,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4380.951445492721,,,0.15832144806947993,rbf,-1,True,0.00010691725511780856,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.01066869533165506,median,minmax,,,,,fast_ica,,,,,,,,,,,deflation,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+77,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.2309302197434343e-05,False,,0.006503403719704897,True,,constant,log,l1,,1.531583361000749e-05,one_hot_encoding,no_coalescense,,most_frequent,robust_scaler,,,0.7530789965606406,0.2430090971909385,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,cosine,1693,,,,,,,,,,,,,,,,,
+80,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5858785544767082,None,0.0,1,14,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,median,robust_scaler,,,0.8409151021946415,0.03951963852061448,fast_ica,,,,,,,,,,,deflation,exp,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+81,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.9680517112466928,None,0.0,1,4,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.012740842287122786,most_frequent,robust_scaler,,,0.889930464615107,0.24974016722405976,feature_agglomeration,,,,,,,,,,,,,,,euclidean,average,25,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+84,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,3.6348307794592436e-10,0.07233284229137055,auto,255,None,1386,47,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,quantile_transformer,466,uniform,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,47.05668163013577,chi2,,,
+88,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,1.134145934367294e-08,0.05597839358087172,auto,255,None,20,50,15,loss,1e-07,0.04047547712408959,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.013656355693947419,most_frequent,robust_scaler,,,0.8162585020545898,0.24147824330789028,feature_agglomeration,,,,,,,,,,,,,,,euclidean,ward,51,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+89,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9.552100559095382e-05,True,,0.007209292257917831,True,,constant,log,l2,,1.70337957134729e-05,no_encoding,minority_coalescer,0.0182109481043058,most_frequent,none,,,,,kernel_pca,,,,,,,,,,,,,,,,,,,,,1.1223991702013292,rbf,152,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+90,none,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.752445508156732e-06,True,,3.4363022662141264e-05,True,,constant,perceptron,l1,,0.0005054929998027575,no_encoding,minority_coalescer,0.010000000000000004,median,standardize,,,,,kernel_pca,,,,,,,,,,,,,,,,,,,0.7300101465014597,2,1.1347040432914681,poly,503,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+92,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.8158497565615254,None,0.0,10,19,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0478920998898566,median,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,
+95,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,7.317088886513159e-10,0.24578100384428256,auto,255,None,21,57,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.011702107724078393,median,normalize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,
+96,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.6368438199103545,None,0.0,1,3,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.010432869286732826,mean,quantile_transformer,833,uniform,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.12239619191468397,fdr,chi2
+98,none,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.8285223054657075,None,0.0,4,7,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0023595334394246165,median,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,
+103,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.8092441205064402,None,0.0,2,6,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.3591460473349783,mean,quantile_transformer,1305,uniform,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,61.99836327945116,chi2,,,
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+111,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.4990576176544739,None,0.0,6,6,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
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+303,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.4427140294863755,None,0.0,8,20,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,robust_scaler,,,0.960103135751788,0.10844954570838759,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+304,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.556350598907517,False,True,squared_hinge,3.1140755492724956e-05,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.1680440617073617,mean,quantile_transformer,1651,normal,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,88.55656428565635,chi2,,,
+310,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,68,2,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010000000000000004,mean,none,,,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.766336759369817,False,,,,,,,,,,,,,,,
+315,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.8408063181862852,True,True,hinge,0.004342389918487474,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.26119501093358377,most_frequent,quantile_transformer,1319,uniform,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.897412266178953,True,,,,,,,,,,,,,,,
+337,weighting,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,minmax,,,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,227.42750098824558,False,True,1,squared_hinge,ovr,l1,0.0018060472646546995,,,,,,,,,,,,,,,,,,,,,,
+341,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.513560345508016,None,0.0,3,2,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,median,quantile_transformer,1044,uniform,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.07550342883878675,,mutual_info_classif
+346,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,0.15762740504817374,0.17692452709577644,auto,255,None,282,9,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.005207945885722629,most_frequent,robust_scaler,,,0.75,0.27100271805667314,feature_agglomeration,,,,,,,,,,,,,,,euclidean,complete,106,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+350,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.2318705830210126,True,True,hinge,0.0002994421772278671,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.13310408167721913,most_frequent,quantile_transformer,1036,uniform,,,kernel_pca,,,,,,,,,,,,,,,,,,,,,,cosine,703,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+360,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.5720469164289109,None,0.0,1,17,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.03310727058091163,median,robust_scaler,,,0.8680123006808074,0.024825227678742904,fast_ica,,,,,,,,,,,parallel,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+367,weighting,adaboost,SAMME,0.2540331297425094,2,187,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,robust_scaler,,,0.9867887266047534,0.041631156927011324,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50.70828969428249,chi2,,,
+371,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,4.198700105243736e-09,0.011070860038925674,auto,255,None,9,116,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010247554402833578,median,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,
+374,none,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.7304811343030777,None,0.0,2,3,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,robust_scaler,,,0.7202654714984674,0.21555973677200435,feature_agglomeration,,,,,,,,,,,,,,,euclidean,average,149,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+378,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,6.485533704735031e-07,0.03500257587365526,auto,255,None,4,7,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00026264639049847547,most_frequent,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+386,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9358939105347388,None,0.0,1,5,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,none,,,,,fast_ica,,,,,,,,,,,parallel,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+398,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.7667112074789202,None,0.0,4,20,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.001721469100933522,median,robust_scaler,,,0.7582814631443476,0.25,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+404,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8.380057363340162,False,True,hinge,0.0020653831067503525,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.05020677662128942,median,quantile_transformer,560,normal,,,kernel_pca,,,,,,,,,,,,,,,,,,,,,,cosine,891,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+407,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.7292011996224884,True,True,hinge,8.162763086383311e-05,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010337761588362885,median,quantile_transformer,877,uniform,,,kernel_pca,,,,,,,,,,,,,,,,,,,,,,cosine,1317,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+415,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,5.215923689311285e-05,0.01709129738847392,auto,255,None,10,19,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0004854193510398933,most_frequent,quantile_transformer,709,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+420,weighting,adaboost,SAMME.R,0.05920795019389743,8,313,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,standardize,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,104,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+426,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1527.3847932648184,False,True,1,squared_hinge,ovr,l2,0.0037778228082614835,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,minmax,,,,,kitchen_sinks,,,,,,,,,,,,,,,,,,,,,,,,0.00025777548503151833,2621,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+430,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,2.2389471617630046e-10,0.07245292952526383,auto,255,None,25,75,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.023490803960861777,median,quantile_transformer,1107,uniform,,,extra_trees_preproc_for_classification,False,gini,None,0.7778748752629213,None,0.0,1,14,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+434,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.4438263804770306,None,0.0,2,10,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,mean,robust_scaler,,,0.9824945141547571,0.24967364012300372,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+438,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,6.090138036399552e-10,0.017305568433437588,auto,255,None,202,188,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,robust_scaler,,,0.7526196011313747,0.1563616005702636,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+443,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.6269076084367177,None,0.0,1,7,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.12943250859943486,most_frequent,standardize,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,62.84526554323969,chi2,,,
+448,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.7463662441763204,None,0.0,20,18,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0004507212632016269,mean,none,,,,,extra_trees_preproc_for_classification,True,gini,None,0.4279327206778048,None,0.0,3,9,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+456,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0322540554793962e-05,False,,0.015117861103299655,True,,invscaling,log,l1,0.558781568719232,0.00016877274953233003,one_hot_encoding,no_coalescense,,median,quantile_transformer,681,normal,,,extra_trees_preproc_for_classification,False,gini,None,0.6320249932267078,None,0.0,17,18,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+461,none,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.9796199105973851,None,0.0,2,13,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.00845950033601661,mean,robust_scaler,,,0.8275228560550734,0.23814615163106348,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
diff --git a/autosklearn/metalearning/files/recall_samples_binary.classification_dense/description.txt b/autosklearn/metalearning/files/recall_samples_binary.classification_dense/description.txt
index 7301ec5e29..f2935a5a24 100644
--- a/autosklearn/metalearning/files/recall_samples_binary.classification_dense/description.txt
+++ b/autosklearn/metalearning/files/recall_samples_binary.classification_dense/description.txt
@@ -1,8 +1,9 @@
 features_cutoff_time: 3600
 features_cutoff_memory: 3072
-number_of_feature_steps: 35
+number_of_feature_steps: 42
 feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances
 feature_step LogNumberOfInstances: LogNumberOfInstances
+feature_step NumberOfClasses: NumberOfClasses
 feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures
 feature_step LogNumberOfFeatures: LogNumberOfFeatures
 feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues
@@ -20,6 +21,11 @@ feature_step DatasetRatio: DatasetRatio, LogDatasetRatio
 feature_step LogDatasetRatio: LogDatasetRatio
 feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio
 feature_step LogInverseDatasetRatio: LogInverseDatasetRatio
+feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD
+feature_step ClassProbabilityMin: ClassProbabilityMin
+feature_step ClassProbabilityMax: ClassProbabilityMax
+feature_step ClassProbabilityMean: ClassProbabilityMean
+feature_step ClassProbabilitySTD: ClassProbabilitySTD
 feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum
 feature_step SymbolsMin: SymbolsMin
 feature_step SymbolsMax: SymbolsMax
@@ -36,11 +42,12 @@ feature_step SkewnessMin: SkewnessMin
 feature_step SkewnessMax: SkewnessMax
 feature_step SkewnessMean: SkewnessMean
 feature_step SkewnessSTD: SkewnessSTD
-features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
+feature_step ClassEntropy: ClassEntropy
+features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy
 features_stochastic: 
-default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
+default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy
 
-algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127
+algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235,236,237,238,239,240,241,242,243,244,245,246,247,248,249,250,251,252,253,254,255,256,257,258,259,260,261,262,263,264,265,266,267,268,269,270,271,272,273,274,275,276,277,278,279,280,281,282,283,284,285,286,287,288,289,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,312,313,314,315,316,317,318,319,320,321,322,323,324,325,326,327,328,329,330,331,332,333,334,335,336,337,338,339,340,341,342,343,344,345,346,347,348,349,350,351,352,353,354,355,356,357,358,359,360,361,362,363,364,365,366,367,368,369,370,371,372,373,374,375,376,377,378,379,380,381,382,383,384,385,386,387,388,389,390,391,392,393,394,395,396,397,398,399,400,401,402,403,404,405,406,407,408,409,410,411,412,413,414,415,416,417,418,419,420,421,422,423,424,425,426,427,428,429,430,431,432,433,434,435,436,437,438,439,440,441,442,443,444,445,446,447,448,449,450,451,452,453,454,455,456,457,458,459,460,461,462,463,464
 algorithms_stochastic: 
 performance_measures: recall_samples
 performance_type: solution_quality
diff --git a/autosklearn/metalearning/files/recall_samples_binary.classification_dense/feature_costs.arff b/autosklearn/metalearning/files/recall_samples_binary.classification_dense/feature_costs.arff
index 0e17e20d22..8512fd9275 100644
--- a/autosklearn/metalearning/files/recall_samples_binary.classification_dense/feature_costs.arff
+++ b/autosklearn/metalearning/files/recall_samples_binary.classification_dense/feature_costs.arff
@@ -4,6 +4,7 @@
 @ATTRIBUTE repetition NUMERIC
 @ATTRIBUTE NumberOfInstances NUMERIC
 @ATTRIBUTE LogNumberOfInstances NUMERIC
+@ATTRIBUTE NumberOfClasses NUMERIC
 @ATTRIBUTE NumberOfFeatures NUMERIC
 @ATTRIBUTE LogNumberOfFeatures NUMERIC
 @ATTRIBUTE MissingValues NUMERIC
@@ -21,6 +22,11 @@
 @ATTRIBUTE LogDatasetRatio NUMERIC
 @ATTRIBUTE InverseDatasetRatio NUMERIC
 @ATTRIBUTE LogInverseDatasetRatio NUMERIC
+@ATTRIBUTE ClassOccurences NUMERIC
+@ATTRIBUTE ClassProbabilityMin NUMERIC
+@ATTRIBUTE ClassProbabilityMax NUMERIC
+@ATTRIBUTE ClassProbabilityMean NUMERIC
+@ATTRIBUTE ClassProbabilitySTD NUMERIC
 @ATTRIBUTE NumSymbols NUMERIC
 @ATTRIBUTE SymbolsMin NUMERIC
 @ATTRIBUTE SymbolsMax NUMERIC
@@ -37,162 +43,138 @@
 @ATTRIBUTE SkewnessMax NUMERIC
 @ATTRIBUTE SkewnessMean NUMERIC
 @ATTRIBUTE SkewnessSTD NUMERIC
+@ATTRIBUTE ClassEntropy NUMERIC
 
 @DATA
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+75234,1.0,1e-05,1e-05,0.00015,1e-05,1e-05,0.0058,0.00534,1e-05,0.00033,1e-05,0.00016,0.0,2e-05,2e-05,7e-05,0.00204,3e-05,1e-05,5e-05,1e-05,0.00024,3e-05,2e-05,0.0001,9e-05,0.00043,0.0,0.0,0.00012,0.00024,6e-05,0.00156,6e-05,7e-05,0.00121,0.00021,0.00054,6e-05,7e-05,9e-05,0.00032,0.00501
+75235,1.0,1e-05,1e-05,0.0003,2e-05,1e-05,0.00461,0.00385,2e-05,0.00055,2e-05,0.00026,1e-05,3e-05,3e-05,0.0001,0.00011,3e-05,1e-05,5e-05,2e-05,0.00026,3e-05,1e-05,8e-05,0.00014,0.00075,1e-05,1e-05,0.0002,0.00043,0.00011,0.00127,0.00017,0.0002,0.00029,0.00061,0.0008,0.00015,0.00012,0.00016,0.00038,0.00242
+75236,1.0,1e-05,1e-05,0.0001,1e-05,1e-05,0.00218,0.00118,2e-05,0.00065,3e-05,0.00041,1e-05,3e-05,3e-05,9e-05,9e-05,4e-05,1e-05,7e-05,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.0006,1e-05,1e-05,0.00021,0.00032,6e-05,0.00043,7e-05,7e-05,9e-05,0.00019,0.00062,7e-05,8e-05,0.0001,0.00037,0.00048
+75237,1.0,2e-05,1e-05,0.00785,2e-05,1e-05,0.31031,0.27854,2e-05,0.01161,3e-05,0.02022,1e-05,4e-05,3e-05,9e-05,0.00024,6e-05,2e-05,9e-05,2e-05,0.00062,7e-05,3e-05,0.00017,0.00035,0.0008,1e-05,1e-05,0.00028,0.00043,8e-05,0.00043,8e-05,7e-05,9e-05,0.0002,0.00044,6e-05,8e-05,9e-05,0.00021,0.14028
+75239,1.0,1e-05,1e-05,7e-05,1e-05,1e-05,0.00105,0.00084,1e-05,0.00015,1e-05,8e-05,1e-05,2e-05,5e-05,4e-05,8e-05,2e-05,1e-05,3e-05,1e-05,0.00016,2e-05,1e-05,5e-05,9e-05,0.00049,0.0,0.0,0.00012,0.00026,0.00011,0.00046,0.00013,8e-05,8e-05,0.00018,0.00044,6e-05,9e-05,0.00011,0.00018,0.00065
+75240,1.0,2e-05,1e-05,0.00036,5e-05,4e-05,0.00845,0.00644,6e-05,0.00122,4e-05,0.00091,1e-05,4e-05,4e-05,9e-05,0.00013,5e-05,2e-05,9e-05,5e-05,0.00031,3e-05,1e-05,7e-05,0.0002,0.00073,3e-05,2e-05,0.00019,0.0003,0.00018,0.00055,9e-05,9e-05,0.00011,0.00025,0.0005,7e-05,8e-05,0.00015,0.0002,0.00365
+75243,1.0,1e-05,1e-05,0.00029,1e-05,1e-05,0.00461,0.00415,1e-05,0.00035,1e-05,0.00015,0.0,2e-05,2e-05,5e-05,7e-05,2e-05,1e-05,4e-05,1e-05,0.00018,2e-05,1e-05,4e-05,0.00011,0.00022,1e-05,1e-05,5e-05,0.00011,5e-05,0.0004,6e-05,6e-05,8e-05,0.0002,0.00039,6e-05,6e-05,8e-05,0.00019,0.00265
+75244,1.0,3e-05,2e-05,0.0004,3e-05,2e-05,0.0173,0.01298,4e-05,0.00267,4e-05,0.00174,1e-05,7e-05,9e-05,0.00024,0.00027,8e-05,2e-05,0.00013,3e-05,0.00045,7e-05,2e-05,0.00011,0.00025,0.00077,6e-05,2e-05,0.00021,0.00034,0.00013,0.00077,0.00014,0.00013,0.00026,0.00023,0.00044,7e-05,8e-05,0.0001,0.00019,0.00649
+75248,1.0,1e-05,1e-05,0.00017,1e-05,1e-05,0.00416,0.00305,1e-05,0.0006,2e-05,0.00054,1e-05,2e-05,2e-05,5e-05,7e-05,2e-05,1e-05,4e-05,1e-05,0.00017,2e-05,1e-05,4e-05,0.0001,0.00029,5e-05,1e-05,6e-05,0.00012,6e-05,0.00081,0.00011,0.00015,0.00016,0.00038,0.0005,7e-05,8e-05,0.00012,0.00024,0.00184
+75249,1.0,1e-05,1e-05,0.00011,1e-05,1e-05,0.00192,0.00153,1e-05,0.00025,1e-05,0.00017,1e-05,2e-05,2e-05,5e-05,7e-05,2e-05,1e-05,5e-05,3e-05,0.00014,2e-05,1e-05,3e-05,8e-05,0.00029,1e-05,3e-05,6e-05,0.00012,7e-05,0.00041,6e-05,8e-05,0.0001,0.00017,0.00043,6e-05,7e-05,9e-05,0.00019,0.00092
+75250,1.0,1e-05,1e-05,0.00375,1e-05,1e-05,0.04994,0.047,1e-05,0.00247,1e-05,0.00049,1e-05,2e-05,2e-05,6e-05,7e-05,2e-05,1e-05,3e-05,1e-05,0.00022,4e-05,1e-05,4e-05,0.00012,0.00041,0.0,1e-05,0.00011,0.00023,6e-05,0.00076,0.00012,0.00012,0.00019,0.00032,0.00086,0.00012,0.00013,0.00018,0.00043,0.04178
diff --git a/autosklearn/metalearning/files/recall_samples_binary.classification_dense/feature_runstatus.arff b/autosklearn/metalearning/files/recall_samples_binary.classification_dense/feature_runstatus.arff
index 0034ad2bd6..5fff2dfed3 100644
--- a/autosklearn/metalearning/files/recall_samples_binary.classification_dense/feature_runstatus.arff
+++ b/autosklearn/metalearning/files/recall_samples_binary.classification_dense/feature_runstatus.arff
@@ -4,6 +4,7 @@
 @ATTRIBUTE repetition NUMERIC
 @ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other}
@@ -21,6 +22,11 @@
 @ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other}
@@ -37,162 +43,138 @@
 @ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other}
 
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diff --git a/autosklearn/metalearning/files/recall_samples_binary.classification_dense/feature_values.arff b/autosklearn/metalearning/files/recall_samples_binary.classification_dense/feature_values.arff
index a60400e3ce..736dc64321 100644
--- a/autosklearn/metalearning/files/recall_samples_binary.classification_dense/feature_values.arff
+++ b/autosklearn/metalearning/files/recall_samples_binary.classification_dense/feature_values.arff
@@ -13,7 +13,6 @@
 @ATTRIBUTE KurtosisMean NUMERIC
 @ATTRIBUTE KurtosisMin NUMERIC
 @ATTRIBUTE KurtosisSTD NUMERIC
-@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC
 @ATTRIBUTE LogDatasetRatio NUMERIC
 @ATTRIBUTE LogInverseDatasetRatio NUMERIC
 @ATTRIBUTE LogNumberOfFeatures NUMERIC
@@ -42,160 +41,135 @@
 @ATTRIBUTE SymbolsSum NUMERIC
 
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diff --git a/autosklearn/metalearning/files/recall_samples_binary.classification_sparse/algorithm_runs.arff b/autosklearn/metalearning/files/recall_samples_binary.classification_sparse/algorithm_runs.arff
index d97e59b7aa..3d31fb4274 100644
--- a/autosklearn/metalearning/files/recall_samples_binary.classification_sparse/algorithm_runs.arff
+++ b/autosklearn/metalearning/files/recall_samples_binary.classification_sparse/algorithm_runs.arff
@@ -7,95 +7,133 @@
 @ATTRIBUTE runstatus {ok, timeout, memout, not_applicable, crash, other}
 
 @DATA
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-75219,1.0,18,?,not_applicable
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 75222,1.0,27,?,not_applicable
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+75106,1.0,358,?,not_applicable
+75120,1.0,365,?,not_applicable
+75193,1.0,369,?,not_applicable
diff --git a/autosklearn/metalearning/files/recall_samples_binary.classification_sparse/configurations.csv b/autosklearn/metalearning/files/recall_samples_binary.classification_sparse/configurations.csv
index 888e169980..c3d201ba5c 100644
--- a/autosklearn/metalearning/files/recall_samples_binary.classification_sparse/configurations.csv
+++ b/autosklearn/metalearning/files/recall_samples_binary.classification_sparse/configurations.csv
@@ -1,93 +1,131 @@
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-88,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7408631116596028,None,0.0,19,2,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,mean,none,,,,,extra_trees_preproc_for_classification,True,entropy,None,0.8873056409960391,None,0.0,11,18,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-89,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.922871100218626,False,True,squared_hinge,0.09957180737451943,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0016563186856411274,median,quantile_transformer,850,uniform,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.09123695282772847,fpr,chi2,
-90,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4834106677905965,False,True,1,squared_hinge,ovr,l2,0.00017625027586147415,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0009157492873444966,most_frequent,quantile_transformer,1150,normal,,,extra_trees_preproc_for_classification,False,entropy,None,0.4981700453792278,None,0.0,9,15,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-91,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.087313242780732,0.0636709914987994,3,0.004481917125024585,poly,-1,False,0.01848880618941367,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.002136015035740733,mean,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-92,weighting,adaboost,SAMME,0.028177459966491795,1,484,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,quantile_transformer,875,normal,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,1.502807354196727,False,True,1,squared_hinge,ovr,l1,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,
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+2,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.8905944749638223,None,0.0,5,4,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.15463552048633444,most_frequent,robust_scaler,,,0.7545842296967857,0.22898551665984185,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.24780801481682346,fwe,chi2,
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+16,weighting,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,99,2,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010033505607662804,median,none,,,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.069760700053376,False,True,1,squared_hinge,ovr,l1,0.06611177025130774,,,,,,,,,,,,,,,,,,,,,
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+22,weighting,adaboost,SAMME.R,0.01561270559347964,4,417,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,quantile_transformer,1617,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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+239,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1359.4305032221287,,,0.18567450957191756,rbf,-1,False,0.00964468225236062,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,none,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,44.51779759183326,chi2,,,,
+241,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.15898494775796565,None,0.0,1,6,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.1952015685805085,median,robust_scaler,,,0.7368704336135686,0.2705737885997559,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,,
+243,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.4427140294863755,None,0.0,8,20,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,robust_scaler,,,0.960103135751788,0.10844954570838759,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+244,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.556350598907517,False,True,squared_hinge,3.1140755492724956e-05,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.1680440617073617,mean,quantile_transformer,1651,normal,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,88.55656428565635,chi2,,,,
+248,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.6607528187073621,None,0.0,2,4,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,quantile_transformer,940,uniform,,,extra_trees_preproc_for_classification,True,gini,None,0.0179591096201271,None,0.0,3,16,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+253,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.9534531912401635,None,0.0,7,6,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.011193163265283827,median,none,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,54.565525966625415,chi2,,,,
+258,none,adaboost,SAMME,0.6791611572325104,6,465,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,quantile_transformer,1464,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+274,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.513560345508016,None,0.0,3,2,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,median,quantile_transformer,1044,uniform,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.07550342883878675,,mutual_info_classif,
+277,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,57,2,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+280,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.2318705830210126,True,True,hinge,0.0002994421772278671,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.13310408167721913,most_frequent,quantile_transformer,1036,uniform,,,kernel_pca,,,,,,,,,,,,,,cosine,703,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+288,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.5719833791553314,None,0.0,16,3,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.13804007452389966,mean,normalize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+290,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.262649106979026,None,0.0,3,19,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,mean,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,,
+293,weighting,adaboost,SAMME,0.2540331297425094,2,187,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,robust_scaler,,,0.9867887266047534,0.041631156927011324,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50.70828969428249,chi2,,,,
+297,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.0014200038703931547,most_frequent,robust_scaler,,,0.7373450748248416,0.26419938540085264,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+300,none,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.7010137052424128,None,0.0,7,10,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.2761813291805052,most_frequent,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,,
+304,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.828038345740254,None,0.0,8,2,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,,
+307,none,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,249,None,,0.0005516162390755682,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010000000000000004,mean,quantile_transformer,1350,uniform,,,kernel_pca,,,,,,,,,,,0.162692683181769,3,1.0,poly,487,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+312,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,60.6761602925822,,,0.038224035513907226,rbf,-1,False,0.0018214343969776353,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.421025538999516,median,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+320,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.7667112074789202,None,0.0,4,20,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.001721469100933522,median,robust_scaler,,,0.7582814631443476,0.25,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+325,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8.380057363340162,False,True,hinge,0.0020653831067503525,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.05020677662128942,median,quantile_transformer,560,normal,,,kernel_pca,,,,,,,,,,,,,,cosine,891,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+328,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.7292011996224884,True,True,hinge,8.162763086383311e-05,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010337761588362885,median,quantile_transformer,877,uniform,,,kernel_pca,,,,,,,,,,,,,,cosine,1317,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+331,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.5,None,0.0,1,5,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.15244281596303316,mean,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+334,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.489205056074126,None,0.0,20,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0007633437565444919,most_frequent,robust_scaler,,,0.75,0.2540789187813349,extra_trees_preproc_for_classification,False,entropy,None,0.22062129922609008,None,0.0,19,6,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+338,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.37035966336010345,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.007136588202285817,mean,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+341,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5287741993191233,None,0.0,12,17,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,normalize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,False,,,,,,,,,,,,,
+345,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.4385746943443949,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,median,none,,,,,extra_trees_preproc_for_classification,False,gini,None,0.4961402916688301,None,0.0,1,18,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+349,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.4438263804770306,None,0.0,2,10,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,mean,robust_scaler,,,0.9824945141547571,0.24967364012300372,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+352,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5746384738423225,None,0.0,1,2,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.014830459445672814,mean,robust_scaler,,,0.7814390720260649,0.2588734594830571,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+353,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.6269076084367177,None,0.0,1,7,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.12943250859943486,most_frequent,standardize,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,62.84526554323969,chi2,,,,
+358,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.7463662441763204,None,0.0,20,18,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0004507212632016269,mean,none,,,,,extra_trees_preproc_for_classification,True,gini,None,0.4279327206778048,None,0.0,3,9,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+365,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0322540554793962e-05,False,,0.015117861103299655,True,,invscaling,log,l1,0.558781568719232,0.00016877274953233003,one_hot_encoding,no_coalescense,,median,quantile_transformer,681,normal,,,extra_trees_preproc_for_classification,False,gini,None,0.6320249932267078,None,0.0,17,18,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+369,none,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.9796199105973851,None,0.0,2,13,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.00845950033601661,mean,robust_scaler,,,0.8275228560550734,0.23814615163106348,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
diff --git a/autosklearn/metalearning/files/recall_samples_binary.classification_sparse/description.txt b/autosklearn/metalearning/files/recall_samples_binary.classification_sparse/description.txt
index a13ccd158b..450318293f 100644
--- a/autosklearn/metalearning/files/recall_samples_binary.classification_sparse/description.txt
+++ b/autosklearn/metalearning/files/recall_samples_binary.classification_sparse/description.txt
@@ -1,8 +1,9 @@
 features_cutoff_time: 3600
 features_cutoff_memory: 3072
-number_of_feature_steps: 35
+number_of_feature_steps: 42
 feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances
 feature_step LogNumberOfInstances: LogNumberOfInstances
+feature_step NumberOfClasses: NumberOfClasses
 feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures
 feature_step LogNumberOfFeatures: LogNumberOfFeatures
 feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues
@@ -20,6 +21,11 @@ feature_step DatasetRatio: DatasetRatio, LogDatasetRatio
 feature_step LogDatasetRatio: LogDatasetRatio
 feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio
 feature_step LogInverseDatasetRatio: LogInverseDatasetRatio
+feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD
+feature_step ClassProbabilityMin: ClassProbabilityMin
+feature_step ClassProbabilityMax: ClassProbabilityMax
+feature_step ClassProbabilityMean: ClassProbabilityMean
+feature_step ClassProbabilitySTD: ClassProbabilitySTD
 feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum
 feature_step SymbolsMin: SymbolsMin
 feature_step SymbolsMax: SymbolsMax
@@ -36,11 +42,12 @@ feature_step SkewnessMin: SkewnessMin
 feature_step SkewnessMax: SkewnessMax
 feature_step SkewnessMean: SkewnessMean
 feature_step SkewnessSTD: SkewnessSTD
-features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
+feature_step ClassEntropy: ClassEntropy
+features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy
 features_stochastic: 
-default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
+default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy
 
-algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92
+algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235,236,237,238,239,240,241,242,243,244,245,246,247,248,249,250,251,252,253,254,255,256,257,258,259,260,261,262,263,264,265,266,267,268,269,270,271,272,273,274,275,276,277,278,279,280,281,282,283,284,285,286,287,288,289,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,312,313,314,315,316,317,318,319,320,321,322,323,324,325,326,327,328,329,330,331,332,333,334,335,336,337,338,339,340,341,342,343,344,345,346,347,348,349,350,351,352,353,354,355,356,357,358,359,360,361,362,363,364,365,366,367,368,369,370,371,372
 algorithms_stochastic: 
 performance_measures: recall_samples
 performance_type: solution_quality
diff --git a/autosklearn/metalearning/files/recall_samples_binary.classification_sparse/feature_costs.arff b/autosklearn/metalearning/files/recall_samples_binary.classification_sparse/feature_costs.arff
index 0e17e20d22..8512fd9275 100644
--- a/autosklearn/metalearning/files/recall_samples_binary.classification_sparse/feature_costs.arff
+++ b/autosklearn/metalearning/files/recall_samples_binary.classification_sparse/feature_costs.arff
@@ -4,6 +4,7 @@
 @ATTRIBUTE repetition NUMERIC
 @ATTRIBUTE NumberOfInstances NUMERIC
 @ATTRIBUTE LogNumberOfInstances NUMERIC
+@ATTRIBUTE NumberOfClasses NUMERIC
 @ATTRIBUTE NumberOfFeatures NUMERIC
 @ATTRIBUTE LogNumberOfFeatures NUMERIC
 @ATTRIBUTE MissingValues NUMERIC
@@ -21,6 +22,11 @@
 @ATTRIBUTE LogDatasetRatio NUMERIC
 @ATTRIBUTE InverseDatasetRatio NUMERIC
 @ATTRIBUTE LogInverseDatasetRatio NUMERIC
+@ATTRIBUTE ClassOccurences NUMERIC
+@ATTRIBUTE ClassProbabilityMin NUMERIC
+@ATTRIBUTE ClassProbabilityMax NUMERIC
+@ATTRIBUTE ClassProbabilityMean NUMERIC
+@ATTRIBUTE ClassProbabilitySTD NUMERIC
 @ATTRIBUTE NumSymbols NUMERIC
 @ATTRIBUTE SymbolsMin NUMERIC
 @ATTRIBUTE SymbolsMax NUMERIC
@@ -37,162 +43,138 @@
 @ATTRIBUTE SkewnessMax NUMERIC
 @ATTRIBUTE SkewnessMean NUMERIC
 @ATTRIBUTE SkewnessSTD NUMERIC
+@ATTRIBUTE ClassEntropy NUMERIC
 
 @DATA
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+75234,1.0,1e-05,1e-05,0.00015,1e-05,1e-05,0.0058,0.00534,1e-05,0.00033,1e-05,0.00016,0.0,2e-05,2e-05,7e-05,0.00204,3e-05,1e-05,5e-05,1e-05,0.00024,3e-05,2e-05,0.0001,9e-05,0.00043,0.0,0.0,0.00012,0.00024,6e-05,0.00156,6e-05,7e-05,0.00121,0.00021,0.00054,6e-05,7e-05,9e-05,0.00032,0.00501
+75235,1.0,1e-05,1e-05,0.0003,2e-05,1e-05,0.00461,0.00385,2e-05,0.00055,2e-05,0.00026,1e-05,3e-05,3e-05,0.0001,0.00011,3e-05,1e-05,5e-05,2e-05,0.00026,3e-05,1e-05,8e-05,0.00014,0.00075,1e-05,1e-05,0.0002,0.00043,0.00011,0.00127,0.00017,0.0002,0.00029,0.00061,0.0008,0.00015,0.00012,0.00016,0.00038,0.00242
+75236,1.0,1e-05,1e-05,0.0001,1e-05,1e-05,0.00218,0.00118,2e-05,0.00065,3e-05,0.00041,1e-05,3e-05,3e-05,9e-05,9e-05,4e-05,1e-05,7e-05,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.0006,1e-05,1e-05,0.00021,0.00032,6e-05,0.00043,7e-05,7e-05,9e-05,0.00019,0.00062,7e-05,8e-05,0.0001,0.00037,0.00048
+75237,1.0,2e-05,1e-05,0.00785,2e-05,1e-05,0.31031,0.27854,2e-05,0.01161,3e-05,0.02022,1e-05,4e-05,3e-05,9e-05,0.00024,6e-05,2e-05,9e-05,2e-05,0.00062,7e-05,3e-05,0.00017,0.00035,0.0008,1e-05,1e-05,0.00028,0.00043,8e-05,0.00043,8e-05,7e-05,9e-05,0.0002,0.00044,6e-05,8e-05,9e-05,0.00021,0.14028
+75239,1.0,1e-05,1e-05,7e-05,1e-05,1e-05,0.00105,0.00084,1e-05,0.00015,1e-05,8e-05,1e-05,2e-05,5e-05,4e-05,8e-05,2e-05,1e-05,3e-05,1e-05,0.00016,2e-05,1e-05,5e-05,9e-05,0.00049,0.0,0.0,0.00012,0.00026,0.00011,0.00046,0.00013,8e-05,8e-05,0.00018,0.00044,6e-05,9e-05,0.00011,0.00018,0.00065
+75240,1.0,2e-05,1e-05,0.00036,5e-05,4e-05,0.00845,0.00644,6e-05,0.00122,4e-05,0.00091,1e-05,4e-05,4e-05,9e-05,0.00013,5e-05,2e-05,9e-05,5e-05,0.00031,3e-05,1e-05,7e-05,0.0002,0.00073,3e-05,2e-05,0.00019,0.0003,0.00018,0.00055,9e-05,9e-05,0.00011,0.00025,0.0005,7e-05,8e-05,0.00015,0.0002,0.00365
+75243,1.0,1e-05,1e-05,0.00029,1e-05,1e-05,0.00461,0.00415,1e-05,0.00035,1e-05,0.00015,0.0,2e-05,2e-05,5e-05,7e-05,2e-05,1e-05,4e-05,1e-05,0.00018,2e-05,1e-05,4e-05,0.00011,0.00022,1e-05,1e-05,5e-05,0.00011,5e-05,0.0004,6e-05,6e-05,8e-05,0.0002,0.00039,6e-05,6e-05,8e-05,0.00019,0.00265
+75244,1.0,3e-05,2e-05,0.0004,3e-05,2e-05,0.0173,0.01298,4e-05,0.00267,4e-05,0.00174,1e-05,7e-05,9e-05,0.00024,0.00027,8e-05,2e-05,0.00013,3e-05,0.00045,7e-05,2e-05,0.00011,0.00025,0.00077,6e-05,2e-05,0.00021,0.00034,0.00013,0.00077,0.00014,0.00013,0.00026,0.00023,0.00044,7e-05,8e-05,0.0001,0.00019,0.00649
+75248,1.0,1e-05,1e-05,0.00017,1e-05,1e-05,0.00416,0.00305,1e-05,0.0006,2e-05,0.00054,1e-05,2e-05,2e-05,5e-05,7e-05,2e-05,1e-05,4e-05,1e-05,0.00017,2e-05,1e-05,4e-05,0.0001,0.00029,5e-05,1e-05,6e-05,0.00012,6e-05,0.00081,0.00011,0.00015,0.00016,0.00038,0.0005,7e-05,8e-05,0.00012,0.00024,0.00184
+75249,1.0,1e-05,1e-05,0.00011,1e-05,1e-05,0.00192,0.00153,1e-05,0.00025,1e-05,0.00017,1e-05,2e-05,2e-05,5e-05,7e-05,2e-05,1e-05,5e-05,3e-05,0.00014,2e-05,1e-05,3e-05,8e-05,0.00029,1e-05,3e-05,6e-05,0.00012,7e-05,0.00041,6e-05,8e-05,0.0001,0.00017,0.00043,6e-05,7e-05,9e-05,0.00019,0.00092
+75250,1.0,1e-05,1e-05,0.00375,1e-05,1e-05,0.04994,0.047,1e-05,0.00247,1e-05,0.00049,1e-05,2e-05,2e-05,6e-05,7e-05,2e-05,1e-05,3e-05,1e-05,0.00022,4e-05,1e-05,4e-05,0.00012,0.00041,0.0,1e-05,0.00011,0.00023,6e-05,0.00076,0.00012,0.00012,0.00019,0.00032,0.00086,0.00012,0.00013,0.00018,0.00043,0.04178
diff --git a/autosklearn/metalearning/files/recall_samples_binary.classification_sparse/feature_runstatus.arff b/autosklearn/metalearning/files/recall_samples_binary.classification_sparse/feature_runstatus.arff
index 0034ad2bd6..5fff2dfed3 100644
--- a/autosklearn/metalearning/files/recall_samples_binary.classification_sparse/feature_runstatus.arff
+++ b/autosklearn/metalearning/files/recall_samples_binary.classification_sparse/feature_runstatus.arff
@@ -4,6 +4,7 @@
 @ATTRIBUTE repetition NUMERIC
 @ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other}
@@ -21,6 +22,11 @@
 @ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other}
@@ -37,162 +43,138 @@
 @ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other}
 
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diff --git a/autosklearn/metalearning/files/recall_samples_binary.classification_sparse/feature_values.arff b/autosklearn/metalearning/files/recall_samples_binary.classification_sparse/feature_values.arff
index a60400e3ce..736dc64321 100644
--- a/autosklearn/metalearning/files/recall_samples_binary.classification_sparse/feature_values.arff
+++ b/autosklearn/metalearning/files/recall_samples_binary.classification_sparse/feature_values.arff
@@ -13,7 +13,6 @@
 @ATTRIBUTE KurtosisMean NUMERIC
 @ATTRIBUTE KurtosisMin NUMERIC
 @ATTRIBUTE KurtosisSTD NUMERIC
-@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC
 @ATTRIBUTE LogDatasetRatio NUMERIC
 @ATTRIBUTE LogInverseDatasetRatio NUMERIC
 @ATTRIBUTE LogNumberOfFeatures NUMERIC
@@ -42,160 +41,135 @@
 @ATTRIBUTE SymbolsSum NUMERIC
 
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diff --git a/autosklearn/metalearning/files/recall_samples_multiclass.classification_dense/algorithm_runs.arff b/autosklearn/metalearning/files/recall_samples_multiclass.classification_dense/algorithm_runs.arff
index 090f0ed822..4176a9b9a8 100644
--- a/autosklearn/metalearning/files/recall_samples_multiclass.classification_dense/algorithm_runs.arff
+++ b/autosklearn/metalearning/files/recall_samples_multiclass.classification_dense/algorithm_runs.arff
@@ -7,130 +7,135 @@
 @ATTRIBUTE runstatus {ok, timeout, memout, not_applicable, crash, other}
 
 @DATA
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 75092,1.0,14,?,not_applicable
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+75225,1.0,279,?,not_applicable
+75100,1.0,286,?,not_applicable
+75132,1.0,293,?,not_applicable
+75210,1.0,298,?,not_applicable
+273,1.0,300,?,not_applicable
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+75121,1.0,304,?,not_applicable
+75098,1.0,310,?,not_applicable
+75115,1.0,315,?,not_applicable
+75217,1.0,337,?,not_applicable
+75134,1.0,341,?,not_applicable
+75096,1.0,346,?,not_applicable
+75203,1.0,350,?,not_applicable
+75123,1.0,360,?,not_applicable
+75125,1.0,367,?,not_applicable
+2120,1.0,371,?,not_applicable
+75232,1.0,374,?,not_applicable
+75103,1.0,378,?,not_applicable
+75230,1.0,386,?,not_applicable
+75240,1.0,398,?,not_applicable
+75198,1.0,404,?,not_applicable
+75201,1.0,407,?,not_applicable
+75105,1.0,415,?,not_applicable
+75154,1.0,420,?,not_applicable
+2117,1.0,426,?,not_applicable
+75156,1.0,430,?,not_applicable
+75097,1.0,434,?,not_applicable
+75101,1.0,438,?,not_applicable
+75172,1.0,443,?,not_applicable
+75106,1.0,448,?,not_applicable
+75120,1.0,456,?,not_applicable
+75193,1.0,461,?,not_applicable
diff --git a/autosklearn/metalearning/files/recall_samples_multiclass.classification_dense/configurations.csv b/autosklearn/metalearning/files/recall_samples_multiclass.classification_dense/configurations.csv
index 8d94f66c45..03d7f03e43 100644
--- a/autosklearn/metalearning/files/recall_samples_multiclass.classification_dense/configurations.csv
+++ b/autosklearn/metalearning/files/recall_samples_multiclass.classification_dense/configurations.csv
@@ -1,128 +1,133 @@
 idx,balancing:strategy,classifier:__choice__,classifier:adaboost:algorithm,classifier:adaboost:learning_rate,classifier:adaboost:max_depth,classifier:adaboost:n_estimators,classifier:bernoulli_nb:alpha,classifier:bernoulli_nb:fit_prior,classifier:decision_tree:criterion,classifier:decision_tree:max_depth_factor,classifier:decision_tree:max_features,classifier:decision_tree:max_leaf_nodes,classifier:decision_tree:min_impurity_decrease,classifier:decision_tree:min_samples_leaf,classifier:decision_tree:min_samples_split,classifier:decision_tree:min_weight_fraction_leaf,classifier:extra_trees:bootstrap,classifier:extra_trees:criterion,classifier:extra_trees:max_depth,classifier:extra_trees:max_features,classifier:extra_trees:max_leaf_nodes,classifier:extra_trees:min_impurity_decrease,classifier:extra_trees:min_samples_leaf,classifier:extra_trees:min_samples_split,classifier:extra_trees:min_weight_fraction_leaf,classifier:gradient_boosting:early_stop,classifier:gradient_boosting:l2_regularization,classifier:gradient_boosting:learning_rate,classifier:gradient_boosting:loss,classifier:gradient_boosting:max_bins,classifier:gradient_boosting:max_depth,classifier:gradient_boosting:max_leaf_nodes,classifier:gradient_boosting:min_samples_leaf,classifier:gradient_boosting:n_iter_no_change,classifier:gradient_boosting:scoring,classifier:gradient_boosting:tol,classifier:gradient_boosting:validation_fraction,classifier:k_nearest_neighbors:n_neighbors,classifier:k_nearest_neighbors:p,classifier:k_nearest_neighbors:weights,classifier:lda:n_components,classifier:lda:shrinkage,classifier:lda:shrinkage_factor,classifier:lda:tol,classifier:liblinear_svc:C,classifier:liblinear_svc:dual,classifier:liblinear_svc:fit_intercept,classifier:liblinear_svc:intercept_scaling,classifier:liblinear_svc:loss,classifier:liblinear_svc:multi_class,classifier:liblinear_svc:penalty,classifier:liblinear_svc:tol,classifier:libsvm_svc:C,classifier:libsvm_svc:coef0,classifier:libsvm_svc:degree,classifier:libsvm_svc:gamma,classifier:libsvm_svc:kernel,classifier:libsvm_svc:max_iter,classifier:libsvm_svc:shrinking,classifier:libsvm_svc:tol,classifier:multinomial_nb:alpha,classifier:multinomial_nb:fit_prior,classifier:passive_aggressive:C,classifier:passive_aggressive:average,classifier:passive_aggressive:fit_intercept,classifier:passive_aggressive:loss,classifier:passive_aggressive:tol,classifier:qda:reg_param,classifier:random_forest:bootstrap,classifier:random_forest:criterion,classifier:random_forest:max_depth,classifier:random_forest:max_features,classifier:random_forest:max_leaf_nodes,classifier:random_forest:min_impurity_decrease,classifier:random_forest:min_samples_leaf,classifier:random_forest:min_samples_split,classifier:random_forest:min_weight_fraction_leaf,classifier:sgd:alpha,classifier:sgd:average,classifier:sgd:epsilon,classifier:sgd:eta0,classifier:sgd:fit_intercept,classifier:sgd:l1_ratio,classifier:sgd:learning_rate,classifier:sgd:loss,classifier:sgd:penalty,classifier:sgd:power_t,classifier:sgd:tol,data_preprocessing:categorical_transformer:categorical_encoding:__choice__,data_preprocessing:categorical_transformer:category_coalescence:__choice__,data_preprocessing:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction,data_preprocessing:numerical_transformer:imputation:strategy,data_preprocessing:numerical_transformer:rescaling:__choice__,data_preprocessing:numerical_transformer:rescaling:quantile_transformer:n_quantiles,data_preprocessing:numerical_transformer:rescaling:quantile_transformer:output_distribution,data_preprocessing:numerical_transformer:rescaling:robust_scaler:q_max,data_preprocessing:numerical_transformer:rescaling:robust_scaler:q_min,feature_preprocessor:__choice__,feature_preprocessor:extra_trees_preproc_for_classification:bootstrap,feature_preprocessor:extra_trees_preproc_for_classification:criterion,feature_preprocessor:extra_trees_preproc_for_classification:max_depth,feature_preprocessor:extra_trees_preproc_for_classification:max_features,feature_preprocessor:extra_trees_preproc_for_classification:max_leaf_nodes,feature_preprocessor:extra_trees_preproc_for_classification:min_impurity_decrease,feature_preprocessor:extra_trees_preproc_for_classification:min_samples_leaf,feature_preprocessor:extra_trees_preproc_for_classification:min_samples_split,feature_preprocessor:extra_trees_preproc_for_classification:min_weight_fraction_leaf,feature_preprocessor:extra_trees_preproc_for_classification:n_estimators,feature_preprocessor:fast_ica:algorithm,feature_preprocessor:fast_ica:fun,feature_preprocessor:fast_ica:n_components,feature_preprocessor:fast_ica:whiten,feature_preprocessor:feature_agglomeration:affinity,feature_preprocessor:feature_agglomeration:linkage,feature_preprocessor:feature_agglomeration:n_clusters,feature_preprocessor:feature_agglomeration:pooling_func,feature_preprocessor:kernel_pca:coef0,feature_preprocessor:kernel_pca:degree,feature_preprocessor:kernel_pca:gamma,feature_preprocessor:kernel_pca:kernel,feature_preprocessor:kernel_pca:n_components,feature_preprocessor:kitchen_sinks:gamma,feature_preprocessor:kitchen_sinks:n_components,feature_preprocessor:liblinear_svc_preprocessor:C,feature_preprocessor:liblinear_svc_preprocessor:dual,feature_preprocessor:liblinear_svc_preprocessor:fit_intercept,feature_preprocessor:liblinear_svc_preprocessor:intercept_scaling,feature_preprocessor:liblinear_svc_preprocessor:loss,feature_preprocessor:liblinear_svc_preprocessor:multi_class,feature_preprocessor:liblinear_svc_preprocessor:penalty,feature_preprocessor:liblinear_svc_preprocessor:tol,feature_preprocessor:nystroem_sampler:coef0,feature_preprocessor:nystroem_sampler:degree,feature_preprocessor:nystroem_sampler:gamma,feature_preprocessor:nystroem_sampler:kernel,feature_preprocessor:nystroem_sampler:n_components,feature_preprocessor:pca:keep_variance,feature_preprocessor:pca:whiten,feature_preprocessor:polynomial:degree,feature_preprocessor:polynomial:include_bias,feature_preprocessor:polynomial:interaction_only,feature_preprocessor:random_trees_embedding:bootstrap,feature_preprocessor:random_trees_embedding:max_depth,feature_preprocessor:random_trees_embedding:max_leaf_nodes,feature_preprocessor:random_trees_embedding:min_samples_leaf,feature_preprocessor:random_trees_embedding:min_samples_split,feature_preprocessor:random_trees_embedding:min_weight_fraction_leaf,feature_preprocessor:random_trees_embedding:n_estimators,feature_preprocessor:select_percentile_classification:percentile,feature_preprocessor:select_percentile_classification:score_func,feature_preprocessor:select_rates_classification:alpha,feature_preprocessor:select_rates_classification:mode,feature_preprocessor:select_rates_classification:score_func
-1,none,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,standardize,,,,,extra_trees_preproc_for_classification,False,entropy,None,0.8629651625821806,None,0.0,10,10,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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-3,none,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,188,None,,0.09976330267940166,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,none,,,,,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,-0.05439674489937307,2,0.051865468486041774,poly,727,,,,,,,,,,,,,,,,,
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-6,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.933900673392165,None,0.0,7,4,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,minmax,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-7,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,170.37509684994612,0.6734219115470694,3,0.08998608839429428,poly,-1,True,0.024147312348267988,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,none,,,,,extra_trees_preproc_for_classification,False,entropy,None,0.9449154369109399,None,0.0,1,14,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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-19,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,199,None,,0.003239674765949011,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,robust_scaler,,,0.7228447795482008,0.25,kernel_pca,,,,,,,,,,,,,,,,,,,0.09440148939740256,3,0.0008114095405154861,poly,1996,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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-21,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,2.5882079595129472e-05,0.36125906161027654,auto,255,None,341,119,16,loss,1e-07,0.2924550184445239,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.02338478303505337,most_frequent,robust_scaler,,,0.7866731278723753,0.29543475006822734,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,68,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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-24,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.21794354428393548,None,0.0,2,16,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0025451910134387575,median,quantile_transformer,1477,uniform,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
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-27,weighting,decision_tree,,,,,,,gini,1.0778738481485166,1.0,None,0.0,14,7,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.002474357425774638,mean,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,
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-29,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9333237919291875,None,0.0,8,3,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,
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-35,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.2697794350876887,None,0.0,20,19,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.01857832166651019,median,robust_scaler,,,0.746850014013939,0.26258045325454815,fast_ica,,,,,,,,,,,deflation,cube,67,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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-37,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7360659359578277,None,0.0,18,5,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.0032431851910047368,mean,robust_scaler,,,0.7204949497300925,0.2277786352264519,fast_ica,,,,,,,,,,,deflation,exp,307,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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-40,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7005303455027131,None,0.0,1,5,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.027348815457551995,mean,quantile_transformer,789,uniform,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,
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-42,weighting,adaboost,SAMME,0.34205296521106404,10,89,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.02025205191860567,most_frequent,minmax,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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-44,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,10,1,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0022606253047811435,median,quantile_transformer,1247,normal,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,25.327793585266683,chi2,,,
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-46,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,92.66144306787051,,,0.04654639302091653,rbf,-1,True,0.0022231544280610005,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.005194838694455963,mean,none,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,15,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-47,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1550807214391849,True,True,hinge,0.00019875748182257,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.05786688075604592,mean,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,
-48,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.4467384469668449,None,0.0,19,19,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.00033708405018786203,median,quantile_transformer,711,normal,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,7.744829113696808,False,True,1,squared_hinge,ovr,l1,0.0001869446320464273,,,,,,,,,,,,,,,,,,,,,,
-49,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,26.502259985828186,,,9.475662388405154e-05,rbf,-1,False,0.00019690635968217866,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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-53,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1252.7997374154156,0.20811431041897355,5,0.0018140593074227453,poly,-1,False,0.03149782358621565,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.001930822730474197,most_frequent,quantile_transformer,233,normal,,,feature_agglomeration,,,,,,,,,,,,,,,euclidean,ward,29,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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-55,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.6109468076475573,0.8748383232154038,4,0.41333070422336576,poly,-1,False,3.591268778393482e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.011804925819789203,median,minmax,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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-58,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,151,None,,0.0003283777607180646,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.2510527431514227,mean,normalize,,,,,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,-0.96093182232312,,0.05762510964607661,sigmoid,2632,,,,,,,,,,,,,,,,,
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-64,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,29.55731828912983,False,True,1,squared_hinge,ovr,l2,0.0011332486951954968,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,
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-78,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,2.925599188129995e-05,0.12560285030651847,auto,255,None,300,193,17,loss,1e-07,0.039442997915546514,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00299400754997757,mean,robust_scaler,,,0.7093292916146515,0.009366983030047793,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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-81,weighting,adaboost,SAMME,0.028939256953011736,2,368,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,quantile_transformer,1954,uniform,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,41.04018582009321,mutual_info,,,
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-88,none,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,230,auto,,0.0014037334852669801,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,quantile_transformer,848,uniform,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,61.06963527679606,f_classif,,,
-89,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,64,auto,,0.00021332620906594865,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.026032882800756493,median,standardize,,,,,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,-0.9513223792709335,,0.00029881582960753004,sigmoid,543,,,,,,,,,,,,,,,,,
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-102,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.7285597583341324,None,0.0,3,20,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.011700861722450575,most_frequent,robust_scaler,,,0.7911119178158159,0.2999762250828743,feature_agglomeration,,,,,,,,,,,,,,,euclidean,ward,151,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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-127,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.6466927589470762,None,0.0,11,2,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.009672465311659264,most_frequent,standardize,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,369,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+1,none,decision_tree,,,,,,,entropy,1.877536950957045,1.0,None,0.0,10,13,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,quantile_transformer,1275,uniform,,,extra_trees_preproc_for_classification,True,gini,None,0.04910706843318435,None,0.0,12,12,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+2,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.8905944749638223,None,0.0,5,4,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.15463552048633444,most_frequent,robust_scaler,,,0.7545842296967857,0.22898551665984185,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.24780801481682346,fwe,chi2
+5,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,9.83838677007206e-09,0.09269618726619562,auto,255,None,37,16,14,loss,1e-07,0.04208303685387712,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
+6,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,4.267426422904654e-06,0.011555121209024403,auto,255,None,121,34,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010064483887441845,most_frequent,quantile_transformer,1194,uniform,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,
+8,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,114,manual,0.11778631707605758,0.0029112721339713465,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.015582195257240751,median,normalize,,,,,kernel_pca,,,,,,,,,,,,,,,,,,,0.42909623253327767,3,0.25407612000343777,poly,1689,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+9,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.685918004243461,True,True,squared_hinge,1.7784504877380735e-05,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010335606354842895,most_frequent,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,
+10,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.7396493120555564,None,0.0,1,18,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010000000000000004,median,robust_scaler,,,0.8908334215036855,0.08666907249785906,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,
+13,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,0.9907352051102745,0.018115439174701313,auto,255,None,84,5,3,loss,1e-07,0.13697766901072808,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.036524416007602305,mean,robust_scaler,,,0.9824116214519696,0.2911868066464925,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,
+14,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,4.629265598705481e-10,0.03894546521791516,auto,255,None,5,22,18,loss,1e-07,0.0837137336215462,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.008397749525484112,mean,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
+15,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.21772092350860472,None,0.0,14,17,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.0020488212891693275,median,standardize,,,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,33.74257606530722,False,True,1,squared_hinge,ovr,l1,8.740452966205111e-05,,,,,,,,,,,,,,,,,,,,,,
+17,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.6591033863118033,None,0.0,10,10,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,none,,,,,fast_ica,,,,,,,,,,,deflation,exp,1506,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+18,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,1.9110915129429343e-09,0.06380311481632683,auto,255,None,20,62,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,robust_scaler,,,0.7307407504330838,0.2660206566992171,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,
+19,weighting,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,99,2,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010033505607662804,median,none,,,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.069760700053376,False,True,1,squared_hinge,ovr,l1,0.06611177025130774,,,,,,,,,,,,,,,,,,,,,,
+23,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,2.0447842449942555e-09,0.04257026709693251,auto,255,None,47,13,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010000000000000004,median,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,
+24,weighting,adaboost,SAMME.R,0.44886160948796244,9,493,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.031007103864624616,mean,robust_scaler,,,0.7575180367976682,0.26457274298753825,feature_agglomeration,,,,,,,,,,,,,,,cosine,complete,211,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+25,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,0.008942041103505539,0.05229567301622349,auto,255,None,6,24,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.01936143670847451,mean,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,
+26,weighting,adaboost,SAMME.R,0.01561270559347964,4,417,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,quantile_transformer,1617,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+27,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.41025844265119926,None,0.0,18,15,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,median,quantile_transformer,1911,normal,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,False,,,,,,,,,,,,
+29,none,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.9034033923099005,False,True,1,squared_hinge,ovr,l2,1.2159485299684885e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.027586423552745792,most_frequent,none,,,,,kernel_pca,,,,,,,,,,,,,,,,,,,,,0.4034250963681743,rbf,735,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+30,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,490.8047233341048,0.13853827450882505,,0.004044309450174347,sigmoid,-1,False,0.005958796387567885,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.008446690235218424,median,minmax,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,229,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+31,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,3.0538220042587883e-07,0.0704056758356995,auto,255,None,9,3,19,loss,1e-07,0.01927933154167906,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.019804174585488932,median,quantile_transformer,921,normal,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,
+32,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,101,auto,,1.553086088086431e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.01137059177001425,most_frequent,normalize,,,,,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,-0.021621751597165284,4,0.7468597264810062,poly,5078,,,,,,,,,,,,,,,,,
+33,none,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.5769165522851386,None,0.0,1,6,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,none,,,,,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,7,None,9,14,1.0,74,,,,,
+34,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,0.06671977293714751,0.04456236025167353,auto,255,None,3,163,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.4333293305857194,mean,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+37,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0618160212153551,True,True,squared_hinge,1.2370453081062464e-05,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.01448339838697973,mean,standardize,,,,,kitchen_sinks,,,,,,,,,,,,,,,,,,,,,,,,0.018879929475513826,1637,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+39,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.6799215694343422,None,0.0,1,5,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.004724310659953132,most_frequent,quantile_transformer,1037,normal,,,fast_ica,,,,,,,,,,,parallel,exp,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+41,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.6965935192702026,None,0.0,12,16,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,none,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,67.60637688434707,f_classif,,,
+43,none,decision_tree,,,,,,,gini,1.1110863621017264,1.0,None,0.0,16,19,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.00043339385698342055,mean,none,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.20957492283649354,fwe,f_classif
+44,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,2.6159836259985015e-05,0.05532294838858921,auto,255,None,116,4,14,loss,1e-07,0.20422881335187132,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,
+49,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00010123414317660679,False,,0.04011567850168966,True,,constant,log,l1,,0.00014179758438189507,one_hot_encoding,minority_coalescer,0.008920898694379564,mean,robust_scaler,,,0.7712963178784032,0.2702398922671455,kernel_pca,,,,,,,,,,,,,,,,,,,,,,cosine,1090,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+54,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,1.0607494292547588e-08,0.014599960935351135,auto,255,None,5,7,1,loss,1e-07,0.01508309574734627,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
+58,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5547994712622472,None,0.0,1,2,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.008028893322827371,most_frequent,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
+59,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9768.124422997415,False,True,1,squared_hinge,ovr,l2,0.002391133479983495,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,robust_scaler,,,0.7418544320601411,0.258610892841927,fast_ica,,,,,,,,,,,parallel,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+60,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.8757952087292588,None,0.0,3,7,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.00023383575324676886,most_frequent,robust_scaler,,,0.75,0.25885426505910014,feature_agglomeration,,,,,,,,,,,,,,,cosine,average,83,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+64,weighting,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.5927254703947399,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.004193053494360822,median,none,,,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.5779022723643873,False,,,,,,,,,,,,,,,
+65,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,4.427693008576421e-07,0.1480512588831196,auto,255,None,5,46,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.07105836118280727,median,quantile_transformer,875,normal,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,False,,,,,,,,,,,,
+66,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,236.02119240292217,False,True,1,squared_hinge,ovr,l2,0.0017427914676337187,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.1020917547461355,mean,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,False,,,,,,,,,,,,
+67,weighting,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2974150304199443,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0054956766969650565,median,standardize,,,,,feature_agglomeration,,,,,,,,,,,,,,,cosine,average,311,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+73,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,6.794013031623291e-09,0.07399415058344509,auto,255,None,4,112,15,loss,1e-07,0.07001899213382194,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,quantile_transformer,1301,normal,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,
+76,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4380.951445492721,,,0.15832144806947993,rbf,-1,True,0.00010691725511780856,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.01066869533165506,median,minmax,,,,,fast_ica,,,,,,,,,,,deflation,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+77,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.2309302197434343e-05,False,,0.006503403719704897,True,,constant,log,l1,,1.531583361000749e-05,one_hot_encoding,no_coalescense,,most_frequent,robust_scaler,,,0.7530789965606406,0.2430090971909385,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,cosine,1693,,,,,,,,,,,,,,,,,
+80,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5858785544767082,None,0.0,1,14,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,median,robust_scaler,,,0.8409151021946415,0.03951963852061448,fast_ica,,,,,,,,,,,deflation,exp,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+81,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.9680517112466928,None,0.0,1,4,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.012740842287122786,most_frequent,robust_scaler,,,0.889930464615107,0.24974016722405976,feature_agglomeration,,,,,,,,,,,,,,,euclidean,average,25,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+84,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,3.6348307794592436e-10,0.07233284229137055,auto,255,None,1386,47,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,quantile_transformer,466,uniform,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,47.05668163013577,chi2,,,
+88,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,1.134145934367294e-08,0.05597839358087172,auto,255,None,20,50,15,loss,1e-07,0.04047547712408959,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.013656355693947419,most_frequent,robust_scaler,,,0.8162585020545898,0.24147824330789028,feature_agglomeration,,,,,,,,,,,,,,,euclidean,ward,51,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+89,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9.552100559095382e-05,True,,0.007209292257917831,True,,constant,log,l2,,1.70337957134729e-05,no_encoding,minority_coalescer,0.0182109481043058,most_frequent,none,,,,,kernel_pca,,,,,,,,,,,,,,,,,,,,,1.1223991702013292,rbf,152,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+90,none,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.752445508156732e-06,True,,3.4363022662141264e-05,True,,constant,perceptron,l1,,0.0005054929998027575,no_encoding,minority_coalescer,0.010000000000000004,median,standardize,,,,,kernel_pca,,,,,,,,,,,,,,,,,,,0.7300101465014597,2,1.1347040432914681,poly,503,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+92,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.8158497565615254,None,0.0,10,19,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0478920998898566,median,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,
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+277,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,0.44498750739470333,0.034703723117214785,auto,255,None,7,17,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.00028984449327810053,most_frequent,robust_scaler,,,0.915126081808246,0.08109563152223943,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.04295334028663175,fwe,f_classif
+279,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.7365488384201175,None,0.0,6,8,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,
+286,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9527105503688407,None,0.0,14,8,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.006862831743478417,mean,none,,,,,extra_trees_preproc_for_classification,True,gini,None,0.04923305036890069,None,0.0,14,5,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+293,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,7.033246623188702e-10,0.07296742801083987,auto,255,None,42,160,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.008719726495868056,median,robust_scaler,,,0.7579931359954756,0.23262028324550565,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,180,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+298,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1359.4305032221287,,,0.18567450957191756,rbf,-1,False,0.00964468225236062,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,none,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,44.51779759183326,chi2,,,
+300,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,3.864103096225689e-10,0.07145430778337199,auto,255,None,9,41,14,loss,1e-07,0.05641439490894152,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.016120397047347555,mean,quantile_transformer,1190,normal,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,
+303,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.4427140294863755,None,0.0,8,20,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,robust_scaler,,,0.960103135751788,0.10844954570838759,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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+310,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,68,2,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010000000000000004,mean,none,,,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.766336759369817,False,,,,,,,,,,,,,,,
+315,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.8408063181862852,True,True,hinge,0.004342389918487474,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.26119501093358377,most_frequent,quantile_transformer,1319,uniform,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.897412266178953,True,,,,,,,,,,,,,,,
+337,weighting,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,minmax,,,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,227.42750098824558,False,True,1,squared_hinge,ovr,l1,0.0018060472646546995,,,,,,,,,,,,,,,,,,,,,,
+341,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.513560345508016,None,0.0,3,2,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,median,quantile_transformer,1044,uniform,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.07550342883878675,,mutual_info_classif
+346,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,0.15762740504817374,0.17692452709577644,auto,255,None,282,9,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.005207945885722629,most_frequent,robust_scaler,,,0.75,0.27100271805667314,feature_agglomeration,,,,,,,,,,,,,,,euclidean,complete,106,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+350,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.2318705830210126,True,True,hinge,0.0002994421772278671,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.13310408167721913,most_frequent,quantile_transformer,1036,uniform,,,kernel_pca,,,,,,,,,,,,,,,,,,,,,,cosine,703,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+360,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.5720469164289109,None,0.0,1,17,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.03310727058091163,median,robust_scaler,,,0.8680123006808074,0.024825227678742904,fast_ica,,,,,,,,,,,parallel,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+367,weighting,adaboost,SAMME,0.2540331297425094,2,187,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,robust_scaler,,,0.9867887266047534,0.041631156927011324,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50.70828969428249,chi2,,,
+371,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,4.198700105243736e-09,0.011070860038925674,auto,255,None,9,116,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010247554402833578,median,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,
+374,none,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.7304811343030777,None,0.0,2,3,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,robust_scaler,,,0.7202654714984674,0.21555973677200435,feature_agglomeration,,,,,,,,,,,,,,,euclidean,average,149,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+378,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,6.485533704735031e-07,0.03500257587365526,auto,255,None,4,7,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00026264639049847547,most_frequent,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+386,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9358939105347388,None,0.0,1,5,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,none,,,,,fast_ica,,,,,,,,,,,parallel,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+398,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.7667112074789202,None,0.0,4,20,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.001721469100933522,median,robust_scaler,,,0.7582814631443476,0.25,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+404,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8.380057363340162,False,True,hinge,0.0020653831067503525,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.05020677662128942,median,quantile_transformer,560,normal,,,kernel_pca,,,,,,,,,,,,,,,,,,,,,,cosine,891,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+407,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.7292011996224884,True,True,hinge,8.162763086383311e-05,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010337761588362885,median,quantile_transformer,877,uniform,,,kernel_pca,,,,,,,,,,,,,,,,,,,,,,cosine,1317,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+415,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,5.215923689311285e-05,0.01709129738847392,auto,255,None,10,19,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0004854193510398933,most_frequent,quantile_transformer,709,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+420,weighting,adaboost,SAMME.R,0.05920795019389743,8,313,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,standardize,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,104,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+426,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1527.3847932648184,False,True,1,squared_hinge,ovr,l2,0.0037778228082614835,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,minmax,,,,,kitchen_sinks,,,,,,,,,,,,,,,,,,,,,,,,0.00025777548503151833,2621,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+430,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,2.2389471617630046e-10,0.07245292952526383,auto,255,None,25,75,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.023490803960861777,median,quantile_transformer,1107,uniform,,,extra_trees_preproc_for_classification,False,gini,None,0.7778748752629213,None,0.0,1,14,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+434,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.4438263804770306,None,0.0,2,10,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,mean,robust_scaler,,,0.9824945141547571,0.24967364012300372,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+438,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,6.090138036399552e-10,0.017305568433437588,auto,255,None,202,188,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,robust_scaler,,,0.7526196011313747,0.1563616005702636,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+443,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.6269076084367177,None,0.0,1,7,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.12943250859943486,most_frequent,standardize,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,62.84526554323969,chi2,,,
+448,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.7463662441763204,None,0.0,20,18,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0004507212632016269,mean,none,,,,,extra_trees_preproc_for_classification,True,gini,None,0.4279327206778048,None,0.0,3,9,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+456,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0322540554793962e-05,False,,0.015117861103299655,True,,invscaling,log,l1,0.558781568719232,0.00016877274953233003,one_hot_encoding,no_coalescense,,median,quantile_transformer,681,normal,,,extra_trees_preproc_for_classification,False,gini,None,0.6320249932267078,None,0.0,17,18,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+461,none,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.9796199105973851,None,0.0,2,13,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.00845950033601661,mean,robust_scaler,,,0.8275228560550734,0.23814615163106348,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
diff --git a/autosklearn/metalearning/files/recall_samples_multiclass.classification_dense/description.txt b/autosklearn/metalearning/files/recall_samples_multiclass.classification_dense/description.txt
index 7301ec5e29..f2935a5a24 100644
--- a/autosklearn/metalearning/files/recall_samples_multiclass.classification_dense/description.txt
+++ b/autosklearn/metalearning/files/recall_samples_multiclass.classification_dense/description.txt
@@ -1,8 +1,9 @@
 features_cutoff_time: 3600
 features_cutoff_memory: 3072
-number_of_feature_steps: 35
+number_of_feature_steps: 42
 feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances
 feature_step LogNumberOfInstances: LogNumberOfInstances
+feature_step NumberOfClasses: NumberOfClasses
 feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures
 feature_step LogNumberOfFeatures: LogNumberOfFeatures
 feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues
@@ -20,6 +21,11 @@ feature_step DatasetRatio: DatasetRatio, LogDatasetRatio
 feature_step LogDatasetRatio: LogDatasetRatio
 feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio
 feature_step LogInverseDatasetRatio: LogInverseDatasetRatio
+feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD
+feature_step ClassProbabilityMin: ClassProbabilityMin
+feature_step ClassProbabilityMax: ClassProbabilityMax
+feature_step ClassProbabilityMean: ClassProbabilityMean
+feature_step ClassProbabilitySTD: ClassProbabilitySTD
 feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum
 feature_step SymbolsMin: SymbolsMin
 feature_step SymbolsMax: SymbolsMax
@@ -36,11 +42,12 @@ feature_step SkewnessMin: SkewnessMin
 feature_step SkewnessMax: SkewnessMax
 feature_step SkewnessMean: SkewnessMean
 feature_step SkewnessSTD: SkewnessSTD
-features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
+feature_step ClassEntropy: ClassEntropy
+features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy
 features_stochastic: 
-default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
+default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy
 
-algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127
+algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235,236,237,238,239,240,241,242,243,244,245,246,247,248,249,250,251,252,253,254,255,256,257,258,259,260,261,262,263,264,265,266,267,268,269,270,271,272,273,274,275,276,277,278,279,280,281,282,283,284,285,286,287,288,289,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,312,313,314,315,316,317,318,319,320,321,322,323,324,325,326,327,328,329,330,331,332,333,334,335,336,337,338,339,340,341,342,343,344,345,346,347,348,349,350,351,352,353,354,355,356,357,358,359,360,361,362,363,364,365,366,367,368,369,370,371,372,373,374,375,376,377,378,379,380,381,382,383,384,385,386,387,388,389,390,391,392,393,394,395,396,397,398,399,400,401,402,403,404,405,406,407,408,409,410,411,412,413,414,415,416,417,418,419,420,421,422,423,424,425,426,427,428,429,430,431,432,433,434,435,436,437,438,439,440,441,442,443,444,445,446,447,448,449,450,451,452,453,454,455,456,457,458,459,460,461,462,463,464
 algorithms_stochastic: 
 performance_measures: recall_samples
 performance_type: solution_quality
diff --git a/autosklearn/metalearning/files/recall_samples_multiclass.classification_dense/feature_costs.arff b/autosklearn/metalearning/files/recall_samples_multiclass.classification_dense/feature_costs.arff
index 0e17e20d22..8512fd9275 100644
--- a/autosklearn/metalearning/files/recall_samples_multiclass.classification_dense/feature_costs.arff
+++ b/autosklearn/metalearning/files/recall_samples_multiclass.classification_dense/feature_costs.arff
@@ -4,6 +4,7 @@
 @ATTRIBUTE repetition NUMERIC
 @ATTRIBUTE NumberOfInstances NUMERIC
 @ATTRIBUTE LogNumberOfInstances NUMERIC
+@ATTRIBUTE NumberOfClasses NUMERIC
 @ATTRIBUTE NumberOfFeatures NUMERIC
 @ATTRIBUTE LogNumberOfFeatures NUMERIC
 @ATTRIBUTE MissingValues NUMERIC
@@ -21,6 +22,11 @@
 @ATTRIBUTE LogDatasetRatio NUMERIC
 @ATTRIBUTE InverseDatasetRatio NUMERIC
 @ATTRIBUTE LogInverseDatasetRatio NUMERIC
+@ATTRIBUTE ClassOccurences NUMERIC
+@ATTRIBUTE ClassProbabilityMin NUMERIC
+@ATTRIBUTE ClassProbabilityMax NUMERIC
+@ATTRIBUTE ClassProbabilityMean NUMERIC
+@ATTRIBUTE ClassProbabilitySTD NUMERIC
 @ATTRIBUTE NumSymbols NUMERIC
 @ATTRIBUTE SymbolsMin NUMERIC
 @ATTRIBUTE SymbolsMax NUMERIC
@@ -37,162 +43,138 @@
 @ATTRIBUTE SkewnessMax NUMERIC
 @ATTRIBUTE SkewnessMean NUMERIC
 @ATTRIBUTE SkewnessSTD NUMERIC
+@ATTRIBUTE ClassEntropy NUMERIC
 
 @DATA
-75123,1.0,1e-05,1e-05,1e-05,1e-05,0.00253,0.00221,1e-05,0.00024,3e-05,0.00011,0.0,2e-05,2e-05,5e-05,8e-05,2e-05,1e-05,7e-05,1e-05,0.00072,1e-05,1e-05,0.00019,0.00041,0.00011,0.00061,0.00012,9e-05,0.00015,0.00025,0.00067,0.00014,0.00011,0.00016,0.00027
-75090,1.0,1e-05,1e-05,2e-05,2e-05,0.00815,0.00368,7e-05,0.00228,9e-05,0.00235,0.0,5e-05,5e-05,0.0001,0.00013,0.00013,1e-05,0.00021,2e-05,0.00064,0.0,0.0,0.00014,0.00038,0.00011,0.00066,0.00011,0.00014,0.00014,0.00026,0.00066,0.00011,0.00013,0.00015,0.00027
-2123,1.0,1e-05,1e-05,1e-05,1e-05,0.00097,0.00079,1e-05,0.00013,1e-05,7e-05,0.0,2e-05,2e-05,5e-05,8e-05,2e-05,1e-05,5e-05,1e-05,0.0007,1e-05,0.0,0.00017,0.00042,0.0001,0.00052,6e-05,8e-05,0.00012,0.00026,0.00049,6e-05,0.00011,9e-05,0.00023
-248,1.0,1e-05,1e-05,1e-05,1e-05,0.00088,0.00077,1e-05,0.0001,1e-05,4e-05,0.0,1e-05,1e-05,4e-05,5e-05,2e-05,1e-05,4e-05,1e-05,0.00041,1e-05,0.0,9e-05,0.00022,9e-05,0.00046,6e-05,8e-05,9e-05,0.00023,0.00049,7e-05,0.0001,0.0001,0.00021
-75101,1.0,1e-05,1e-05,1e-05,1e-05,0.0418,0.03546,1e-05,0.00389,1e-05,0.00248,0.0,2e-05,2e-05,4e-05,6e-05,2e-05,1e-05,5e-05,2e-05,0.00045,1e-05,0.0,0.00011,0.00023,0.0001,0.00041,6e-05,8e-05,0.0001,0.00018,0.00047,6e-05,0.0001,8e-05,0.00023
-75191,1.0,1e-05,1e-05,3e-05,1e-05,0.53436,0.40777,2e-05,0.09655,3e-05,0.0301,1e-05,2e-05,3e-05,5e-05,0.00011,7e-05,1e-05,0.00011,3e-05,0.00084,5e-05,7e-05,0.00017,0.00033,0.00023,0.00088,0.00012,0.00017,0.00032,0.00026,0.00068,9e-05,0.00012,0.00013,0.00035
-75187,1.0,1e-05,1e-05,2e-05,1e-05,0.00548,0.00473,1e-05,0.0005,2e-05,0.00029,1e-05,2e-05,2e-05,7e-05,9e-05,3e-05,1e-05,6e-05,1e-05,0.00062,1e-05,1e-05,0.00014,0.00033,0.00013,0.00038,7e-05,7e-05,8e-05,0.00016,0.00047,6e-05,9e-05,9e-05,0.00023
-266,1.0,1e-05,1e-05,1e-05,1e-05,0.0018,0.00146,1e-05,0.00023,2e-05,0.00015,2e-05,2e-05,2e-05,6e-05,8e-05,3e-05,1e-05,6e-05,1e-05,0.00059,1e-05,1e-05,0.00014,0.00031,0.00013,0.0004,5e-05,7e-05,8e-05,0.0002,0.00044,8e-05,8e-05,8e-05,0.0002
-253,1.0,1e-05,1e-05,1e-05,1e-05,0.00082,0.00063,1e-05,0.00016,2e-05,5e-05,0.0,1e-05,1e-05,4e-05,5e-05,2e-05,1e-05,5e-05,2e-05,0.00046,1e-05,0.0,9e-05,0.00024,0.00012,0.00044,6e-05,7e-05,8e-05,0.00023,0.00043,6e-05,0.0001,8e-05,0.00019
-75217,1.0,1e-05,1e-05,2e-05,1e-05,0.00196,0.00158,1e-05,0.00026,2e-05,0.00016,1e-05,3e-05,3e-05,7e-05,0.0001,3e-05,1e-05,7e-05,2e-05,0.00089,1e-05,1e-05,0.00022,0.00053,0.00013,0.00074,9e-05,0.0001,0.00019,0.00036,0.00065,0.00013,0.0001,0.00011,0.00031
-75176,1.0,1e-05,1e-05,2e-05,1e-05,0.01406,0.01285,1e-05,0.00094,2e-05,0.00031,1e-05,3e-05,3e-05,6e-05,9e-05,3e-05,1e-05,7e-05,2e-05,0.00069,1e-05,1e-05,0.00017,0.00035,0.00015,0.00085,0.00018,0.00011,0.0002,0.00036,0.0011,0.00022,0.00023,0.00023,0.00042
-2117,1.0,1e-05,1e-05,1e-05,1e-05,0.01735,0.0154,1e-05,0.00141,1e-05,0.00057,0.0,1e-05,1e-05,3e-05,6e-05,2e-05,1e-05,5e-05,1e-05,0.0004,1e-05,0.0,8e-05,0.00021,0.0001,0.00065,0.00012,0.00011,0.00015,0.00026,0.00055,7e-05,0.00014,9e-05,0.00025
-75148,1.0,1e-05,1e-05,2e-05,2e-05,0.00129,0.00115,1e-05,0.00012,1e-05,4e-05,0.0,2e-05,2e-05,4e-05,5e-05,2e-05,1e-05,4e-05,1e-05,0.0004,1e-05,0.0,9e-05,0.00021,9e-05,0.00036,7e-05,7e-05,8e-05,0.00015,0.00041,6e-05,8e-05,8e-05,0.00019
-75103,1.0,1e-05,1e-05,1e-05,1e-05,0.01302,0.00819,3e-05,0.00234,3e-05,0.00255,0.0,2e-05,2e-05,6e-05,0.00012,5e-05,1e-05,0.00011,1e-05,0.0005,1e-05,0.0,9e-05,0.00029,0.00011,0.00054,8e-05,0.00011,0.00011,0.00024,0.00066,7e-05,0.00015,0.00011,0.00032
-75168,1.0,1e-05,0.0,1e-05,1e-05,0.02353,0.00601,0.00016,0.01754,0.00018,0.00031,0.0,0.00013,0.00013,0.00027,0.0003,0.00032,1e-05,0.00042,1e-05,0.0004,1e-05,0.0,8e-05,0.00021,0.0001,0.0011,0.00022,0.00025,0.00026,0.00037,0.00124,0.00022,0.00025,0.00028,0.00049
-75157,1.0,1e-05,1e-05,1e-05,1e-05,0.00125,0.00106,1e-05,0.00015,2e-05,7e-05,0.0,2e-05,2e-05,5e-05,6e-05,2e-05,1e-05,4e-05,1e-05,0.00048,2e-05,0.0,0.0001,0.00026,0.0001,0.00039,6e-05,7e-05,8e-05,0.00019,0.00043,6e-05,9e-05,8e-05,0.0002
-75092,1.0,1e-05,1e-05,1e-05,1e-05,0.00099,0.00072,1e-05,0.00018,1e-05,0.00011,0.0,2e-05,2e-05,4e-05,6e-05,2e-05,1e-05,5e-05,1e-05,0.00058,1e-05,0.0,0.00014,0.00034,9e-05,0.00041,6e-05,8e-05,9e-05,0.00017,0.00045,7e-05,0.0001,9e-05,0.00019
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-75227,1.0,1e-05,1e-05,2e-05,1e-05,0.00416,0.00373,1e-05,0.00038,2e-05,9e-05,1e-05,2e-05,2e-05,6e-05,0.0001,3e-05,1e-05,7e-05,2e-05,0.00063,1e-05,1e-05,0.00015,0.00034,0.00013,0.00037,6e-05,8e-05,8e-05,0.00016,0.00043,6e-05,9e-05,9e-05,0.0002
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-75192,1.0,1e-05,1e-05,2e-05,1e-05,0.00188,0.00165,1e-05,0.00016,1e-05,9e-05,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,1e-05,4e-05,1e-05,0.00039,1e-05,0.0,9e-05,0.00021,9e-05,0.00048,5e-05,8e-05,0.0001,0.00025,0.00045,6e-05,0.0001,0.0001,0.0002
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-75132,1.0,1e-05,0.0,2e-05,1e-05,0.45642,0.41356,1e-05,0.03273,1e-05,0.01015,0.0,1e-05,1e-05,3e-05,6e-05,2e-05,1e-05,6e-05,2e-05,0.00049,1e-05,0.0,0.00012,0.00023,0.00013,0.00046,7e-05,8e-05,8e-05,0.00023,0.00053,6e-05,0.0001,9e-05,0.00028
-75212,1.0,1e-05,1e-05,1e-05,1e-05,0.0008,0.00059,1e-05,0.00015,1e-05,9e-05,0.0,2e-05,1e-05,3e-05,5e-05,2e-05,1e-05,4e-05,1e-05,0.00037,0.0,0.0,8e-05,0.00019,9e-05,0.00055,8e-05,0.00012,0.00011,0.00024,0.00047,6e-05,0.0001,9e-05,0.00022
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+75235,1.0,1e-05,1e-05,0.0003,2e-05,1e-05,0.00461,0.00385,2e-05,0.00055,2e-05,0.00026,1e-05,3e-05,3e-05,0.0001,0.00011,3e-05,1e-05,5e-05,2e-05,0.00026,3e-05,1e-05,8e-05,0.00014,0.00075,1e-05,1e-05,0.0002,0.00043,0.00011,0.00127,0.00017,0.0002,0.00029,0.00061,0.0008,0.00015,0.00012,0.00016,0.00038,0.00242
+75236,1.0,1e-05,1e-05,0.0001,1e-05,1e-05,0.00218,0.00118,2e-05,0.00065,3e-05,0.00041,1e-05,3e-05,3e-05,9e-05,9e-05,4e-05,1e-05,7e-05,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.0006,1e-05,1e-05,0.00021,0.00032,6e-05,0.00043,7e-05,7e-05,9e-05,0.00019,0.00062,7e-05,8e-05,0.0001,0.00037,0.00048
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+75249,1.0,1e-05,1e-05,0.00011,1e-05,1e-05,0.00192,0.00153,1e-05,0.00025,1e-05,0.00017,1e-05,2e-05,2e-05,5e-05,7e-05,2e-05,1e-05,5e-05,3e-05,0.00014,2e-05,1e-05,3e-05,8e-05,0.00029,1e-05,3e-05,6e-05,0.00012,7e-05,0.00041,6e-05,8e-05,0.0001,0.00017,0.00043,6e-05,7e-05,9e-05,0.00019,0.00092
+75250,1.0,1e-05,1e-05,0.00375,1e-05,1e-05,0.04994,0.047,1e-05,0.00247,1e-05,0.00049,1e-05,2e-05,2e-05,6e-05,7e-05,2e-05,1e-05,3e-05,1e-05,0.00022,4e-05,1e-05,4e-05,0.00012,0.00041,0.0,1e-05,0.00011,0.00023,6e-05,0.00076,0.00012,0.00012,0.00019,0.00032,0.00086,0.00012,0.00013,0.00018,0.00043,0.04178
diff --git a/autosklearn/metalearning/files/recall_samples_multiclass.classification_dense/feature_runstatus.arff b/autosklearn/metalearning/files/recall_samples_multiclass.classification_dense/feature_runstatus.arff
index 0034ad2bd6..5fff2dfed3 100644
--- a/autosklearn/metalearning/files/recall_samples_multiclass.classification_dense/feature_runstatus.arff
+++ b/autosklearn/metalearning/files/recall_samples_multiclass.classification_dense/feature_runstatus.arff
@@ -4,6 +4,7 @@
 @ATTRIBUTE repetition NUMERIC
 @ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other}
@@ -21,6 +22,11 @@
 @ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other}
@@ -37,162 +43,138 @@
 @ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other}
 
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diff --git a/autosklearn/metalearning/files/recall_samples_multiclass.classification_dense/feature_values.arff b/autosklearn/metalearning/files/recall_samples_multiclass.classification_dense/feature_values.arff
index a60400e3ce..736dc64321 100644
--- a/autosklearn/metalearning/files/recall_samples_multiclass.classification_dense/feature_values.arff
+++ b/autosklearn/metalearning/files/recall_samples_multiclass.classification_dense/feature_values.arff
@@ -13,7 +13,6 @@
 @ATTRIBUTE KurtosisMean NUMERIC
 @ATTRIBUTE KurtosisMin NUMERIC
 @ATTRIBUTE KurtosisSTD NUMERIC
-@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC
 @ATTRIBUTE LogDatasetRatio NUMERIC
 @ATTRIBUTE LogInverseDatasetRatio NUMERIC
 @ATTRIBUTE LogNumberOfFeatures NUMERIC
@@ -42,160 +41,135 @@
 @ATTRIBUTE SymbolsSum NUMERIC
 
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diff --git a/autosklearn/metalearning/files/recall_samples_multiclass.classification_sparse/algorithm_runs.arff b/autosklearn/metalearning/files/recall_samples_multiclass.classification_sparse/algorithm_runs.arff
index d97e59b7aa..3d31fb4274 100644
--- a/autosklearn/metalearning/files/recall_samples_multiclass.classification_sparse/algorithm_runs.arff
+++ b/autosklearn/metalearning/files/recall_samples_multiclass.classification_sparse/algorithm_runs.arff
@@ -7,95 +7,133 @@
 @ATTRIBUTE runstatus {ok, timeout, memout, not_applicable, crash, other}
 
 @DATA
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-275,1.0,23,?,not_applicable
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 75222,1.0,27,?,not_applicable
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-75121,1.0,59,?,not_applicable
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-2119,1.0,64,?,not_applicable
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-75113,1.0,66,?,not_applicable
-75203,1.0,67,?,not_applicable
-75182,1.0,68,?,not_applicable
-251,1.0,69,?,not_applicable
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-75227,1.0,77,?,not_applicable
-2120,1.0,78,?,not_applicable
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-2117,1.0,86,?,not_applicable
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diff --git a/autosklearn/metalearning/files/recall_samples_multiclass.classification_sparse/configurations.csv b/autosklearn/metalearning/files/recall_samples_multiclass.classification_sparse/configurations.csv
index 888e169980..c3d201ba5c 100644
--- a/autosklearn/metalearning/files/recall_samples_multiclass.classification_sparse/configurations.csv
+++ b/autosklearn/metalearning/files/recall_samples_multiclass.classification_sparse/configurations.csv
@@ -1,93 +1,131 @@
 idx,balancing:strategy,classifier:__choice__,classifier:adaboost:algorithm,classifier:adaboost:learning_rate,classifier:adaboost:max_depth,classifier:adaboost:n_estimators,classifier:bernoulli_nb:alpha,classifier:bernoulli_nb:fit_prior,classifier:decision_tree:criterion,classifier:decision_tree:max_depth_factor,classifier:decision_tree:max_features,classifier:decision_tree:max_leaf_nodes,classifier:decision_tree:min_impurity_decrease,classifier:decision_tree:min_samples_leaf,classifier:decision_tree:min_samples_split,classifier:decision_tree:min_weight_fraction_leaf,classifier:extra_trees:bootstrap,classifier:extra_trees:criterion,classifier:extra_trees:max_depth,classifier:extra_trees:max_features,classifier:extra_trees:max_leaf_nodes,classifier:extra_trees:min_impurity_decrease,classifier:extra_trees:min_samples_leaf,classifier:extra_trees:min_samples_split,classifier:extra_trees:min_weight_fraction_leaf,classifier:gradient_boosting:early_stop,classifier:gradient_boosting:l2_regularization,classifier:gradient_boosting:learning_rate,classifier:gradient_boosting:loss,classifier:gradient_boosting:max_bins,classifier:gradient_boosting:max_depth,classifier:gradient_boosting:max_leaf_nodes,classifier:gradient_boosting:min_samples_leaf,classifier:gradient_boosting:n_iter_no_change,classifier:gradient_boosting:scoring,classifier:gradient_boosting:tol,classifier:gradient_boosting:validation_fraction,classifier:k_nearest_neighbors:n_neighbors,classifier:k_nearest_neighbors:p,classifier:k_nearest_neighbors:weights,classifier:lda:n_components,classifier:lda:shrinkage,classifier:lda:shrinkage_factor,classifier:lda:tol,classifier:liblinear_svc:C,classifier:liblinear_svc:dual,classifier:liblinear_svc:fit_intercept,classifier:liblinear_svc:intercept_scaling,classifier:liblinear_svc:loss,classifier:liblinear_svc:multi_class,classifier:liblinear_svc:penalty,classifier:liblinear_svc:tol,classifier:libsvm_svc:C,classifier:libsvm_svc:coef0,classifier:libsvm_svc:degree,classifier:libsvm_svc:gamma,classifier:libsvm_svc:kernel,classifier:libsvm_svc:max_iter,classifier:libsvm_svc:shrinking,classifier:libsvm_svc:tol,classifier:multinomial_nb:alpha,classifier:multinomial_nb:fit_prior,classifier:passive_aggressive:C,classifier:passive_aggressive:average,classifier:passive_aggressive:fit_intercept,classifier:passive_aggressive:loss,classifier:passive_aggressive:tol,classifier:qda:reg_param,classifier:random_forest:bootstrap,classifier:random_forest:criterion,classifier:random_forest:max_depth,classifier:random_forest:max_features,classifier:random_forest:max_leaf_nodes,classifier:random_forest:min_impurity_decrease,classifier:random_forest:min_samples_leaf,classifier:random_forest:min_samples_split,classifier:random_forest:min_weight_fraction_leaf,classifier:sgd:alpha,classifier:sgd:average,classifier:sgd:epsilon,classifier:sgd:eta0,classifier:sgd:fit_intercept,classifier:sgd:l1_ratio,classifier:sgd:learning_rate,classifier:sgd:loss,classifier:sgd:penalty,classifier:sgd:power_t,classifier:sgd:tol,data_preprocessing:categorical_transformer:categorical_encoding:__choice__,data_preprocessing:categorical_transformer:category_coalescence:__choice__,data_preprocessing:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction,data_preprocessing:numerical_transformer:imputation:strategy,data_preprocessing:numerical_transformer:rescaling:__choice__,data_preprocessing:numerical_transformer:rescaling:quantile_transformer:n_quantiles,data_preprocessing:numerical_transformer:rescaling:quantile_transformer:output_distribution,data_preprocessing:numerical_transformer:rescaling:robust_scaler:q_max,data_preprocessing:numerical_transformer:rescaling:robust_scaler:q_min,feature_preprocessor:__choice__,feature_preprocessor:extra_trees_preproc_for_classification:bootstrap,feature_preprocessor:extra_trees_preproc_for_classification:criterion,feature_preprocessor:extra_trees_preproc_for_classification:max_depth,feature_preprocessor:extra_trees_preproc_for_classification:max_features,feature_preprocessor:extra_trees_preproc_for_classification:max_leaf_nodes,feature_preprocessor:extra_trees_preproc_for_classification:min_impurity_decrease,feature_preprocessor:extra_trees_preproc_for_classification:min_samples_leaf,feature_preprocessor:extra_trees_preproc_for_classification:min_samples_split,feature_preprocessor:extra_trees_preproc_for_classification:min_weight_fraction_leaf,feature_preprocessor:extra_trees_preproc_for_classification:n_estimators,feature_preprocessor:kernel_pca:coef0,feature_preprocessor:kernel_pca:degree,feature_preprocessor:kernel_pca:gamma,feature_preprocessor:kernel_pca:kernel,feature_preprocessor:kernel_pca:n_components,feature_preprocessor:kitchen_sinks:gamma,feature_preprocessor:kitchen_sinks:n_components,feature_preprocessor:liblinear_svc_preprocessor:C,feature_preprocessor:liblinear_svc_preprocessor:dual,feature_preprocessor:liblinear_svc_preprocessor:fit_intercept,feature_preprocessor:liblinear_svc_preprocessor:intercept_scaling,feature_preprocessor:liblinear_svc_preprocessor:loss,feature_preprocessor:liblinear_svc_preprocessor:multi_class,feature_preprocessor:liblinear_svc_preprocessor:penalty,feature_preprocessor:liblinear_svc_preprocessor:tol,feature_preprocessor:nystroem_sampler:coef0,feature_preprocessor:nystroem_sampler:degree,feature_preprocessor:nystroem_sampler:gamma,feature_preprocessor:nystroem_sampler:kernel,feature_preprocessor:nystroem_sampler:n_components,feature_preprocessor:polynomial:degree,feature_preprocessor:polynomial:include_bias,feature_preprocessor:polynomial:interaction_only,feature_preprocessor:random_trees_embedding:bootstrap,feature_preprocessor:random_trees_embedding:max_depth,feature_preprocessor:random_trees_embedding:max_leaf_nodes,feature_preprocessor:random_trees_embedding:min_samples_leaf,feature_preprocessor:random_trees_embedding:min_samples_split,feature_preprocessor:random_trees_embedding:min_weight_fraction_leaf,feature_preprocessor:random_trees_embedding:n_estimators,feature_preprocessor:select_percentile_classification:percentile,feature_preprocessor:select_percentile_classification:score_func,feature_preprocessor:select_rates_classification:alpha,feature_preprocessor:select_rates_classification:mode,feature_preprocessor:select_rates_classification:score_func,feature_preprocessor:truncatedSVD:target_dim
-1,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.3163020577549344,None,0.0,15,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.01322981552715978,most_frequent,robust_scaler,,,0.9746667009556722,0.1974876581478311,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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-48,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.41019363453549706,None,0.0,15,2,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.0003507389224306018,median,none,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.20614098085343638,fdr,chi2,
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-76,weighting,adaboost,SAMME,0.3348531172093427,7,198,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.31611881738182174,most_frequent,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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-79,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.6728827282646637,None,0.0,18,8,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,robust_scaler,,,0.8512985751386942,0.14092824896182807,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,,
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-82,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.02021502857757827,True,,0.055848937053280076,True,,constant,log,l2,,0.00016356162584832537,no_encoding,no_coalescense,,median,quantile_transformer,27,normal,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.11873149622606233,fwe,chi2,
-83,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8.24807027221455,-0.23458710218209156,,0.0001030034352044511,sigmoid,-1,True,0.0002726309373020207,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,quantile_transformer,1974,normal,,,extra_trees_preproc_for_classification,True,entropy,None,0.9395220389606214,None,0.0,2,20,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-84,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.4864284879550203,None,0.0,1,4,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.008665854799630503,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-85,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.43699642289829416,None,0.0,6,20,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,quantile_transformer,1604,uniform,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-86,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.9289590349108043,None,0.0,8,16,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,quantile_transformer,1207,uniform,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.20885612826860853,fwe,chi2,
-87,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.44727252219812963,None,0.0,2,10,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,standardize,,,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l1,8.17533189014765e-05,,,,,,,,,,,,,,,,,,,,,
-88,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7408631116596028,None,0.0,19,2,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,mean,none,,,,,extra_trees_preproc_for_classification,True,entropy,None,0.8873056409960391,None,0.0,11,18,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-89,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.922871100218626,False,True,squared_hinge,0.09957180737451943,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0016563186856411274,median,quantile_transformer,850,uniform,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.09123695282772847,fpr,chi2,
-90,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4834106677905965,False,True,1,squared_hinge,ovr,l2,0.00017625027586147415,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0009157492873444966,most_frequent,quantile_transformer,1150,normal,,,extra_trees_preproc_for_classification,False,entropy,None,0.4981700453792278,None,0.0,9,15,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-91,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.087313242780732,0.0636709914987994,3,0.004481917125024585,poly,-1,False,0.01848880618941367,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.002136015035740733,mean,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-92,weighting,adaboost,SAMME,0.028177459966491795,1,484,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,quantile_transformer,875,normal,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,1.502807354196727,False,True,1,squared_hinge,ovr,l1,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,
+1,none,decision_tree,,,,,,,entropy,1.877536950957045,1.0,None,0.0,10,13,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,quantile_transformer,1275,uniform,,,extra_trees_preproc_for_classification,True,gini,None,0.04910706843318435,None,0.0,12,12,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+2,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.8905944749638223,None,0.0,5,4,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.15463552048633444,most_frequent,robust_scaler,,,0.7545842296967857,0.22898551665984185,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.24780801481682346,fwe,chi2,
+5,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.17926334619345924,None,0.0,11,6,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.012362486878126662,most_frequent,robust_scaler,,,0.7088361967862001,0.21919818267993,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,44.77696516370458,chi2,,,,
+6,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,114,manual,0.11778631707605758,0.0029112721339713465,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.015582195257240751,median,normalize,,,,,kernel_pca,,,,,,,,,,,0.42909623253327767,3,0.25407612000343777,poly,1689,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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+22,weighting,adaboost,SAMME.R,0.01561270559347964,4,417,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,quantile_transformer,1617,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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diff --git a/autosklearn/metalearning/files/recall_samples_multiclass.classification_sparse/description.txt b/autosklearn/metalearning/files/recall_samples_multiclass.classification_sparse/description.txt
index a13ccd158b..450318293f 100644
--- a/autosklearn/metalearning/files/recall_samples_multiclass.classification_sparse/description.txt
+++ b/autosklearn/metalearning/files/recall_samples_multiclass.classification_sparse/description.txt
@@ -1,8 +1,9 @@
 features_cutoff_time: 3600
 features_cutoff_memory: 3072
-number_of_feature_steps: 35
+number_of_feature_steps: 42
 feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances
 feature_step LogNumberOfInstances: LogNumberOfInstances
+feature_step NumberOfClasses: NumberOfClasses
 feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures
 feature_step LogNumberOfFeatures: LogNumberOfFeatures
 feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues
@@ -20,6 +21,11 @@ feature_step DatasetRatio: DatasetRatio, LogDatasetRatio
 feature_step LogDatasetRatio: LogDatasetRatio
 feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio
 feature_step LogInverseDatasetRatio: LogInverseDatasetRatio
+feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD
+feature_step ClassProbabilityMin: ClassProbabilityMin
+feature_step ClassProbabilityMax: ClassProbabilityMax
+feature_step ClassProbabilityMean: ClassProbabilityMean
+feature_step ClassProbabilitySTD: ClassProbabilitySTD
 feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum
 feature_step SymbolsMin: SymbolsMin
 feature_step SymbolsMax: SymbolsMax
@@ -36,11 +42,12 @@ feature_step SkewnessMin: SkewnessMin
 feature_step SkewnessMax: SkewnessMax
 feature_step SkewnessMean: SkewnessMean
 feature_step SkewnessSTD: SkewnessSTD
-features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
+feature_step ClassEntropy: ClassEntropy
+features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy
 features_stochastic: 
-default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
+default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy
 
-algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92
+algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235,236,237,238,239,240,241,242,243,244,245,246,247,248,249,250,251,252,253,254,255,256,257,258,259,260,261,262,263,264,265,266,267,268,269,270,271,272,273,274,275,276,277,278,279,280,281,282,283,284,285,286,287,288,289,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,312,313,314,315,316,317,318,319,320,321,322,323,324,325,326,327,328,329,330,331,332,333,334,335,336,337,338,339,340,341,342,343,344,345,346,347,348,349,350,351,352,353,354,355,356,357,358,359,360,361,362,363,364,365,366,367,368,369,370,371,372
 algorithms_stochastic: 
 performance_measures: recall_samples
 performance_type: solution_quality
diff --git a/autosklearn/metalearning/files/recall_samples_multiclass.classification_sparse/feature_costs.arff b/autosklearn/metalearning/files/recall_samples_multiclass.classification_sparse/feature_costs.arff
index 0e17e20d22..8512fd9275 100644
--- a/autosklearn/metalearning/files/recall_samples_multiclass.classification_sparse/feature_costs.arff
+++ b/autosklearn/metalearning/files/recall_samples_multiclass.classification_sparse/feature_costs.arff
@@ -4,6 +4,7 @@
 @ATTRIBUTE repetition NUMERIC
 @ATTRIBUTE NumberOfInstances NUMERIC
 @ATTRIBUTE LogNumberOfInstances NUMERIC
+@ATTRIBUTE NumberOfClasses NUMERIC
 @ATTRIBUTE NumberOfFeatures NUMERIC
 @ATTRIBUTE LogNumberOfFeatures NUMERIC
 @ATTRIBUTE MissingValues NUMERIC
@@ -21,6 +22,11 @@
 @ATTRIBUTE LogDatasetRatio NUMERIC
 @ATTRIBUTE InverseDatasetRatio NUMERIC
 @ATTRIBUTE LogInverseDatasetRatio NUMERIC
+@ATTRIBUTE ClassOccurences NUMERIC
+@ATTRIBUTE ClassProbabilityMin NUMERIC
+@ATTRIBUTE ClassProbabilityMax NUMERIC
+@ATTRIBUTE ClassProbabilityMean NUMERIC
+@ATTRIBUTE ClassProbabilitySTD NUMERIC
 @ATTRIBUTE NumSymbols NUMERIC
 @ATTRIBUTE SymbolsMin NUMERIC
 @ATTRIBUTE SymbolsMax NUMERIC
@@ -37,162 +43,138 @@
 @ATTRIBUTE SkewnessMax NUMERIC
 @ATTRIBUTE SkewnessMean NUMERIC
 @ATTRIBUTE SkewnessSTD NUMERIC
+@ATTRIBUTE ClassEntropy NUMERIC
 
 @DATA
-75123,1.0,1e-05,1e-05,1e-05,1e-05,0.00253,0.00221,1e-05,0.00024,3e-05,0.00011,0.0,2e-05,2e-05,5e-05,8e-05,2e-05,1e-05,7e-05,1e-05,0.00072,1e-05,1e-05,0.00019,0.00041,0.00011,0.00061,0.00012,9e-05,0.00015,0.00025,0.00067,0.00014,0.00011,0.00016,0.00027
-75090,1.0,1e-05,1e-05,2e-05,2e-05,0.00815,0.00368,7e-05,0.00228,9e-05,0.00235,0.0,5e-05,5e-05,0.0001,0.00013,0.00013,1e-05,0.00021,2e-05,0.00064,0.0,0.0,0.00014,0.00038,0.00011,0.00066,0.00011,0.00014,0.00014,0.00026,0.00066,0.00011,0.00013,0.00015,0.00027
-2123,1.0,1e-05,1e-05,1e-05,1e-05,0.00097,0.00079,1e-05,0.00013,1e-05,7e-05,0.0,2e-05,2e-05,5e-05,8e-05,2e-05,1e-05,5e-05,1e-05,0.0007,1e-05,0.0,0.00017,0.00042,0.0001,0.00052,6e-05,8e-05,0.00012,0.00026,0.00049,6e-05,0.00011,9e-05,0.00023
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-75217,1.0,1e-05,1e-05,2e-05,1e-05,0.00196,0.00158,1e-05,0.00026,2e-05,0.00016,1e-05,3e-05,3e-05,7e-05,0.0001,3e-05,1e-05,7e-05,2e-05,0.00089,1e-05,1e-05,0.00022,0.00053,0.00013,0.00074,9e-05,0.0001,0.00019,0.00036,0.00065,0.00013,0.0001,0.00011,0.00031
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+75234,1.0,1e-05,1e-05,0.00015,1e-05,1e-05,0.0058,0.00534,1e-05,0.00033,1e-05,0.00016,0.0,2e-05,2e-05,7e-05,0.00204,3e-05,1e-05,5e-05,1e-05,0.00024,3e-05,2e-05,0.0001,9e-05,0.00043,0.0,0.0,0.00012,0.00024,6e-05,0.00156,6e-05,7e-05,0.00121,0.00021,0.00054,6e-05,7e-05,9e-05,0.00032,0.00501
+75235,1.0,1e-05,1e-05,0.0003,2e-05,1e-05,0.00461,0.00385,2e-05,0.00055,2e-05,0.00026,1e-05,3e-05,3e-05,0.0001,0.00011,3e-05,1e-05,5e-05,2e-05,0.00026,3e-05,1e-05,8e-05,0.00014,0.00075,1e-05,1e-05,0.0002,0.00043,0.00011,0.00127,0.00017,0.0002,0.00029,0.00061,0.0008,0.00015,0.00012,0.00016,0.00038,0.00242
+75236,1.0,1e-05,1e-05,0.0001,1e-05,1e-05,0.00218,0.00118,2e-05,0.00065,3e-05,0.00041,1e-05,3e-05,3e-05,9e-05,9e-05,4e-05,1e-05,7e-05,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.0006,1e-05,1e-05,0.00021,0.00032,6e-05,0.00043,7e-05,7e-05,9e-05,0.00019,0.00062,7e-05,8e-05,0.0001,0.00037,0.00048
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+75244,1.0,3e-05,2e-05,0.0004,3e-05,2e-05,0.0173,0.01298,4e-05,0.00267,4e-05,0.00174,1e-05,7e-05,9e-05,0.00024,0.00027,8e-05,2e-05,0.00013,3e-05,0.00045,7e-05,2e-05,0.00011,0.00025,0.00077,6e-05,2e-05,0.00021,0.00034,0.00013,0.00077,0.00014,0.00013,0.00026,0.00023,0.00044,7e-05,8e-05,0.0001,0.00019,0.00649
+75248,1.0,1e-05,1e-05,0.00017,1e-05,1e-05,0.00416,0.00305,1e-05,0.0006,2e-05,0.00054,1e-05,2e-05,2e-05,5e-05,7e-05,2e-05,1e-05,4e-05,1e-05,0.00017,2e-05,1e-05,4e-05,0.0001,0.00029,5e-05,1e-05,6e-05,0.00012,6e-05,0.00081,0.00011,0.00015,0.00016,0.00038,0.0005,7e-05,8e-05,0.00012,0.00024,0.00184
+75249,1.0,1e-05,1e-05,0.00011,1e-05,1e-05,0.00192,0.00153,1e-05,0.00025,1e-05,0.00017,1e-05,2e-05,2e-05,5e-05,7e-05,2e-05,1e-05,5e-05,3e-05,0.00014,2e-05,1e-05,3e-05,8e-05,0.00029,1e-05,3e-05,6e-05,0.00012,7e-05,0.00041,6e-05,8e-05,0.0001,0.00017,0.00043,6e-05,7e-05,9e-05,0.00019,0.00092
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diff --git a/autosklearn/metalearning/files/recall_samples_multiclass.classification_sparse/feature_runstatus.arff b/autosklearn/metalearning/files/recall_samples_multiclass.classification_sparse/feature_runstatus.arff
index 0034ad2bd6..5fff2dfed3 100644
--- a/autosklearn/metalearning/files/recall_samples_multiclass.classification_sparse/feature_runstatus.arff
+++ b/autosklearn/metalearning/files/recall_samples_multiclass.classification_sparse/feature_runstatus.arff
@@ -4,6 +4,7 @@
 @ATTRIBUTE repetition NUMERIC
 @ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other}
@@ -21,6 +22,11 @@
 @ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other}
@@ -37,162 +43,138 @@
 @ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other}
 
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diff --git a/autosklearn/metalearning/files/recall_samples_multiclass.classification_sparse/feature_values.arff b/autosklearn/metalearning/files/recall_samples_multiclass.classification_sparse/feature_values.arff
index a60400e3ce..736dc64321 100644
--- a/autosklearn/metalearning/files/recall_samples_multiclass.classification_sparse/feature_values.arff
+++ b/autosklearn/metalearning/files/recall_samples_multiclass.classification_sparse/feature_values.arff
@@ -13,7 +13,6 @@
 @ATTRIBUTE KurtosisMean NUMERIC
 @ATTRIBUTE KurtosisMin NUMERIC
 @ATTRIBUTE KurtosisSTD NUMERIC
-@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC
 @ATTRIBUTE LogDatasetRatio NUMERIC
 @ATTRIBUTE LogInverseDatasetRatio NUMERIC
 @ATTRIBUTE LogNumberOfFeatures NUMERIC
@@ -42,160 +41,135 @@
 @ATTRIBUTE SymbolsSum NUMERIC
 
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diff --git a/autosklearn/metalearning/files/recall_weighted_binary.classification_dense/algorithm_runs.arff b/autosklearn/metalearning/files/recall_weighted_binary.classification_dense/algorithm_runs.arff
index f8fb3b5d97..0ea97209d1 100644
--- a/autosklearn/metalearning/files/recall_weighted_binary.classification_dense/algorithm_runs.arff
+++ b/autosklearn/metalearning/files/recall_weighted_binary.classification_dense/algorithm_runs.arff
@@ -7,130 +7,135 @@
 @ATTRIBUTE runstatus {ok, timeout, memout, not_applicable, crash, other}
 
 @DATA
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diff --git a/autosklearn/metalearning/files/recall_weighted_binary.classification_dense/configurations.csv b/autosklearn/metalearning/files/recall_weighted_binary.classification_dense/configurations.csv
index 14ccf74d94..85e767fd63 100644
--- a/autosklearn/metalearning/files/recall_weighted_binary.classification_dense/configurations.csv
+++ b/autosklearn/metalearning/files/recall_weighted_binary.classification_dense/configurations.csv
@@ -1,128 +1,133 @@
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-4,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,23916.78290516781,False,True,1,squared_hinge,ovr,l2,0.0006655607726658531,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.008808715242823137,median,none,,,,,kernel_pca,,,,,,,,,,,,,,,,,,,0.2633153190620261,2,0.004770332714291669,poly,1483,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-5,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,2,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,none,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,94.23876232993393,mutual_info,,,
-6,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.933900673392165,None,0.0,7,4,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,minmax,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-7,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.3700222208089159,None,0.0,14,19,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,minmax,,,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,1.0533068843554318,False,True,1,squared_hinge,ovr,l1,0.00618591414610778,,,,,,,,,,,,,,,,,,,,,,
-8,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5238814331135578,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,median,none,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,28,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-9,none,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13,None,,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,none,,,,,feature_agglomeration,,,,,,,,,,,,,,,euclidean,average,74,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-10,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.5244313211416313,None,0.0,1,2,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.454300236339658,most_frequent,robust_scaler,,,0.7858830918128734,0.2705174720960696,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-11,weighting,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0006294780694492238,most_frequent,standardize,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.063715750591364,fdr,chi2
-12,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.541827838541001,True,True,squared_hinge,0.0001129851063838815,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,standardize,,,,,extra_trees_preproc_for_classification,True,entropy,None,0.5533459595240017,None,0.0,18,4,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-13,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.3652103526812861,None,0.0,6,8,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,quantile_transformer,79,uniform,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.17357047764133562,fwe,chi2
-14,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6185424163558029,False,True,1,squared_hinge,ovr,l2,4.385767395120581e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010397452590017174,median,quantile_transformer,961,normal,,,feature_agglomeration,,,,,,,,,,,,,,,cosine,average,25,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-15,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.643761610990255,None,0.0,20,6,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.039859381630488695,mean,normalize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,
-16,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7947177310891914,None,0.0,13,3,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,mean,robust_scaler,,,0.7681430822715739,0.23411421138225857,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,16.747604337291705,f_classif,,,
-17,none,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.1769332447629957,mean,minmax,,,,,fast_ica,,,,,,,,,,,deflation,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-18,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.07518858116896743,None,0.0,7,10,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0027133920607872518,mean,robust_scaler,,,0.7873452530915046,0.16215139072179952,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-19,none,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,200,None,,0.0017457216340699586,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,none,,,,,kernel_pca,,,,,,,,,,,,,,,,,,,0.0,4,0.0007494648939847078,poly,1996,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-20,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.6039096182414764,None,0.0,2,20,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.1939609823704447,most_frequent,robust_scaler,,,0.7331327008874781,0.29747488162877694,extra_trees_preproc_for_classification,True,entropy,None,0.33401603690569853,None,0.0,4,4,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-21,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.8054434875626825,None,0.0,1,20,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,median,standardize,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4241048505416849,fpr,f_classif
-22,none,adaboost,SAMME,0.011233995624432622,9,477,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.018370622484682127,mean,standardize,,,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6039710338898471,False,,,,,,,,,,,,,,,
-23,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7734.521233796252,,,2.6048661611376747,rbf,-1,True,4.68717607435371e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,none,,,,,feature_agglomeration,,,,,,,,,,,,,,,euclidean,average,25,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-24,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.8367655423352977,None,0.0,1,5,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.01866043949164283,median,standardize,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.3824115704370156,fpr,f_classif
-25,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5010952533165973,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.005838375247601407,mean,normalize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,
-26,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.7297122444166401,False,True,1,squared_hinge,ovr,l2,0.00023416745444541063,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.03941815152197094,most_frequent,normalize,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0973649546833947,fpr,chi2
-27,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.4426097579931897,None,0.0,16,4,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,mean,robust_scaler,,,0.7407323456920554,0.0804756921037867,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,
-28,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5607168729746175,None,0.0,2,3,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.009810589644392154,most_frequent,quantile_transformer,1132,normal,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.06211089008167909,fdr,chi2
-29,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9333237919291875,None,0.0,8,3,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,
-30,none,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.9728532763078892,None,0.0,13,9,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,none,,,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6651071190864879,False,,,,,,,,,,,,,,,
-31,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9561075301148197,None,0.0,6,2,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,
-32,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.557321265077371,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-33,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,1.153931482107762e-10,0.011666190464009397,auto,255,None,29,190,18,loss,1e-07,0.147868307124396,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,
-34,none,adaboost,SAMME,0.6255555220658239,6,446,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.008833097306364968,most_frequent,none,,,,,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,10,None,4,17,1.0,76,,,,,
-35,none,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.563056219822946,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.32793677336996485,most_frequent,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-36,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,3.6797768538445263e-09,0.05511004217375807,auto,255,None,72,62,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,robust_scaler,,,0.7241481576763391,0.0501033899901458,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,36,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-37,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.7521969592385672,None,0.0,9,5,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.020402264943872365,median,robust_scaler,,,0.737227213630259,0.20752747663727789,fast_ica,,,,,,,,,,,parallel,exp,312,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-38,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.887898394612674,None,0.0,10,19,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,robust_scaler,,,0.8350698288864961,0.2347163538066168,extra_trees_preproc_for_classification,False,entropy,None,0.49795108918064074,None,0.0,13,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-39,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,1.6343482806592892e-09,0.19537524572679268,auto,255,None,32,29,13,loss,1e-07,0.05061738224302791,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-40,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7088164344533493,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,mean,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,
-41,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,1.943186107984505e-07,0.08852455702036988,auto,255,None,5,72,19,loss,1e-07,0.039837460762271175,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.01591265419367627,median,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,
-42,weighting,adaboost,SAMME,0.5210651042094924,10,401,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,standardize,,,,,feature_agglomeration,,,,,,,,,,,,,,,euclidean,complete,89,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-43,none,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0686374607394679,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,minmax,,,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.8490260132946401,False,,,,,,,,,,,,,,,
-44,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.6931271368243409,None,0.0,11,10,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.362940004438799,most_frequent,robust_scaler,,,0.9177587796305063,0.17084038458597867,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,30.491514144623952,mutual_info,,,
-45,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.9398869869697849,None,0.0,20,15,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,normalize,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,269,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-46,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,92.66144306787051,,,0.04654639302091653,rbf,-1,True,0.0022231544280610005,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.005194838694455963,mean,none,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,15,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-47,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2681.998807317996,-0.9558745731731211,,0.0011299948421773457,sigmoid,-1,False,1.5292507354478086e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-48,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.8095522998548572,None,0.0,17,10,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.031940595071539304,mean,quantile_transformer,1563,normal,,,extra_trees_preproc_for_classification,False,entropy,None,0.9330145598603196,None,0.0,18,10,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-49,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,26.502259985828186,,,9.475662388405154e-05,rbf,-1,False,0.00019690635968217866,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-50,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,2.5360196477156387e-07,0.011018756408558218,auto,255,None,40,122,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.00011749164080669445,most_frequent,quantile_transformer,757,uniform,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,5.097834697156853,False,True,1,squared_hinge,ovr,l1,0.053877834919477424,,,,,,,,,,,,,,,,,,,,,,
-51,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5255991794296913,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-52,weighting,decision_tree,,,,,,,gini,1.7853447328017091,1.0,None,0.0,13,7,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0001265964225445088,median,normalize,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.26399878867042303,,mutual_info_classif
-53,weighting,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.7776312540090575,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0005253715056715056,mean,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,False,,,,,,,,,,,,
-54,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,1.488333818193668e-10,0.09526676471267805,auto,255,None,258,87,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,normalize,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.3561953264350478,fpr,f_classif
-55,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.9523435078770708,0.8748383232154038,4,0.2195992292488049,poly,-1,True,4.849685143365339e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,minmax,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-56,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.7610308078756552,None,0.0,1,2,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.010000000000000004,mean,robust_scaler,,,0.9205545465103644,0.20272761060743316,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-57,weighting,adaboost,SAMME.R,0.03127273017917347,10,496,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,standardize,,,,,fast_ica,,,,,,,,,,,deflation,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-58,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,151,None,,0.0003283777607180646,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.2510527431514227,mean,normalize,,,,,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,-0.96093182232312,,0.05762510964607661,sigmoid,2632,,,,,,,,,,,,,,,,,
-59,none,bernoulli_nb,,,,,0.6524090653939782,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.005164169126808402,most_frequent,quantile_transformer,16,normal,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.033082145443631535,False,True,1,squared_hinge,ovr,l1,0.002615020898325393,,,,,,,,,,,,,,,,,,,,,,
-60,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.6453555269772124,None,0.0,1,4,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,normalize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-61,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,940.9437776449834,,,1.5839338084625942,rbf,-1,False,0.023072781592565438,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,none,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,77.25763685070707,mutual_info,,,
-62,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,220,auto,,0.0013824567992319003,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00021328932075284388,mean,minmax,,,,,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.039972474835314076,,0.01017810882899714,sigmoid,295,,,,,,,,,,,,,,,,,
-63,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.7326858450533382,0.02513634776687268,2,0.8635510319471909,poly,-1,True,0.0014598272110141993,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.06447266871225255,most_frequent,quantile_transformer,1000,normal,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,79.01961823033078,f_classif,,,
-64,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,29.55731828912983,False,True,1,squared_hinge,ovr,l2,0.0011332486951954968,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,
-65,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,1.1387795876755026e-06,0.12245605190343567,auto,255,None,58,7,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,robust_scaler,,,0.79305915648885,0.1841487406876042,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-66,none,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.563056219822946,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.32793677336996485,most_frequent,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-67,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,0.2638627266631576,0.022360820165005443,auto,255,None,171,6,19,loss,1e-07,0.016209060012924756,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.008763072395677399,mean,standardize,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,78,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-68,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,925.3053743248586,False,True,1,squared_hinge,ovr,l2,0.02764481198395717,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.3850718018366796,most_frequent,quantile_transformer,786,uniform,,,feature_agglomeration,,,,,,,,,,,,,,,cosine,average,156,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-69,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7573078019937971,None,0.0,2,5,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,robust_scaler,,,0.7265312295801113,0.28332515882236425,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.09660564590146253,fpr,f_classif
-70,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.005753626505113041,False,True,squared_hinge,0.00924401505172238,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.3192271696445429,median,quantile_transformer,913,normal,,,kernel_pca,,,,,,,,,,,,,,,,,,,,,,cosine,1963,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-71,none,adaboost,SAMME.R,0.22802213935525836,6,284,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,robust_scaler,,,0.7468645381288924,0.22838821662367073,feature_agglomeration,,,,,,,,,,,,,,,cosine,average,128,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-72,none,adaboost,SAMME,0.1280458968526125,7,125,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,robust_scaler,,,0.7156460334356771,0.2981902862926971,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,65.16171885308417,chi2,,,
-73,none,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9931779675565967,None,0.0,18,17,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,robust_scaler,,,0.75,0.2945917010472818,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,
-74,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.08646118576686276,None,0.0,4,9,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,most_frequent,quantile_transformer,959,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-75,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,1.2430504242164082e-09,0.03359568307446533,auto,255,None,1609,29,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,standardize,,,,,feature_agglomeration,,,,,,,,,,,,,,,cosine,complete,40,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-76,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7.491039171224502,True,True,squared_hinge,2.8058138048327884e-05,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,minmax,,,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.7754372809893839,False,True,1,squared_hinge,ovr,l1,3.9643594596766314e-05,,,,,,,,,,,,,,,,,,,,,,
-77,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.34936025988397124,None,0.0,2,13,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00018601309647406987,median,robust_scaler,,,0.8634309348843472,0.26976989465367607,extra_trees_preproc_for_classification,False,entropy,None,0.48025175288975036,None,0.0,6,17,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-78,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,0.011547155223520492,0.13665554140837183,auto,255,None,13,5,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.1824584492028449,median,minmax,,,,,feature_agglomeration,,,,,,,,,,,,,,,euclidean,average,261,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-79,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.933900673392165,None,0.0,7,4,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,minmax,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-80,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1661.2274747409913,,,0.025821208852387936,rbf,-1,False,0.0017011435104921824,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,none,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,306,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-81,none,adaboost,SAMME,0.011233995624432622,9,477,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.018370622484682127,mean,standardize,,,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6039710338898471,False,,,,,,,,,,,,,,,
-82,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,21.85207494001697,0.18872999589786676,4,1.1863646021868761,poly,-1,False,0.0021338371901294443,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0034080852834770217,mean,normalize,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.11326005109886934,fdr,f_classif
-83,none,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.563056219822946,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.32793677336996485,most_frequent,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-84,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,0.037849720944694,0.1268815770019783,auto,255,None,11,99,11,loss,1e-07,0.10654523627313527,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,
-85,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.593728171200453,None,0.0,7,20,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.02830941310359363,mean,normalize,,,,,extra_trees_preproc_for_classification,False,entropy,None,0.7389446586281172,None,0.0,20,11,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-86,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,1,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.019797877010737826,mean,normalize,,,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.8239678741128165,False,,,,,,,,,,,,,,,
-87,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.46057831591617715,False,True,hinge,0.04557857428827514,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00027457445401600137,median,standardize,,,,,extra_trees_preproc_for_classification,True,gini,None,0.48190346970486964,None,0.0,17,18,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-88,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,38,auto,,0.00019398431171606024,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010000000000000004,median,quantile_transformer,1440,normal,,,extra_trees_preproc_for_classification,True,gini,None,0.5126639956845847,None,0.0,16,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-89,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5722917944959481,None,0.0,1,14,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,
-90,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.26356754187780895,None,0.0,4,3,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010966178401758774,mean,robust_scaler,,,0.7817043861798886,0.24371720112407158,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
-91,weighting,adaboost,SAMME.R,0.13547552520095074,4,62,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.011851011202809002,most_frequent,standardize,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4888776875314595,fpr,chi2
-92,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,80.0028902468169,False,True,1,squared_hinge,ovr,l2,0.0024481999829574722,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.15047970487774687,mean,quantile_transformer,1130,normal,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,1.1912646635544712,False,True,1,squared_hinge,ovr,l1,0.00014314728486450857,,,,,,,,,,,,,,,,,,,,,,
-93,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.22879311080499845,None,0.0,8,4,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.014320022547615783,median,minmax,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.238630703440461,mutual_info,,,
-94,weighting,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,2,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,none,,,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6342574285315175,False,,,,,,,,,,,,,,,
-95,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.107967527639527e-05,True,True,hinge,0.00448294745544775,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,quantile_transformer,953,normal,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,35.00114345405331,chi2,,,
-96,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.14148638702297855,None,0.0,2,6,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.0038499650338378643,most_frequent,quantile_transformer,1759,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-97,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,1.1522414705935405e-10,0.6132800183073315,auto,255,None,1607,67,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010096911249562407,mean,robust_scaler,,,0.7663493326967942,0.136094217337434,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-98,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2746221958279041,False,True,1,squared_hinge,ovr,l2,6.546077664669006e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,quantile_transformer,1054,normal,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,
-99,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.479405900046494,,,0.013340778632154543,rbf,-1,False,6.913586060890114e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,robust_scaler,,,0.9953232616818098,0.06450577886132274,fast_ica,,,,,,,,,,,deflation,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-100,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.825844582229201,None,0.0,12,9,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0008422508222149647,median,robust_scaler,,,0.8658803880857064,0.2528423686293341,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-101,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2574971638664341,True,True,hinge,1.8027411099296446e-05,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.00536396328125103,median,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,
-102,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.7033210835501171,None,0.0,3,19,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,robust_scaler,,,0.7699195245692886,0.29258311638476237,feature_agglomeration,,,,,,,,,,,,,,,euclidean,ward,143,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-103,none,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,128,manual,0.8574560771111307,0.010886139381599514,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.24115007944216788,most_frequent,normalize,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,62.26514170991452,mutual_info,,,
-104,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.6282663389679584,None,0.0,1,3,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,median,minmax,,,,,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-105,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,1.430083734915855e-09,0.08779621406981941,auto,255,None,17,20,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.29749837779115257,mean,robust_scaler,,,0.7236445277814435,0.25,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
-106,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.3294588572341214,None,0.0,1,3,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
-107,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,39.22980757485479,,,0.13597389869623427,rbf,-1,False,0.00011639060052687874,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.006288306814260224,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-108,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.7922837297504705,False,True,1,squared_hinge,ovr,l2,0.0005476597326342352,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.012674631437254894,most_frequent,none,,,,,extra_trees_preproc_for_classification,False,entropy,None,0.23145146971319638,None,0.0,7,20,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-109,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.18060139887649052,None,0.0,10,12,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.010000000000000004,most_frequent,robust_scaler,,,0.9298612094255864,0.1263597946722308,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
-110,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.4111348710613201,None,0.0,16,8,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.01224348527546641,most_frequent,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,
-111,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6427543483503397,True,True,hinge,0.00045318775930916944,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.009338484332306904,median,quantile_transformer,174,normal,,,kernel_pca,,,,,,,,,,,,,,,,,,,,,,cosine,1848,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-112,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.04981080944617379,True,,0.07309386939525211,True,,constant,log,l2,,0.0006721243213777075,no_encoding,no_coalescense,,mean,quantile_transformer,574,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-113,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,9.029473608568458e-09,0.15779990576964315,auto,255,None,37,71,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.018093177195644097,mean,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,False,,,,,,,,,,,,
-114,none,adaboost,SAMME,0.011233995624432622,9,477,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.018370622484682127,mean,standardize,,,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6039710338898471,False,,,,,,,,,,,,,,,
-115,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l2,2.9468189962573666e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.014870368357163507,most_frequent,quantile_transformer,1000,uniform,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,83.43414935094258,f_classif,,,
-116,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.4864284879550203,None,0.0,1,4,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.008665854799630503,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-117,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.43699642289829416,None,0.0,6,20,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,quantile_transformer,1604,uniform,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-118,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.6922752267318475,True,True,squared_hinge,0.06299408576730353,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,quantile_transformer,1742,uniform,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,107,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-119,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.44727252219812963,None,0.0,2,10,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,standardize,,,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l1,8.17533189014765e-05,,,,,,,,,,,,,,,,,,,,,,
-120,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.46057831591617715,False,True,hinge,0.04557857428827514,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00027457445401600137,median,standardize,,,,,extra_trees_preproc_for_classification,True,gini,None,0.48190346970486964,None,0.0,17,18,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-121,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.4210978663047845,None,0.0,5,6,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.006365988550833196,most_frequent,robust_scaler,,,0.8348891074186082,0.00974961635730845,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0695919708890599,fpr,f_classif
-122,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4424195769042466,False,True,hinge,0.02490432909709204,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,mean,quantile_transformer,1000,uniform,,,kernel_pca,,,,,,,,,,,,,,,,,,,,,,cosine,769,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-123,weighting,decision_tree,,,,,,,entropy,1.0435267397863155,1.0,None,0.0,15,3,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.36913659113454333,most_frequent,normalize,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4947786362821451,fpr,f_classif
-124,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1561.1002600303584,,,0.0024048551704194507,rbf,-1,False,0.0001936214667496851,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,minmax,,,,,fast_ica,,,,,,,,,,,parallel,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-125,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.087313242780732,0.0636709914987994,3,0.004481917125024585,poly,-1,False,0.01848880618941367,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.002136015035740733,mean,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-126,weighting,adaboost,SAMME.R,1.696588612003168,9,455,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,minmax,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,82.18726095926662,chi2,,,
-127,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.6466927589470762,None,0.0,11,2,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.009672465311659264,most_frequent,standardize,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,369,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+1,none,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1514.059969351301,False,True,1,squared_hinge,ovr,l2,0.002448814309966599,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,standardize,,,,,extra_trees_preproc_for_classification,True,entropy,None,0.8221481751476923,None,0.0,2,3,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+3,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5707458022222313,None,0.0,3,12,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,minmax,,,,,extra_trees_preproc_for_classification,False,gini,None,0.5651056128131684,None,0.0,9,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+6,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.7283683762613216,None,0.0,12,20,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,median,standardize,,,,,fast_ica,,,,,,,,,,,deflation,exp,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+8,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,114,manual,0.11778631707605758,0.0029112721339713465,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.015582195257240751,median,normalize,,,,,kernel_pca,,,,,,,,,,,,,,,,,,,0.42909623253327767,3,0.25407612000343777,poly,1689,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+11,weighting,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,2,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.3865962574104293,mean,normalize,,,,,fast_ica,,,,,,,,,,,deflation,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+13,weighting,adaboost,SAMME,0.17983222024694556,4,357,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0008898781197407956,mean,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+15,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.21772092350860472,None,0.0,14,17,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.0020488212891693275,median,standardize,,,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,33.74257606530722,False,True,1,squared_hinge,ovr,l1,8.740452966205111e-05,,,,,,,,,,,,,,,,,,,,,,
+18,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,2.4243922131268786e-05,0.07570328162448307,auto,255,None,71,42,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,robust_scaler,,,0.8898729094898769,0.12138152991327475,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,94.96086286583851,chi2,,,
+20,none,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.9866058571747695,None,0.0,16,9,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.1680676743605393,most_frequent,robust_scaler,,,0.8840072797399463,0.08535534311393947,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,False,,,,,,,,,,,,
+23,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,0.00022373024203887573,0.027149343206135476,auto,255,None,49,13,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.00777614320416325,most_frequent,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,
+27,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.41025844265119926,None,0.0,18,15,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,median,quantile_transformer,1911,normal,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,False,,,,,,,,,,,,
+29,none,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.563056219822946,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.32793677336996485,most_frequent,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+31,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,8.757412830636845e-06,0.10095376320667454,auto,255,None,3,2,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.015401766535365336,median,quantile_transformer,647,normal,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,
+32,none,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,174,manual,0.21501892897746921,2.0347110826262054e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.013754893527625522,most_frequent,normalize,,,,,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,-0.019221513778605592,3,0.10190948951339239,poly,2521,,,,,,,,,,,,,,,,,
+35,none,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9.608707084865884,False,True,1,squared_hinge,ovr,l2,0.008086910150491696,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.020901889243957545,median,minmax,,,,,kitchen_sinks,,,,,,,,,,,,,,,,,,,,,,,,0.5694399784333516,3230,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+39,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.6799215694343422,None,0.0,1,5,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.004724310659953132,most_frequent,quantile_transformer,1037,normal,,,fast_ica,,,,,,,,,,,parallel,exp,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+41,none,extra_trees,,,,,,,,,,,,,,,True,gini,None,0.5033866291997137,None,0.0,2,14,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,minmax,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+43,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.9018061166635376,None,0.0,7,9,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,quantile_transformer,1802,normal,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,84.54647012968938,chi2,,,
+44,none,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l2,2.858526169098402e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0007827505640681123,median,standardize,,,,,extra_trees_preproc_for_classification,False,entropy,None,0.3443310459451119,None,0.0,3,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+49,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.049504283446131e-05,False,,0.0004438773852753879,True,2.4110816673432424e-07,constant,log,elasticnet,,0.01321814853186568,one_hot_encoding,minority_coalescer,0.01510416593631535,most_frequent,none,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,66.65285268958851,chi2,,,
+52,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,6.421059381158897e-08,0.11261253191288709,auto,255,None,45,28,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,
+55,none,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.9822179211645446,None,0.0,14,8,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.013966871874533052,most_frequent,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,
+58,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5214197490559657,None,0.0,1,2,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.00826182798588248,mean,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,
+63,none,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,130,manual,0.5446276346297756,0.00846307015171434,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.005736348762561667,median,quantile_transformer,973,uniform,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.08483615095863663,fdr,chi2
+65,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,1.2822815440468518e-10,0.12922700208645135,auto,255,None,31,44,13,loss,1e-07,0.08789566421608311,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.012230423571314038,mean,robust_scaler,,,0.71734690066056,0.20283418288016852,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,
+68,weighting,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,minmax,,,,,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+75,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13.284077389890939,-0.2621334760621076,5,0.9060908941898536,poly,-1,False,0.06323853917206591,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0011743021592446861,most_frequent,normalize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,
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+447,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.3431705351883592,None,0.0,4,16,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.09444471061261189,most_frequent,normalize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+450,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,7.837701348094735e-09,0.05315388879642303,auto,255,None,67,2,10,loss,1e-07,0.11655723047843726,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.001857226018763921,median,none,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.014648191862400293,fdr,f_classif
+458,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,5.581658271288433e-10,0.06171988234311011,auto,255,None,25,6,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.014859405637537319,most_frequent,robust_scaler,,,0.7983900845251328,0.2545046978458648,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,1.380085213170737,False,True,1,squared_hinge,ovr,l1,0.0061450732015354784,,,,,,,,,,,,,,,,,,,,,,
+462,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6,1,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.017471096998391875,median,none,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,67.7801145492284,f_classif,,,
diff --git a/autosklearn/metalearning/files/recall_weighted_binary.classification_dense/description.txt b/autosklearn/metalearning/files/recall_weighted_binary.classification_dense/description.txt
index 4fb11c56ab..2a6f0c9b23 100644
--- a/autosklearn/metalearning/files/recall_weighted_binary.classification_dense/description.txt
+++ b/autosklearn/metalearning/files/recall_weighted_binary.classification_dense/description.txt
@@ -1,8 +1,9 @@
 features_cutoff_time: 3600
 features_cutoff_memory: 3072
-number_of_feature_steps: 35
+number_of_feature_steps: 42
 feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances
 feature_step LogNumberOfInstances: LogNumberOfInstances
+feature_step NumberOfClasses: NumberOfClasses
 feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures
 feature_step LogNumberOfFeatures: LogNumberOfFeatures
 feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues
@@ -20,6 +21,11 @@ feature_step DatasetRatio: DatasetRatio, LogDatasetRatio
 feature_step LogDatasetRatio: LogDatasetRatio
 feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio
 feature_step LogInverseDatasetRatio: LogInverseDatasetRatio
+feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD
+feature_step ClassProbabilityMin: ClassProbabilityMin
+feature_step ClassProbabilityMax: ClassProbabilityMax
+feature_step ClassProbabilityMean: ClassProbabilityMean
+feature_step ClassProbabilitySTD: ClassProbabilitySTD
 feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum
 feature_step SymbolsMin: SymbolsMin
 feature_step SymbolsMax: SymbolsMax
@@ -36,11 +42,12 @@ feature_step SkewnessMin: SkewnessMin
 feature_step SkewnessMax: SkewnessMax
 feature_step SkewnessMean: SkewnessMean
 feature_step SkewnessSTD: SkewnessSTD
-features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
+feature_step ClassEntropy: ClassEntropy
+features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy
 features_stochastic: 
-default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
+default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy
 
-algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127
+algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235,236,237,238,239,240,241,242,243,244,245,246,247,248,249,250,251,252,253,254,255,256,257,258,259,260,261,262,263,264,265,266,267,268,269,270,271,272,273,274,275,276,277,278,279,280,281,282,283,284,285,286,287,288,289,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,312,313,314,315,316,317,318,319,320,321,322,323,324,325,326,327,328,329,330,331,332,333,334,335,336,337,338,339,340,341,342,343,344,345,346,347,348,349,350,351,352,353,354,355,356,357,358,359,360,361,362,363,364,365,366,367,368,369,370,371,372,373,374,375,376,377,378,379,380,381,382,383,384,385,386,387,388,389,390,391,392,393,394,395,396,397,398,399,400,401,402,403,404,405,406,407,408,409,410,411,412,413,414,415,416,417,418,419,420,421,422,423,424,425,426,427,428,429,430,431,432,433,434,435,436,437,438,439,440,441,442,443,444,445,446,447,448,449,450,451,452,453,454,455,456,457,458,459,460,461,462,463,464
 algorithms_stochastic: 
 performance_measures: recall_weighted
 performance_type: solution_quality
diff --git a/autosklearn/metalearning/files/recall_weighted_binary.classification_dense/feature_costs.arff b/autosklearn/metalearning/files/recall_weighted_binary.classification_dense/feature_costs.arff
index 0e17e20d22..8512fd9275 100644
--- a/autosklearn/metalearning/files/recall_weighted_binary.classification_dense/feature_costs.arff
+++ b/autosklearn/metalearning/files/recall_weighted_binary.classification_dense/feature_costs.arff
@@ -4,6 +4,7 @@
 @ATTRIBUTE repetition NUMERIC
 @ATTRIBUTE NumberOfInstances NUMERIC
 @ATTRIBUTE LogNumberOfInstances NUMERIC
+@ATTRIBUTE NumberOfClasses NUMERIC
 @ATTRIBUTE NumberOfFeatures NUMERIC
 @ATTRIBUTE LogNumberOfFeatures NUMERIC
 @ATTRIBUTE MissingValues NUMERIC
@@ -21,6 +22,11 @@
 @ATTRIBUTE LogDatasetRatio NUMERIC
 @ATTRIBUTE InverseDatasetRatio NUMERIC
 @ATTRIBUTE LogInverseDatasetRatio NUMERIC
+@ATTRIBUTE ClassOccurences NUMERIC
+@ATTRIBUTE ClassProbabilityMin NUMERIC
+@ATTRIBUTE ClassProbabilityMax NUMERIC
+@ATTRIBUTE ClassProbabilityMean NUMERIC
+@ATTRIBUTE ClassProbabilitySTD NUMERIC
 @ATTRIBUTE NumSymbols NUMERIC
 @ATTRIBUTE SymbolsMin NUMERIC
 @ATTRIBUTE SymbolsMax NUMERIC
@@ -37,162 +43,138 @@
 @ATTRIBUTE SkewnessMax NUMERIC
 @ATTRIBUTE SkewnessMean NUMERIC
 @ATTRIBUTE SkewnessSTD NUMERIC
+@ATTRIBUTE ClassEntropy NUMERIC
 
 @DATA
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diff --git a/autosklearn/metalearning/files/recall_weighted_binary.classification_dense/feature_runstatus.arff b/autosklearn/metalearning/files/recall_weighted_binary.classification_dense/feature_runstatus.arff
index 0034ad2bd6..5fff2dfed3 100644
--- a/autosklearn/metalearning/files/recall_weighted_binary.classification_dense/feature_runstatus.arff
+++ b/autosklearn/metalearning/files/recall_weighted_binary.classification_dense/feature_runstatus.arff
@@ -4,6 +4,7 @@
 @ATTRIBUTE repetition NUMERIC
 @ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other}
@@ -21,6 +22,11 @@
 @ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other}
@@ -37,162 +43,138 @@
 @ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other}
 
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diff --git a/autosklearn/metalearning/files/recall_weighted_binary.classification_dense/feature_values.arff b/autosklearn/metalearning/files/recall_weighted_binary.classification_dense/feature_values.arff
index a60400e3ce..736dc64321 100644
--- a/autosklearn/metalearning/files/recall_weighted_binary.classification_dense/feature_values.arff
+++ b/autosklearn/metalearning/files/recall_weighted_binary.classification_dense/feature_values.arff
@@ -13,7 +13,6 @@
 @ATTRIBUTE KurtosisMean NUMERIC
 @ATTRIBUTE KurtosisMin NUMERIC
 @ATTRIBUTE KurtosisSTD NUMERIC
-@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC
 @ATTRIBUTE LogDatasetRatio NUMERIC
 @ATTRIBUTE LogInverseDatasetRatio NUMERIC
 @ATTRIBUTE LogNumberOfFeatures NUMERIC
@@ -42,160 +41,135 @@
 @ATTRIBUTE SymbolsSum NUMERIC
 
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diff --git a/autosklearn/metalearning/files/recall_weighted_binary.classification_sparse/algorithm_runs.arff b/autosklearn/metalearning/files/recall_weighted_binary.classification_sparse/algorithm_runs.arff
index bd9fe33e02..402934b928 100644
--- a/autosklearn/metalearning/files/recall_weighted_binary.classification_sparse/algorithm_runs.arff
+++ b/autosklearn/metalearning/files/recall_weighted_binary.classification_sparse/algorithm_runs.arff
@@ -7,95 +7,133 @@
 @ATTRIBUTE runstatus {ok, timeout, memout, not_applicable, crash, other}
 
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diff --git a/autosklearn/metalearning/files/recall_weighted_binary.classification_sparse/configurations.csv b/autosklearn/metalearning/files/recall_weighted_binary.classification_sparse/configurations.csv
index 75662496d2..36fd991e13 100644
--- a/autosklearn/metalearning/files/recall_weighted_binary.classification_sparse/configurations.csv
+++ b/autosklearn/metalearning/files/recall_weighted_binary.classification_sparse/configurations.csv
@@ -1,93 +1,131 @@
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-91,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.087313242780732,0.0636709914987994,3,0.004481917125024585,poly,-1,False,0.01848880618941367,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.002136015035740733,mean,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-92,weighting,adaboost,SAMME,0.029932847627447023,8,483,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,quantile_transformer,1290,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+1,none,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1514.059969351301,False,True,1,squared_hinge,ovr,l2,0.002448814309966599,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,standardize,,,,,extra_trees_preproc_for_classification,True,entropy,None,0.8221481751476923,None,0.0,2,3,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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+287,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.3850609070926397,None,0.0,1,2,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.013992309764275707,median,normalize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,,
+289,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.818514683854827,None,0.0,15,20,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.4928285185491301,most_frequent,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,,
+292,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.0479982468066316,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,quantile_transformer,1097,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+293,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7040496889211508,None,0.0,17,19,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.016450784709133564,mean,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+300,none,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.7010137052424128,None,0.0,7,10,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.2761813291805052,most_frequent,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,,
+306,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2677516008848513,True,True,hinge,0.05752557410901496,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0010152857217605206,median,quantile_transformer,1000,uniform,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,51.60974796845029,chi2,,,,
+307,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,33,None,,0.006687546229372618,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.06231343712708962,mean,none,,,,,kernel_pca,,,,,,,,,,,-0.7000777299811654,3,0.008780219231661734,poly,498,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+312,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,60.6761602925822,,,0.038224035513907226,rbf,-1,False,0.0018214343969776353,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.421025538999516,median,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+313,weighting,adaboost,SAMME,1.2423888874490505,5,284,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.01582563952987033,most_frequent,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+316,weighting,adaboost,SAMME,1.2615483379313783,8,190,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.00017789451703497617,mean,robust_scaler,,,0.7345811776810758,0.054715769043996304,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+317,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.4572679693563934,None,0.0,1,11,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,mean,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,,
+318,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.843098530645432,None,0.0,1,4,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,mean,quantile_transformer,930,normal,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,,
+321,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.5864398404787432,None,0.0,1,14,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0070663750850608895,most_frequent,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,,
+326,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0001329242404968632,False,True,squared_hinge,0.000416219546803132,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.007568009633214092,median,quantile_transformer,1126,normal,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,29.06367291960815,chi2,,,,
+329,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.6642356723981304,False,True,1,squared_hinge,ovr,l2,7.912577426155621e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.08464913801800422,most_frequent,normalize,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,53.35800942135187,chi2,,,,
+331,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.5,None,0.0,1,5,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.15244281596303316,mean,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+332,none,adaboost,SAMME,0.22805500898886877,8,172,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.019649593891454008,mean,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+334,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7516795829719571,None,0.0,14,7,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0010013681780844724,mean,none,,,,,extra_trees_preproc_for_classification,True,entropy,None,0.6123323873969666,None,0.0,17,7,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+339,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.36374112587320206,None,0.0,2,10,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010870084126959173,most_frequent,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,,
+343,none,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9062834897256731,None,0.0,20,9,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.005187379382274718,mean,quantile_transformer,349,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+345,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5321814610661173,None,0.0,1,3,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,standardize,,,,,extra_trees_preproc_for_classification,True,gini,None,0.5,None,0.0,1,15,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+350,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.44903177399085925,None,0.0,2,17,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,mean,robust_scaler,,,0.7812408516672696,0.12791233577400205,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,,
+352,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5988524108452234,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,mean,robust_scaler,,,0.75,0.26961884796358077,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+355,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,68.60833575985336,,,0.03905068757955671,rbf,-1,False,0.02846559747114773,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.04775815990561371,mean,standardize,,,,,extra_trees_preproc_for_classification,False,entropy,None,0.45727987956076366,None,0.0,13,11,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+357,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.3431705351883592,None,0.0,4,16,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.09444471061261189,most_frequent,normalize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+359,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.9282407375709691,None,0.0,16,3,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.015995805068365877,mean,standardize,,,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.10042232633120375,False,True,1,squared_hinge,ovr,l1,0.004451009526629745,,,,,,,,,,,,,,,,,,,,,
+367,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.938372431886818e-05,True,True,hinge,0.0016143408148969647,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00024949978971758427,median,quantile_transformer,754,uniform,,,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,-0.22339839702350717,2,0.5718982387915436,poly,5260,,,,,,,,,,,,,,,,
+371,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4,1,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.01020427226149318,median,none,,,,,extra_trees_preproc_for_classification,True,entropy,None,0.3663460889081009,None,0.0,2,7,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
diff --git a/autosklearn/metalearning/files/recall_weighted_binary.classification_sparse/description.txt b/autosklearn/metalearning/files/recall_weighted_binary.classification_sparse/description.txt
index 9a8b7ce951..ff65bc2934 100644
--- a/autosklearn/metalearning/files/recall_weighted_binary.classification_sparse/description.txt
+++ b/autosklearn/metalearning/files/recall_weighted_binary.classification_sparse/description.txt
@@ -1,8 +1,9 @@
 features_cutoff_time: 3600
 features_cutoff_memory: 3072
-number_of_feature_steps: 35
+number_of_feature_steps: 42
 feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances
 feature_step LogNumberOfInstances: LogNumberOfInstances
+feature_step NumberOfClasses: NumberOfClasses
 feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures
 feature_step LogNumberOfFeatures: LogNumberOfFeatures
 feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues
@@ -20,6 +21,11 @@ feature_step DatasetRatio: DatasetRatio, LogDatasetRatio
 feature_step LogDatasetRatio: LogDatasetRatio
 feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio
 feature_step LogInverseDatasetRatio: LogInverseDatasetRatio
+feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD
+feature_step ClassProbabilityMin: ClassProbabilityMin
+feature_step ClassProbabilityMax: ClassProbabilityMax
+feature_step ClassProbabilityMean: ClassProbabilityMean
+feature_step ClassProbabilitySTD: ClassProbabilitySTD
 feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum
 feature_step SymbolsMin: SymbolsMin
 feature_step SymbolsMax: SymbolsMax
@@ -36,11 +42,12 @@ feature_step SkewnessMin: SkewnessMin
 feature_step SkewnessMax: SkewnessMax
 feature_step SkewnessMean: SkewnessMean
 feature_step SkewnessSTD: SkewnessSTD
-features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
+feature_step ClassEntropy: ClassEntropy
+features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy
 features_stochastic: 
-default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
+default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy
 
-algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92
+algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235,236,237,238,239,240,241,242,243,244,245,246,247,248,249,250,251,252,253,254,255,256,257,258,259,260,261,262,263,264,265,266,267,268,269,270,271,272,273,274,275,276,277,278,279,280,281,282,283,284,285,286,287,288,289,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,312,313,314,315,316,317,318,319,320,321,322,323,324,325,326,327,328,329,330,331,332,333,334,335,336,337,338,339,340,341,342,343,344,345,346,347,348,349,350,351,352,353,354,355,356,357,358,359,360,361,362,363,364,365,366,367,368,369,370,371,372
 algorithms_stochastic: 
 performance_measures: recall_weighted
 performance_type: solution_quality
diff --git a/autosklearn/metalearning/files/recall_weighted_binary.classification_sparse/feature_costs.arff b/autosklearn/metalearning/files/recall_weighted_binary.classification_sparse/feature_costs.arff
index 0e17e20d22..8512fd9275 100644
--- a/autosklearn/metalearning/files/recall_weighted_binary.classification_sparse/feature_costs.arff
+++ b/autosklearn/metalearning/files/recall_weighted_binary.classification_sparse/feature_costs.arff
@@ -4,6 +4,7 @@
 @ATTRIBUTE repetition NUMERIC
 @ATTRIBUTE NumberOfInstances NUMERIC
 @ATTRIBUTE LogNumberOfInstances NUMERIC
+@ATTRIBUTE NumberOfClasses NUMERIC
 @ATTRIBUTE NumberOfFeatures NUMERIC
 @ATTRIBUTE LogNumberOfFeatures NUMERIC
 @ATTRIBUTE MissingValues NUMERIC
@@ -21,6 +22,11 @@
 @ATTRIBUTE LogDatasetRatio NUMERIC
 @ATTRIBUTE InverseDatasetRatio NUMERIC
 @ATTRIBUTE LogInverseDatasetRatio NUMERIC
+@ATTRIBUTE ClassOccurences NUMERIC
+@ATTRIBUTE ClassProbabilityMin NUMERIC
+@ATTRIBUTE ClassProbabilityMax NUMERIC
+@ATTRIBUTE ClassProbabilityMean NUMERIC
+@ATTRIBUTE ClassProbabilitySTD NUMERIC
 @ATTRIBUTE NumSymbols NUMERIC
 @ATTRIBUTE SymbolsMin NUMERIC
 @ATTRIBUTE SymbolsMax NUMERIC
@@ -37,162 +43,138 @@
 @ATTRIBUTE SkewnessMax NUMERIC
 @ATTRIBUTE SkewnessMean NUMERIC
 @ATTRIBUTE SkewnessSTD NUMERIC
+@ATTRIBUTE ClassEntropy NUMERIC
 
 @DATA
-75123,1.0,1e-05,1e-05,1e-05,1e-05,0.00253,0.00221,1e-05,0.00024,3e-05,0.00011,0.0,2e-05,2e-05,5e-05,8e-05,2e-05,1e-05,7e-05,1e-05,0.00072,1e-05,1e-05,0.00019,0.00041,0.00011,0.00061,0.00012,9e-05,0.00015,0.00025,0.00067,0.00014,0.00011,0.00016,0.00027
-75090,1.0,1e-05,1e-05,2e-05,2e-05,0.00815,0.00368,7e-05,0.00228,9e-05,0.00235,0.0,5e-05,5e-05,0.0001,0.00013,0.00013,1e-05,0.00021,2e-05,0.00064,0.0,0.0,0.00014,0.00038,0.00011,0.00066,0.00011,0.00014,0.00014,0.00026,0.00066,0.00011,0.00013,0.00015,0.00027
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+75236,1.0,1e-05,1e-05,0.0001,1e-05,1e-05,0.00218,0.00118,2e-05,0.00065,3e-05,0.00041,1e-05,3e-05,3e-05,9e-05,9e-05,4e-05,1e-05,7e-05,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.0006,1e-05,1e-05,0.00021,0.00032,6e-05,0.00043,7e-05,7e-05,9e-05,0.00019,0.00062,7e-05,8e-05,0.0001,0.00037,0.00048
+75237,1.0,2e-05,1e-05,0.00785,2e-05,1e-05,0.31031,0.27854,2e-05,0.01161,3e-05,0.02022,1e-05,4e-05,3e-05,9e-05,0.00024,6e-05,2e-05,9e-05,2e-05,0.00062,7e-05,3e-05,0.00017,0.00035,0.0008,1e-05,1e-05,0.00028,0.00043,8e-05,0.00043,8e-05,7e-05,9e-05,0.0002,0.00044,6e-05,8e-05,9e-05,0.00021,0.14028
+75239,1.0,1e-05,1e-05,7e-05,1e-05,1e-05,0.00105,0.00084,1e-05,0.00015,1e-05,8e-05,1e-05,2e-05,5e-05,4e-05,8e-05,2e-05,1e-05,3e-05,1e-05,0.00016,2e-05,1e-05,5e-05,9e-05,0.00049,0.0,0.0,0.00012,0.00026,0.00011,0.00046,0.00013,8e-05,8e-05,0.00018,0.00044,6e-05,9e-05,0.00011,0.00018,0.00065
+75240,1.0,2e-05,1e-05,0.00036,5e-05,4e-05,0.00845,0.00644,6e-05,0.00122,4e-05,0.00091,1e-05,4e-05,4e-05,9e-05,0.00013,5e-05,2e-05,9e-05,5e-05,0.00031,3e-05,1e-05,7e-05,0.0002,0.00073,3e-05,2e-05,0.00019,0.0003,0.00018,0.00055,9e-05,9e-05,0.00011,0.00025,0.0005,7e-05,8e-05,0.00015,0.0002,0.00365
+75243,1.0,1e-05,1e-05,0.00029,1e-05,1e-05,0.00461,0.00415,1e-05,0.00035,1e-05,0.00015,0.0,2e-05,2e-05,5e-05,7e-05,2e-05,1e-05,4e-05,1e-05,0.00018,2e-05,1e-05,4e-05,0.00011,0.00022,1e-05,1e-05,5e-05,0.00011,5e-05,0.0004,6e-05,6e-05,8e-05,0.0002,0.00039,6e-05,6e-05,8e-05,0.00019,0.00265
+75244,1.0,3e-05,2e-05,0.0004,3e-05,2e-05,0.0173,0.01298,4e-05,0.00267,4e-05,0.00174,1e-05,7e-05,9e-05,0.00024,0.00027,8e-05,2e-05,0.00013,3e-05,0.00045,7e-05,2e-05,0.00011,0.00025,0.00077,6e-05,2e-05,0.00021,0.00034,0.00013,0.00077,0.00014,0.00013,0.00026,0.00023,0.00044,7e-05,8e-05,0.0001,0.00019,0.00649
+75248,1.0,1e-05,1e-05,0.00017,1e-05,1e-05,0.00416,0.00305,1e-05,0.0006,2e-05,0.00054,1e-05,2e-05,2e-05,5e-05,7e-05,2e-05,1e-05,4e-05,1e-05,0.00017,2e-05,1e-05,4e-05,0.0001,0.00029,5e-05,1e-05,6e-05,0.00012,6e-05,0.00081,0.00011,0.00015,0.00016,0.00038,0.0005,7e-05,8e-05,0.00012,0.00024,0.00184
+75249,1.0,1e-05,1e-05,0.00011,1e-05,1e-05,0.00192,0.00153,1e-05,0.00025,1e-05,0.00017,1e-05,2e-05,2e-05,5e-05,7e-05,2e-05,1e-05,5e-05,3e-05,0.00014,2e-05,1e-05,3e-05,8e-05,0.00029,1e-05,3e-05,6e-05,0.00012,7e-05,0.00041,6e-05,8e-05,0.0001,0.00017,0.00043,6e-05,7e-05,9e-05,0.00019,0.00092
+75250,1.0,1e-05,1e-05,0.00375,1e-05,1e-05,0.04994,0.047,1e-05,0.00247,1e-05,0.00049,1e-05,2e-05,2e-05,6e-05,7e-05,2e-05,1e-05,3e-05,1e-05,0.00022,4e-05,1e-05,4e-05,0.00012,0.00041,0.0,1e-05,0.00011,0.00023,6e-05,0.00076,0.00012,0.00012,0.00019,0.00032,0.00086,0.00012,0.00013,0.00018,0.00043,0.04178
diff --git a/autosklearn/metalearning/files/recall_weighted_binary.classification_sparse/feature_runstatus.arff b/autosklearn/metalearning/files/recall_weighted_binary.classification_sparse/feature_runstatus.arff
index 0034ad2bd6..5fff2dfed3 100644
--- a/autosklearn/metalearning/files/recall_weighted_binary.classification_sparse/feature_runstatus.arff
+++ b/autosklearn/metalearning/files/recall_weighted_binary.classification_sparse/feature_runstatus.arff
@@ -4,6 +4,7 @@
 @ATTRIBUTE repetition NUMERIC
 @ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other}
@@ -21,6 +22,11 @@
 @ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other}
@@ -37,162 +43,138 @@
 @ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other}
 
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diff --git a/autosklearn/metalearning/files/recall_weighted_binary.classification_sparse/feature_values.arff b/autosklearn/metalearning/files/recall_weighted_binary.classification_sparse/feature_values.arff
index a60400e3ce..736dc64321 100644
--- a/autosklearn/metalearning/files/recall_weighted_binary.classification_sparse/feature_values.arff
+++ b/autosklearn/metalearning/files/recall_weighted_binary.classification_sparse/feature_values.arff
@@ -13,7 +13,6 @@
 @ATTRIBUTE KurtosisMean NUMERIC
 @ATTRIBUTE KurtosisMin NUMERIC
 @ATTRIBUTE KurtosisSTD NUMERIC
-@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC
 @ATTRIBUTE LogDatasetRatio NUMERIC
 @ATTRIBUTE LogInverseDatasetRatio NUMERIC
 @ATTRIBUTE LogNumberOfFeatures NUMERIC
@@ -42,160 +41,135 @@
 @ATTRIBUTE SymbolsSum NUMERIC
 
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diff --git a/autosklearn/metalearning/files/recall_weighted_multiclass.classification_dense/algorithm_runs.arff b/autosklearn/metalearning/files/recall_weighted_multiclass.classification_dense/algorithm_runs.arff
index f8fb3b5d97..0ea97209d1 100644
--- a/autosklearn/metalearning/files/recall_weighted_multiclass.classification_dense/algorithm_runs.arff
+++ b/autosklearn/metalearning/files/recall_weighted_multiclass.classification_dense/algorithm_runs.arff
@@ -7,130 +7,135 @@
 @ATTRIBUTE runstatus {ok, timeout, memout, not_applicable, crash, other}
 
 @DATA
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diff --git a/autosklearn/metalearning/files/recall_weighted_multiclass.classification_dense/configurations.csv b/autosklearn/metalearning/files/recall_weighted_multiclass.classification_dense/configurations.csv
index 14ccf74d94..85e767fd63 100644
--- a/autosklearn/metalearning/files/recall_weighted_multiclass.classification_dense/configurations.csv
+++ b/autosklearn/metalearning/files/recall_weighted_multiclass.classification_dense/configurations.csv
@@ -1,128 +1,133 @@
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-27,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.4426097579931897,None,0.0,16,4,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,mean,robust_scaler,,,0.7407323456920554,0.0804756921037867,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,
-28,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5607168729746175,None,0.0,2,3,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.009810589644392154,most_frequent,quantile_transformer,1132,normal,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.06211089008167909,fdr,chi2
-29,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9333237919291875,None,0.0,8,3,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,
-30,none,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.9728532763078892,None,0.0,13,9,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,none,,,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6651071190864879,False,,,,,,,,,,,,,,,
-31,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9561075301148197,None,0.0,6,2,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,
-32,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.557321265077371,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-33,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,1.153931482107762e-10,0.011666190464009397,auto,255,None,29,190,18,loss,1e-07,0.147868307124396,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,
-34,none,adaboost,SAMME,0.6255555220658239,6,446,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.008833097306364968,most_frequent,none,,,,,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,10,None,4,17,1.0,76,,,,,
-35,none,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.563056219822946,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.32793677336996485,most_frequent,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-36,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,3.6797768538445263e-09,0.05511004217375807,auto,255,None,72,62,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,robust_scaler,,,0.7241481576763391,0.0501033899901458,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,36,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-37,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.7521969592385672,None,0.0,9,5,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.020402264943872365,median,robust_scaler,,,0.737227213630259,0.20752747663727789,fast_ica,,,,,,,,,,,parallel,exp,312,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-38,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.887898394612674,None,0.0,10,19,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,robust_scaler,,,0.8350698288864961,0.2347163538066168,extra_trees_preproc_for_classification,False,entropy,None,0.49795108918064074,None,0.0,13,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-39,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,1.6343482806592892e-09,0.19537524572679268,auto,255,None,32,29,13,loss,1e-07,0.05061738224302791,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-40,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7088164344533493,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,mean,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,
-41,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,1.943186107984505e-07,0.08852455702036988,auto,255,None,5,72,19,loss,1e-07,0.039837460762271175,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.01591265419367627,median,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,
-42,weighting,adaboost,SAMME,0.5210651042094924,10,401,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,standardize,,,,,feature_agglomeration,,,,,,,,,,,,,,,euclidean,complete,89,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-43,none,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0686374607394679,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,minmax,,,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.8490260132946401,False,,,,,,,,,,,,,,,
-44,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.6931271368243409,None,0.0,11,10,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.362940004438799,most_frequent,robust_scaler,,,0.9177587796305063,0.17084038458597867,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,30.491514144623952,mutual_info,,,
-45,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.9398869869697849,None,0.0,20,15,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,normalize,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,269,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-46,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,92.66144306787051,,,0.04654639302091653,rbf,-1,True,0.0022231544280610005,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.005194838694455963,mean,none,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,15,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-47,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2681.998807317996,-0.9558745731731211,,0.0011299948421773457,sigmoid,-1,False,1.5292507354478086e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-48,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.8095522998548572,None,0.0,17,10,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.031940595071539304,mean,quantile_transformer,1563,normal,,,extra_trees_preproc_for_classification,False,entropy,None,0.9330145598603196,None,0.0,18,10,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-49,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,26.502259985828186,,,9.475662388405154e-05,rbf,-1,False,0.00019690635968217866,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-50,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,2.5360196477156387e-07,0.011018756408558218,auto,255,None,40,122,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.00011749164080669445,most_frequent,quantile_transformer,757,uniform,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,5.097834697156853,False,True,1,squared_hinge,ovr,l1,0.053877834919477424,,,,,,,,,,,,,,,,,,,,,,
-51,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5255991794296913,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-52,weighting,decision_tree,,,,,,,gini,1.7853447328017091,1.0,None,0.0,13,7,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0001265964225445088,median,normalize,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.26399878867042303,,mutual_info_classif
-53,weighting,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.7776312540090575,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0005253715056715056,mean,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,False,,,,,,,,,,,,
-54,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,1.488333818193668e-10,0.09526676471267805,auto,255,None,258,87,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,normalize,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.3561953264350478,fpr,f_classif
-55,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.9523435078770708,0.8748383232154038,4,0.2195992292488049,poly,-1,True,4.849685143365339e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,minmax,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-56,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.7610308078756552,None,0.0,1,2,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.010000000000000004,mean,robust_scaler,,,0.9205545465103644,0.20272761060743316,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-57,weighting,adaboost,SAMME.R,0.03127273017917347,10,496,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,standardize,,,,,fast_ica,,,,,,,,,,,deflation,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-58,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,151,None,,0.0003283777607180646,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.2510527431514227,mean,normalize,,,,,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,-0.96093182232312,,0.05762510964607661,sigmoid,2632,,,,,,,,,,,,,,,,,
-59,none,bernoulli_nb,,,,,0.6524090653939782,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.005164169126808402,most_frequent,quantile_transformer,16,normal,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.033082145443631535,False,True,1,squared_hinge,ovr,l1,0.002615020898325393,,,,,,,,,,,,,,,,,,,,,,
-60,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.6453555269772124,None,0.0,1,4,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,normalize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-61,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,940.9437776449834,,,1.5839338084625942,rbf,-1,False,0.023072781592565438,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,none,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,77.25763685070707,mutual_info,,,
-62,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,220,auto,,0.0013824567992319003,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00021328932075284388,mean,minmax,,,,,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.039972474835314076,,0.01017810882899714,sigmoid,295,,,,,,,,,,,,,,,,,
-63,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.7326858450533382,0.02513634776687268,2,0.8635510319471909,poly,-1,True,0.0014598272110141993,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.06447266871225255,most_frequent,quantile_transformer,1000,normal,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,79.01961823033078,f_classif,,,
-64,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,29.55731828912983,False,True,1,squared_hinge,ovr,l2,0.0011332486951954968,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,
-65,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,1.1387795876755026e-06,0.12245605190343567,auto,255,None,58,7,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,robust_scaler,,,0.79305915648885,0.1841487406876042,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-66,none,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.563056219822946,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.32793677336996485,most_frequent,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-67,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,0.2638627266631576,0.022360820165005443,auto,255,None,171,6,19,loss,1e-07,0.016209060012924756,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.008763072395677399,mean,standardize,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,78,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-68,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,925.3053743248586,False,True,1,squared_hinge,ovr,l2,0.02764481198395717,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.3850718018366796,most_frequent,quantile_transformer,786,uniform,,,feature_agglomeration,,,,,,,,,,,,,,,cosine,average,156,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-69,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7573078019937971,None,0.0,2,5,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,robust_scaler,,,0.7265312295801113,0.28332515882236425,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.09660564590146253,fpr,f_classif
-70,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.005753626505113041,False,True,squared_hinge,0.00924401505172238,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.3192271696445429,median,quantile_transformer,913,normal,,,kernel_pca,,,,,,,,,,,,,,,,,,,,,,cosine,1963,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-71,none,adaboost,SAMME.R,0.22802213935525836,6,284,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,robust_scaler,,,0.7468645381288924,0.22838821662367073,feature_agglomeration,,,,,,,,,,,,,,,cosine,average,128,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-72,none,adaboost,SAMME,0.1280458968526125,7,125,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,robust_scaler,,,0.7156460334356771,0.2981902862926971,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,65.16171885308417,chi2,,,
-73,none,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9931779675565967,None,0.0,18,17,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,robust_scaler,,,0.75,0.2945917010472818,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,
-74,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.08646118576686276,None,0.0,4,9,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,most_frequent,quantile_transformer,959,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-75,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,1.2430504242164082e-09,0.03359568307446533,auto,255,None,1609,29,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,standardize,,,,,feature_agglomeration,,,,,,,,,,,,,,,cosine,complete,40,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-76,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7.491039171224502,True,True,squared_hinge,2.8058138048327884e-05,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,minmax,,,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.7754372809893839,False,True,1,squared_hinge,ovr,l1,3.9643594596766314e-05,,,,,,,,,,,,,,,,,,,,,,
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-79,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.933900673392165,None,0.0,7,4,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,minmax,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-80,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1661.2274747409913,,,0.025821208852387936,rbf,-1,False,0.0017011435104921824,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,none,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,306,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-81,none,adaboost,SAMME,0.011233995624432622,9,477,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.018370622484682127,mean,standardize,,,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6039710338898471,False,,,,,,,,,,,,,,,
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-83,none,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.563056219822946,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.32793677336996485,most_frequent,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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-85,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.593728171200453,None,0.0,7,20,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.02830941310359363,mean,normalize,,,,,extra_trees_preproc_for_classification,False,entropy,None,0.7389446586281172,None,0.0,20,11,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-86,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,1,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.019797877010737826,mean,normalize,,,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.8239678741128165,False,,,,,,,,,,,,,,,
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-88,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,38,auto,,0.00019398431171606024,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010000000000000004,median,quantile_transformer,1440,normal,,,extra_trees_preproc_for_classification,True,gini,None,0.5126639956845847,None,0.0,16,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-89,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5722917944959481,None,0.0,1,14,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,
-90,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.26356754187780895,None,0.0,4,3,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010966178401758774,mean,robust_scaler,,,0.7817043861798886,0.24371720112407158,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
-91,weighting,adaboost,SAMME.R,0.13547552520095074,4,62,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.011851011202809002,most_frequent,standardize,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4888776875314595,fpr,chi2
-92,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,80.0028902468169,False,True,1,squared_hinge,ovr,l2,0.0024481999829574722,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.15047970487774687,mean,quantile_transformer,1130,normal,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,1.1912646635544712,False,True,1,squared_hinge,ovr,l1,0.00014314728486450857,,,,,,,,,,,,,,,,,,,,,,
-93,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.22879311080499845,None,0.0,8,4,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.014320022547615783,median,minmax,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.238630703440461,mutual_info,,,
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-96,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.14148638702297855,None,0.0,2,6,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.0038499650338378643,most_frequent,quantile_transformer,1759,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-97,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,1.1522414705935405e-10,0.6132800183073315,auto,255,None,1607,67,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010096911249562407,mean,robust_scaler,,,0.7663493326967942,0.136094217337434,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-98,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2746221958279041,False,True,1,squared_hinge,ovr,l2,6.546077664669006e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,quantile_transformer,1054,normal,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,
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-100,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.825844582229201,None,0.0,12,9,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0008422508222149647,median,robust_scaler,,,0.8658803880857064,0.2528423686293341,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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-103,none,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,128,manual,0.8574560771111307,0.010886139381599514,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.24115007944216788,most_frequent,normalize,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,62.26514170991452,mutual_info,,,
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-105,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,1.430083734915855e-09,0.08779621406981941,auto,255,None,17,20,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.29749837779115257,mean,robust_scaler,,,0.7236445277814435,0.25,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
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-124,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1561.1002600303584,,,0.0024048551704194507,rbf,-1,False,0.0001936214667496851,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,minmax,,,,,fast_ica,,,,,,,,,,,parallel,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-125,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.087313242780732,0.0636709914987994,3,0.004481917125024585,poly,-1,False,0.01848880618941367,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.002136015035740733,mean,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-126,weighting,adaboost,SAMME.R,1.696588612003168,9,455,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,minmax,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,82.18726095926662,chi2,,,
-127,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.6466927589470762,None,0.0,11,2,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.009672465311659264,most_frequent,standardize,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,369,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+1,none,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1514.059969351301,False,True,1,squared_hinge,ovr,l2,0.002448814309966599,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,standardize,,,,,extra_trees_preproc_for_classification,True,entropy,None,0.8221481751476923,None,0.0,2,3,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+3,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5707458022222313,None,0.0,3,12,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,minmax,,,,,extra_trees_preproc_for_classification,False,gini,None,0.5651056128131684,None,0.0,9,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+6,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.7283683762613216,None,0.0,12,20,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,median,standardize,,,,,fast_ica,,,,,,,,,,,deflation,exp,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+8,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,114,manual,0.11778631707605758,0.0029112721339713465,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.015582195257240751,median,normalize,,,,,kernel_pca,,,,,,,,,,,,,,,,,,,0.42909623253327767,3,0.25407612000343777,poly,1689,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+11,weighting,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,2,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.3865962574104293,mean,normalize,,,,,fast_ica,,,,,,,,,,,deflation,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+13,weighting,adaboost,SAMME,0.17983222024694556,4,357,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0008898781197407956,mean,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+15,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.21772092350860472,None,0.0,14,17,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.0020488212891693275,median,standardize,,,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,33.74257606530722,False,True,1,squared_hinge,ovr,l1,8.740452966205111e-05,,,,,,,,,,,,,,,,,,,,,,
+18,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,2.4243922131268786e-05,0.07570328162448307,auto,255,None,71,42,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,robust_scaler,,,0.8898729094898769,0.12138152991327475,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,94.96086286583851,chi2,,,
+20,none,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.9866058571747695,None,0.0,16,9,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.1680676743605393,most_frequent,robust_scaler,,,0.8840072797399463,0.08535534311393947,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,False,,,,,,,,,,,,
+23,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,0.00022373024203887573,0.027149343206135476,auto,255,None,49,13,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.00777614320416325,most_frequent,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,
+27,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.41025844265119926,None,0.0,18,15,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,median,quantile_transformer,1911,normal,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,False,,,,,,,,,,,,
+29,none,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.563056219822946,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.32793677336996485,most_frequent,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+31,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,8.757412830636845e-06,0.10095376320667454,auto,255,None,3,2,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.015401766535365336,median,quantile_transformer,647,normal,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,
+32,none,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,174,manual,0.21501892897746921,2.0347110826262054e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.013754893527625522,most_frequent,normalize,,,,,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,-0.019221513778605592,3,0.10190948951339239,poly,2521,,,,,,,,,,,,,,,,,
+35,none,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9.608707084865884,False,True,1,squared_hinge,ovr,l2,0.008086910150491696,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.020901889243957545,median,minmax,,,,,kitchen_sinks,,,,,,,,,,,,,,,,,,,,,,,,0.5694399784333516,3230,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+39,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.6799215694343422,None,0.0,1,5,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.004724310659953132,most_frequent,quantile_transformer,1037,normal,,,fast_ica,,,,,,,,,,,parallel,exp,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+41,none,extra_trees,,,,,,,,,,,,,,,True,gini,None,0.5033866291997137,None,0.0,2,14,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,minmax,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+43,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.9018061166635376,None,0.0,7,9,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,quantile_transformer,1802,normal,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,84.54647012968938,chi2,,,
+44,none,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l2,2.858526169098402e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0007827505640681123,median,standardize,,,,,extra_trees_preproc_for_classification,False,entropy,None,0.3443310459451119,None,0.0,3,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+49,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.049504283446131e-05,False,,0.0004438773852753879,True,2.4110816673432424e-07,constant,log,elasticnet,,0.01321814853186568,one_hot_encoding,minority_coalescer,0.01510416593631535,most_frequent,none,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,66.65285268958851,chi2,,,
+52,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,6.421059381158897e-08,0.11261253191288709,auto,255,None,45,28,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,
+55,none,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.9822179211645446,None,0.0,14,8,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.013966871874533052,most_frequent,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,
+58,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5214197490559657,None,0.0,1,2,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.00826182798588248,mean,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,
+63,none,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,130,manual,0.5446276346297756,0.00846307015171434,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.005736348762561667,median,quantile_transformer,973,uniform,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.08483615095863663,fdr,chi2
+65,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,1.2822815440468518e-10,0.12922700208645135,auto,255,None,31,44,13,loss,1e-07,0.08789566421608311,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.012230423571314038,mean,robust_scaler,,,0.71734690066056,0.20283418288016852,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,
+68,weighting,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,minmax,,,,,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+75,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13.284077389890939,-0.2621334760621076,5,0.9060908941898536,poly,-1,False,0.06323853917206591,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0011743021592446861,most_frequent,normalize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,
+82,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,3.79982070767762e-10,0.10307173798074214,auto,255,None,30,25,6,loss,1e-07,0.09705336338563596,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.004889186641013006,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+84,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.5507560981489436,None,0.0,7,4,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,
+91,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,0.0006322356877253852,0.02155479872417127,auto,255,None,6,15,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
+93,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5012519528536651,None,0.0,1,8,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,mean,standardize,,,,,feature_agglomeration,,,,,,,,,,,,,,,cosine,complete,323,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+96,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5602498483292023,None,0.0,1,3,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.3195602228752535,mean,standardize,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.09782235573464702,fdr,chi2
+99,weighting,adaboost,SAMME,0.17983222024694556,4,357,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0008898781197407956,mean,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+105,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.268699692198443e-05,True,,4.1225573912733725e-06,True,,constant,log,l2,,4.729985534114764e-05,no_encoding,no_coalescense,,median,quantile_transformer,246,normal,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,20.73090458774975,chi2,,,
+106,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,2.047681354714616e-06,0.016073071519249763,auto,255,None,127,60,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.005262156195293648,median,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,
+110,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0021814768111094564,False,True,hinge,8.726644999887521e-05,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,none,,,,,kitchen_sinks,,,,,,,,,,,,,,,,,,,,,,,,0.0010909910768059797,970,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+111,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.4990576176544739,None,0.0,6,6,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
+114,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,83,auto,,0.024875973599406742,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,normalize,,,,,kitchen_sinks,,,,,,,,,,,,,,,,,,,,,,,,0.041059688084044955,7269,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+115,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.463780563279276,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.011474025594258192,mean,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,
+116,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,236,None,,0.08905291537442941,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,normalize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,
+121,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.08836825866738698,None,0.0,5,19,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0003943043347109662,median,robust_scaler,,,0.7564808439539721,0.23469821956116474,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2206955933275169,fdr,f_classif
+124,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.3955965159441456,None,0.0,3,7,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,median,minmax,,,,,extra_trees_preproc_for_classification,False,entropy,None,0.029027785145223306,None,0.0,17,7,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+126,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,1.4547955251450584e-08,0.03465981099276111,auto,255,None,10,4,7,loss,1e-07,0.12579700249284784,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.001559260617305279,most_frequent,normalize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,False,,,,,,,,,,,,
+130,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,9.471664460969401e-09,0.05921990503832894,auto,255,None,1357,22,15,loss,1e-07,0.10690431328779236,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.08690713722954983,most_frequent,robust_scaler,,,0.9305626247167209,0.22989015341001506,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.33284956824843637,fwe,f_classif
+136,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,4.136413423055797e-09,0.13801185065309887,auto,255,None,186,1,15,loss,1e-07,0.042836514118739795,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,none,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,372,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+138,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,6.611729692973355e-06,0.057217701969255366,auto,255,None,8,1,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,minmax,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+139,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,10.855406135042964,0.9747671863774952,5,0.2387142708486043,poly,-1,False,0.015747719198070633,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.1749714579410719,median,minmax,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,53.581101959413125,mutual_info,,,
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+335,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.005376856768687352,True,True,squared_hinge,0.00022063788135710262,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.007755252735957179,mean,quantile_transformer,1290,uniform,,,fast_ica,,,,,,,,,,,parallel,exp,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+337,weighting,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,minmax,,,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,227.42750098824558,False,True,1,squared_hinge,ovr,l1,0.0018060472646546995,,,,,,,,,,,,,,,,,,,,,,
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+344,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,0.0001229071652181646,0.030445374086776,auto,255,None,18,70,4,loss,1e-07,0.10054981687084993,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.018690394487291782,mean,robust_scaler,,,0.75,0.22345866985551235,fast_ica,,,,,,,,,,,deflation,exp,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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+352,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.002017899394165588,False,,0.0005911937660372945,True,0.17801822098174286,constant,log,elasticnet,,0.00041682529601986016,no_encoding,no_coalescense,,median,quantile_transformer,1335,normal,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0642825062494559,fpr,chi2
+354,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.47037910442880493,None,0.0,10,3,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.17511596636550678,mean,none,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,87.25836994493127,mutual_info,,,
+362,none,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,147,None,,0.020167732354570436,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,minmax,,,,,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.39502590219018424,rbf,117,,,,,,,,,,,,,,,,,
+365,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.0479982468066316,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,quantile_transformer,1097,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+369,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.9347805195624181,None,0.0,2,19,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.1594813217854149,mean,normalize,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,52.65009549586377,f_classif,,,
+374,none,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.7304811343030777,None,0.0,2,3,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,robust_scaler,,,0.7202654714984674,0.21555973677200435,feature_agglomeration,,,,,,,,,,,,,,,euclidean,average,149,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+379,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,1.2940289605295536e-09,0.15591343717459372,auto,255,None,7,59,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0005549095909238961,mean,quantile_transformer,297,uniform,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,58.10560700979111,chi2,,,
+388,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,53.67009896349757,,,0.03687753298495132,rbf,-1,False,0.028791286245157307,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.047859755696856916,mean,standardize,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,70,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+392,none,adaboost,SAMME,0.7387448781351068,8,450,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0473229636375385,median,standardize,,,,,feature_agglomeration,,,,,,,,,,,,,,,euclidean,ward,344,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+399,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.5864398404787432,None,0.0,1,14,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0070663750850608895,most_frequent,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
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+405,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0001329242404968632,False,True,squared_hinge,0.000416219546803132,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.007568009633214092,median,quantile_transformer,1126,normal,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,29.06367291960815,chi2,,,
+408,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.6642356723981304,False,True,1,squared_hinge,ovr,l2,7.912577426155621e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.08464913801800422,most_frequent,normalize,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,53.35800942135187,chi2,,,
+413,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,7.194851113809193e-10,0.013882834218943657,auto,255,None,18,2,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0005124238915083231,mean,quantile_transformer,948,normal,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,60.45885911036194,f_classif,,,
+415,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,0.00037966471646911845,0.016422945536911454,auto,255,None,10,56,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.002419439132687246,median,quantile_transformer,828,uniform,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+422,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.36374112587320206,None,0.0,2,10,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010870084126959173,most_frequent,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,
+424,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,2.344883102482138e-07,0.0328548789988159,auto,255,None,42,24,16,loss,1e-07,0.13015466346727866,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,most_frequent,robust_scaler,,,0.7010898774332888,0.2534280821273264,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,
+428,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,3.738096432152635e-10,0.04468500715913894,auto,255,None,31,156,20,loss,1e-07,0.10279783687118692,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010000000000000004,most_frequent,quantile_transformer,1132,normal,,,feature_agglomeration,,,,,,,,,,,,,,,euclidean,ward,43,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+430,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5321814610661173,None,0.0,1,3,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,standardize,,,,,extra_trees_preproc_for_classification,True,gini,None,0.5,None,0.0,1,15,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+435,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.44903177399085925,None,0.0,2,17,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,mean,robust_scaler,,,0.7812408516672696,0.12791233577400205,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,
+438,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,6.090138036399552e-10,0.017305568433437588,auto,255,None,202,188,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,robust_scaler,,,0.7526196011313747,0.1563616005702636,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+445,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,68.60833575985336,,,0.03905068757955671,rbf,-1,False,0.02846559747114773,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.04775815990561371,mean,standardize,,,,,extra_trees_preproc_for_classification,False,entropy,None,0.45727987956076366,None,0.0,13,11,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+447,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.3431705351883592,None,0.0,4,16,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.09444471061261189,most_frequent,normalize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+450,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,7.837701348094735e-09,0.05315388879642303,auto,255,None,67,2,10,loss,1e-07,0.11655723047843726,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.001857226018763921,median,none,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.014648191862400293,fdr,f_classif
+458,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,5.581658271288433e-10,0.06171988234311011,auto,255,None,25,6,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.014859405637537319,most_frequent,robust_scaler,,,0.7983900845251328,0.2545046978458648,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,1.380085213170737,False,True,1,squared_hinge,ovr,l1,0.0061450732015354784,,,,,,,,,,,,,,,,,,,,,,
+462,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6,1,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.017471096998391875,median,none,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,67.7801145492284,f_classif,,,
diff --git a/autosklearn/metalearning/files/recall_weighted_multiclass.classification_dense/description.txt b/autosklearn/metalearning/files/recall_weighted_multiclass.classification_dense/description.txt
index 4fb11c56ab..2a6f0c9b23 100644
--- a/autosklearn/metalearning/files/recall_weighted_multiclass.classification_dense/description.txt
+++ b/autosklearn/metalearning/files/recall_weighted_multiclass.classification_dense/description.txt
@@ -1,8 +1,9 @@
 features_cutoff_time: 3600
 features_cutoff_memory: 3072
-number_of_feature_steps: 35
+number_of_feature_steps: 42
 feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances
 feature_step LogNumberOfInstances: LogNumberOfInstances
+feature_step NumberOfClasses: NumberOfClasses
 feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures
 feature_step LogNumberOfFeatures: LogNumberOfFeatures
 feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues
@@ -20,6 +21,11 @@ feature_step DatasetRatio: DatasetRatio, LogDatasetRatio
 feature_step LogDatasetRatio: LogDatasetRatio
 feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio
 feature_step LogInverseDatasetRatio: LogInverseDatasetRatio
+feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD
+feature_step ClassProbabilityMin: ClassProbabilityMin
+feature_step ClassProbabilityMax: ClassProbabilityMax
+feature_step ClassProbabilityMean: ClassProbabilityMean
+feature_step ClassProbabilitySTD: ClassProbabilitySTD
 feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum
 feature_step SymbolsMin: SymbolsMin
 feature_step SymbolsMax: SymbolsMax
@@ -36,11 +42,12 @@ feature_step SkewnessMin: SkewnessMin
 feature_step SkewnessMax: SkewnessMax
 feature_step SkewnessMean: SkewnessMean
 feature_step SkewnessSTD: SkewnessSTD
-features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
+feature_step ClassEntropy: ClassEntropy
+features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy
 features_stochastic: 
-default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
+default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy
 
-algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127
+algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235,236,237,238,239,240,241,242,243,244,245,246,247,248,249,250,251,252,253,254,255,256,257,258,259,260,261,262,263,264,265,266,267,268,269,270,271,272,273,274,275,276,277,278,279,280,281,282,283,284,285,286,287,288,289,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,312,313,314,315,316,317,318,319,320,321,322,323,324,325,326,327,328,329,330,331,332,333,334,335,336,337,338,339,340,341,342,343,344,345,346,347,348,349,350,351,352,353,354,355,356,357,358,359,360,361,362,363,364,365,366,367,368,369,370,371,372,373,374,375,376,377,378,379,380,381,382,383,384,385,386,387,388,389,390,391,392,393,394,395,396,397,398,399,400,401,402,403,404,405,406,407,408,409,410,411,412,413,414,415,416,417,418,419,420,421,422,423,424,425,426,427,428,429,430,431,432,433,434,435,436,437,438,439,440,441,442,443,444,445,446,447,448,449,450,451,452,453,454,455,456,457,458,459,460,461,462,463,464
 algorithms_stochastic: 
 performance_measures: recall_weighted
 performance_type: solution_quality
diff --git a/autosklearn/metalearning/files/recall_weighted_multiclass.classification_dense/feature_costs.arff b/autosklearn/metalearning/files/recall_weighted_multiclass.classification_dense/feature_costs.arff
index 0e17e20d22..8512fd9275 100644
--- a/autosklearn/metalearning/files/recall_weighted_multiclass.classification_dense/feature_costs.arff
+++ b/autosklearn/metalearning/files/recall_weighted_multiclass.classification_dense/feature_costs.arff
@@ -4,6 +4,7 @@
 @ATTRIBUTE repetition NUMERIC
 @ATTRIBUTE NumberOfInstances NUMERIC
 @ATTRIBUTE LogNumberOfInstances NUMERIC
+@ATTRIBUTE NumberOfClasses NUMERIC
 @ATTRIBUTE NumberOfFeatures NUMERIC
 @ATTRIBUTE LogNumberOfFeatures NUMERIC
 @ATTRIBUTE MissingValues NUMERIC
@@ -21,6 +22,11 @@
 @ATTRIBUTE LogDatasetRatio NUMERIC
 @ATTRIBUTE InverseDatasetRatio NUMERIC
 @ATTRIBUTE LogInverseDatasetRatio NUMERIC
+@ATTRIBUTE ClassOccurences NUMERIC
+@ATTRIBUTE ClassProbabilityMin NUMERIC
+@ATTRIBUTE ClassProbabilityMax NUMERIC
+@ATTRIBUTE ClassProbabilityMean NUMERIC
+@ATTRIBUTE ClassProbabilitySTD NUMERIC
 @ATTRIBUTE NumSymbols NUMERIC
 @ATTRIBUTE SymbolsMin NUMERIC
 @ATTRIBUTE SymbolsMax NUMERIC
@@ -37,162 +43,138 @@
 @ATTRIBUTE SkewnessMax NUMERIC
 @ATTRIBUTE SkewnessMean NUMERIC
 @ATTRIBUTE SkewnessSTD NUMERIC
+@ATTRIBUTE ClassEntropy NUMERIC
 
 @DATA
-75123,1.0,1e-05,1e-05,1e-05,1e-05,0.00253,0.00221,1e-05,0.00024,3e-05,0.00011,0.0,2e-05,2e-05,5e-05,8e-05,2e-05,1e-05,7e-05,1e-05,0.00072,1e-05,1e-05,0.00019,0.00041,0.00011,0.00061,0.00012,9e-05,0.00015,0.00025,0.00067,0.00014,0.00011,0.00016,0.00027
-75090,1.0,1e-05,1e-05,2e-05,2e-05,0.00815,0.00368,7e-05,0.00228,9e-05,0.00235,0.0,5e-05,5e-05,0.0001,0.00013,0.00013,1e-05,0.00021,2e-05,0.00064,0.0,0.0,0.00014,0.00038,0.00011,0.00066,0.00011,0.00014,0.00014,0.00026,0.00066,0.00011,0.00013,0.00015,0.00027
-2123,1.0,1e-05,1e-05,1e-05,1e-05,0.00097,0.00079,1e-05,0.00013,1e-05,7e-05,0.0,2e-05,2e-05,5e-05,8e-05,2e-05,1e-05,5e-05,1e-05,0.0007,1e-05,0.0,0.00017,0.00042,0.0001,0.00052,6e-05,8e-05,0.00012,0.00026,0.00049,6e-05,0.00011,9e-05,0.00023
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-266,1.0,1e-05,1e-05,1e-05,1e-05,0.0018,0.00146,1e-05,0.00023,2e-05,0.00015,2e-05,2e-05,2e-05,6e-05,8e-05,3e-05,1e-05,6e-05,1e-05,0.00059,1e-05,1e-05,0.00014,0.00031,0.00013,0.0004,5e-05,7e-05,8e-05,0.0002,0.00044,8e-05,8e-05,8e-05,0.0002
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-75217,1.0,1e-05,1e-05,2e-05,1e-05,0.00196,0.00158,1e-05,0.00026,2e-05,0.00016,1e-05,3e-05,3e-05,7e-05,0.0001,3e-05,1e-05,7e-05,2e-05,0.00089,1e-05,1e-05,0.00022,0.00053,0.00013,0.00074,9e-05,0.0001,0.00019,0.00036,0.00065,0.00013,0.0001,0.00011,0.00031
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-2117,1.0,1e-05,1e-05,1e-05,1e-05,0.01735,0.0154,1e-05,0.00141,1e-05,0.00057,0.0,1e-05,1e-05,3e-05,6e-05,2e-05,1e-05,5e-05,1e-05,0.0004,1e-05,0.0,8e-05,0.00021,0.0001,0.00065,0.00012,0.00011,0.00015,0.00026,0.00055,7e-05,0.00014,9e-05,0.00025
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-75103,1.0,1e-05,1e-05,1e-05,1e-05,0.01302,0.00819,3e-05,0.00234,3e-05,0.00255,0.0,2e-05,2e-05,6e-05,0.00012,5e-05,1e-05,0.00011,1e-05,0.0005,1e-05,0.0,9e-05,0.00029,0.00011,0.00054,8e-05,0.00011,0.00011,0.00024,0.00066,7e-05,0.00015,0.00011,0.00032
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-75157,1.0,1e-05,1e-05,1e-05,1e-05,0.00125,0.00106,1e-05,0.00015,2e-05,7e-05,0.0,2e-05,2e-05,5e-05,6e-05,2e-05,1e-05,4e-05,1e-05,0.00048,2e-05,0.0,0.0001,0.00026,0.0001,0.00039,6e-05,7e-05,8e-05,0.00019,0.00043,6e-05,9e-05,8e-05,0.0002
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+75236,1.0,1e-05,1e-05,0.0001,1e-05,1e-05,0.00218,0.00118,2e-05,0.00065,3e-05,0.00041,1e-05,3e-05,3e-05,9e-05,9e-05,4e-05,1e-05,7e-05,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.0006,1e-05,1e-05,0.00021,0.00032,6e-05,0.00043,7e-05,7e-05,9e-05,0.00019,0.00062,7e-05,8e-05,0.0001,0.00037,0.00048
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+75249,1.0,1e-05,1e-05,0.00011,1e-05,1e-05,0.00192,0.00153,1e-05,0.00025,1e-05,0.00017,1e-05,2e-05,2e-05,5e-05,7e-05,2e-05,1e-05,5e-05,3e-05,0.00014,2e-05,1e-05,3e-05,8e-05,0.00029,1e-05,3e-05,6e-05,0.00012,7e-05,0.00041,6e-05,8e-05,0.0001,0.00017,0.00043,6e-05,7e-05,9e-05,0.00019,0.00092
+75250,1.0,1e-05,1e-05,0.00375,1e-05,1e-05,0.04994,0.047,1e-05,0.00247,1e-05,0.00049,1e-05,2e-05,2e-05,6e-05,7e-05,2e-05,1e-05,3e-05,1e-05,0.00022,4e-05,1e-05,4e-05,0.00012,0.00041,0.0,1e-05,0.00011,0.00023,6e-05,0.00076,0.00012,0.00012,0.00019,0.00032,0.00086,0.00012,0.00013,0.00018,0.00043,0.04178
diff --git a/autosklearn/metalearning/files/recall_weighted_multiclass.classification_dense/feature_runstatus.arff b/autosklearn/metalearning/files/recall_weighted_multiclass.classification_dense/feature_runstatus.arff
index 0034ad2bd6..5fff2dfed3 100644
--- a/autosklearn/metalearning/files/recall_weighted_multiclass.classification_dense/feature_runstatus.arff
+++ b/autosklearn/metalearning/files/recall_weighted_multiclass.classification_dense/feature_runstatus.arff
@@ -4,6 +4,7 @@
 @ATTRIBUTE repetition NUMERIC
 @ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other}
@@ -21,6 +22,11 @@
 @ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other}
@@ -37,162 +43,138 @@
 @ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other}
 
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diff --git a/autosklearn/metalearning/files/recall_weighted_multiclass.classification_dense/feature_values.arff b/autosklearn/metalearning/files/recall_weighted_multiclass.classification_dense/feature_values.arff
index a60400e3ce..736dc64321 100644
--- a/autosklearn/metalearning/files/recall_weighted_multiclass.classification_dense/feature_values.arff
+++ b/autosklearn/metalearning/files/recall_weighted_multiclass.classification_dense/feature_values.arff
@@ -13,7 +13,6 @@
 @ATTRIBUTE KurtosisMean NUMERIC
 @ATTRIBUTE KurtosisMin NUMERIC
 @ATTRIBUTE KurtosisSTD NUMERIC
-@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC
 @ATTRIBUTE LogDatasetRatio NUMERIC
 @ATTRIBUTE LogInverseDatasetRatio NUMERIC
 @ATTRIBUTE LogNumberOfFeatures NUMERIC
@@ -42,160 +41,135 @@
 @ATTRIBUTE SymbolsSum NUMERIC
 
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diff --git a/autosklearn/metalearning/files/recall_weighted_multiclass.classification_sparse/algorithm_runs.arff b/autosklearn/metalearning/files/recall_weighted_multiclass.classification_sparse/algorithm_runs.arff
index bd9fe33e02..402934b928 100644
--- a/autosklearn/metalearning/files/recall_weighted_multiclass.classification_sparse/algorithm_runs.arff
+++ b/autosklearn/metalearning/files/recall_weighted_multiclass.classification_sparse/algorithm_runs.arff
@@ -7,95 +7,133 @@
 @ATTRIBUTE runstatus {ok, timeout, memout, not_applicable, crash, other}
 
 @DATA
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diff --git a/autosklearn/metalearning/files/recall_weighted_multiclass.classification_sparse/configurations.csv b/autosklearn/metalearning/files/recall_weighted_multiclass.classification_sparse/configurations.csv
index 75662496d2..36fd991e13 100644
--- a/autosklearn/metalearning/files/recall_weighted_multiclass.classification_sparse/configurations.csv
+++ b/autosklearn/metalearning/files/recall_weighted_multiclass.classification_sparse/configurations.csv
@@ -1,93 +1,131 @@
 idx,balancing:strategy,classifier:__choice__,classifier:adaboost:algorithm,classifier:adaboost:learning_rate,classifier:adaboost:max_depth,classifier:adaboost:n_estimators,classifier:bernoulli_nb:alpha,classifier:bernoulli_nb:fit_prior,classifier:decision_tree:criterion,classifier:decision_tree:max_depth_factor,classifier:decision_tree:max_features,classifier:decision_tree:max_leaf_nodes,classifier:decision_tree:min_impurity_decrease,classifier:decision_tree:min_samples_leaf,classifier:decision_tree:min_samples_split,classifier:decision_tree:min_weight_fraction_leaf,classifier:extra_trees:bootstrap,classifier:extra_trees:criterion,classifier:extra_trees:max_depth,classifier:extra_trees:max_features,classifier:extra_trees:max_leaf_nodes,classifier:extra_trees:min_impurity_decrease,classifier:extra_trees:min_samples_leaf,classifier:extra_trees:min_samples_split,classifier:extra_trees:min_weight_fraction_leaf,classifier:gradient_boosting:early_stop,classifier:gradient_boosting:l2_regularization,classifier:gradient_boosting:learning_rate,classifier:gradient_boosting:loss,classifier:gradient_boosting:max_bins,classifier:gradient_boosting:max_depth,classifier:gradient_boosting:max_leaf_nodes,classifier:gradient_boosting:min_samples_leaf,classifier:gradient_boosting:n_iter_no_change,classifier:gradient_boosting:scoring,classifier:gradient_boosting:tol,classifier:gradient_boosting:validation_fraction,classifier:k_nearest_neighbors:n_neighbors,classifier:k_nearest_neighbors:p,classifier:k_nearest_neighbors:weights,classifier:lda:n_components,classifier:lda:shrinkage,classifier:lda:shrinkage_factor,classifier:lda:tol,classifier:liblinear_svc:C,classifier:liblinear_svc:dual,classifier:liblinear_svc:fit_intercept,classifier:liblinear_svc:intercept_scaling,classifier:liblinear_svc:loss,classifier:liblinear_svc:multi_class,classifier:liblinear_svc:penalty,classifier:liblinear_svc:tol,classifier:libsvm_svc:C,classifier:libsvm_svc:coef0,classifier:libsvm_svc:degree,classifier:libsvm_svc:gamma,classifier:libsvm_svc:kernel,classifier:libsvm_svc:max_iter,classifier:libsvm_svc:shrinking,classifier:libsvm_svc:tol,classifier:multinomial_nb:alpha,classifier:multinomial_nb:fit_prior,classifier:passive_aggressive:C,classifier:passive_aggressive:average,classifier:passive_aggressive:fit_intercept,classifier:passive_aggressive:loss,classifier:passive_aggressive:tol,classifier:qda:reg_param,classifier:random_forest:bootstrap,classifier:random_forest:criterion,classifier:random_forest:max_depth,classifier:random_forest:max_features,classifier:random_forest:max_leaf_nodes,classifier:random_forest:min_impurity_decrease,classifier:random_forest:min_samples_leaf,classifier:random_forest:min_samples_split,classifier:random_forest:min_weight_fraction_leaf,classifier:sgd:alpha,classifier:sgd:average,classifier:sgd:epsilon,classifier:sgd:eta0,classifier:sgd:fit_intercept,classifier:sgd:l1_ratio,classifier:sgd:learning_rate,classifier:sgd:loss,classifier:sgd:penalty,classifier:sgd:power_t,classifier:sgd:tol,data_preprocessing:categorical_transformer:categorical_encoding:__choice__,data_preprocessing:categorical_transformer:category_coalescence:__choice__,data_preprocessing:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction,data_preprocessing:numerical_transformer:imputation:strategy,data_preprocessing:numerical_transformer:rescaling:__choice__,data_preprocessing:numerical_transformer:rescaling:quantile_transformer:n_quantiles,data_preprocessing:numerical_transformer:rescaling:quantile_transformer:output_distribution,data_preprocessing:numerical_transformer:rescaling:robust_scaler:q_max,data_preprocessing:numerical_transformer:rescaling:robust_scaler:q_min,feature_preprocessor:__choice__,feature_preprocessor:extra_trees_preproc_for_classification:bootstrap,feature_preprocessor:extra_trees_preproc_for_classification:criterion,feature_preprocessor:extra_trees_preproc_for_classification:max_depth,feature_preprocessor:extra_trees_preproc_for_classification:max_features,feature_preprocessor:extra_trees_preproc_for_classification:max_leaf_nodes,feature_preprocessor:extra_trees_preproc_for_classification:min_impurity_decrease,feature_preprocessor:extra_trees_preproc_for_classification:min_samples_leaf,feature_preprocessor:extra_trees_preproc_for_classification:min_samples_split,feature_preprocessor:extra_trees_preproc_for_classification:min_weight_fraction_leaf,feature_preprocessor:extra_trees_preproc_for_classification:n_estimators,feature_preprocessor:kernel_pca:coef0,feature_preprocessor:kernel_pca:degree,feature_preprocessor:kernel_pca:gamma,feature_preprocessor:kernel_pca:kernel,feature_preprocessor:kernel_pca:n_components,feature_preprocessor:kitchen_sinks:gamma,feature_preprocessor:kitchen_sinks:n_components,feature_preprocessor:liblinear_svc_preprocessor:C,feature_preprocessor:liblinear_svc_preprocessor:dual,feature_preprocessor:liblinear_svc_preprocessor:fit_intercept,feature_preprocessor:liblinear_svc_preprocess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-13,none,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,200,None,,0.0017457216340699586,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,none,,,,,kernel_pca,,,,,,,,,,,0.0,4,0.0007494648939847078,poly,1996,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-14,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.6039096182414764,None,0.0,2,20,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.1939609823704447,most_frequent,robust_scaler,,,0.7331327008874781,0.29747488162877694,extra_trees_preproc_for_classification,True,entropy,None,0.33401603690569853,None,0.0,4,4,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-15,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7865652301288171,None,0.0,1,4,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,median,robust_scaler,,,0.8623635641193496,0.13298297278730625,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-16,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.905401147837377,-0.32832122529048147,3,1.001896309040499,poly,-1,False,5.1523484258030785e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-17,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.3304182089730454,None,0.0,1,11,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.015634546384927752,median,quantile_transformer,332,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-18,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5010952533165973,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.005838375247601407,mean,normalize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,,
-19,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.7297122444166401,False,True,1,squared_hinge,ovr,l2,0.00023416745444541063,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.03941815152197094,most_frequent,normalize,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0973649546833947,fpr,chi2,
-20,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.4426097579931897,None,0.0,16,4,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,mean,robust_scaler,,,0.7407323456920554,0.0804756921037867,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,,
-21,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5607168729746175,None,0.0,2,3,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.009810589644392154,most_frequent,quantile_transformer,1132,normal,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.06211089008167909,fdr,chi2,
-22,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9333237919291875,None,0.0,8,3,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,,
-23,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.557321265077371,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-24,none,adaboost,SAMME,0.6255555220658239,6,446,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.008833097306364968,most_frequent,none,,,,,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,10,None,4,17,1.0,76,,,,,,
-25,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.7772914874049878,None,0.0,10,15,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00015285482513309234,mean,robust_scaler,,,0.8373404000074934,0.14303151080827242,extra_trees_preproc_for_classification,True,entropy,None,0.5021713909357703,None,0.0,13,16,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-26,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7422641456052917,None,0.0,8,15,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.005561351266169067,most_frequent,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-27,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.887898394612674,None,0.0,10,19,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,robust_scaler,,,0.8350698288864961,0.2347163538066168,extra_trees_preproc_for_classification,False,entropy,None,0.49795108918064074,None,0.0,13,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-28,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7088164344533493,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,mean,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,,
-29,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,10,1,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0022606253047811435,median,quantile_transformer,1247,normal,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,25.327793585266683,chi2,,,,
-30,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,24.786439230569382,,,0.03411197494635044,rbf,-1,True,0.003003351499585181,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0007994838629325874,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-31,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2681.998807317996,-0.9558745731731211,,0.0011299948421773457,sigmoid,-1,False,1.5292507354478086e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-32,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.8095522998548572,None,0.0,17,10,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.031940595071539304,mean,quantile_transformer,1563,normal,,,extra_trees_preproc_for_classification,False,entropy,None,0.9330145598603196,None,0.0,18,10,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-33,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,26.502259985828186,,,9.475662388405154e-05,rbf,-1,False,0.00019690635968217866,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-34,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.339600708038189,False,True,1,squared_hinge,ovr,l2,9.572641158475304e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,quantile_transformer,1193,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-35,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5255991794296913,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-36,weighting,decision_tree,,,,,,,gini,1.7853447328017091,1.0,None,0.0,13,7,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0001265964225445088,median,normalize,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.26399878867042303,,mutual_info_classif,
-37,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.7610308078756552,None,0.0,1,2,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.010000000000000004,mean,robust_scaler,,,0.9205545465103644,0.20272761060743316,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-38,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,249.12499213127998,,,0.0005283374995918348,rbf,-1,False,0.0016932707039261864,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.48910068367909115,most_frequent,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-39,none,bernoulli_nb,,,,,0.6524090653939782,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.005164169126808402,most_frequent,quantile_transformer,16,normal,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.033082145443631535,False,True,1,squared_hinge,ovr,l1,0.002615020898325393,,,,,,,,,,,,,,,,,,,,,
-40,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.6453555269772124,None,0.0,1,4,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,normalize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-41,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.20911875507660596,None,0.0,12,8,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,median,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,,
-42,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5138705053438934,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.14411802159506462,most_frequent,quantile_transformer,857,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-43,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,29.55731828912983,False,True,1,squared_hinge,ovr,l2,0.0011332486951954968,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,,
-44,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.37801720673725603,True,True,squared_hinge,0.0001255132393635067,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.025993178344125793,most_frequent,standardize,,,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,1.4421737618429475,False,True,1,squared_hinge,ovr,l1,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,
-45,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.10581257327556097,None,0.0,7,12,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.004219392676189224,median,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,,
-46,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.4188764793561401,None,0.0,5,5,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.010589444903605501,mean,robust_scaler,,,0.9796997860990453,0.27262832749694405,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-47,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.005753626505113041,False,True,squared_hinge,0.00924401505172238,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.3192271696445429,median,quantile_transformer,913,normal,,,kernel_pca,,,,,,,,,,,,,,cosine,1963,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-48,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.41019363453549706,None,0.0,15,2,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.0003507389224306018,median,none,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.20614098085343638,fdr,chi2,
-49,none,adaboost,SAMME,0.1280458968526125,7,125,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,robust_scaler,,,0.7156460334356771,0.2981902862926971,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,65.16171885308417,chi2,,,,
-50,none,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9931779675565967,None,0.0,18,17,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,robust_scaler,,,0.75,0.2945917010472818,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,,
-51,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.08646118576686276,None,0.0,4,9,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,most_frequent,quantile_transformer,959,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-52,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.6271213423927893,None,0.0,1,4,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.009403509495453675,mean,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,,
-53,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8.85547546146731,True,True,squared_hinge,4.0111304608748804e-05,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-54,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.34936025988397124,None,0.0,2,13,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00018601309647406987,median,robust_scaler,,,0.8634309348843472,0.26976989465367607,extra_trees_preproc_for_classification,False,entropy,None,0.48025175288975036,None,0.0,6,17,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-55,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.8793175987664956,None,0.0,10,5,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,robust_scaler,,,0.7645748130904642,0.2677156877155214,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,130.79089036625916,False,True,1,squared_hinge,ovr,l1,0.005538878421721295,,,,,,,,,,,,,,,,,,,,,
-56,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.014911374426433241,None,0.0,6,2,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,,
-57,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0016941428709087083,True,,,True,,optimal,squared_hinge,l1,,5.124265599423001e-05,no_encoding,no_coalescense,,most_frequent,quantile_transformer,461,uniform,,,extra_trees_preproc_for_classification,True,gini,None,0.31371431180257103,None,0.0,5,17,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-58,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.7591396576940418,-0.02447104417007273,2,0.7325180366342191,poly,-1,True,0.00045922794962860523,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,standardize,,,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,2.057731506094293,False,True,1,squared_hinge,ovr,l1,0.03575998247131604,,,,,,,,,,,,,,,,,,,,,
-59,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.593728171200453,None,0.0,7,20,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.02830941310359363,mean,normalize,,,,,extra_trees_preproc_for_classification,False,entropy,None,0.7389446586281172,None,0.0,20,11,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-60,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5542793596955801,None,0.0,1,3,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,most_frequent,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-61,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.46057831591617715,False,True,hinge,0.04557857428827514,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00027457445401600137,median,standardize,,,,,extra_trees_preproc_for_classification,True,gini,None,0.48190346970486964,None,0.0,17,18,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-62,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.46057831591617715,False,True,hinge,0.04557857428827514,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00027457445401600137,median,standardize,,,,,extra_trees_preproc_for_classification,True,gini,None,0.48190346970486964,None,0.0,17,18,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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+367,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.938372431886818e-05,True,True,hinge,0.0016143408148969647,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00024949978971758427,median,quantile_transformer,754,uniform,,,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,-0.22339839702350717,2,0.5718982387915436,poly,5260,,,,,,,,,,,,,,,,
+371,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4,1,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.01020427226149318,median,none,,,,,extra_trees_preproc_for_classification,True,entropy,None,0.3663460889081009,None,0.0,2,7,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
diff --git a/autosklearn/metalearning/files/recall_weighted_multiclass.classification_sparse/description.txt b/autosklearn/metalearning/files/recall_weighted_multiclass.classification_sparse/description.txt
index 9a8b7ce951..ff65bc2934 100644
--- a/autosklearn/metalearning/files/recall_weighted_multiclass.classification_sparse/description.txt
+++ b/autosklearn/metalearning/files/recall_weighted_multiclass.classification_sparse/description.txt
@@ -1,8 +1,9 @@
 features_cutoff_time: 3600
 features_cutoff_memory: 3072
-number_of_feature_steps: 35
+number_of_feature_steps: 42
 feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances
 feature_step LogNumberOfInstances: LogNumberOfInstances
+feature_step NumberOfClasses: NumberOfClasses
 feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures
 feature_step LogNumberOfFeatures: LogNumberOfFeatures
 feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues
@@ -20,6 +21,11 @@ feature_step DatasetRatio: DatasetRatio, LogDatasetRatio
 feature_step LogDatasetRatio: LogDatasetRatio
 feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio
 feature_step LogInverseDatasetRatio: LogInverseDatasetRatio
+feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD
+feature_step ClassProbabilityMin: ClassProbabilityMin
+feature_step ClassProbabilityMax: ClassProbabilityMax
+feature_step ClassProbabilityMean: ClassProbabilityMean
+feature_step ClassProbabilitySTD: ClassProbabilitySTD
 feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum
 feature_step SymbolsMin: SymbolsMin
 feature_step SymbolsMax: SymbolsMax
@@ -36,11 +42,12 @@ feature_step SkewnessMin: SkewnessMin
 feature_step SkewnessMax: SkewnessMax
 feature_step SkewnessMean: SkewnessMean
 feature_step SkewnessSTD: SkewnessSTD
-features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
+feature_step ClassEntropy: ClassEntropy
+features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy
 features_stochastic: 
-default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
+default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy
 
-algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92
+algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235,236,237,238,239,240,241,242,243,244,245,246,247,248,249,250,251,252,253,254,255,256,257,258,259,260,261,262,263,264,265,266,267,268,269,270,271,272,273,274,275,276,277,278,279,280,281,282,283,284,285,286,287,288,289,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,312,313,314,315,316,317,318,319,320,321,322,323,324,325,326,327,328,329,330,331,332,333,334,335,336,337,338,339,340,341,342,343,344,345,346,347,348,349,350,351,352,353,354,355,356,357,358,359,360,361,362,363,364,365,366,367,368,369,370,371,372
 algorithms_stochastic: 
 performance_measures: recall_weighted
 performance_type: solution_quality
diff --git a/autosklearn/metalearning/files/recall_weighted_multiclass.classification_sparse/feature_costs.arff b/autosklearn/metalearning/files/recall_weighted_multiclass.classification_sparse/feature_costs.arff
index 0e17e20d22..8512fd9275 100644
--- a/autosklearn/metalearning/files/recall_weighted_multiclass.classification_sparse/feature_costs.arff
+++ b/autosklearn/metalearning/files/recall_weighted_multiclass.classification_sparse/feature_costs.arff
@@ -4,6 +4,7 @@
 @ATTRIBUTE repetition NUMERIC
 @ATTRIBUTE NumberOfInstances NUMERIC
 @ATTRIBUTE LogNumberOfInstances NUMERIC
+@ATTRIBUTE NumberOfClasses NUMERIC
 @ATTRIBUTE NumberOfFeatures NUMERIC
 @ATTRIBUTE LogNumberOfFeatures NUMERIC
 @ATTRIBUTE MissingValues NUMERIC
@@ -21,6 +22,11 @@
 @ATTRIBUTE LogDatasetRatio NUMERIC
 @ATTRIBUTE InverseDatasetRatio NUMERIC
 @ATTRIBUTE LogInverseDatasetRatio NUMERIC
+@ATTRIBUTE ClassOccurences NUMERIC
+@ATTRIBUTE ClassProbabilityMin NUMERIC
+@ATTRIBUTE ClassProbabilityMax NUMERIC
+@ATTRIBUTE ClassProbabilityMean NUMERIC
+@ATTRIBUTE ClassProbabilitySTD NUMERIC
 @ATTRIBUTE NumSymbols NUMERIC
 @ATTRIBUTE SymbolsMin NUMERIC
 @ATTRIBUTE SymbolsMax NUMERIC
@@ -37,162 +43,138 @@
 @ATTRIBUTE SkewnessMax NUMERIC
 @ATTRIBUTE SkewnessMean NUMERIC
 @ATTRIBUTE SkewnessSTD NUMERIC
+@ATTRIBUTE ClassEntropy NUMERIC
 
 @DATA
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diff --git a/autosklearn/metalearning/files/recall_weighted_multiclass.classification_sparse/feature_runstatus.arff b/autosklearn/metalearning/files/recall_weighted_multiclass.classification_sparse/feature_runstatus.arff
index 0034ad2bd6..5fff2dfed3 100644
--- a/autosklearn/metalearning/files/recall_weighted_multiclass.classification_sparse/feature_runstatus.arff
+++ b/autosklearn/metalearning/files/recall_weighted_multiclass.classification_sparse/feature_runstatus.arff
@@ -4,6 +4,7 @@
 @ATTRIBUTE repetition NUMERIC
 @ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other}
@@ -21,6 +22,11 @@
 @ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other}
@@ -37,162 +43,138 @@
 @ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other}
 
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diff --git a/autosklearn/metalearning/files/recall_weighted_multiclass.classification_sparse/feature_values.arff b/autosklearn/metalearning/files/recall_weighted_multiclass.classification_sparse/feature_values.arff
index a60400e3ce..736dc64321 100644
--- a/autosklearn/metalearning/files/recall_weighted_multiclass.classification_sparse/feature_values.arff
+++ b/autosklearn/metalearning/files/recall_weighted_multiclass.classification_sparse/feature_values.arff
@@ -13,7 +13,6 @@
 @ATTRIBUTE KurtosisMean NUMERIC
 @ATTRIBUTE KurtosisMin NUMERIC
 @ATTRIBUTE KurtosisSTD NUMERIC
-@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC
 @ATTRIBUTE LogDatasetRatio NUMERIC
 @ATTRIBUTE LogInverseDatasetRatio NUMERIC
 @ATTRIBUTE LogNumberOfFeatures NUMERIC
@@ -42,160 +41,135 @@
 @ATTRIBUTE SymbolsSum NUMERIC
 
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diff --git a/autosklearn/metalearning/files/roc_auc_binary.classification_dense/algorithm_runs.arff b/autosklearn/metalearning/files/roc_auc_binary.classification_dense/algorithm_runs.arff
index bc4b150759..079687669c 100644
--- a/autosklearn/metalearning/files/roc_auc_binary.classification_dense/algorithm_runs.arff
+++ b/autosklearn/metalearning/files/roc_auc_binary.classification_dense/algorithm_runs.arff
@@ -7,130 +7,135 @@
 @ATTRIBUTE runstatus {ok, timeout, memout, not_applicable, crash, other}
 
 @DATA
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-75212,1.0,2,0.16776802017215897,ok
-252,1.0,3,?,not_applicable
-246,1.0,4,?,not_applicable
-75178,1.0,5,?,not_applicable
-75239,1.0,6,0.0,ok
-75171,1.0,7,0.085367576363505,ok
-75233,1.0,8,0.017730661245038193,ok
-248,1.0,9,?,not_applicable
-75231,1.0,10,?,not_applicable
-2123,1.0,11,?,not_applicable
-75196,1.0,12,0.00010402219140082103,ok
-75188,1.0,13,?,not_applicable
-75092,1.0,14,0.05392684238838086,ok
-75248,1.0,15,0.14433373101792535,ok
-75126,1.0,16,0.023670347116980572,ok
-75234,1.0,17,0.0024113922352499984,ok
-75150,1.0,18,0.2117404483293388,ok
-258,1.0,19,?,not_applicable
-75168,1.0,20,?,not_applicable
-75235,1.0,21,?,not_applicable
-75159,1.0,22,0.10926239932174653,ok
-244,1.0,23,?,not_applicable
-75221,1.0,24,?,not_applicable
-75219,1.0,25,0.0057392216673344265,ok
-75202,1.0,26,?,not_applicable
-3043,1.0,27,0.004649259057134425,ok
-75205,1.0,28,?,not_applicable
-75174,1.0,29,0.05163238469196885,ok
-288,1.0,30,?,not_applicable
-75250,1.0,31,?,not_applicable
-275,1.0,32,?,not_applicable
-75142,1.0,33,0.015037365399820257,ok
-75213,1.0,34,0.014899487583760251,ok
-75099,1.0,35,0.1453030700603516,ok
-75243,1.0,36,?,not_applicable
-75184,1.0,37,0.05189413820838129,ok
-75222,1.0,38,0.03970238095238099,ok
-75175,1.0,39,0.03519678530261172,ok
-233,1.0,40,5.0658561296823557e-05,ok
-75161,1.0,41,0.011699165921616905,ok
-75176,1.0,42,0.0009101691094206554,ok
-75090,1.0,43,?,not_applicable
-75114,1.0,44,0.01408065618591936,ok
-260,1.0,45,?,not_applicable
-236,1.0,46,?,not_applicable
-75141,1.0,47,0.0081607580609383,ok
-75107,1.0,48,0.14134705527275548,ok
-262,1.0,49,?,not_applicable
-75146,1.0,50,0.03916579176290613,ok
-75189,1.0,51,0.003230156186084243,ok
-2350,1.0,52,0.41965124135644105,ok
-253,1.0,53,?,not_applicable
-2122,1.0,54,?,not_applicable
-75110,1.0,55,?,not_applicable
-75249,1.0,56,0.0003071909392029504,ok
-75108,1.0,57,0.0,ok
-242,1.0,58,?,not_applicable
-75117,1.0,59,0.0615488847196165,ok
-75191,1.0,60,0.07897784609606961,ok
-75226,1.0,61,3.536062890940883e-06,ok
-261,1.0,62,0.21168393895666626,ok
-75236,1.0,63,?,not_applicable
-75095,1.0,64,0.007955151737153554,ok
-75148,1.0,65,0.05226054310909389,ok
-75093,1.0,66,0.252596209769244,ok
-75223,1.0,67,?,not_applicable
-75244,1.0,68,0.1053494919818827,ok
-75109,1.0,69,?,not_applicable
-75197,1.0,70,?,not_applicable
-75127,1.0,71,0.27616944025774925,ok
-75143,1.0,72,0.005653857287595931,ok
-75153,1.0,73,0.020757455431716698,ok
-75173,1.0,74,0.05042045454545452,ok
-75215,1.0,75,0.0032187627971225208,ok
-75195,1.0,76,0.0,ok
-75207,1.0,77,?,not_applicable
-266,1.0,78,?,not_applicable
-75225,1.0,79,0.07323392554991537,ok
-75166,1.0,80,0.029769676311315885,ok
-75100,1.0,81,0.13935764833968423,ok
-75169,1.0,82,?,not_applicable
-75132,1.0,83,0.285681171421424,ok
-273,1.0,84,0.011504602571595579,ok
-75121,1.0,85,0.0,ok
-75098,1.0,86,?,not_applicable
-75115,1.0,87,0.06279966645820301,ok
-75116,1.0,88,0.004569600731136103,ok
-75185,1.0,89,0.04990297944254485,ok
-2119,1.0,90,?,not_applicable
-75157,1.0,91,0.4186635220125786,ok
-75113,1.0,92,0.0005957883923985996,ok
-75134,1.0,93,?,not_applicable
-75096,1.0,94,?,not_applicable
-75203,1.0,95,?,not_applicable
-75182,1.0,96,0.0543816257602735,ok
-251,1.0,97,?,not_applicable
-75123,1.0,98,?,not_applicable
-75237,1.0,99,3.1735632675111702e-06,ok
-75125,1.0,100,0.02981286849525755,ok
-75232,1.0,101,0.0815382921727642,ok
-75103,1.0,102,0.0010105294215273508,ok
-75192,1.0,103,0.5007924520802488,ok
-75230,1.0,104,?,not_applicable
-75139,1.0,105,0.0006225063537530495,ok
-75227,1.0,106,0.03668027868170054,ok
-2120,1.0,107,?,not_applicable
-75124,1.0,108,0.09452406839972327,ok
-75240,1.0,109,0.008759763486385852,ok
-75129,1.0,110,0.15417843495597272,ok
-75198,1.0,111,?,not_applicable
-75201,1.0,112,?,not_applicable
-75112,1.0,113,0.05837172814070124,ok
-75133,1.0,114,0.07049778663774142,ok
-75105,1.0,115,0.20636840824268865,ok
-75154,1.0,116,?,not_applicable
-75177,1.0,117,0.004187481633852408,ok
-2117,1.0,118,0.08771911890288053,ok
-75156,1.0,119,0.1365301400403729,ok
-75097,1.0,120,0.16544692942208472,ok
-75101,1.0,121,0.19636435238838335,ok
-75172,1.0,122,?,not_applicable
-75106,1.0,123,0.3067981875963971,ok
-75179,1.0,124,0.1047071451478837,ok
-75187,1.0,125,0.0012569524975303281,ok
-75120,1.0,126,0.07627811860940703,ok
-75193,1.0,127,?,not_applicable
+75192,1.0,1,0.48914415175333126,ok
+75119,1.0,2,0.03586776859504137,ok
+75139,1.0,321,0.000592063689743072,ok
+75212,1.0,397,0.17037648900095648,ok
+246,1.0,8,?,not_applicable
+252,1.0,9,?,not_applicable
+75178,1.0,10,?,not_applicable
+75177,1.0,13,0.0007976155493052417,ok
+75092,1.0,52,0.04872691411152952,ok
+75239,1.0,15,0.0,ok
+75173,1.0,264,0.04976298701298698,ok
+75215,1.0,269,0.003011411213182491,ok
+75171,1.0,21,0.08390184823006619,ok
+75112,1.0,410,0.056506595005561766,ok
+75227,1.0,24,0.03564418557308702,ok
+75233,1.0,102,0.014237583502759277,ok
+75182,1.0,356,0.052365535678483566,ok
+253,1.0,27,?,not_applicable
+75157,1.0,29,0.4139033018867925,ok
+75187,1.0,267,0.001194541081475875,ok
+75124,1.0,395,0.08288524266086794,ok
+75090,1.0,32,?,not_applicable
+75222,1.0,285,0.032916666666666594,ok
+75231,1.0,34,?,not_applicable
+75185,1.0,37,0.0492198320581817,ok
+2123,1.0,39,?,not_applicable
+75150,1.0,72,0.20365148738192596,ok
+75143,1.0,43,0.0009815096251266642,ok
+75196,1.0,45,0.00010402219140082103,ok
+75188,1.0,49,?,not_applicable
+75248,1.0,54,0.13847340668404695,ok
+75249,1.0,204,0.00027157459842586107,ok
+75113,1.0,59,0.00035914702581374414,ok
+75126,1.0,63,0.01998989680305996,ok
+288,1.0,64,?,not_applicable
+251,1.0,65,?,not_applicable
+75184,1.0,156,0.05192249912367086,ok
+75234,1.0,68,0.0024341981701589965,ok
+258,1.0,73,?,not_applicable
+75166,1.0,281,0.027273444208829156,ok
+75168,1.0,77,?,not_applicable
+75148,1.0,276,0.05714372744025953,ok
+75235,1.0,81,?,not_applicable
+75159,1.0,85,0.07428995337007216,ok
+75146,1.0,88,0.03902390737018924,ok
+244,1.0,89,?,not_applicable
+75141,1.0,163,0.008309207869546142,ok
+75221,1.0,92,?,not_applicable
+75219,1.0,101,0.003017615815757768,ok
+75202,1.0,96,?,not_applicable
+3043,1.0,99,0.0007976155493052417,ok
+75205,1.0,103,?,not_applicable
+75174,1.0,106,0.046689314765498646,ok
+75250,1.0,111,?,not_applicable
+75179,1.0,238,0.10433997211841184,ok
+275,1.0,115,?,not_applicable
+242,1.0,116,?,not_applicable
+75207,1.0,117,?,not_applicable
+75142,1.0,118,0.01486433691665967,ok
+75099,1.0,171,0.13256035161374957,ok
+75243,1.0,126,?,not_applicable
+75175,1.0,253,0.03438767711211699,ok
+233,1.0,137,7.236937328125581e-05,ok
+75161,1.0,139,0.010965392944143892,ok
+75176,1.0,143,0.0008155640317971669,ok
+262,1.0,146,?,not_applicable
+75129,1.0,147,0.1410533311181258,ok
+261,1.0,221,0.20118938300756473,ok
+75114,1.0,152,0.01289587605377085,ok
+75093,1.0,230,0.24427512963129827,ok
+260,1.0,158,?,not_applicable
+236,1.0,160,?,not_applicable
+254,1.0,166,0.0,ok
+75107,1.0,167,0.13454992985733727,ok
+75181,1.0,175,?,not_applicable
+75189,1.0,187,0.0039052183844204835,ok
+75163,1.0,189,0.02211436849684123,ok
+2350,1.0,191,0.41524352522082464,ok
+2122,1.0,195,?,not_applicable
+75110,1.0,198,?,not_applicable
+75213,1.0,394,0.013125739061884079,ok
+75095,1.0,203,0.0069040205766888185,ok
+75108,1.0,207,0.0,ok
+75117,1.0,212,0.05331457160725439,ok
+75191,1.0,216,0.07801659888081602,ok
+75226,1.0,219,7.70861710237547e-05,ok
+75244,1.0,222,0.10109230957560011,ok
+75236,1.0,223,?,not_applicable
+75169,1.0,227,?,not_applicable
+75116,1.0,319,0.002741760438681573,ok
+75223,1.0,235,?,not_applicable
+75109,1.0,242,?,not_applicable
+75197,1.0,245,?,not_applicable
+75237,1.0,364,4.241221283507457e-06,ok
+248,1.0,250,?,not_applicable
+2119,1.0,254,?,not_applicable
+75127,1.0,255,0.2750824723734884,ok
+75153,1.0,263,0.022855656269682556,ok
+75195,1.0,271,2.2912493125026856e-08,ok
+266,1.0,277,?,not_applicable
+75225,1.0,279,0.06236780879864645,ok
+75100,1.0,286,0.12582626953884435,ok
+75132,1.0,293,0.27152591159294626,ok
+75210,1.0,298,0.0,ok
+273,1.0,300,0.01124981735827002,ok
+75133,1.0,303,0.03146345753569224,ok
+75121,1.0,304,0.0,ok
+75098,1.0,310,?,not_applicable
+75115,1.0,315,0.028559516364394355,ok
+75217,1.0,337,?,not_applicable
+75134,1.0,341,?,not_applicable
+75096,1.0,346,?,not_applicable
+75203,1.0,350,?,not_applicable
+75123,1.0,360,?,not_applicable
+75125,1.0,369,0.021532940271725254,ok
+2120,1.0,371,?,not_applicable
+75232,1.0,374,0.07496972588161899,ok
+75103,1.0,380,0.0010392963174684633,ok
+75230,1.0,386,?,not_applicable
+75240,1.0,399,0.007972654303551208,ok
+75198,1.0,404,?,not_applicable
+75201,1.0,407,?,not_applicable
+75105,1.0,415,0.16475344537756154,ok
+75154,1.0,420,?,not_applicable
+2117,1.0,429,0.08514254760590934,ok
+75156,1.0,430,0.13042017382504711,ok
+75097,1.0,434,0.14857355521175386,ok
+75101,1.0,438,0.1892655265868073,ok
+75172,1.0,443,?,not_applicable
+75106,1.0,449,0.2627889170037707,ok
+75120,1.0,456,0.05153374233128838,ok
+75193,1.0,461,?,not_applicable
diff --git a/autosklearn/metalearning/files/roc_auc_binary.classification_dense/configurations.csv b/autosklearn/metalearning/files/roc_auc_binary.classification_dense/configurations.csv
index 7c928e9c5b..bc2613dc18 100644
--- a/autosklearn/metalearning/files/roc_auc_binary.classification_dense/configurations.csv
+++ b/autosklearn/metalearning/files/roc_auc_binary.classification_dense/configurations.csv
@@ -1,128 +1,133 @@
 idx,balancing:strategy,classifier:__choice__,classifier:adaboost:algorithm,classifier:adaboost:learning_rate,classifier:adaboost:max_depth,classifier:adaboost:n_estimators,classifier:bernoulli_nb:alpha,classifier:bernoulli_nb:fit_prior,classifier:decision_tree:criterion,classifier:decision_tree:max_depth_factor,classifier:decision_tree:max_features,classifier:decision_tree:max_leaf_nodes,classifier:decision_tree:min_impurity_decrease,classifier:decision_tree:min_samples_leaf,classifier:decision_tree:min_samples_split,classifier:decision_tree:min_weight_fraction_leaf,classifier:extra_trees:bootstrap,classifier:extra_trees:criterion,classifier:extra_trees:max_depth,classifier:extra_trees:max_features,classifier:extra_trees:max_leaf_nodes,classifier:extra_trees:min_impurity_decrease,classifier:extra_trees:min_samples_leaf,classifier:extra_trees:min_samples_split,classifier:extra_trees:min_weight_fraction_leaf,classifier:gradient_boosting:early_stop,classifier:gradient_boosting:l2_regularization,classifier:gradient_boosting:learning_rate,classifier:gradient_boosting:loss,classifier:gradient_boosting:max_bins,classifier:gradient_boosting:max_depth,classifier:gradient_boosting:max_leaf_nodes,classifier:gradient_boosting:min_samples_leaf,classifier:gradient_boosting:n_iter_no_change,classifier:gradient_boosting:scoring,classifier:gradient_boosting:tol,classifier:gradient_boosting:validation_fraction,classifier:k_nearest_neighbors:n_neighbors,classifier:k_nearest_neighbors:p,classifier:k_nearest_neighbors:weights,classifier:lda:n_components,classifier:lda:shrinkage,classifier:lda:shrinkage_factor,classifier:lda:tol,classifier:liblinear_svc:C,classifier:liblinear_svc:dual,classifier:liblinear_svc:fit_intercept,classifier:liblinear_svc:intercept_scaling,classifier:liblinear_svc:loss,classifier:liblinear_svc:multi_class,classifier:liblinear_svc:penalty,classifier:liblinear_svc:tol,classifier:libsvm_svc:C,classifier:libsvm_svc:coef0,classifier:libsvm_svc:degree,classifier:libsvm_svc:gamma,classifier:libsvm_svc:kernel,classifier:libsvm_svc:max_iter,classifier:libsvm_svc:shrinking,classifier:libsvm_svc:tol,classifier:multinomial_nb:alpha,classifier:multinomial_nb:fit_prior,classifier:passive_aggressive:C,classifier:passive_aggressive:average,classifier:passive_aggressive:fit_intercept,classifier:passive_aggressive:loss,classifier:passive_aggressive:tol,classifier:qda:reg_param,classifier:random_forest:bootstrap,classifier:random_forest:criterion,classifier:random_forest:max_depth,classifier:random_forest:max_features,classifier:random_forest:max_leaf_nodes,classifier:random_forest:min_impurity_decrease,classifier:random_forest:min_samples_leaf,classifier:random_forest:min_samples_split,classifier:random_forest:min_weight_fraction_leaf,classifier:sgd:alpha,classifier:sgd:average,classifier:sgd:epsilon,classifier:sgd:eta0,classifier:sgd:fit_intercept,classifier:sgd:l1_ratio,classifier:sgd:learning_rate,classifier:sgd:loss,classifier:sgd:penalty,classifier:sgd:power_t,classifier:sgd:tol,data_preprocessing:categorical_transformer:categorical_encoding:__choice__,data_preprocessing:categorical_transformer:category_coalescence:__choice__,data_preprocessing:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction,data_preprocessing:numerical_transformer:imputation:strategy,data_preprocessing:numerical_transformer:rescaling:__choice__,data_preprocessing:numerical_transformer:rescaling:quantile_transformer:n_quantiles,data_preprocessing:numerical_transformer:rescaling:quantile_transformer:output_distribution,data_preprocessing:numerical_transformer:rescaling:robust_scaler:q_max,data_preprocessing:numerical_transformer:rescaling:robust_scaler:q_min,feature_preprocessor:__choice__,feature_preprocessor:extra_trees_preproc_for_classification:bootstrap,feature_preprocessor:extra_trees_preproc_for_classification:criterion,feature_preprocessor:extra_trees_preproc_for_classification:max_depth,feature_preprocessor:extra_trees_preproc_for_classification:max_features,feature_preprocessor:extra_trees_preproc_for_classification:max_leaf_nodes,feature_preprocessor:extra_trees_preproc_for_classification:min_impurity_decrease,feature_preprocessor:extra_trees_preproc_for_classification:min_samples_leaf,feature_preprocessor:extra_trees_preproc_for_classification:min_samples_split,feature_preprocessor:extra_trees_preproc_for_classification:min_weight_fraction_leaf,feature_preprocessor:extra_trees_preproc_for_classification:n_estimators,feature_preprocessor:fast_ica:algorithm,feature_preprocessor:fast_ica:fun,feature_preprocessor:fast_ica:n_components,feature_preprocessor:fast_ica:whiten,feature_preprocessor:feature_agglomeration:affinity,feature_preprocessor:feature_agglomeration:linkage,feature_preprocessor:feature_agglomeration:n_clusters,feature_preprocessor:feature_agglomeration:pooling_func,feature_preprocessor:kernel_pca:coef0,feature_preprocessor:kernel_pca:degree,feature_preprocessor:kernel_pca:gamma,feature_preprocessor:kernel_pca:kernel,feature_preprocessor:kernel_pca:n_components,feature_preprocessor:kitchen_sinks:gamma,feature_preprocessor:kitchen_sinks:n_components,feature_preprocessor:liblinear_svc_preprocessor:C,feature_preprocessor:liblinear_svc_preprocessor:dual,feature_preprocessor:liblinear_svc_preprocessor:fit_intercept,feature_preprocessor:liblinear_svc_preprocessor:intercept_scaling,feature_preprocessor:liblinear_svc_preprocessor:loss,feature_preprocessor:liblinear_svc_preprocessor:multi_class,feature_preprocessor:liblinear_svc_preprocessor:penalty,feature_preprocessor:liblinear_svc_preprocessor:tol,feature_preprocessor:nystroem_sampler:coef0,feature_preprocessor:nystroem_sampler:degree,feature_preprocessor:nystroem_sampler:gamma,feature_preprocessor:nystroem_sampler:kernel,feature_preprocessor:nystroem_sampler:n_components,feature_preprocessor:pca:keep_variance,feature_preprocessor:pca:whiten,feature_preprocessor:polynomial:degree,feature_preprocessor:polynomial:include_bias,feature_preprocessor:polynomial:interaction_only,feature_preprocessor:random_trees_embedding:bootstrap,feature_preprocessor:random_trees_embedding:max_depth,feature_preprocessor:random_trees_embedding:max_leaf_nodes,feature_preprocessor:random_trees_embedding:min_samples_leaf,feature_preprocessor:random_trees_embedding:min_samples_split,feature_preprocessor:random_trees_embedding:min_weight_fraction_leaf,feature_preprocessor:random_trees_embedding:n_estimators,feature_preprocessor:select_percentile_classification:percentile,feature_preprocessor:select_percentile_classification:score_func,feature_preprocessor:select_rates_classification:alpha,feature_preprocessor:select_rates_classification:mode,feature_preprocessor:select_rates_classification:score_func
-1,none,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,standardize,,,,,extra_trees_preproc_for_classification,False,entropy,None,0.8629651625821806,None,0.0,10,10,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-2,none,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.9525269450493825,None,0.0,4,20,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,standardize,,,,,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-3,none,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,188,None,,0.09976330267940166,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,none,,,,,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,-0.05439674489937307,2,0.051865468486041774,poly,727,,,,,,,,,,,,,,,,,
-4,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,23916.78290516781,False,True,1,squared_hinge,ovr,l2,0.0006655607726658531,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.008808715242823137,median,none,,,,,kernel_pca,,,,,,,,,,,,,,,,,,,0.2633153190620261,2,0.004770332714291669,poly,1483,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-5,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,2,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010091246322497477,median,none,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,95.37976833605768,mutual_info,,,
-6,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.933900673392165,None,0.0,7,4,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,minmax,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-7,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,0.9455840414651977,0.035051597802663065,auto,255,None,6,39,20,loss,1e-07,0.03762410007700571,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,robust_scaler,,,0.8083718526277422,0.24604827304064597,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,1377.6677844231401,False,True,1,squared_hinge,ovr,l1,0.0019190146350085739,,,,,,,,,,,,,,,,,,,,,,
-8,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5238814331135578,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,median,none,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,28,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-9,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1292.7448076998514,0.004489932344506231,2,1.3088036376373702,poly,-1,False,1.7943262139196537e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,minmax,,,,,feature_agglomeration,,,,,,,,,,,,,,,cosine,average,386,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-10,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.5244313211416313,None,0.0,1,2,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.454300236339658,most_frequent,robust_scaler,,,0.7858830918128734,0.2705174720960696,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-11,weighting,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0006294780694492238,most_frequent,standardize,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.063715750591364,fdr,chi2
-12,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.541827838541001,True,True,squared_hinge,0.0001129851063838815,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,standardize,,,,,extra_trees_preproc_for_classification,True,entropy,None,0.5533459595240017,None,0.0,18,4,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-13,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.25657910619102237,None,0.0,6,6,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.021086303324492208,most_frequent,quantile_transformer,1247,uniform,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.16021572815319468,fpr,chi2
-14,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6185424163558029,False,True,1,squared_hinge,ovr,l2,4.385767395120581e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010397452590017174,median,quantile_transformer,961,normal,,,feature_agglomeration,,,,,,,,,,,,,,,cosine,average,25,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-15,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.5890308372885505,None,0.0,19,10,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.02193054860417746,median,robust_scaler,,,0.75,0.25,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,87.97956633784086,f_classif,,,
-16,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.6931271368243409,None,0.0,11,10,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.362940004438799,most_frequent,robust_scaler,,,0.9177587796305063,0.17084038458597867,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,30.491514144623952,mutual_info,,,
-17,none,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.1769332447629957,mean,minmax,,,,,fast_ica,,,,,,,,,,,deflation,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-18,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,40,2,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,robust_scaler,,,0.8108128057144657,0.1573950644227126,fast_ica,,,,,,,,,,,parallel,exp,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-19,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,199,None,,0.003239674765949011,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,robust_scaler,,,0.7228447795482008,0.25,kernel_pca,,,,,,,,,,,,,,,,,,,0.09440148939740256,3,0.0008114095405154861,poly,1996,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-20,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.6039096182414764,None,0.0,2,20,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.1939609823704447,most_frequent,robust_scaler,,,0.7331327008874781,0.29747488162877694,extra_trees_preproc_for_classification,True,entropy,None,0.33401603690569853,None,0.0,4,4,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-21,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,2.5882079595129472e-05,0.36125906161027654,auto,255,None,341,119,16,loss,1e-07,0.2924550184445239,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.02338478303505337,most_frequent,robust_scaler,,,0.7866731278723753,0.29543475006822734,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,68,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-22,weighting,adaboost,SAMME.R,0.17707648730809677,1,58,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.009258091334065716,most_frequent,quantile_transformer,1249,normal,,,feature_agglomeration,,,,,,,,,,,,,,,cosine,average,151,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-23,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7725.740943204492,,,3.122840783722913,rbf,-1,True,1.4116065061807418e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.14280178218510184,most_frequent,none,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,27,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-24,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.21794354428393548,None,0.0,2,16,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0025451910134387575,median,quantile_transformer,1477,uniform,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
-25,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5010952533165973,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.005838375247601407,mean,normalize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,
-26,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5514127836113225,None,0.0,4,20,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.010750756228608195,most_frequent,robust_scaler,,,0.7471420685032172,0.2916152663822578,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.22557315057515023,fwe,chi2
-27,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.9663114854040722,None,0.0,11,12,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.26932389682904634,median,quantile_transformer,23,uniform,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l1,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,
-28,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.506204385946851,None,0.0,2,8,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.03433007982751011,mean,none,,,,,extra_trees_preproc_for_classification,False,gini,None,0.32676415738837183,None,0.0,5,6,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-29,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9333237919291875,None,0.0,8,3,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,
-30,none,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.9728532763078892,None,0.0,13,9,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,none,,,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6651071190864879,False,,,,,,,,,,,,,,,
-31,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9561075301148197,None,0.0,6,2,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,
-32,none,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,184,manual,0.5022041786628263,2.3064966255468173e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,most_frequent,standardize,,,,,kernel_pca,,,,,,,,,,,,,,,,,,,-0.32023224593255817,2,0.09479590655633498,poly,1382,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-33,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.8832772200272849,None,0.0,17,3,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,quantile_transformer,1733,normal,,,feature_agglomeration,,,,,,,,,,,,,,,euclidean,complete,262,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-34,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,22194.242946902177,0.45210849669021247,,0.0005669713702171556,sigmoid,-1,False,0.03542142266081451,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,quantile_transformer,291,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-35,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.47147939823444396,None,0.0,18,13,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,robust_scaler,,,0.9414880591550729,0.19219631546028484,fast_ica,,,,,,,,,,,parallel,logcosh,1978,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-36,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,0.032732094595234795,0.09918078053098744,auto,255,None,97,54,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.019135985037746533,median,quantile_transformer,1000,uniform,,,feature_agglomeration,,,,,,,,,,,,,,,euclidean,average,37,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-37,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.7521969592385672,None,0.0,9,5,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.020402264943872365,median,robust_scaler,,,0.737227213630259,0.20752747663727789,fast_ica,,,,,,,,,,,parallel,exp,312,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-38,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6796.975345686287,-0.06771468190846153,,0.00012110674895907204,sigmoid,-1,False,0.002353331562303236,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0004461878087253007,median,standardize,,,,,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-39,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,3.6796787331285204e-07,0.14399102828508656,auto,255,None,18,12,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,quantile_transformer,965,normal,,,feature_agglomeration,,,,,,,,,,,,,,,cosine,average,365,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-40,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7088164344533493,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,mean,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,
-41,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,1.943186107984505e-07,0.08852455702036988,auto,255,None,5,72,19,loss,1e-07,0.039837460762271175,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.01591265419367627,median,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,
-42,weighting,adaboost,SAMME,0.5210651042094924,10,401,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,standardize,,,,,feature_agglomeration,,,,,,,,,,,,,,,euclidean,complete,89,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-43,none,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0686374607394679,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,minmax,,,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.8490260132946401,False,,,,,,,,,,,,,,,
-44,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.6931271368243409,None,0.0,11,10,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.362940004438799,most_frequent,robust_scaler,,,0.9177587796305063,0.17084038458597867,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,30.491514144623952,mutual_info,,,
-45,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.3663040729445102,None,0.0,20,19,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.010000000000000004,median,robust_scaler,,,0.7338553275791654,0.25,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,331,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-46,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,92.66144306787051,,,0.04654639302091653,rbf,-1,True,0.0022231544280610005,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.005194838694455963,mean,none,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,15,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-47,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0663383838629283,,,0.06227886986638805,rbf,-1,False,5.002833516846625e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0005633176938817242,mean,standardize,,,,,feature_agglomeration,,,,,,,,,,,,,,,cosine,complete,64,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-48,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.4467384469668449,None,0.0,19,19,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.00033708405018786203,median,quantile_transformer,711,normal,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,7.744829113696808,False,True,1,squared_hinge,ovr,l1,0.0001869446320464273,,,,,,,,,,,,,,,,,,,,,,
-49,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,26.502259985828186,,,9.475662388405154e-05,rbf,-1,False,0.00019690635968217866,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-50,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,2.5360196477156387e-07,0.011018756408558218,auto,255,None,40,122,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.00011749164080669445,most_frequent,quantile_transformer,757,uniform,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,5.097834697156853,False,True,1,squared_hinge,ovr,l1,0.053877834919477424,,,,,,,,,,,,,,,,,,,,,,
-51,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5255991794296913,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-52,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.34697723213923637,None,0.0,15,18,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.1923594385988973,mean,normalize,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,25.77499302945827,chi2,,,
-53,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1252.7997374154156,0.20811431041897355,5,0.0018140593074227453,poly,-1,False,0.03149782358621565,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.001930822730474197,most_frequent,quantile_transformer,233,normal,,,feature_agglomeration,,,,,,,,,,,,,,,euclidean,ward,29,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-54,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,1.5661823810486637e-10,0.08996863554015262,auto,255,None,156,89,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.009331521286159971,median,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,
-55,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.6109468076475573,0.8748383232154038,4,0.41333070422336576,poly,-1,False,3.591268778393482e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.011804925819789203,median,minmax,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-56,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.7610308078756552,None,0.0,1,2,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.010000000000000004,mean,robust_scaler,,,0.9205545465103644,0.20272761060743316,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-57,weighting,adaboost,SAMME.R,0.03127273017917347,10,496,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,standardize,,,,,fast_ica,,,,,,,,,,,deflation,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-58,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,151,None,,0.0003283777607180646,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.2510527431514227,mean,normalize,,,,,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,-0.96093182232312,,0.05762510964607661,sigmoid,2632,,,,,,,,,,,,,,,,,
-59,none,bernoulli_nb,,,,,0.6524090653939782,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.005164169126808402,most_frequent,quantile_transformer,16,normal,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.033082145443631535,False,True,1,squared_hinge,ovr,l1,0.002615020898325393,,,,,,,,,,,,,,,,,,,,,,
-60,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.6453555269772124,None,0.0,1,4,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,normalize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-61,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,202.1500193823052,,,1.4635959201092044,rbf,-1,True,0.02257461509273525,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,none,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,75.98663128595764,mutual_info,,,
-62,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.20911875507660596,None,0.0,12,8,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,median,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,
-63,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.34534888916828577,0.02513634776687268,2,1.0733091880109409,poly,-1,True,0.0006974263047918307,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,quantile_transformer,1000,normal,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,77.99934218022861,mutual_info,,,
-64,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,168.83067109296323,,,4.3654257342094485e-05,rbf,-1,False,0.04136843105299338,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0013312674340867563,mean,robust_scaler,,,0.7949633765736044,0.27598937864544565,feature_agglomeration,,,,,,,,,,,,,,,euclidean,ward,58,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-65,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,1.1387795876755026e-06,0.12245605190343567,auto,255,None,58,7,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,robust_scaler,,,0.79305915648885,0.1841487406876042,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-66,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.8425560317492612,None,0.0,8,15,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.015276988502366128,median,none,,,,,fast_ica,,,,,,,,,,,parallel,cube,17,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-67,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,0.2638627266631576,0.022360820165005443,auto,255,None,171,6,19,loss,1e-07,0.016209060012924756,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.008763072395677399,mean,standardize,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,78,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-68,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,925.3053743248586,False,True,1,squared_hinge,ovr,l2,0.02764481198395717,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.3850718018366796,most_frequent,quantile_transformer,786,uniform,,,feature_agglomeration,,,,,,,,,,,,,,,cosine,average,156,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-69,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.8278163313806293,None,0.0,3,5,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,robust_scaler,,,0.725835793765774,0.2571431961341018,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.07784020380291123,fdr,f_classif
-70,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0023391956112474004,False,True,squared_hinge,0.011792446147470903,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.08295440990272292,most_frequent,quantile_transformer,962,normal,,,kernel_pca,,,,,,,,,,,,,,,,,,,,,,cosine,1778,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-71,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.41019363453549706,None,0.0,15,2,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.0003507389224306018,median,none,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.20614098085343638,fdr,chi2
-72,none,adaboost,SAMME,0.1280458968526125,7,125,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,robust_scaler,,,0.7156460334356771,0.2981902862926971,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,65.16171885308417,chi2,,,
-73,none,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.9931779675565967,None,0.0,18,17,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,robust_scaler,,,0.75,0.2945917010472818,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,
-74,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.01084024283715146,None,0.0,12,3,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,False,,,,,,,,,,,,
-75,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,5.576808764359008e-10,0.028224996367483435,auto,255,None,296,29,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,standardize,,,,,feature_agglomeration,,,,,,,,,,,,,,,cosine,complete,105,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-76,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7.975058275181761,True,True,squared_hinge,1.4887256000993565e-05,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,minmax,,,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.7747665215782399,False,True,1,squared_hinge,ovr,l1,3.926086263836518e-05,,,,,,,,,,,,,,,,,,,,,,
-77,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.34936025988397124,None,0.0,2,13,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00018601309647406987,median,robust_scaler,,,0.8634309348843472,0.26976989465367607,extra_trees_preproc_for_classification,False,entropy,None,0.48025175288975036,None,0.0,6,17,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-78,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,2.925599188129995e-05,0.12560285030651847,auto,255,None,300,193,17,loss,1e-07,0.039442997915546514,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00299400754997757,mean,robust_scaler,,,0.7093292916146515,0.009366983030047793,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-79,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.933900673392165,None,0.0,7,4,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,minmax,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-80,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1661.2274747409913,,,0.025821208852387936,rbf,-1,False,0.0017011435104921824,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,none,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,306,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-81,weighting,adaboost,SAMME,0.08979115391186342,1,74,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,minmax,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4922034012869809,fpr,f_classif
-82,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,21.85207494001697,0.18872999589786676,4,1.1863646021868761,poly,-1,False,0.0021338371901294443,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0034080852834770217,mean,normalize,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.11326005109886934,fdr,f_classif
-83,weighting,adaboost,SAMME,0.38341570193537416,9,250,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,minmax,,,,,feature_agglomeration,,,,,,,,,,,,,,,euclidean,average,13,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-84,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,3.205350717972069e-06,0.12860421150351484,auto,255,None,10,99,11,loss,1e-07,0.04653414943059845,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.012276606117657,median,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
-85,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.9499894634982698,None,0.0,20,15,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.01161399161402058,mean,normalize,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,42.76696665279698,f_classif,,,
-86,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,1,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.019797877010737826,mean,normalize,,,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.8239678741128165,False,,,,,,,,,,,,,,,
-87,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.46057831591617715,False,True,hinge,0.04557857428827514,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00027457445401600137,median,standardize,,,,,extra_trees_preproc_for_classification,True,gini,None,0.48190346970486964,None,0.0,17,18,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-88,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.46057831591617715,False,True,hinge,0.04557857428827514,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00027457445401600137,median,standardize,,,,,extra_trees_preproc_for_classification,True,gini,None,0.48190346970486964,None,0.0,17,18,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-89,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,64,auto,,0.00021332620906594865,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.026032882800756493,median,standardize,,,,,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,-0.9513223792709335,,0.00029881582960753004,sigmoid,543,,,,,,,,,,,,,,,,,
-90,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.26356754187780895,None,0.0,4,3,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010966178401758774,mean,robust_scaler,,,0.7817043861798886,0.24371720112407158,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
-91,none,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.563056219822946,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.32793677336996485,most_frequent,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-92,weighting,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.9990376970503236,None,0.0,11,8,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,normalize,,,,,feature_agglomeration,,,,,,,,,,,,,,,cosine,complete,109,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-93,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.22879311080499845,None,0.0,8,4,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.014320022547615783,median,minmax,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.238630703440461,mutual_info,,,
-94,weighting,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,2,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0073667222552306465,median,robust_scaler,,,0.701595034693991,0.29514640404971515,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6066051105210388,False,,,,,,,,,,,,,,,
-95,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.107967527639527e-05,True,True,hinge,0.00448294745544775,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,quantile_transformer,953,normal,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,35.00114345405331,chi2,,,
-96,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.14148638702297855,None,0.0,2,6,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.0038499650338378643,most_frequent,quantile_transformer,1759,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-97,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,1.1522414705935405e-10,0.6132800183073315,auto,255,None,1607,67,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010096911249562407,mean,robust_scaler,,,0.7663493326967942,0.136094217337434,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-98,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1228.3897711257332,0.6862670093933454,5,1.113863025113022,poly,-1,True,1.9271116749907707e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.018161834200128672,mean,normalize,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.06278549919408385,fpr,chi2
-99,none,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.4093238036872129,None,0.0,1,9,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.008254106845490612,most_frequent,robust_scaler,,,0.7823045687170416,0.23189540562406433,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,6,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-100,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.9815182201168626,None,0.0,15,19,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,robust_scaler,,,0.8878662977383482,0.25666364929970126,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,44.15748219826322,mutual_info,,,
-101,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2574971638664341,True,True,hinge,1.8027411099296446e-05,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.00536396328125103,median,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,
-102,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.7613982255598998,None,0.0,3,19,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010127721455862085,mean,standardize,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,340,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-103,none,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,128,manual,0.8574560771111307,0.010886139381599514,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.24115007944216788,most_frequent,normalize,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,62.26514170991452,mutual_info,,,
-104,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.6282663389679584,None,0.0,1,3,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,median,minmax,,,,,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-105,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,8.14394583618027e-09,0.1314274861878701,auto,255,None,27,15,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.3598067419611515,mean,robust_scaler,,,0.7494615433236279,0.260649229829574,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
-106,none,adaboost,SAMME.R,0.9261146078592859,9,476,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.012453052108117785,median,robust_scaler,,,0.7492076537583867,0.25853088636132765,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,6,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-107,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,11.046854203258679,,,0.07809571976769347,rbf,-1,False,0.00036193159909053734,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.006445557635410205,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-108,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.6728827282646637,None,0.0,18,8,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,robust_scaler,,,0.8512985751386942,0.14092824896182807,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,
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-111,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6427543483503397,True,True,hinge,0.00045318775930916944,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.009338484332306904,median,quantile_transformer,174,normal,,,kernel_pca,,,,,,,,,,,,,,,,,,,,,,cosine,1848,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-112,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.02021502857757827,True,,0.055848937053280076,True,,constant,log,l2,,0.00016356162584832537,no_encoding,no_coalescense,,median,quantile_transformer,27,normal,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.11873149622606233,fwe,chi2
-113,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,9.029473608568458e-09,0.15779990576964315,auto,255,None,37,71,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.018093177195644097,mean,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,False,,,,,,,,,,,,
-114,weighting,adaboost,SAMME.R,0.23766684501868082,10,97,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.004737885672001703,median,none,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50.0,chi2,,,
-115,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.3747863651224668,True,True,hinge,0.00019106780876479388,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.002442315142014378,median,quantile_transformer,1850,uniform,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.570194774022733,f_classif,,,
-116,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.4864284879550203,None,0.0,1,4,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.008665854799630503,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-117,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.9370653361074645,None,0.0,5,18,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.006095662608922393,mean,quantile_transformer,1825,uniform,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.09335752950021645,fdr,f_classif
-118,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.6922752267318475,True,True,squared_hinge,0.06299408576730353,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,quantile_transformer,1742,uniform,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,107,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-119,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.44727252219812963,None,0.0,2,10,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,standardize,,,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l1,8.17533189014765e-05,,,,,,,,,,,,,,,,,,,,,,
-120,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.6047465690000334,None,0.0,7,19,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,quantile_transformer,924,uniform,,,extra_trees_preproc_for_classification,True,gini,None,0.47688457431800646,None,0.0,9,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-121,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.4210978663047845,None,0.0,5,6,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.006365988550833196,most_frequent,robust_scaler,,,0.8348891074186082,0.00974961635730845,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0695919708890599,fpr,f_classif
-122,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.922871100218626,False,True,squared_hinge,0.09957180737451943,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0016563186856411274,median,quantile_transformer,850,uniform,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.09123695282772847,fpr,chi2
-123,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.20612848067724507,False,True,1,squared_hinge,ovr,l2,0.00017832980955957004,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0007502816173508168,median,quantile_transformer,1083,uniform,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.02820386332530596,fwe,f_classif
-124,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1561.1002600303584,,,0.0024048551704194507,rbf,-1,False,0.0001936214667496851,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,minmax,,,,,fast_ica,,,,,,,,,,,parallel,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-125,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.087313242780732,0.0636709914987994,3,0.004481917125024585,poly,-1,False,0.01848880618941367,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.002136015035740733,mean,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-126,weighting,adaboost,SAMME.R,1.696588612003168,9,455,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,minmax,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,82.18726095926662,chi2,,,
-127,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.6466927589470762,None,0.0,11,2,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.009672465311659264,most_frequent,standardize,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,369,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+1,none,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1514.059969351301,False,True,1,squared_hinge,ovr,l2,0.002448814309966599,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,standardize,,,,,extra_trees_preproc_for_classification,True,entropy,None,0.8221481751476923,None,0.0,2,3,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+2,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.8739761373635383,None,0.0,5,9,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,quantile_transformer,1135,uniform,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.06130526963953546,fdr,f_classif
+8,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,114,manual,0.11778631707605758,0.0029112721339713465,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.015582195257240751,median,normalize,,,,,kernel_pca,,,,,,,,,,,,,,,,,,,0.42909623253327767,3,0.25407612000343777,poly,1689,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+9,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.685918004243461,True,True,squared_hinge,1.7784504877380735e-05,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010335606354842895,most_frequent,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,
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+13,weighting,adaboost,SAMME,0.17983222024694556,4,357,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0008898781197407956,mean,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+15,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.21772092350860472,None,0.0,14,17,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.0020488212891693275,median,standardize,,,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,33.74257606530722,False,True,1,squared_hinge,ovr,l1,8.740452966205111e-05,,,,,,,,,,,,,,,,,,,,,,
+21,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,378.090682455998,,,1.5153998334214391,rbf,-1,False,4.744451572262126e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,minmax,,,,,extra_trees_preproc_for_classification,False,gini,None,0.4326333081739829,None,0.0,13,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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+29,weighting,bernoulli_nb,,,,,3.227246427900751,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,none,,,,,fast_ica,,,,,,,,,,,parallel,exp,788,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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+37,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,161,auto,,0.005011915149502186,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.02713788966372092,median,minmax,,,,,kitchen_sinks,,,,,,,,,,,,,,,,,,,,,,,,0.07896167589979168,2189,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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+43,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.9479098792950027,None,0.0,12,14,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,quantile_transformer,1758,normal,,,feature_agglomeration,,,,,,,,,,,,,,,cosine,complete,244,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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+63,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.957787813103507e-05,False,True,squared_hinge,0.046147943721042806,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.4778210360579751,mean,quantile_transformer,315,uniform,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,1315.2870319104447,False,True,1,squared_hinge,ovr,l1,0.030203490583956487,,,,,,,,,,,,,,,,,,,,,,
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+68,weighting,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,minmax,,,,,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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+434,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.4438263804770306,None,0.0,2,10,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,mean,robust_scaler,,,0.9824945141547571,0.24967364012300372,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+438,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,6.090138036399552e-10,0.017305568433437588,auto,255,None,202,188,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,robust_scaler,,,0.7526196011313747,0.1563616005702636,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+443,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.6269076084367177,None,0.0,1,7,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.12943250859943486,most_frequent,standardize,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,62.84526554323969,chi2,,,
+449,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,2.074711735072034e-07,0.01106799034691714,auto,255,None,13,24,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.020949205931423413,mean,standardize,,,,,extra_trees_preproc_for_classification,True,gini,None,0.43505400875624267,None,0.0,20,8,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+456,none,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.772617001926524e-05,False,,0.00531060153046999,True,,constant,log,l1,,0.00016225369247634786,one_hot_encoding,no_coalescense,,median,quantile_transformer,745,uniform,,,extra_trees_preproc_for_classification,False,entropy,None,0.6017654648980271,None,0.0,17,9,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+461,none,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.9796199105973851,None,0.0,2,13,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.00845950033601661,mean,robust_scaler,,,0.8275228560550734,0.23814615163106348,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
diff --git a/autosklearn/metalearning/files/roc_auc_binary.classification_dense/description.txt b/autosklearn/metalearning/files/roc_auc_binary.classification_dense/description.txt
index ff856cb9a4..27f7a52b3f 100644
--- a/autosklearn/metalearning/files/roc_auc_binary.classification_dense/description.txt
+++ b/autosklearn/metalearning/files/roc_auc_binary.classification_dense/description.txt
@@ -1,8 +1,9 @@
 features_cutoff_time: 3600
 features_cutoff_memory: 3072
-number_of_feature_steps: 35
+number_of_feature_steps: 42
 feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances
 feature_step LogNumberOfInstances: LogNumberOfInstances
+feature_step NumberOfClasses: NumberOfClasses
 feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures
 feature_step LogNumberOfFeatures: LogNumberOfFeatures
 feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues
@@ -20,6 +21,11 @@ feature_step DatasetRatio: DatasetRatio, LogDatasetRatio
 feature_step LogDatasetRatio: LogDatasetRatio
 feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio
 feature_step LogInverseDatasetRatio: LogInverseDatasetRatio
+feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD
+feature_step ClassProbabilityMin: ClassProbabilityMin
+feature_step ClassProbabilityMax: ClassProbabilityMax
+feature_step ClassProbabilityMean: ClassProbabilityMean
+feature_step ClassProbabilitySTD: ClassProbabilitySTD
 feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum
 feature_step SymbolsMin: SymbolsMin
 feature_step SymbolsMax: SymbolsMax
@@ -36,11 +42,12 @@ feature_step SkewnessMin: SkewnessMin
 feature_step SkewnessMax: SkewnessMax
 feature_step SkewnessMean: SkewnessMean
 feature_step SkewnessSTD: SkewnessSTD
-features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
+feature_step ClassEntropy: ClassEntropy
+features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy
 features_stochastic: 
-default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
+default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy
 
-algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127
+algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235,236,237,238,239,240,241,242,243,244,245,246,247,248,249,250,251,252,253,254,255,256,257,258,259,260,261,262,263,264,265,266,267,268,269,270,271,272,273,274,275,276,277,278,279,280,281,282,283,284,285,286,287,288,289,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,312,313,314,315,316,317,318,319,320,321,322,323,324,325,326,327,328,329,330,331,332,333,334,335,336,337,338,339,340,341,342,343,344,345,346,347,348,349,350,351,352,353,354,355,356,357,358,359,360,361,362,363,364,365,366,367,368,369,370,371,372,373,374,375,376,377,378,379,380,381,382,383,384,385,386,387,388,389,390,391,392,393,394,395,396,397,398,399,400,401,402,403,404,405,406,407,408,409,410,411,412,413,414,415,416,417,418,419,420,421,422,423,424,425,426,427,428,429,430,431,432,433,434,435,436,437,438,439,440,441,442,443,444,445,446,447,448,449,450,451,452,453,454,455,456,457,458,459,460,461,462,463,464
 algorithms_stochastic: 
 performance_measures: roc_auc
 performance_type: solution_quality
diff --git a/autosklearn/metalearning/files/roc_auc_binary.classification_dense/feature_costs.arff b/autosklearn/metalearning/files/roc_auc_binary.classification_dense/feature_costs.arff
index 0e17e20d22..8512fd9275 100644
--- a/autosklearn/metalearning/files/roc_auc_binary.classification_dense/feature_costs.arff
+++ b/autosklearn/metalearning/files/roc_auc_binary.classification_dense/feature_costs.arff
@@ -4,6 +4,7 @@
 @ATTRIBUTE repetition NUMERIC
 @ATTRIBUTE NumberOfInstances NUMERIC
 @ATTRIBUTE LogNumberOfInstances NUMERIC
+@ATTRIBUTE NumberOfClasses NUMERIC
 @ATTRIBUTE NumberOfFeatures NUMERIC
 @ATTRIBUTE LogNumberOfFeatures NUMERIC
 @ATTRIBUTE MissingValues NUMERIC
@@ -21,6 +22,11 @@
 @ATTRIBUTE LogDatasetRatio NUMERIC
 @ATTRIBUTE InverseDatasetRatio NUMERIC
 @ATTRIBUTE LogInverseDatasetRatio NUMERIC
+@ATTRIBUTE ClassOccurences NUMERIC
+@ATTRIBUTE ClassProbabilityMin NUMERIC
+@ATTRIBUTE ClassProbabilityMax NUMERIC
+@ATTRIBUTE ClassProbabilityMean NUMERIC
+@ATTRIBUTE ClassProbabilitySTD NUMERIC
 @ATTRIBUTE NumSymbols NUMERIC
 @ATTRIBUTE SymbolsMin NUMERIC
 @ATTRIBUTE SymbolsMax NUMERIC
@@ -37,162 +43,138 @@
 @ATTRIBUTE SkewnessMax NUMERIC
 @ATTRIBUTE SkewnessMean NUMERIC
 @ATTRIBUTE SkewnessSTD NUMERIC
+@ATTRIBUTE ClassEntropy NUMERIC
 
 @DATA
-75123,1.0,1e-05,1e-05,1e-05,1e-05,0.00253,0.00221,1e-05,0.00024,3e-05,0.00011,0.0,2e-05,2e-05,5e-05,8e-05,2e-05,1e-05,7e-05,1e-05,0.00072,1e-05,1e-05,0.00019,0.00041,0.00011,0.00061,0.00012,9e-05,0.00015,0.00025,0.00067,0.00014,0.00011,0.00016,0.00027
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-2123,1.0,1e-05,1e-05,1e-05,1e-05,0.00097,0.00079,1e-05,0.00013,1e-05,7e-05,0.0,2e-05,2e-05,5e-05,8e-05,2e-05,1e-05,5e-05,1e-05,0.0007,1e-05,0.0,0.00017,0.00042,0.0001,0.00052,6e-05,8e-05,0.00012,0.00026,0.00049,6e-05,0.00011,9e-05,0.00023
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-75101,1.0,1e-05,1e-05,1e-05,1e-05,0.0418,0.03546,1e-05,0.00389,1e-05,0.00248,0.0,2e-05,2e-05,4e-05,6e-05,2e-05,1e-05,5e-05,2e-05,0.00045,1e-05,0.0,0.00011,0.00023,0.0001,0.00041,6e-05,8e-05,0.0001,0.00018,0.00047,6e-05,0.0001,8e-05,0.00023
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-75187,1.0,1e-05,1e-05,2e-05,1e-05,0.00548,0.00473,1e-05,0.0005,2e-05,0.00029,1e-05,2e-05,2e-05,7e-05,9e-05,3e-05,1e-05,6e-05,1e-05,0.00062,1e-05,1e-05,0.00014,0.00033,0.00013,0.00038,7e-05,7e-05,8e-05,0.00016,0.00047,6e-05,9e-05,9e-05,0.00023
-266,1.0,1e-05,1e-05,1e-05,1e-05,0.0018,0.00146,1e-05,0.00023,2e-05,0.00015,2e-05,2e-05,2e-05,6e-05,8e-05,3e-05,1e-05,6e-05,1e-05,0.00059,1e-05,1e-05,0.00014,0.00031,0.00013,0.0004,5e-05,7e-05,8e-05,0.0002,0.00044,8e-05,8e-05,8e-05,0.0002
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-75217,1.0,1e-05,1e-05,2e-05,1e-05,0.00196,0.00158,1e-05,0.00026,2e-05,0.00016,1e-05,3e-05,3e-05,7e-05,0.0001,3e-05,1e-05,7e-05,2e-05,0.00089,1e-05,1e-05,0.00022,0.00053,0.00013,0.00074,9e-05,0.0001,0.00019,0.00036,0.00065,0.00013,0.0001,0.00011,0.00031
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diff --git a/autosklearn/metalearning/files/roc_auc_binary.classification_dense/feature_runstatus.arff b/autosklearn/metalearning/files/roc_auc_binary.classification_dense/feature_runstatus.arff
index 0034ad2bd6..5fff2dfed3 100644
--- a/autosklearn/metalearning/files/roc_auc_binary.classification_dense/feature_runstatus.arff
+++ b/autosklearn/metalearning/files/roc_auc_binary.classification_dense/feature_runstatus.arff
@@ -4,6 +4,7 @@
 @ATTRIBUTE repetition NUMERIC
 @ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other}
@@ -21,6 +22,11 @@
 @ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other}
@@ -37,162 +43,138 @@
 @ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other}
 
 @DATA
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diff --git a/autosklearn/metalearning/files/roc_auc_binary.classification_dense/feature_values.arff b/autosklearn/metalearning/files/roc_auc_binary.classification_dense/feature_values.arff
index a60400e3ce..736dc64321 100644
--- a/autosklearn/metalearning/files/roc_auc_binary.classification_dense/feature_values.arff
+++ b/autosklearn/metalearning/files/roc_auc_binary.classification_dense/feature_values.arff
@@ -13,7 +13,6 @@
 @ATTRIBUTE KurtosisMean NUMERIC
 @ATTRIBUTE KurtosisMin NUMERIC
 @ATTRIBUTE KurtosisSTD NUMERIC
-@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC
 @ATTRIBUTE LogDatasetRatio NUMERIC
 @ATTRIBUTE LogInverseDatasetRatio NUMERIC
 @ATTRIBUTE LogNumberOfFeatures NUMERIC
@@ -42,160 +41,135 @@
 @ATTRIBUTE SymbolsSum NUMERIC
 
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diff --git a/autosklearn/metalearning/files/roc_auc_binary.classification_sparse/algorithm_runs.arff b/autosklearn/metalearning/files/roc_auc_binary.classification_sparse/algorithm_runs.arff
index 38dda6f0ca..c3d3504dc1 100644
--- a/autosklearn/metalearning/files/roc_auc_binary.classification_sparse/algorithm_runs.arff
+++ b/autosklearn/metalearning/files/roc_auc_binary.classification_sparse/algorithm_runs.arff
@@ -7,95 +7,133 @@
 @ATTRIBUTE runstatus {ok, timeout, memout, not_applicable, crash, other}
 
 @DATA
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diff --git a/autosklearn/metalearning/files/roc_auc_binary.classification_sparse/configurations.csv b/autosklearn/metalearning/files/roc_auc_binary.classification_sparse/configurations.csv
index 40a9ae0202..0f2a8ecd9d 100644
--- a/autosklearn/metalearning/files/roc_auc_binary.classification_sparse/configurations.csv
+++ b/autosklearn/metalearning/files/roc_auc_binary.classification_sparse/configurations.csv
@@ -1,93 +1,131 @@
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-90,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4834106677905965,False,True,1,squared_hinge,ovr,l2,0.00017625027586147415,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0009157492873444966,most_frequent,quantile_transformer,1150,normal,,,extra_trees_preproc_for_classification,False,entropy,None,0.4981700453792278,None,0.0,9,15,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-91,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.087313242780732,0.0636709914987994,3,0.004481917125024585,poly,-1,False,0.01848880618941367,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.002136015035740733,mean,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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+274,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.513560345508016,None,0.0,3,2,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,median,quantile_transformer,1044,uniform,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.07550342883878675,,mutual_info_classif,
+277,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,57,2,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+280,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.2318705830210126,True,True,hinge,0.0002994421772278671,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.13310408167721913,most_frequent,quantile_transformer,1036,uniform,,,kernel_pca,,,,,,,,,,,,,,cosine,703,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+284,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5609113236355627,None,0.0,2,2,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.47917908217902894,mean,quantile_transformer,673,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+288,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.5719833791553314,None,0.0,16,3,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.13804007452389966,mean,normalize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+291,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.11789257899273609,None,0.0,2,3,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.4082847014868829,median,robust_scaler,,,0.9073691418507908,0.1954896244813517,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,,
+294,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.7174117044245482,None,0.0,1,10,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,quantile_transformer,988,uniform,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+297,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,2,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.0014200038703931547,most_frequent,robust_scaler,,,0.7373450748248416,0.26419938540085264,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+298,weighting,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.9607287110710755,None,0.0,19,17,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,quantile_transformer,1000,normal,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,,
+300,none,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.7010137052424128,None,0.0,7,10,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.2761813291805052,most_frequent,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,,
+306,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2677516008848513,True,True,hinge,0.05752557410901496,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0010152857217605206,median,quantile_transformer,1000,uniform,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,51.60974796845029,chi2,,,,
+307,none,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,249,None,,0.0005516162390755682,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010000000000000004,mean,quantile_transformer,1350,uniform,,,kernel_pca,,,,,,,,,,,0.162692683181769,3,1.0,poly,487,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+312,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,60.6761602925822,,,0.038224035513907226,rbf,-1,False,0.0018214343969776353,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.421025538999516,median,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+317,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.4572679693563934,None,0.0,1,11,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,mean,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,,
+319,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5663133321395308,None,0.0,5,19,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0244551766287634,median,robust_scaler,,,0.75,0.21219212178051497,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,,
+321,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.5922009836550511,None,0.0,4,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.008308749517077297,median,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,,
+325,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8.380057363340162,False,True,hinge,0.0020653831067503525,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.05020677662128942,median,quantile_transformer,560,normal,,,kernel_pca,,,,,,,,,,,,,,cosine,891,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+328,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.7292011996224884,True,True,hinge,8.162763086383311e-05,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010337761588362885,median,quantile_transformer,877,uniform,,,kernel_pca,,,,,,,,,,,,,,cosine,1317,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+331,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.5,None,0.0,1,5,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.15244281596303316,mean,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+334,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.489205056074126,None,0.0,20,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0007633437565444919,most_frequent,robust_scaler,,,0.75,0.2540789187813349,extra_trees_preproc_for_classification,False,entropy,None,0.22062129922609008,None,0.0,19,6,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+338,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.37035966336010345,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.007136588202285817,mean,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+344,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.9039555800017817e-05,False,True,squared_hinge,0.0011536981768375681,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,quantile_transformer,1122,uniform,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,,
+348,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7717211744793793,None,0.0,3,17,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,median,robust_scaler,,,0.7426192562776642,0.2500337305337367,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+349,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.4438263804770306,None,0.0,2,10,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,mean,robust_scaler,,,0.9824945141547571,0.24967364012300372,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+352,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5988524108452234,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,mean,robust_scaler,,,0.75,0.26961884796358077,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+353,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.6269076084367177,None,0.0,1,7,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.12943250859943486,most_frequent,standardize,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,62.84526554323969,chi2,,,,
+358,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.7463662441763204,None,0.0,20,18,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0004507212632016269,mean,none,,,,,extra_trees_preproc_for_classification,True,gini,None,0.4279327206778048,None,0.0,3,9,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+363,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.1393173890820956,False,True,1,squared_hinge,ovr,l2,8.283470008521272e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.015152352014390857,mean,normalize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,False,,,,,,,,,,,,,
+365,none,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.772617001926524e-05,False,,0.00531060153046999,True,,constant,log,l1,,0.00016225369247634786,one_hot_encoding,no_coalescense,,median,quantile_transformer,745,uniform,,,extra_trees_preproc_for_classification,False,entropy,None,0.6017654648980271,None,0.0,17,9,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+369,none,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.9796199105973851,None,0.0,2,13,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.00845950033601661,mean,robust_scaler,,,0.8275228560550734,0.23814615163106348,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
diff --git a/autosklearn/metalearning/files/roc_auc_binary.classification_sparse/description.txt b/autosklearn/metalearning/files/roc_auc_binary.classification_sparse/description.txt
index 4d3a5c3ec1..0bd0fa165a 100644
--- a/autosklearn/metalearning/files/roc_auc_binary.classification_sparse/description.txt
+++ b/autosklearn/metalearning/files/roc_auc_binary.classification_sparse/description.txt
@@ -1,8 +1,9 @@
 features_cutoff_time: 3600
 features_cutoff_memory: 3072
-number_of_feature_steps: 35
+number_of_feature_steps: 42
 feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances
 feature_step LogNumberOfInstances: LogNumberOfInstances
+feature_step NumberOfClasses: NumberOfClasses
 feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures
 feature_step LogNumberOfFeatures: LogNumberOfFeatures
 feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues
@@ -20,6 +21,11 @@ feature_step DatasetRatio: DatasetRatio, LogDatasetRatio
 feature_step LogDatasetRatio: LogDatasetRatio
 feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio
 feature_step LogInverseDatasetRatio: LogInverseDatasetRatio
+feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD
+feature_step ClassProbabilityMin: ClassProbabilityMin
+feature_step ClassProbabilityMax: ClassProbabilityMax
+feature_step ClassProbabilityMean: ClassProbabilityMean
+feature_step ClassProbabilitySTD: ClassProbabilitySTD
 feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum
 feature_step SymbolsMin: SymbolsMin
 feature_step SymbolsMax: SymbolsMax
@@ -36,11 +42,12 @@ feature_step SkewnessMin: SkewnessMin
 feature_step SkewnessMax: SkewnessMax
 feature_step SkewnessMean: SkewnessMean
 feature_step SkewnessSTD: SkewnessSTD
-features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
+feature_step ClassEntropy: ClassEntropy
+features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy
 features_stochastic: 
-default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
+default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy
 
-algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92
+algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235,236,237,238,239,240,241,242,243,244,245,246,247,248,249,250,251,252,253,254,255,256,257,258,259,260,261,262,263,264,265,266,267,268,269,270,271,272,273,274,275,276,277,278,279,280,281,282,283,284,285,286,287,288,289,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,312,313,314,315,316,317,318,319,320,321,322,323,324,325,326,327,328,329,330,331,332,333,334,335,336,337,338,339,340,341,342,343,344,345,346,347,348,349,350,351,352,353,354,355,356,357,358,359,360,361,362,363,364,365,366,367,368,369,370,371,372
 algorithms_stochastic: 
 performance_measures: roc_auc
 performance_type: solution_quality
diff --git a/autosklearn/metalearning/files/roc_auc_binary.classification_sparse/feature_costs.arff b/autosklearn/metalearning/files/roc_auc_binary.classification_sparse/feature_costs.arff
index 0e17e20d22..8512fd9275 100644
--- a/autosklearn/metalearning/files/roc_auc_binary.classification_sparse/feature_costs.arff
+++ b/autosklearn/metalearning/files/roc_auc_binary.classification_sparse/feature_costs.arff
@@ -4,6 +4,7 @@
 @ATTRIBUTE repetition NUMERIC
 @ATTRIBUTE NumberOfInstances NUMERIC
 @ATTRIBUTE LogNumberOfInstances NUMERIC
+@ATTRIBUTE NumberOfClasses NUMERIC
 @ATTRIBUTE NumberOfFeatures NUMERIC
 @ATTRIBUTE LogNumberOfFeatures NUMERIC
 @ATTRIBUTE MissingValues NUMERIC
@@ -21,6 +22,11 @@
 @ATTRIBUTE LogDatasetRatio NUMERIC
 @ATTRIBUTE InverseDatasetRatio NUMERIC
 @ATTRIBUTE LogInverseDatasetRatio NUMERIC
+@ATTRIBUTE ClassOccurences NUMERIC
+@ATTRIBUTE ClassProbabilityMin NUMERIC
+@ATTRIBUTE ClassProbabilityMax NUMERIC
+@ATTRIBUTE ClassProbabilityMean NUMERIC
+@ATTRIBUTE ClassProbabilitySTD NUMERIC
 @ATTRIBUTE NumSymbols NUMERIC
 @ATTRIBUTE SymbolsMin NUMERIC
 @ATTRIBUTE SymbolsMax NUMERIC
@@ -37,162 +43,138 @@
 @ATTRIBUTE SkewnessMax NUMERIC
 @ATTRIBUTE SkewnessMean NUMERIC
 @ATTRIBUTE SkewnessSTD NUMERIC
+@ATTRIBUTE ClassEntropy NUMERIC
 
 @DATA
-75123,1.0,1e-05,1e-05,1e-05,1e-05,0.00253,0.00221,1e-05,0.00024,3e-05,0.00011,0.0,2e-05,2e-05,5e-05,8e-05,2e-05,1e-05,7e-05,1e-05,0.00072,1e-05,1e-05,0.00019,0.00041,0.00011,0.00061,0.00012,9e-05,0.00015,0.00025,0.00067,0.00014,0.00011,0.00016,0.00027
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+75235,1.0,1e-05,1e-05,0.0003,2e-05,1e-05,0.00461,0.00385,2e-05,0.00055,2e-05,0.00026,1e-05,3e-05,3e-05,0.0001,0.00011,3e-05,1e-05,5e-05,2e-05,0.00026,3e-05,1e-05,8e-05,0.00014,0.00075,1e-05,1e-05,0.0002,0.00043,0.00011,0.00127,0.00017,0.0002,0.00029,0.00061,0.0008,0.00015,0.00012,0.00016,0.00038,0.00242
+75236,1.0,1e-05,1e-05,0.0001,1e-05,1e-05,0.00218,0.00118,2e-05,0.00065,3e-05,0.00041,1e-05,3e-05,3e-05,9e-05,9e-05,4e-05,1e-05,7e-05,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.0006,1e-05,1e-05,0.00021,0.00032,6e-05,0.00043,7e-05,7e-05,9e-05,0.00019,0.00062,7e-05,8e-05,0.0001,0.00037,0.00048
+75237,1.0,2e-05,1e-05,0.00785,2e-05,1e-05,0.31031,0.27854,2e-05,0.01161,3e-05,0.02022,1e-05,4e-05,3e-05,9e-05,0.00024,6e-05,2e-05,9e-05,2e-05,0.00062,7e-05,3e-05,0.00017,0.00035,0.0008,1e-05,1e-05,0.00028,0.00043,8e-05,0.00043,8e-05,7e-05,9e-05,0.0002,0.00044,6e-05,8e-05,9e-05,0.00021,0.14028
+75239,1.0,1e-05,1e-05,7e-05,1e-05,1e-05,0.00105,0.00084,1e-05,0.00015,1e-05,8e-05,1e-05,2e-05,5e-05,4e-05,8e-05,2e-05,1e-05,3e-05,1e-05,0.00016,2e-05,1e-05,5e-05,9e-05,0.00049,0.0,0.0,0.00012,0.00026,0.00011,0.00046,0.00013,8e-05,8e-05,0.00018,0.00044,6e-05,9e-05,0.00011,0.00018,0.00065
+75240,1.0,2e-05,1e-05,0.00036,5e-05,4e-05,0.00845,0.00644,6e-05,0.00122,4e-05,0.00091,1e-05,4e-05,4e-05,9e-05,0.00013,5e-05,2e-05,9e-05,5e-05,0.00031,3e-05,1e-05,7e-05,0.0002,0.00073,3e-05,2e-05,0.00019,0.0003,0.00018,0.00055,9e-05,9e-05,0.00011,0.00025,0.0005,7e-05,8e-05,0.00015,0.0002,0.00365
+75243,1.0,1e-05,1e-05,0.00029,1e-05,1e-05,0.00461,0.00415,1e-05,0.00035,1e-05,0.00015,0.0,2e-05,2e-05,5e-05,7e-05,2e-05,1e-05,4e-05,1e-05,0.00018,2e-05,1e-05,4e-05,0.00011,0.00022,1e-05,1e-05,5e-05,0.00011,5e-05,0.0004,6e-05,6e-05,8e-05,0.0002,0.00039,6e-05,6e-05,8e-05,0.00019,0.00265
+75244,1.0,3e-05,2e-05,0.0004,3e-05,2e-05,0.0173,0.01298,4e-05,0.00267,4e-05,0.00174,1e-05,7e-05,9e-05,0.00024,0.00027,8e-05,2e-05,0.00013,3e-05,0.00045,7e-05,2e-05,0.00011,0.00025,0.00077,6e-05,2e-05,0.00021,0.00034,0.00013,0.00077,0.00014,0.00013,0.00026,0.00023,0.00044,7e-05,8e-05,0.0001,0.00019,0.00649
+75248,1.0,1e-05,1e-05,0.00017,1e-05,1e-05,0.00416,0.00305,1e-05,0.0006,2e-05,0.00054,1e-05,2e-05,2e-05,5e-05,7e-05,2e-05,1e-05,4e-05,1e-05,0.00017,2e-05,1e-05,4e-05,0.0001,0.00029,5e-05,1e-05,6e-05,0.00012,6e-05,0.00081,0.00011,0.00015,0.00016,0.00038,0.0005,7e-05,8e-05,0.00012,0.00024,0.00184
+75249,1.0,1e-05,1e-05,0.00011,1e-05,1e-05,0.00192,0.00153,1e-05,0.00025,1e-05,0.00017,1e-05,2e-05,2e-05,5e-05,7e-05,2e-05,1e-05,5e-05,3e-05,0.00014,2e-05,1e-05,3e-05,8e-05,0.00029,1e-05,3e-05,6e-05,0.00012,7e-05,0.00041,6e-05,8e-05,0.0001,0.00017,0.00043,6e-05,7e-05,9e-05,0.00019,0.00092
+75250,1.0,1e-05,1e-05,0.00375,1e-05,1e-05,0.04994,0.047,1e-05,0.00247,1e-05,0.00049,1e-05,2e-05,2e-05,6e-05,7e-05,2e-05,1e-05,3e-05,1e-05,0.00022,4e-05,1e-05,4e-05,0.00012,0.00041,0.0,1e-05,0.00011,0.00023,6e-05,0.00076,0.00012,0.00012,0.00019,0.00032,0.00086,0.00012,0.00013,0.00018,0.00043,0.04178
diff --git a/autosklearn/metalearning/files/roc_auc_binary.classification_sparse/feature_runstatus.arff b/autosklearn/metalearning/files/roc_auc_binary.classification_sparse/feature_runstatus.arff
index 0034ad2bd6..5fff2dfed3 100644
--- a/autosklearn/metalearning/files/roc_auc_binary.classification_sparse/feature_runstatus.arff
+++ b/autosklearn/metalearning/files/roc_auc_binary.classification_sparse/feature_runstatus.arff
@@ -4,6 +4,7 @@
 @ATTRIBUTE repetition NUMERIC
 @ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other}
@@ -21,6 +22,11 @@
 @ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other}
@@ -37,162 +43,138 @@
 @ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other}
 
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diff --git a/autosklearn/metalearning/files/roc_auc_binary.classification_sparse/feature_values.arff b/autosklearn/metalearning/files/roc_auc_binary.classification_sparse/feature_values.arff
index a60400e3ce..736dc64321 100644
--- a/autosklearn/metalearning/files/roc_auc_binary.classification_sparse/feature_values.arff
+++ b/autosklearn/metalearning/files/roc_auc_binary.classification_sparse/feature_values.arff
@@ -13,7 +13,6 @@
 @ATTRIBUTE KurtosisMean NUMERIC
 @ATTRIBUTE KurtosisMin NUMERIC
 @ATTRIBUTE KurtosisSTD NUMERIC
-@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC
 @ATTRIBUTE LogDatasetRatio NUMERIC
 @ATTRIBUTE LogInverseDatasetRatio NUMERIC
 @ATTRIBUTE LogNumberOfFeatures NUMERIC
@@ -42,160 +41,135 @@
 @ATTRIBUTE SymbolsSum NUMERIC
 
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+75230,1.0,6.61834612757962,0.014925373134328358,0.01,0.005597014925373134,0.0018660447388134309,0.05970149253731343,16.75,8.500021934509277,3.735363759100437,0.01982259750366211,2.361585237546769,-2.8183982582710754,2.8183982582710754,4.1588830833596715,6.977281341630747,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.0,0.0,0.0,0.0,0.0,2.3237264156341553,1.4173028748482466,0.3571271300315857,0.6533592091248933,0.0,0.0,0.0,0.0,0.0
+75231,1.0,6.620420668885037,0.013059701492537313,0.01,0.005597014925373134,0.0017703248006382775,0.05970149253731343,16.75,115.78911590576172,15.754332467913628,0.9003109931945801,18.061812803398542,-2.8183982582710754,2.8183982582710754,4.1588830833596715,6.977281341630747,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.0,0.0,0.0,0.0,0.0,8.885228157043457,3.1392695950344205,0.9206903576850891,1.4797961507751884,0.0,0.0,0.0,0.0,0.0
+75232,1.0,0.9201702027239149,0.6647807637906648,0.5,0.3352192362093352,0.16478076379066478,0.05799151343705799,17.24390243902439,585.4510498046875,41.48030315044416,-0.2529125213623047,95.17568446650067,-2.847458599192265,2.8474585991922647,3.713572066704308,6.561030665896573,0.0,2.0,41.0,0.0,707.0,0.0,0.0,41.0,0.0,0.0,0.0,0.0,0.0,23.23834228515625,3.832647103316537,-1.5003634691238403,4.508387764393873,0.0,0.0,0.0,0.0,0.0
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+75235,1.0,1.7067919449664923,0.4105579868708972,0.25,0.05579868708971553,0.1492903085996621,0.006564551422319475,152.33333333333334,13.76956558227539,2.346758103856697,-1.6145207422400454,4.524616106763882,-5.026071102226095,5.026071102226095,3.1780538303479458,8.204124932574041,0.0,4.0,24.0,0.0,3656.0,0.0,0.0,24.0,0.0,0.0,0.0,0.0,0.0,3.779214382171631,1.515954251983203,0.004355928860604763,1.0657977665170062,0.0,0.0,0.0,0.0,0.0
+75236,1.0,3.3208751934243548,0.10486891385767791,0.1,0.09363295880149813,0.003810297743328803,0.2397003745318352,4.171875,22.422517776489258,-0.9225467168815645,-1.9997756481170654,1.9352856443240884,-1.4283655750405777,1.4283655750405777,5.545177444479562,6.97354301952014,0.0,10.0,256.0,0.0,1068.0,0.0,0.0,256.0,0.0,0.0,0.0,0.0,0.0,4.94191312789917,0.8186543448100565,-0.6194983720779419,0.6381679017395948,0.0,0.0,0.0,0.0,0.0
+75237,1.0,0.7378736147990802,0.7918800894091566,0.5,0.20811991059084348,0.29188008940915655,1.8271625991997027e-05,54729.666666666664,-0.5975548261535093,-0.7926032911766772,-0.9140236377716064,0.13929334047064063,-10.910161193614513,10.910161193614513,1.0986122886681098,12.008773482282624,0.0,2.0,3.0,0.0,164189.0,0.0,0.0,3.0,0.0,0.0,0.0,0.0,0.0,-0.039879728108644485,-0.3348021370669206,-0.6223916411399841,0.2378660138627058,0.0,0.0,0.0,0.0,0.0
+75239,1.0,0.9443547030267275,0.6379707916986933,0.5,0.3620292083013067,0.1379707916986933,0.02536510376633359,39.42424242424242,30.96304702758789,5.229958094222913,-1.8830041885375977,9.159290860687747,-3.674380917046025,3.674380917046025,3.4965075614664802,7.170888478512505,0.0,2.0,33.0,0.0,1301.0,0.0,0.0,33.0,0.0,0.0,0.0,0.0,0.0,5.591684341430664,1.5151810998266393,-0.8763390183448792,1.68213778251707,0.0,0.0,0.0,0.0,0.0
+75240,1.0,0.9473321982231081,0.6342759940463534,0.5,0.3657240059536466,0.13427599404635338,0.01275781416117372,78.38333333333334,1911.103482572295,38.512800597597035,-3.0,145.76440092342054,-4.361611319722948,4.361611319722948,4.0943445622221,8.455955881945048,27.0,2.0,60.0,17.0,4703.0,4703.0,29447.0,33.0,0.2833333333333333,1.0,0.10435537600113402,0.8181818181818182,1.2222222222222223,43.144344696960424,3.913912678196276,-22.79380338813253,4.943976022970944,173.0,22.59259259259259,1.0,43.77997111758117,610.0
+75243,1.0,1.7118844784475225,0.33529140750979036,0.25,0.024648698456576825,0.13045500689907696,0.0009214466712739,1085.25,0.37898291200086787,-0.9656219973073414,-1.9999312246360998,0.7132002663612025,-6.989565654179703,6.989565654179703,2.0794415416798357,9.06900719585954,8.0,4.0,8.0,0.0,8682.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.5423951867147632,0.9342552225820131,-0.008293091335592482,0.40192683634075954,5.0,3.375,2.0,0.8569568250501305,27.0
+75244,1.0,0.33901826511567296,0.9370570367870401,0.5,0.06294296321295983,0.43705703678704017,0.009449881876476544,105.82142857142857,1180.200844451951,40.51648623113333,-3.0,132.43484378998235,-4.661753037398361,4.661753037398361,4.02535169073515,8.68710472813351,41.0,2.0,56.0,14.0,5926.0,5926.0,23694.0,15.0,0.25,1.0,0.0713984378766694,2.7333333333333334,0.36585365853658536,34.38314768097812,3.7769009011075547,-8.659484722748674,5.307279668054912,16.0,5.975609756097561,1.0,3.692363938923924,245.0
+75248,1.0,0.4971128374263667,0.8909272183449651,0.5,0.1090727816550349,0.39092721834496513,0.010967098703888335,91.18181818181819,5010.000199441565,104.23108191482348,-3.0,468.735807382562,-4.512855515163565,4.512855515163565,4.007333185232471,8.520188700396035,39.0,2.0,55.0,12.0,5015.0,4938.0,21515.0,16.0,0.21818181818181817,0.9846460618145564,0.07800235656666364,2.4375,0.41025641025641024,70.79548149028699,4.06998328231248,-50.04498375424548,9.467860234489164,14.0,5.769230769230769,1.0,3.407845687351005,225.0
+75249,1.0,0.3891675492147647,0.9236550632911392,0.5,0.07634493670886076,0.4236550632911392,0.011471518987341773,87.17241379310344,416.3357123975682,46.57141260912883,-3.0,84.69777944086748,-4.467887925280274,4.467887925280274,3.367295829986474,7.8351837552667485,22.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,7.0,0.2413793103448276,1.0,0.056048123090353556,3.142857142857143,0.3181818181818182,20.453256767506936,0.8884855226401125,-20.453256767506936,6.7466910388456185,5.0,2.0454545454545454,1.0,0.7056443043754556,45.0
+75250,1.0,3.992354626842552,0.146342438507591,0.04545454545454545,0.005796927628356971,0.03949259311161838,3.997881123004808e-05,25013.25,2.1476065760596077,1.4719570787187288,0.3045189380645752,0.6973345081454226,-10.127160963449944,10.127160963449944,1.3862943611198906,11.513455324569835,0.0,22.0,4.0,0.0,100053.0,0.0,0.0,4.0,0.0,0.0,0.0,0.0,0.0,1.2135857343673706,0.3996680751442909,-0.3896799087524414,0.7616029272290122,0.0,0.0,0.0,0.0,0.0
diff --git a/autosklearn/metalearning/files/roc_auc_multiclass.classification_dense/algorithm_runs.arff b/autosklearn/metalearning/files/roc_auc_multiclass.classification_dense/algorithm_runs.arff
index bc4b150759..079687669c 100644
--- a/autosklearn/metalearning/files/roc_auc_multiclass.classification_dense/algorithm_runs.arff
+++ b/autosklearn/metalearning/files/roc_auc_multiclass.classification_dense/algorithm_runs.arff
@@ -7,130 +7,135 @@
 @ATTRIBUTE runstatus {ok, timeout, memout, not_applicable, crash, other}
 
 @DATA
-75119,1.0,1,0.06314049586776849,ok
-75212,1.0,2,0.16776802017215897,ok
-252,1.0,3,?,not_applicable
-246,1.0,4,?,not_applicable
-75178,1.0,5,?,not_applicable
-75239,1.0,6,0.0,ok
-75171,1.0,7,0.085367576363505,ok
-75233,1.0,8,0.017730661245038193,ok
-248,1.0,9,?,not_applicable
-75231,1.0,10,?,not_applicable
-2123,1.0,11,?,not_applicable
-75196,1.0,12,0.00010402219140082103,ok
-75188,1.0,13,?,not_applicable
-75092,1.0,14,0.05392684238838086,ok
-75248,1.0,15,0.14433373101792535,ok
-75126,1.0,16,0.023670347116980572,ok
-75234,1.0,17,0.0024113922352499984,ok
-75150,1.0,18,0.2117404483293388,ok
-258,1.0,19,?,not_applicable
-75168,1.0,20,?,not_applicable
-75235,1.0,21,?,not_applicable
-75159,1.0,22,0.10926239932174653,ok
-244,1.0,23,?,not_applicable
-75221,1.0,24,?,not_applicable
-75219,1.0,25,0.0057392216673344265,ok
-75202,1.0,26,?,not_applicable
-3043,1.0,27,0.004649259057134425,ok
-75205,1.0,28,?,not_applicable
-75174,1.0,29,0.05163238469196885,ok
-288,1.0,30,?,not_applicable
-75250,1.0,31,?,not_applicable
-275,1.0,32,?,not_applicable
-75142,1.0,33,0.015037365399820257,ok
-75213,1.0,34,0.014899487583760251,ok
-75099,1.0,35,0.1453030700603516,ok
-75243,1.0,36,?,not_applicable
-75184,1.0,37,0.05189413820838129,ok
-75222,1.0,38,0.03970238095238099,ok
-75175,1.0,39,0.03519678530261172,ok
-233,1.0,40,5.0658561296823557e-05,ok
-75161,1.0,41,0.011699165921616905,ok
-75176,1.0,42,0.0009101691094206554,ok
-75090,1.0,43,?,not_applicable
-75114,1.0,44,0.01408065618591936,ok
-260,1.0,45,?,not_applicable
-236,1.0,46,?,not_applicable
-75141,1.0,47,0.0081607580609383,ok
-75107,1.0,48,0.14134705527275548,ok
-262,1.0,49,?,not_applicable
-75146,1.0,50,0.03916579176290613,ok
-75189,1.0,51,0.003230156186084243,ok
-2350,1.0,52,0.41965124135644105,ok
-253,1.0,53,?,not_applicable
-2122,1.0,54,?,not_applicable
-75110,1.0,55,?,not_applicable
-75249,1.0,56,0.0003071909392029504,ok
-75108,1.0,57,0.0,ok
-242,1.0,58,?,not_applicable
-75117,1.0,59,0.0615488847196165,ok
-75191,1.0,60,0.07897784609606961,ok
-75226,1.0,61,3.536062890940883e-06,ok
-261,1.0,62,0.21168393895666626,ok
-75236,1.0,63,?,not_applicable
-75095,1.0,64,0.007955151737153554,ok
-75148,1.0,65,0.05226054310909389,ok
-75093,1.0,66,0.252596209769244,ok
-75223,1.0,67,?,not_applicable
-75244,1.0,68,0.1053494919818827,ok
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-75197,1.0,70,?,not_applicable
-75127,1.0,71,0.27616944025774925,ok
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-75153,1.0,73,0.020757455431716698,ok
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-75215,1.0,75,0.0032187627971225208,ok
-75195,1.0,76,0.0,ok
-75207,1.0,77,?,not_applicable
-266,1.0,78,?,not_applicable
-75225,1.0,79,0.07323392554991537,ok
-75166,1.0,80,0.029769676311315885,ok
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-273,1.0,84,0.011504602571595579,ok
-75121,1.0,85,0.0,ok
-75098,1.0,86,?,not_applicable
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-75116,1.0,88,0.004569600731136103,ok
-75185,1.0,89,0.04990297944254485,ok
-2119,1.0,90,?,not_applicable
-75157,1.0,91,0.4186635220125786,ok
-75113,1.0,92,0.0005957883923985996,ok
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-75182,1.0,96,0.0543816257602735,ok
-251,1.0,97,?,not_applicable
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-75237,1.0,99,3.1735632675111702e-06,ok
-75125,1.0,100,0.02981286849525755,ok
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-75230,1.0,104,?,not_applicable
-75139,1.0,105,0.0006225063537530495,ok
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-75154,1.0,116,?,not_applicable
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-75172,1.0,122,?,not_applicable
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-75193,1.0,127,?,not_applicable
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+75177,1.0,13,0.0007976155493052417,ok
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+253,1.0,27,?,not_applicable
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+75124,1.0,395,0.08288524266086794,ok
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+75222,1.0,285,0.032916666666666594,ok
+75231,1.0,34,?,not_applicable
+75185,1.0,37,0.0492198320581817,ok
+2123,1.0,39,?,not_applicable
+75150,1.0,72,0.20365148738192596,ok
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+75196,1.0,45,0.00010402219140082103,ok
+75188,1.0,49,?,not_applicable
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+75184,1.0,156,0.05192249912367086,ok
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+258,1.0,73,?,not_applicable
+75166,1.0,281,0.027273444208829156,ok
+75168,1.0,77,?,not_applicable
+75148,1.0,276,0.05714372744025953,ok
+75235,1.0,81,?,not_applicable
+75159,1.0,85,0.07428995337007216,ok
+75146,1.0,88,0.03902390737018924,ok
+244,1.0,89,?,not_applicable
+75141,1.0,163,0.008309207869546142,ok
+75221,1.0,92,?,not_applicable
+75219,1.0,101,0.003017615815757768,ok
+75202,1.0,96,?,not_applicable
+3043,1.0,99,0.0007976155493052417,ok
+75205,1.0,103,?,not_applicable
+75174,1.0,106,0.046689314765498646,ok
+75250,1.0,111,?,not_applicable
+75179,1.0,238,0.10433997211841184,ok
+275,1.0,115,?,not_applicable
+242,1.0,116,?,not_applicable
+75207,1.0,117,?,not_applicable
+75142,1.0,118,0.01486433691665967,ok
+75099,1.0,171,0.13256035161374957,ok
+75243,1.0,126,?,not_applicable
+75175,1.0,253,0.03438767711211699,ok
+233,1.0,137,7.236937328125581e-05,ok
+75161,1.0,139,0.010965392944143892,ok
+75176,1.0,143,0.0008155640317971669,ok
+262,1.0,146,?,not_applicable
+75129,1.0,147,0.1410533311181258,ok
+261,1.0,221,0.20118938300756473,ok
+75114,1.0,152,0.01289587605377085,ok
+75093,1.0,230,0.24427512963129827,ok
+260,1.0,158,?,not_applicable
+236,1.0,160,?,not_applicable
+254,1.0,166,0.0,ok
+75107,1.0,167,0.13454992985733727,ok
+75181,1.0,175,?,not_applicable
+75189,1.0,187,0.0039052183844204835,ok
+75163,1.0,189,0.02211436849684123,ok
+2350,1.0,191,0.41524352522082464,ok
+2122,1.0,195,?,not_applicable
+75110,1.0,198,?,not_applicable
+75213,1.0,394,0.013125739061884079,ok
+75095,1.0,203,0.0069040205766888185,ok
+75108,1.0,207,0.0,ok
+75117,1.0,212,0.05331457160725439,ok
+75191,1.0,216,0.07801659888081602,ok
+75226,1.0,219,7.70861710237547e-05,ok
+75244,1.0,222,0.10109230957560011,ok
+75236,1.0,223,?,not_applicable
+75169,1.0,227,?,not_applicable
+75116,1.0,319,0.002741760438681573,ok
+75223,1.0,235,?,not_applicable
+75109,1.0,242,?,not_applicable
+75197,1.0,245,?,not_applicable
+75237,1.0,364,4.241221283507457e-06,ok
+248,1.0,250,?,not_applicable
+2119,1.0,254,?,not_applicable
+75127,1.0,255,0.2750824723734884,ok
+75153,1.0,263,0.022855656269682556,ok
+75195,1.0,271,2.2912493125026856e-08,ok
+266,1.0,277,?,not_applicable
+75225,1.0,279,0.06236780879864645,ok
+75100,1.0,286,0.12582626953884435,ok
+75132,1.0,293,0.27152591159294626,ok
+75210,1.0,298,0.0,ok
+273,1.0,300,0.01124981735827002,ok
+75133,1.0,303,0.03146345753569224,ok
+75121,1.0,304,0.0,ok
+75098,1.0,310,?,not_applicable
+75115,1.0,315,0.028559516364394355,ok
+75217,1.0,337,?,not_applicable
+75134,1.0,341,?,not_applicable
+75096,1.0,346,?,not_applicable
+75203,1.0,350,?,not_applicable
+75123,1.0,360,?,not_applicable
+75125,1.0,369,0.021532940271725254,ok
+2120,1.0,371,?,not_applicable
+75232,1.0,374,0.07496972588161899,ok
+75103,1.0,380,0.0010392963174684633,ok
+75230,1.0,386,?,not_applicable
+75240,1.0,399,0.007972654303551208,ok
+75198,1.0,404,?,not_applicable
+75201,1.0,407,?,not_applicable
+75105,1.0,415,0.16475344537756154,ok
+75154,1.0,420,?,not_applicable
+2117,1.0,429,0.08514254760590934,ok
+75156,1.0,430,0.13042017382504711,ok
+75097,1.0,434,0.14857355521175386,ok
+75101,1.0,438,0.1892655265868073,ok
+75172,1.0,443,?,not_applicable
+75106,1.0,449,0.2627889170037707,ok
+75120,1.0,456,0.05153374233128838,ok
+75193,1.0,461,?,not_applicable
diff --git a/autosklearn/metalearning/files/roc_auc_multiclass.classification_dense/configurations.csv b/autosklearn/metalearning/files/roc_auc_multiclass.classification_dense/configurations.csv
index 7c928e9c5b..bc2613dc18 100644
--- a/autosklearn/metalearning/files/roc_auc_multiclass.classification_dense/configurations.csv
+++ b/autosklearn/metalearning/files/roc_auc_multiclass.classification_dense/configurations.csv
@@ -1,128 +1,133 @@
 idx,balancing:strategy,classifier:__choice__,classifier:adaboost:algorithm,classifier:adaboost:learning_rate,classifier:adaboost:max_depth,classifier:adaboost:n_estimators,classifier:bernoulli_nb:alpha,classifier:bernoulli_nb:fit_prior,classifier:decision_tree:criterion,classifier:decision_tree:max_depth_factor,classifier:decision_tree:max_features,classifier:decision_tree:max_leaf_nodes,classifier:decision_tree:min_impurity_decrease,classifier:decision_tree:min_samples_leaf,classifier:decision_tree:min_samples_split,classifier:decision_tree:min_weight_fraction_leaf,classifier:extra_trees:bootstrap,classifier:extra_trees:criterion,classifier:extra_trees:max_depth,classifier:extra_trees:max_features,classifier:extra_trees:max_leaf_nodes,classifier:extra_trees:min_impurity_decrease,classifier:extra_trees:min_samples_leaf,classifier:extra_trees:min_samples_split,classifier:extra_trees:min_weight_fraction_leaf,classifier:gradient_boosting:early_stop,classifier:gradient_boosting:l2_regularization,classifier:gradient_boosting:learning_rate,classifier:gradient_boosting:loss,classifier:gradient_boosting:max_bins,classifier:gradient_boosting:max_depth,classifier:gradient_boosting:max_leaf_nodes,classifier:gradient_boosting:min_samples_leaf,classifier:gradient_boosting:n_iter_no_change,classifier:gradient_boosting:scoring,classifier:gradient_boosting:tol,classifier:gradient_boosting:validation_fraction,classifier:k_nearest_neighbors:n_neighbors,classifier:k_nearest_neighbors:p,classifier:k_nearest_neighbors:weights,classifier:lda:n_components,classifier:lda:shrinkage,classifier:lda:shrinkage_factor,classifier:lda:tol,classifier:liblinear_svc:C,classifier:liblinear_svc:dual,classifier:liblinear_svc:fit_intercept,classifier:liblinear_svc:intercept_scaling,classifier:liblinear_svc:loss,classifier:liblinear_svc:multi_class,classifier:liblinear_svc:penalty,classifier:liblinear_svc:tol,classifier:libsvm_svc:C,classifier:libsvm_svc:coef0,classifier:libsvm_svc:degree,classifier:libsvm_svc:gamma,classifier:libsvm_svc:kernel,classifier:libsvm_svc:max_iter,classifier:libsvm_svc:shrinking,classifier:libsvm_svc:tol,classifier:multinomial_nb:alpha,classifier:multinomial_nb:fit_prior,classifier:passive_aggressive:C,classifier:passive_aggressive:average,classifier:passive_aggressive:fit_intercept,classifier:passive_aggressive:loss,classifier:passive_aggressive:tol,classifier:qda:reg_param,classifier:random_forest:bootstrap,classifier:random_forest:criterion,classifier:random_forest:max_depth,classifier:random_forest:max_features,classifier:random_forest:max_leaf_nodes,classifier:random_forest:min_impurity_decrease,classifier:random_forest:min_samples_leaf,classifier:random_forest:min_samples_split,classifier:random_forest:min_weight_fraction_leaf,classifier:sgd:alpha,classifier:sgd:average,classifier:sgd:epsilon,classifier:sgd:eta0,classifier:sgd:fit_intercept,classifier:sgd:l1_ratio,classifier:sgd:learning_rate,classifier:sgd:loss,classifier:sgd:penalty,classifier:sgd:power_t,classifier:sgd:tol,data_preprocessing:categorical_transformer:categorical_encoding:__choice__,data_preprocessing:categorical_transformer:category_coalescence:__choice__,data_preprocessing:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction,data_preprocessing:numerical_transformer:imputation:strategy,data_preprocessing:numerical_transformer:rescaling:__choice__,data_preprocessing:numerical_transformer:rescaling:quantile_transformer:n_quantiles,data_preprocessing:numerical_transformer:rescaling:quantile_transformer:output_distribution,data_preprocessing:numerical_transformer:rescaling:robust_scaler:q_max,data_preprocessing:numerical_transformer:rescaling:robust_scaler:q_min,feature_preprocessor:__choice__,feature_preprocessor:extra_trees_preproc_for_classification:bootstrap,feature_preprocessor:extra_trees_preproc_for_classification:criterion,feature_preprocessor:extra_trees_preproc_for_classification:max_depth,feature_preprocessor:extra_trees_preproc_for_classification:max_features,feature_preprocessor:extra_trees_preproc_for_classification:max_leaf_nodes,feature_preprocessor:extra_trees_preproc_for_classification:min_impurity_decrease,feature_preprocessor:extra_trees_preproc_for_classification:min_samples_leaf,feature_preprocessor:extra_trees_preproc_for_classification:min_samples_split,feature_preprocessor:extra_trees_preproc_for_classification:min_weight_fraction_leaf,feature_preprocessor:extra_trees_preproc_for_classification:n_estimators,feature_preprocessor:fast_ica:algorithm,feature_preprocessor:fast_ica:fun,feature_preprocessor:fast_ica:n_components,feature_preprocessor:fast_ica:whiten,feature_preprocessor:feature_agglomeration:affinity,feature_preprocessor:feature_agglomeration:linkage,feature_preprocessor:feature_agglomeration:n_clusters,feature_preprocessor:feature_agglomeration:pooling_func,feature_preprocessor:kernel_pca:coef0,feature_preprocessor:kernel_pca:degree,feature_preprocessor:kernel_pca:gamma,feature_preprocessor:kernel_pca:kernel,feature_preprocessor:kernel_pca:n_components,feature_preprocessor:kitchen_sinks:gamma,feature_preprocessor:kitchen_sinks:n_components,feature_preprocessor:liblinear_svc_preprocessor:C,feature_preprocessor:liblinear_svc_preprocessor:dual,feature_preprocessor:liblinear_svc_preprocessor:fit_intercept,feature_preprocessor:liblinear_svc_preprocessor:intercept_scaling,feature_preprocessor:liblinear_svc_preprocessor:loss,feature_preprocessor:liblinear_svc_preprocessor:multi_class,feature_preprocessor:liblinear_svc_preprocessor:penalty,feature_preprocessor:liblinear_svc_preprocessor:tol,feature_preprocessor:nystroem_sampler:coef0,feature_preprocessor:nystroem_sampler:degree,feature_preprocessor:nystroem_sampler:gamma,feature_preprocessor:nystroem_sampler:kernel,feature_preprocessor:nystroem_sampler:n_components,feature_preprocessor:pca:keep_variance,feature_preprocessor:pca:whiten,feature_preprocessor:polynomial:degree,feature_preprocessor:polynomial:include_bias,feature_preprocessor:polynomial:interaction_only,feature_preprocessor:random_trees_embedding:bootstrap,feature_preprocessor:random_trees_embedding:max_depth,feature_preprocessor:random_trees_embedding:max_leaf_nodes,feature_preprocessor:random_trees_embedding:min_samples_leaf,feature_preprocessor:random_trees_embedding:min_samples_split,feature_preprocessor:random_trees_embedding:min_weight_fraction_leaf,feature_preprocessor:random_trees_embedding:n_estimators,feature_preprocessor:select_percentile_classification:percentile,feature_preprocessor:select_percentile_classification:score_func,feature_preprocessor:select_rates_classification:alpha,feature_preprocessor:select_rates_classification:mode,feature_preprocessor:select_rates_classification:score_func
-1,none,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,standardize,,,,,extra_trees_preproc_for_classification,False,entropy,None,0.8629651625821806,None,0.0,10,10,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-2,none,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.9525269450493825,None,0.0,4,20,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,standardize,,,,,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-3,none,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,188,None,,0.09976330267940166,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,none,,,,,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,-0.05439674489937307,2,0.051865468486041774,poly,727,,,,,,,,,,,,,,,,,
-4,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,23916.78290516781,False,True,1,squared_hinge,ovr,l2,0.0006655607726658531,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.008808715242823137,median,none,,,,,kernel_pca,,,,,,,,,,,,,,,,,,,0.2633153190620261,2,0.004770332714291669,poly,1483,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-5,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,2,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010091246322497477,median,none,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,95.37976833605768,mutual_info,,,
-6,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.933900673392165,None,0.0,7,4,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,minmax,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-7,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,0.9455840414651977,0.035051597802663065,auto,255,None,6,39,20,loss,1e-07,0.03762410007700571,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,robust_scaler,,,0.8083718526277422,0.24604827304064597,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,1377.6677844231401,False,True,1,squared_hinge,ovr,l1,0.0019190146350085739,,,,,,,,,,,,,,,,,,,,,,
-8,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5238814331135578,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,median,none,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,28,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-9,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1292.7448076998514,0.004489932344506231,2,1.3088036376373702,poly,-1,False,1.7943262139196537e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,minmax,,,,,feature_agglomeration,,,,,,,,,,,,,,,cosine,average,386,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-10,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.5244313211416313,None,0.0,1,2,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.454300236339658,most_frequent,robust_scaler,,,0.7858830918128734,0.2705174720960696,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-11,weighting,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0006294780694492238,most_frequent,standardize,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.063715750591364,fdr,chi2
-12,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.541827838541001,True,True,squared_hinge,0.0001129851063838815,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,standardize,,,,,extra_trees_preproc_for_classification,True,entropy,None,0.5533459595240017,None,0.0,18,4,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-13,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.25657910619102237,None,0.0,6,6,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.021086303324492208,most_frequent,quantile_transformer,1247,uniform,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.16021572815319468,fpr,chi2
-14,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6185424163558029,False,True,1,squared_hinge,ovr,l2,4.385767395120581e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010397452590017174,median,quantile_transformer,961,normal,,,feature_agglomeration,,,,,,,,,,,,,,,cosine,average,25,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-15,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.5890308372885505,None,0.0,19,10,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.02193054860417746,median,robust_scaler,,,0.75,0.25,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,87.97956633784086,f_classif,,,
-16,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.6931271368243409,None,0.0,11,10,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.362940004438799,most_frequent,robust_scaler,,,0.9177587796305063,0.17084038458597867,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,30.491514144623952,mutual_info,,,
-17,none,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.1769332447629957,mean,minmax,,,,,fast_ica,,,,,,,,,,,deflation,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-18,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,40,2,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,robust_scaler,,,0.8108128057144657,0.1573950644227126,fast_ica,,,,,,,,,,,parallel,exp,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-19,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,199,None,,0.003239674765949011,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,robust_scaler,,,0.7228447795482008,0.25,kernel_pca,,,,,,,,,,,,,,,,,,,0.09440148939740256,3,0.0008114095405154861,poly,1996,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-20,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.6039096182414764,None,0.0,2,20,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.1939609823704447,most_frequent,robust_scaler,,,0.7331327008874781,0.29747488162877694,extra_trees_preproc_for_classification,True,entropy,None,0.33401603690569853,None,0.0,4,4,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-21,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,2.5882079595129472e-05,0.36125906161027654,auto,255,None,341,119,16,loss,1e-07,0.2924550184445239,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.02338478303505337,most_frequent,robust_scaler,,,0.7866731278723753,0.29543475006822734,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,68,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-22,weighting,adaboost,SAMME.R,0.17707648730809677,1,58,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.009258091334065716,most_frequent,quantile_transformer,1249,normal,,,feature_agglomeration,,,,,,,,,,,,,,,cosine,average,151,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-23,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7725.740943204492,,,3.122840783722913,rbf,-1,True,1.4116065061807418e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.14280178218510184,most_frequent,none,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,27,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-24,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.21794354428393548,None,0.0,2,16,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0025451910134387575,median,quantile_transformer,1477,uniform,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
-25,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5010952533165973,None,0.0,1,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.005838375247601407,mean,normalize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,
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-34,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,22194.242946902177,0.45210849669021247,,0.0005669713702171556,sigmoid,-1,False,0.03542142266081451,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,quantile_transformer,291,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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-43,none,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0686374607394679,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,minmax,,,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.8490260132946401,False,,,,,,,,,,,,,,,
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-46,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,92.66144306787051,,,0.04654639302091653,rbf,-1,True,0.0022231544280610005,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.005194838694455963,mean,none,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,15,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-47,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0663383838629283,,,0.06227886986638805,rbf,-1,False,5.002833516846625e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0005633176938817242,mean,standardize,,,,,feature_agglomeration,,,,,,,,,,,,,,,cosine,complete,64,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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-58,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,151,None,,0.0003283777607180646,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.2510527431514227,mean,normalize,,,,,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,-0.96093182232312,,0.05762510964607661,sigmoid,2632,,,,,,,,,,,,,,,,,
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-64,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,168.83067109296323,,,4.3654257342094485e-05,rbf,-1,False,0.04136843105299338,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0013312674340867563,mean,robust_scaler,,,0.7949633765736044,0.27598937864544565,feature_agglomeration,,,,,,,,,,,,,,,euclidean,ward,58,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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-69,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.8278163313806293,None,0.0,3,5,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,robust_scaler,,,0.725835793765774,0.2571431961341018,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.07784020380291123,fdr,f_classif
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-72,none,adaboost,SAMME,0.1280458968526125,7,125,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,robust_scaler,,,0.7156460334356771,0.2981902862926971,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,65.16171885308417,chi2,,,
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-74,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.01084024283715146,None,0.0,12,3,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,False,,,,,,,,,,,,
-75,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,5.576808764359008e-10,0.028224996367483435,auto,255,None,296,29,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,standardize,,,,,feature_agglomeration,,,,,,,,,,,,,,,cosine,complete,105,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-76,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7.975058275181761,True,True,squared_hinge,1.4887256000993565e-05,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,minmax,,,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.7747665215782399,False,True,1,squared_hinge,ovr,l1,3.926086263836518e-05,,,,,,,,,,,,,,,,,,,,,,
-77,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.34936025988397124,None,0.0,2,13,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00018601309647406987,median,robust_scaler,,,0.8634309348843472,0.26976989465367607,extra_trees_preproc_for_classification,False,entropy,None,0.48025175288975036,None,0.0,6,17,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-78,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,2.925599188129995e-05,0.12560285030651847,auto,255,None,300,193,17,loss,1e-07,0.039442997915546514,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00299400754997757,mean,robust_scaler,,,0.7093292916146515,0.009366983030047793,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-79,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.933900673392165,None,0.0,7,4,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,minmax,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-80,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1661.2274747409913,,,0.025821208852387936,rbf,-1,False,0.0017011435104921824,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,none,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,306,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-81,weighting,adaboost,SAMME,0.08979115391186342,1,74,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,minmax,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4922034012869809,fpr,f_classif
-82,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,21.85207494001697,0.18872999589786676,4,1.1863646021868761,poly,-1,False,0.0021338371901294443,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0034080852834770217,mean,normalize,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.11326005109886934,fdr,f_classif
-83,weighting,adaboost,SAMME,0.38341570193537416,9,250,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,minmax,,,,,feature_agglomeration,,,,,,,,,,,,,,,euclidean,average,13,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-84,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,valid,3.205350717972069e-06,0.12860421150351484,auto,255,None,10,99,11,loss,1e-07,0.04653414943059845,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.012276606117657,median,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
-85,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.9499894634982698,None,0.0,20,15,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.01161399161402058,mean,normalize,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,42.76696665279698,f_classif,,,
-86,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,1,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.019797877010737826,mean,normalize,,,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.8239678741128165,False,,,,,,,,,,,,,,,
-87,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.46057831591617715,False,True,hinge,0.04557857428827514,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00027457445401600137,median,standardize,,,,,extra_trees_preproc_for_classification,True,gini,None,0.48190346970486964,None,0.0,17,18,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-88,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.46057831591617715,False,True,hinge,0.04557857428827514,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.00027457445401600137,median,standardize,,,,,extra_trees_preproc_for_classification,True,gini,None,0.48190346970486964,None,0.0,17,18,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-89,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,64,auto,,0.00021332620906594865,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.026032882800756493,median,standardize,,,,,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,-0.9513223792709335,,0.00029881582960753004,sigmoid,543,,,,,,,,,,,,,,,,,
-90,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.26356754187780895,None,0.0,4,3,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010966178401758774,mean,robust_scaler,,,0.7817043861798886,0.24371720112407158,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
-91,none,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.563056219822946,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.32793677336996485,most_frequent,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-92,weighting,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.9990376970503236,None,0.0,11,8,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,normalize,,,,,feature_agglomeration,,,,,,,,,,,,,,,cosine,complete,109,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-93,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.22879311080499845,None,0.0,8,4,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.014320022547615783,median,minmax,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.238630703440461,mutual_info,,,
-94,weighting,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,2,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0073667222552306465,median,robust_scaler,,,0.701595034693991,0.29514640404971515,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6066051105210388,False,,,,,,,,,,,,,,,
-95,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.107967527639527e-05,True,True,hinge,0.00448294745544775,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,quantile_transformer,953,normal,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,35.00114345405331,chi2,,,
-96,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.14148638702297855,None,0.0,2,6,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.0038499650338378643,most_frequent,quantile_transformer,1759,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-97,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,1.1522414705935405e-10,0.6132800183073315,auto,255,None,1607,67,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010096911249562407,mean,robust_scaler,,,0.7663493326967942,0.136094217337434,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-98,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1228.3897711257332,0.6862670093933454,5,1.113863025113022,poly,-1,True,1.9271116749907707e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.018161834200128672,mean,normalize,,,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.06278549919408385,fpr,chi2
-99,none,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.4093238036872129,None,0.0,1,9,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.008254106845490612,most_frequent,robust_scaler,,,0.7823045687170416,0.23189540562406433,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,6,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-100,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.9815182201168626,None,0.0,15,19,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,robust_scaler,,,0.8878662977383482,0.25666364929970126,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,44.15748219826322,mutual_info,,,
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-102,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.7613982255598998,None,0.0,3,19,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010127721455862085,mean,standardize,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,340,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-103,none,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,128,manual,0.8574560771111307,0.010886139381599514,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.24115007944216788,most_frequent,normalize,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,62.26514170991452,mutual_info,,,
-104,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.6282663389679584,None,0.0,1,3,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,median,minmax,,,,,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-105,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,8.14394583618027e-09,0.1314274861878701,auto,255,None,27,15,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.3598067419611515,mean,robust_scaler,,,0.7494615433236279,0.260649229829574,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
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-107,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,11.046854203258679,,,0.07809571976769347,rbf,-1,False,0.00036193159909053734,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.006445557635410205,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-108,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.6728827282646637,None,0.0,18,8,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,robust_scaler,,,0.8512985751386942,0.14092824896182807,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,
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-110,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.4111348710613201,None,0.0,16,8,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.01224348527546641,most_frequent,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,
-111,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6427543483503397,True,True,hinge,0.00045318775930916944,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.009338484332306904,median,quantile_transformer,174,normal,,,kernel_pca,,,,,,,,,,,,,,,,,,,,,,cosine,1848,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-112,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.02021502857757827,True,,0.055848937053280076,True,,constant,log,l2,,0.00016356162584832537,no_encoding,no_coalescense,,median,quantile_transformer,27,normal,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.11873149622606233,fwe,chi2
-113,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,9.029473608568458e-09,0.15779990576964315,auto,255,None,37,71,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.018093177195644097,mean,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,False,,,,,,,,,,,,
-114,weighting,adaboost,SAMME.R,0.23766684501868082,10,97,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.004737885672001703,median,none,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50.0,chi2,,,
-115,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.3747863651224668,True,True,hinge,0.00019106780876479388,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.002442315142014378,median,quantile_transformer,1850,uniform,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.570194774022733,f_classif,,,
-116,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.4864284879550203,None,0.0,1,4,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.008665854799630503,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-117,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.9370653361074645,None,0.0,5,18,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.006095662608922393,mean,quantile_transformer,1825,uniform,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.09335752950021645,fdr,f_classif
-118,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.6922752267318475,True,True,squared_hinge,0.06299408576730353,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,quantile_transformer,1742,uniform,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,107,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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-120,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.6047465690000334,None,0.0,7,19,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,quantile_transformer,924,uniform,,,extra_trees_preproc_for_classification,True,gini,None,0.47688457431800646,None,0.0,9,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-121,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.4210978663047845,None,0.0,5,6,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.006365988550833196,most_frequent,robust_scaler,,,0.8348891074186082,0.00974961635730845,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0695919708890599,fpr,f_classif
-122,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.922871100218626,False,True,squared_hinge,0.09957180737451943,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0016563186856411274,median,quantile_transformer,850,uniform,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.09123695282772847,fpr,chi2
-123,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.20612848067724507,False,True,1,squared_hinge,ovr,l2,0.00017832980955957004,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0007502816173508168,median,quantile_transformer,1083,uniform,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.02820386332530596,fwe,f_classif
-124,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1561.1002600303584,,,0.0024048551704194507,rbf,-1,False,0.0001936214667496851,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,minmax,,,,,fast_ica,,,,,,,,,,,parallel,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-125,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.087313242780732,0.0636709914987994,3,0.004481917125024585,poly,-1,False,0.01848880618941367,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.002136015035740733,mean,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-126,weighting,adaboost,SAMME.R,1.696588612003168,9,455,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,minmax,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,82.18726095926662,chi2,,,
-127,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.6466927589470762,None,0.0,11,2,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.009672465311659264,most_frequent,standardize,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,369,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+1,none,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1514.059969351301,False,True,1,squared_hinge,ovr,l2,0.002448814309966599,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,median,standardize,,,,,extra_trees_preproc_for_classification,True,entropy,None,0.8221481751476923,None,0.0,2,3,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+2,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.8739761373635383,None,0.0,5,9,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,most_frequent,quantile_transformer,1135,uniform,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.06130526963953546,fdr,f_classif
+8,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,114,manual,0.11778631707605758,0.0029112721339713465,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.015582195257240751,median,normalize,,,,,kernel_pca,,,,,,,,,,,,,,,,,,,0.42909623253327767,3,0.25407612000343777,poly,1689,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+9,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.685918004243461,True,True,squared_hinge,1.7784504877380735e-05,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010335606354842895,most_frequent,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,
+10,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.7396493120555564,None,0.0,1,18,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010000000000000004,median,robust_scaler,,,0.8908334215036855,0.08666907249785906,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,
+13,weighting,adaboost,SAMME,0.17983222024694556,4,357,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0008898781197407956,mean,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+15,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.21772092350860472,None,0.0,14,17,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.0020488212891693275,median,standardize,,,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,33.74257606530722,False,True,1,squared_hinge,ovr,l1,8.740452966205111e-05,,,,,,,,,,,,,,,,,,,,,,
+21,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,378.090682455998,,,1.5153998334214391,rbf,-1,False,4.744451572262126e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,minmax,,,,,extra_trees_preproc_for_classification,False,gini,None,0.4326333081739829,None,0.0,13,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+24,weighting,adaboost,SAMME.R,0.460612885139835,9,500,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.0033638377904512094,mean,quantile_transformer,1611,uniform,,,feature_agglomeration,,,,,,,,,,,,,,,euclidean,complete,201,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+27,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.41025844265119926,None,0.0,18,15,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,median,quantile_transformer,1911,normal,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,False,,,,,,,,,,,,
+29,weighting,bernoulli_nb,,,,,3.227246427900751,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,none,,,,,fast_ica,,,,,,,,,,,parallel,exp,788,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+32,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,101,auto,,1.553086088086431e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.01137059177001425,most_frequent,normalize,,,,,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,-0.021621751597165284,4,0.7468597264810062,poly,5078,,,,,,,,,,,,,,,,,
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+37,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,161,auto,,0.005011915149502186,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.02713788966372092,median,minmax,,,,,kitchen_sinks,,,,,,,,,,,,,,,,,,,,,,,,0.07896167589979168,2189,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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+43,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.9479098792950027,None,0.0,12,14,0.0,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,quantile_transformer,1758,normal,,,feature_agglomeration,,,,,,,,,,,,,,,cosine,complete,244,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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+63,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.957787813103507e-05,False,True,squared_hinge,0.046147943721042806,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.4778210360579751,mean,quantile_transformer,315,uniform,,,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,1315.2870319104447,False,True,1,squared_hinge,ovr,l1,0.030203490583956487,,,,,,,,,,,,,,,,,,,,,,
+64,weighting,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.5927254703947399,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.004193053494360822,median,none,,,,,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.5779022723643873,False,,,,,,,,,,,,,,,
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+68,weighting,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,minmax,,,,,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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+77,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.2309302197434343e-05,False,,0.006503403719704897,True,,constant,log,l1,,1.531583361000749e-05,one_hot_encoding,no_coalescense,,most_frequent,robust_scaler,,,0.7530789965606406,0.2430090971909385,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,cosine,1693,,,,,,,,,,,,,,,,,
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+89,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9.552100559095382e-05,True,,0.007209292257917831,True,,constant,log,l2,,1.70337957134729e-05,no_encoding,minority_coalescer,0.0182109481043058,most_frequent,none,,,,,kernel_pca,,,,,,,,,,,,,,,,,,,,,1.1223991702013292,rbf,152,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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+101,weighting,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7,2,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.11337911417375755,most_frequent,quantile_transformer,824,normal,,,fast_ica,,,,,,,,,,,deflation,cube,21,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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+360,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.5720469164289109,None,0.0,1,17,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.03310727058091163,median,robust_scaler,,,0.8680123006808074,0.024825227678742904,fast_ica,,,,,,,,,,,parallel,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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+371,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,4.198700105243736e-09,0.011070860038925674,auto,255,None,9,116,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010247554402833578,median,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,
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+380,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2677516008848513,True,True,hinge,0.05752557410901496,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0010152857217605206,median,quantile_transformer,1000,uniform,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,51.60974796845029,chi2,,,
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+394,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0025736560290786094,False,True,squared_hinge,0.0006196872282311157,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.042446796272035237,most_frequent,quantile_transformer,773,normal,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,89.84127958910369,f_classif,,,
+395,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.4572679693563934,None,0.0,1,11,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.010000000000000004,mean,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,
+397,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5663133321395308,None,0.0,5,19,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0244551766287634,median,robust_scaler,,,0.75,0.21219212178051497,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
+399,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.5922009836550511,None,0.0,4,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.008308749517077297,median,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
+404,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8.380057363340162,False,True,hinge,0.0020653831067503525,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.05020677662128942,median,quantile_transformer,560,normal,,,kernel_pca,,,,,,,,,,,,,,,,,,,,,,cosine,891,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+407,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.7292011996224884,True,True,hinge,8.162763086383311e-05,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.010337761588362885,median,quantile_transformer,877,uniform,,,kernel_pca,,,,,,,,,,,,,,,,,,,,,,cosine,1317,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+410,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1,6,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.01498658368871822,mean,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
+415,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.489205056074126,None,0.0,20,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0007633437565444919,most_frequent,robust_scaler,,,0.75,0.2540789187813349,extra_trees_preproc_for_classification,False,entropy,None,0.22062129922609008,None,0.0,19,6,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+420,weighting,adaboost,SAMME.R,0.05920795019389743,8,313,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,no_coalescense,,mean,standardize,,,,,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,104,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+429,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0002272680572529066,True,True,squared_hinge,0.02605444434617768,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,
+430,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5561457370812579,None,0.0,1,9,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.05979631185093126,mean,robust_scaler,,,0.7474358923293394,0.2037091020436292,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,48.16522288361584,f_classif,,,
+434,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.4438263804770306,None,0.0,2,10,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,mean,robust_scaler,,,0.9824945141547571,0.24967364012300372,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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+443,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.6269076084367177,None,0.0,1,7,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.12943250859943486,most_frequent,standardize,,,,,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,62.84526554323969,chi2,,,
+449,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,off,2.074711735072034e-07,0.01106799034691714,auto,255,None,13,24,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.020949205931423413,mean,standardize,,,,,extra_trees_preproc_for_classification,True,gini,None,0.43505400875624267,None,0.0,20,8,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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+461,none,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.9796199105973851,None,0.0,2,13,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.00845950033601661,mean,robust_scaler,,,0.8275228560550734,0.23814615163106348,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
diff --git a/autosklearn/metalearning/files/roc_auc_multiclass.classification_dense/description.txt b/autosklearn/metalearning/files/roc_auc_multiclass.classification_dense/description.txt
index ff856cb9a4..27f7a52b3f 100644
--- a/autosklearn/metalearning/files/roc_auc_multiclass.classification_dense/description.txt
+++ b/autosklearn/metalearning/files/roc_auc_multiclass.classification_dense/description.txt
@@ -1,8 +1,9 @@
 features_cutoff_time: 3600
 features_cutoff_memory: 3072
-number_of_feature_steps: 35
+number_of_feature_steps: 42
 feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances
 feature_step LogNumberOfInstances: LogNumberOfInstances
+feature_step NumberOfClasses: NumberOfClasses
 feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures
 feature_step LogNumberOfFeatures: LogNumberOfFeatures
 feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues
@@ -20,6 +21,11 @@ feature_step DatasetRatio: DatasetRatio, LogDatasetRatio
 feature_step LogDatasetRatio: LogDatasetRatio
 feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio
 feature_step LogInverseDatasetRatio: LogInverseDatasetRatio
+feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD
+feature_step ClassProbabilityMin: ClassProbabilityMin
+feature_step ClassProbabilityMax: ClassProbabilityMax
+feature_step ClassProbabilityMean: ClassProbabilityMean
+feature_step ClassProbabilitySTD: ClassProbabilitySTD
 feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum
 feature_step SymbolsMin: SymbolsMin
 feature_step SymbolsMax: SymbolsMax
@@ -36,11 +42,12 @@ feature_step SkewnessMin: SkewnessMin
 feature_step SkewnessMax: SkewnessMax
 feature_step SkewnessMean: SkewnessMean
 feature_step SkewnessSTD: SkewnessSTD
-features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
+feature_step ClassEntropy: ClassEntropy
+features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy
 features_stochastic: 
-default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
+default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy
 
-algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127
+algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235,236,237,238,239,240,241,242,243,244,245,246,247,248,249,250,251,252,253,254,255,256,257,258,259,260,261,262,263,264,265,266,267,268,269,270,271,272,273,274,275,276,277,278,279,280,281,282,283,284,285,286,287,288,289,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,312,313,314,315,316,317,318,319,320,321,322,323,324,325,326,327,328,329,330,331,332,333,334,335,336,337,338,339,340,341,342,343,344,345,346,347,348,349,350,351,352,353,354,355,356,357,358,359,360,361,362,363,364,365,366,367,368,369,370,371,372,373,374,375,376,377,378,379,380,381,382,383,384,385,386,387,388,389,390,391,392,393,394,395,396,397,398,399,400,401,402,403,404,405,406,407,408,409,410,411,412,413,414,415,416,417,418,419,420,421,422,423,424,425,426,427,428,429,430,431,432,433,434,435,436,437,438,439,440,441,442,443,444,445,446,447,448,449,450,451,452,453,454,455,456,457,458,459,460,461,462,463,464
 algorithms_stochastic: 
 performance_measures: roc_auc
 performance_type: solution_quality
diff --git a/autosklearn/metalearning/files/roc_auc_multiclass.classification_dense/feature_costs.arff b/autosklearn/metalearning/files/roc_auc_multiclass.classification_dense/feature_costs.arff
index 0e17e20d22..8512fd9275 100644
--- a/autosklearn/metalearning/files/roc_auc_multiclass.classification_dense/feature_costs.arff
+++ b/autosklearn/metalearning/files/roc_auc_multiclass.classification_dense/feature_costs.arff
@@ -4,6 +4,7 @@
 @ATTRIBUTE repetition NUMERIC
 @ATTRIBUTE NumberOfInstances NUMERIC
 @ATTRIBUTE LogNumberOfInstances NUMERIC
+@ATTRIBUTE NumberOfClasses NUMERIC
 @ATTRIBUTE NumberOfFeatures NUMERIC
 @ATTRIBUTE LogNumberOfFeatures NUMERIC
 @ATTRIBUTE MissingValues NUMERIC
@@ -21,6 +22,11 @@
 @ATTRIBUTE LogDatasetRatio NUMERIC
 @ATTRIBUTE InverseDatasetRatio NUMERIC
 @ATTRIBUTE LogInverseDatasetRatio NUMERIC
+@ATTRIBUTE ClassOccurences NUMERIC
+@ATTRIBUTE ClassProbabilityMin NUMERIC
+@ATTRIBUTE ClassProbabilityMax NUMERIC
+@ATTRIBUTE ClassProbabilityMean NUMERIC
+@ATTRIBUTE ClassProbabilitySTD NUMERIC
 @ATTRIBUTE NumSymbols NUMERIC
 @ATTRIBUTE SymbolsMin NUMERIC
 @ATTRIBUTE SymbolsMax NUMERIC
@@ -37,162 +43,138 @@
 @ATTRIBUTE SkewnessMax NUMERIC
 @ATTRIBUTE SkewnessMean NUMERIC
 @ATTRIBUTE SkewnessSTD NUMERIC
+@ATTRIBUTE ClassEntropy NUMERIC
 
 @DATA
-75123,1.0,1e-05,1e-05,1e-05,1e-05,0.00253,0.00221,1e-05,0.00024,3e-05,0.00011,0.0,2e-05,2e-05,5e-05,8e-05,2e-05,1e-05,7e-05,1e-05,0.00072,1e-05,1e-05,0.00019,0.00041,0.00011,0.00061,0.00012,9e-05,0.00015,0.00025,0.00067,0.00014,0.00011,0.00016,0.00027
-75090,1.0,1e-05,1e-05,2e-05,2e-05,0.00815,0.00368,7e-05,0.00228,9e-05,0.00235,0.0,5e-05,5e-05,0.0001,0.00013,0.00013,1e-05,0.00021,2e-05,0.00064,0.0,0.0,0.00014,0.00038,0.00011,0.00066,0.00011,0.00014,0.00014,0.00026,0.00066,0.00011,0.00013,0.00015,0.00027
-2123,1.0,1e-05,1e-05,1e-05,1e-05,0.00097,0.00079,1e-05,0.00013,1e-05,7e-05,0.0,2e-05,2e-05,5e-05,8e-05,2e-05,1e-05,5e-05,1e-05,0.0007,1e-05,0.0,0.00017,0.00042,0.0001,0.00052,6e-05,8e-05,0.00012,0.00026,0.00049,6e-05,0.00011,9e-05,0.00023
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-75217,1.0,1e-05,1e-05,2e-05,1e-05,0.00196,0.00158,1e-05,0.00026,2e-05,0.00016,1e-05,3e-05,3e-05,7e-05,0.0001,3e-05,1e-05,7e-05,2e-05,0.00089,1e-05,1e-05,0.00022,0.00053,0.00013,0.00074,9e-05,0.0001,0.00019,0.00036,0.00065,0.00013,0.0001,0.00011,0.00031
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-2117,1.0,1e-05,1e-05,1e-05,1e-05,0.01735,0.0154,1e-05,0.00141,1e-05,0.00057,0.0,1e-05,1e-05,3e-05,6e-05,2e-05,1e-05,5e-05,1e-05,0.0004,1e-05,0.0,8e-05,0.00021,0.0001,0.00065,0.00012,0.00011,0.00015,0.00026,0.00055,7e-05,0.00014,9e-05,0.00025
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-75103,1.0,1e-05,1e-05,1e-05,1e-05,0.01302,0.00819,3e-05,0.00234,3e-05,0.00255,0.0,2e-05,2e-05,6e-05,0.00012,5e-05,1e-05,0.00011,1e-05,0.0005,1e-05,0.0,9e-05,0.00029,0.00011,0.00054,8e-05,0.00011,0.00011,0.00024,0.00066,7e-05,0.00015,0.00011,0.00032
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-75157,1.0,1e-05,1e-05,1e-05,1e-05,0.00125,0.00106,1e-05,0.00015,2e-05,7e-05,0.0,2e-05,2e-05,5e-05,6e-05,2e-05,1e-05,4e-05,1e-05,0.00048,2e-05,0.0,0.0001,0.00026,0.0001,0.00039,6e-05,7e-05,8e-05,0.00019,0.00043,6e-05,9e-05,8e-05,0.0002
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diff --git a/autosklearn/metalearning/files/roc_auc_multiclass.classification_dense/feature_runstatus.arff b/autosklearn/metalearning/files/roc_auc_multiclass.classification_dense/feature_runstatus.arff
index 0034ad2bd6..5fff2dfed3 100644
--- a/autosklearn/metalearning/files/roc_auc_multiclass.classification_dense/feature_runstatus.arff
+++ b/autosklearn/metalearning/files/roc_auc_multiclass.classification_dense/feature_runstatus.arff
@@ -4,6 +4,7 @@
 @ATTRIBUTE repetition NUMERIC
 @ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other}
@@ -21,6 +22,11 @@
 @ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other}
@@ -37,162 +43,138 @@
 @ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other}
 
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diff --git a/autosklearn/metalearning/files/roc_auc_multiclass.classification_dense/feature_values.arff b/autosklearn/metalearning/files/roc_auc_multiclass.classification_dense/feature_values.arff
index a60400e3ce..736dc64321 100644
--- a/autosklearn/metalearning/files/roc_auc_multiclass.classification_dense/feature_values.arff
+++ b/autosklearn/metalearning/files/roc_auc_multiclass.classification_dense/feature_values.arff
@@ -13,7 +13,6 @@
 @ATTRIBUTE KurtosisMean NUMERIC
 @ATTRIBUTE KurtosisMin NUMERIC
 @ATTRIBUTE KurtosisSTD NUMERIC
-@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC
 @ATTRIBUTE LogDatasetRatio NUMERIC
 @ATTRIBUTE LogInverseDatasetRatio NUMERIC
 @ATTRIBUTE LogNumberOfFeatures NUMERIC
@@ -42,160 +41,135 @@
 @ATTRIBUTE SymbolsSum NUMERIC
 
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diff --git a/autosklearn/metalearning/files/roc_auc_multiclass.classification_sparse/algorithm_runs.arff b/autosklearn/metalearning/files/roc_auc_multiclass.classification_sparse/algorithm_runs.arff
index 38dda6f0ca..c3d3504dc1 100644
--- a/autosklearn/metalearning/files/roc_auc_multiclass.classification_sparse/algorithm_runs.arff
+++ b/autosklearn/metalearning/files/roc_auc_multiclass.classification_sparse/algorithm_runs.arff
@@ -7,95 +7,133 @@
 @ATTRIBUTE runstatus {ok, timeout, memout, not_applicable, crash, other}
 
 @DATA
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-75171,1.0,4,0.08579628952319207,ok
-248,1.0,5,?,not_applicable
-75231,1.0,6,?,not_applicable
-75196,1.0,7,0.00010402219140082103,ok
-75188,1.0,8,?,not_applicable
-75248,1.0,9,0.14520458662896019,ok
-75126,1.0,10,0.034783863751172706,ok
-75234,1.0,11,0.0067478736819510665,ok
-75150,1.0,12,0.22344212603975744,ok
-258,1.0,13,?,not_applicable
-75168,1.0,14,?,not_applicable
-75235,1.0,15,?,not_applicable
-244,1.0,16,?,not_applicable
-75221,1.0,17,?,not_applicable
-75219,1.0,18,0.0057392216673344265,ok
-75202,1.0,19,?,not_applicable
-3043,1.0,20,0.004649259057134425,ok
-75205,1.0,21,?,not_applicable
-75174,1.0,22,0.05163238469196885,ok
-275,1.0,23,?,not_applicable
-75213,1.0,24,0.014899487583760251,ok
-75099,1.0,25,0.17469168197323537,ok
-75184,1.0,26,0.08323225410390944,ok
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-233,1.0,28,5.0658561296823557e-05,ok
-75114,1.0,29,0.027944862155388495,ok
-236,1.0,30,?,not_applicable
-75141,1.0,31,0.008669241313410958,ok
-75107,1.0,32,0.14134705527275548,ok
-262,1.0,33,?,not_applicable
-75146,1.0,34,0.04598874123846386,ok
-75189,1.0,35,0.003230156186084243,ok
-2350,1.0,36,0.41965124135644105,ok
-75249,1.0,37,0.0003071909392029504,ok
-242,1.0,38,?,not_applicable
-75117,1.0,39,0.0615488847196165,ok
-75191,1.0,40,0.07897784609606961,ok
-261,1.0,41,0.21168393895666626,ok
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-75095,1.0,43,0.01223930752753244,ok
-75093,1.0,44,0.29719127067342743,ok
-75223,1.0,45,?,not_applicable
-75109,1.0,46,?,not_applicable
-75197,1.0,47,?,not_applicable
-75127,1.0,48,0.27616944025774925,ok
-75143,1.0,49,0.005653857287595931,ok
-75153,1.0,50,0.020757455431716698,ok
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-75215,1.0,52,0.004909969378587009,ok
-75195,1.0,53,1.5809620277362768e-06,ok
-75207,1.0,54,?,not_applicable
-75225,1.0,55,0.10215736040609136,ok
-75166,1.0,56,0.06468132787718406,ok
-75100,1.0,57,0.21144723539933108,ok
-75169,1.0,58,?,not_applicable
-75121,1.0,59,0.0,ok
-75098,1.0,60,?,not_applicable
-75115,1.0,61,0.06279966645820301,ok
-75116,1.0,62,0.004569600731136103,ok
-75185,1.0,63,0.05508280393670073,ok
-2119,1.0,64,?,not_applicable
-75157,1.0,65,0.45919025157232707,ok
-75113,1.0,66,0.0006330727234682421,ok
-75203,1.0,67,?,not_applicable
-75182,1.0,68,0.0543816257602735,ok
-251,1.0,69,?,not_applicable
-75123,1.0,70,?,not_applicable
-75125,1.0,71,0.03076134324532176,ok
-75232,1.0,72,0.08535466588382068,ok
-75103,1.0,73,0.0014877648490659867,ok
-75192,1.0,74,0.5175556703796814,ok
-75230,1.0,75,?,not_applicable
-75139,1.0,76,0.00123966217867999,ok
-75227,1.0,77,0.037206680825593086,ok
-2120,1.0,78,?,not_applicable
-75124,1.0,79,0.09452406839972327,ok
-75240,1.0,80,0.008759763486385852,ok
-75198,1.0,81,?,not_applicable
-75201,1.0,82,?,not_applicable
-75133,1.0,83,0.07049778663774142,ok
-75154,1.0,84,?,not_applicable
-75177,1.0,85,0.007272994416691159,ok
-2117,1.0,86,0.09297581704141133,ok
-75156,1.0,87,0.1365301400403729,ok
-75097,1.0,88,0.16544692942208472,ok
-75172,1.0,89,?,not_applicable
-75106,1.0,90,0.3188148296549189,ok
-75187,1.0,91,0.0012569524975303281,ok
-75120,1.0,92,0.16390593047034763,ok
+75192,1.0,1,0.48914415175333126,ok
+75119,1.0,3,0.037231404958677605,ok
+75212,1.0,319,0.17037648900095648,ok
+246,1.0,6,?,not_applicable
+252,1.0,7,?,not_applicable
+75178,1.0,8,?,not_applicable
+75177,1.0,11,0.0007976155493052417,ok
+75092,1.0,31,0.04980276134122297,ok
+75239,1.0,13,0.0,ok
+75215,1.0,220,0.003553397998912078,ok
+75171,1.0,17,0.08407705667336118,ok
+75227,1.0,20,0.038193399995746224,ok
+75233,1.0,73,0.01712363249104465,ok
+75182,1.0,284,0.05561145788299382,ok
+253,1.0,23,?,not_applicable
+75157,1.0,172,0.4265841194968554,ok
+75124,1.0,317,0.08288524266086794,ok
+75222,1.0,231,0.032916666666666705,ok
+75231,1.0,28,?,not_applicable
+75185,1.0,30,0.04999071532856669,ok
+2123,1.0,32,?,not_applicable
+75150,1.0,59,0.20495559001832797,ok
+75143,1.0,273,0.0010478482173009729,ok
+75196,1.0,37,0.00010402219140082103,ok
+75188,1.0,40,?,not_applicable
+75248,1.0,45,0.1405612507916869,ok
+75249,1.0,164,0.00027157459842586107,ok
+75113,1.0,50,0.00035914702581374414,ok
+75126,1.0,52,0.01998989680305996,ok
+251,1.0,53,?,not_applicable
+75184,1.0,122,0.05192249912367086,ok
+75234,1.0,56,0.004611494191194221,ok
+258,1.0,60,?,not_applicable
+75166,1.0,227,0.027273444208829156,ok
+75168,1.0,63,?,not_applicable
+75148,1.0,183,0.06679967711966373,ok
+75235,1.0,67,?,not_applicable
+75159,1.0,68,0.11615091140313694,ok
+244,1.0,71,?,not_applicable
+75141,1.0,129,0.008309207869546142,ok
+75221,1.0,74,?,not_applicable
+75219,1.0,82,0.00501200567492921,ok
+75202,1.0,77,?,not_applicable
+3043,1.0,80,0.0007976155493052417,ok
+75205,1.0,84,?,not_applicable
+75174,1.0,88,0.05050614765799111,ok
+288,1.0,89,?,not_applicable
+75250,1.0,90,?,not_applicable
+75179,1.0,363,0.10877419185936166,ok
+275,1.0,92,?,not_applicable
+75207,1.0,93,?,not_applicable
+75142,1.0,95,0.015018578376675529,ok
+75099,1.0,136,0.16903371818420365,ok
+75243,1.0,99,?,not_applicable
+75175,1.0,211,0.041282912611327105,ok
+233,1.0,107,7.236937328125581e-05,ok
+75161,1.0,109,0.011912347235347065,ok
+75176,1.0,110,0.0008155640317971669,ok
+262,1.0,113,?,not_applicable
+75129,1.0,272,0.14215401229440106,ok
+261,1.0,179,0.20118938300756473,ok
+75090,1.0,116,?,not_applicable
+75114,1.0,118,0.01289587605377085,ok
+75093,1.0,187,0.2537896163896557,ok
+260,1.0,124,?,not_applicable
+236,1.0,126,?,not_applicable
+254,1.0,132,0.0,ok
+75107,1.0,135,0.13495286025254738,ok
+75139,1.0,137,0.0006315469024589238,ok
+75146,1.0,139,0.04063053064162947,ok
+75189,1.0,145,0.0025315308375581935,ok
+75163,1.0,150,0.02211436849684123,ok
+2350,1.0,152,0.41524352522082464,ok
+2122,1.0,156,?,not_applicable
+75110,1.0,159,?,not_applicable
+75213,1.0,162,0.01986598344501389,ok
+75095,1.0,163,0.0069040205766888185,ok
+75108,1.0,168,0.0,ok
+75117,1.0,193,0.054773816968938904,ok
+75191,1.0,174,0.07801659888081602,ok
+75226,1.0,177,0.00012941990181059015,ok
+75244,1.0,298,0.10177688635272186,ok
+75236,1.0,181,?,not_applicable
+75169,1.0,184,?,not_applicable
+75116,1.0,256,0.003055920488947317,ok
+75223,1.0,189,?,not_applicable
+75109,1.0,196,?,not_applicable
+75197,1.0,199,?,not_applicable
+248,1.0,203,?,not_applicable
+2119,1.0,206,?,not_applicable
+75127,1.0,207,0.2750824723734884,ok
+75153,1.0,215,0.022855656269682556,ok
+75173,1.0,217,0.050985389610389786,ok
+75187,1.0,218,0.001194541081475875,ok
+75195,1.0,223,3.8951238356954576e-07,ok
+75225,1.0,226,0.06530245346869723,ok
+75100,1.0,232,0.15938253363403054,ok
+75132,1.0,237,0.30124427895247907,ok
+75210,1.0,239,0.0,ok
+273,1.0,241,0.011658021624780912,ok
+75133,1.0,243,0.03146345753569224,ok
+75121,1.0,244,0.0,ok
+75098,1.0,248,?,not_applicable
+75115,1.0,253,0.03695017719407956,ok
+266,1.0,258,?,not_applicable
+75134,1.0,274,?,not_applicable
+75096,1.0,277,?,not_applicable
+75203,1.0,280,?,not_applicable
+75123,1.0,288,?,not_applicable
+75237,1.0,291,4.241221283507457e-06,ok
+75125,1.0,294,0.028966931556011266,ok
+2120,1.0,297,?,not_applicable
+75232,1.0,300,0.07775861436277565,ok
+75103,1.0,306,0.0010392963174684633,ok
+242,1.0,307,?,not_applicable
+75230,1.0,312,?,not_applicable
+75240,1.0,321,0.007972654303551208,ok
+75198,1.0,325,?,not_applicable
+75201,1.0,328,?,not_applicable
+75112,1.0,331,0.05830771036972615,ok
+75105,1.0,334,0.16475344537756154,ok
+75154,1.0,338,?,not_applicable
+2117,1.0,344,0.085290931483747,ok
+75156,1.0,348,0.13308842519145536,ok
+75097,1.0,349,0.14857355521175386,ok
+75101,1.0,352,0.1980053598723278,ok
+75172,1.0,353,?,not_applicable
+75106,1.0,358,0.2669621656631299,ok
+75120,1.0,365,0.05153374233128838,ok
+75193,1.0,369,?,not_applicable
diff --git a/autosklearn/metalearning/files/roc_auc_multiclass.classification_sparse/configurations.csv b/autosklearn/metalearning/files/roc_auc_multiclass.classification_sparse/configurations.csv
index 40a9ae0202..0f2a8ecd9d 100644
--- a/autosklearn/metalearning/files/roc_auc_multiclass.classification_sparse/configurations.csv
+++ b/autosklearn/metalearning/files/roc_auc_multiclass.classification_sparse/configurations.csv
@@ -1,93 +1,131 @@
 idx,balancing:strategy,classifier:__choice__,classifier:adaboost:algorithm,classifier:adaboost:learning_rate,classifier:adaboost:max_depth,classifier:adaboost:n_estimators,classifier:bernoulli_nb:alpha,classifier:bernoulli_nb:fit_prior,classifier:decision_tree:criterion,classifier:decision_tree:max_depth_factor,classifier:decision_tree:max_features,classifier:decision_tree:max_leaf_nodes,classifier:decision_tree:min_impurity_decrease,classifier:decision_tree:min_samples_leaf,classifier:decision_tree:min_samples_split,classifier:decision_tree:min_weight_fraction_leaf,classifier:extra_trees:bootstrap,classifier:extra_trees:criterion,classifier:extra_trees:max_depth,classifier:extra_trees:max_features,classifier:extra_trees:max_leaf_nodes,classifier:extra_trees:min_impurity_decrease,classifier:extra_trees:min_samples_leaf,classifier:extra_trees:min_samples_split,classifier:extra_trees:min_weight_fraction_leaf,classifier:gradient_boosting:early_stop,classifier:gradient_boosting:l2_regularization,classifier:gradient_boosting:learning_rate,classifier:gradient_boosting:loss,classifier:gradient_boosting:max_bins,classifier:gradient_boosting:max_depth,classifier:gradient_boosting:max_leaf_nodes,classifier:gradient_boosting:min_samples_leaf,classifier:gradient_boosting:n_iter_no_change,classifier:gradient_boosting:scoring,classifier:gradient_boosting:tol,classifier:gradient_boosting:validation_fraction,classifier:k_nearest_neighbors:n_neighbors,classifier:k_nearest_neighbors:p,classifier:k_nearest_neighbors:weights,classifier:lda:n_components,classifier:lda:shrinkage,classifier:lda:shrinkage_factor,classifier:lda:tol,classifier:liblinear_svc:C,classifier:liblinear_svc:dual,classifier:liblinear_svc:fit_intercept,classifier:liblinear_svc:intercept_scaling,classifier:liblinear_svc:loss,classifier:liblinear_svc:multi_class,classifier:liblinear_svc:penalty,classifier:liblinear_svc:tol,classifier:libsvm_svc:C,classifier:libsvm_svc:coef0,classifier:libsvm_svc:degree,classifier:libsvm_svc:gamma,classifier:libsvm_svc:kernel,classifier:libsvm_svc:max_iter,classifier:libsvm_svc:shrinking,classifier:libsvm_svc:tol,classifier:multinomial_nb:alpha,classifier:multinomial_nb:fit_prior,classifier:passive_aggressive:C,classifier:passive_aggressive:average,classifier:passive_aggressive:fit_intercept,classifier:passive_aggressive:loss,classifier:passive_aggressive:tol,classifier:qda:reg_param,classifier:random_forest:bootstrap,classifier:random_forest:criterion,classifier:random_forest:max_depth,classifier:random_forest:max_features,classifier:random_forest:max_leaf_nodes,classifier:random_forest:min_impurity_decrease,classifier:random_forest:min_samples_leaf,classifier:random_forest:min_samples_split,classifier:random_forest:min_weight_fraction_leaf,classifier:sgd:alpha,classifier:sgd:average,classifier:sgd:epsilon,classifier:sgd:eta0,classifier:sgd:fit_intercept,classifier:sgd:l1_ratio,classifier:sgd:learning_rate,classifier:sgd:loss,classifier:sgd:penalty,classifier:sgd:power_t,classifier:sgd:tol,data_preprocessing:categorical_transformer:categorical_encoding:__choice__,data_preprocessing:categorical_transformer:category_coalescence:__choice__,data_preprocessing:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction,data_preprocessing:numerical_transformer:imputation:strategy,data_preprocessing:numerical_transformer:rescaling:__choice__,data_preprocessing:numerical_transformer:rescaling:quantile_transformer:n_quantiles,data_preprocessing:numerical_transformer:rescaling:quantile_transformer:output_distribution,data_preprocessing:numerical_transformer:rescaling:robust_scaler:q_max,data_preprocessing:numerical_transformer:rescaling:robust_scaler:q_min,feature_preprocessor:__choice__,feature_preprocessor:extra_trees_preproc_for_classification:bootstrap,feature_preprocessor:extra_trees_preproc_for_classification:criterion,feature_preprocessor:extra_trees_preproc_for_classification:max_depth,feature_preprocessor:extra_trees_preproc_for_classification:max_features,feature_preprocessor:extra_trees_preproc_for_classification:max_leaf_nodes,feature_preprocessor:extra_trees_preproc_for_classification:min_impurity_decrease,feature_preprocessor:extra_trees_preproc_for_classification:min_samples_leaf,feature_preprocessor:extra_trees_preproc_for_classification:min_samples_split,feature_preprocessor:extra_trees_preproc_for_classification:min_weight_fraction_leaf,feature_preprocessor:extra_trees_preproc_for_classification:n_estimators,feature_preprocessor:kernel_pca:coef0,feature_preprocessor:kernel_pca:degree,feature_preprocessor:kernel_pca:gamma,feature_preprocessor:kernel_pca:kernel,feature_preprocessor:kernel_pca:n_components,feature_preprocessor:kitchen_sinks:gamma,feature_preprocessor:kitchen_sinks:n_components,feature_preprocessor:liblinear_svc_preprocessor:C,feature_preprocessor:liblinear_svc_preprocessor:dual,feature_preprocessor:liblinear_svc_preprocessor:fit_intercept,feature_preprocessor:liblinear_svc_preprocessor:intercept_scaling,feature_preprocessor:liblinear_svc_preprocessor:loss,feature_preprocessor:liblinear_svc_preprocessor:multi_class,feature_preprocessor:liblinear_svc_preprocessor:penalty,feature_preprocessor:liblinear_svc_preprocessor:tol,feature_preprocessor:nystroem_sampler:coef0,feature_preprocessor:nystroem_sampler:degree,feature_preprocessor:nystroem_sampler:gamma,feature_preprocessor:nystroem_sampler:kernel,feature_preprocessor:nystroem_sampler:n_components,feature_preprocessor:polynomial:degree,feature_preprocessor:polynomial:include_bias,feature_preprocessor:polynomial:interaction_only,feature_preprocessor:random_trees_embedding:bootstrap,feature_preprocessor:random_trees_embedding:max_depth,feature_preprocessor:random_trees_embedding:max_leaf_nodes,feature_preprocessor:random_trees_embedding:min_samples_leaf,feature_preprocessor:random_trees_embedding:min_samples_split,feature_preprocessor:random_trees_embedding:min_weight_fraction_leaf,feature_preprocessor:random_trees_embedding:n_estimators,feature_preprocessor:select_percentile_classification:percentile,feature_preprocessor:select_percentile_classification:score_func,feature_preprocessor:select_rates_classification:alpha,feature_preprocessor:select_rates_classification:mode,feature_preprocessor:select_rates_classification:score_func,feature_preprocessor:truncatedSVD:target_dim
-1,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.3163020577549344,None,0.0,15,2,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.01322981552715978,most_frequent,robust_scaler,,,0.9746667009556722,0.1974876581478311,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-2,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,23916.78290516781,False,True,1,squared_hinge,ovr,l2,0.0006655607726658531,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.008808715242823137,median,none,,,,,kernel_pca,,,,,,,,,,,0.2633153190620261,2,0.004770332714291669,poly,1483,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-3,weighting,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,88,2,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.254263872179305,median,normalize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-4,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.8295237320761981,None,0.0,14,13,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.4912899096849949,most_frequent,robust_scaler,,,0.8602962469260254,0.1681311280324052,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l1,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,
-5,none,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00011009762574324903,True,,0.031641726094190054,True,,invscaling,hinge,l2,0.2208821470267501,0.007895311091089785,one_hot_encoding,minority_coalescer,0.13807502937372682,mean,quantile_transformer,1340,normal,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,,
-6,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,0.5244313211416313,None,0.0,1,2,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.454300236339658,most_frequent,robust_scaler,,,0.7858830918128734,0.2705174720960696,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-7,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.541827838541001,True,True,squared_hinge,0.0001129851063838815,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,most_frequent,standardize,,,,,extra_trees_preproc_for_classification,True,entropy,None,0.5533459595240017,None,0.0,18,4,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-8,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.25657910619102237,None,0.0,6,6,0.0,,,,,,,,,,,,no_encoding,minority_coalescer,0.021086303324492208,most_frequent,quantile_transformer,1247,uniform,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.16021572815319468,fpr,chi2,
-9,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,0.643761610990255,None,0.0,20,6,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.039859381630488695,mean,normalize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,,
-10,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.8253905225980761,None,0.0,18,20,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.003725255016700434,mean,robust_scaler,,,0.7701705977765594,0.2396520157991559,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.3579038776690903,False,True,1,squared_hinge,ovr,l1,4.994232534734023e-05,,,,,,,,,,,,,,,,,,,,,
-11,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.06263739023781334,None,0.0,18,8,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.02206379295100344,most_frequent,standardize,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,,
-12,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.07518858116896743,None,0.0,7,10,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0027133920607872518,mean,robust_scaler,,,0.7873452530915046,0.16215139072179952,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-13,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,199,None,,0.003239674765949011,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,mean,robust_scaler,,,0.7228447795482008,0.25,kernel_pca,,,,,,,,,,,0.09440148939740256,3,0.0008114095405154861,poly,1996,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-14,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.6039096182414764,None,0.0,2,20,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.1939609823704447,most_frequent,robust_scaler,,,0.7331327008874781,0.29747488162877694,extra_trees_preproc_for_classification,True,entropy,None,0.33401603690569853,None,0.0,4,4,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-15,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7865652301288171,None,0.0,1,4,0.0,,,,,,,,,,,,no_encoding,no_coalescense,,median,robust_scaler,,,0.8623635641193496,0.13298297278730625,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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-17,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.21794354428393548,None,0.0,2,16,0.0,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0025451910134387575,median,quantile_transformer,1477,uniform,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,,
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-33,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,26.502259985828186,,,9.475662388405154e-05,rbf,-1,False,0.00019690635968217866,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,no_coalescense,,median,none,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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-82,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.02021502857757827,True,,0.055848937053280076,True,,constant,log,l2,,0.00016356162584832537,no_encoding,no_coalescense,,median,quantile_transformer,27,normal,,,select_rates_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.11873149622606233,fwe,chi2,
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-90,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4834106677905965,False,True,1,squared_hinge,ovr,l2,0.00017625027586147415,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,one_hot_encoding,minority_coalescer,0.0009157492873444966,most_frequent,quantile_transformer,1150,normal,,,extra_trees_preproc_for_classification,False,entropy,None,0.4981700453792278,None,0.0,9,15,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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+369,none,extra_trees,,,,,,,,,,,,,,,True,entropy,None,0.9796199105973851,None,0.0,2,13,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,no_encoding,minority_coalescer,0.00845950033601661,mean,robust_scaler,,,0.8275228560550734,0.23814615163106348,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
diff --git a/autosklearn/metalearning/files/roc_auc_multiclass.classification_sparse/description.txt b/autosklearn/metalearning/files/roc_auc_multiclass.classification_sparse/description.txt
index 4d3a5c3ec1..0bd0fa165a 100644
--- a/autosklearn/metalearning/files/roc_auc_multiclass.classification_sparse/description.txt
+++ b/autosklearn/metalearning/files/roc_auc_multiclass.classification_sparse/description.txt
@@ -1,8 +1,9 @@
 features_cutoff_time: 3600
 features_cutoff_memory: 3072
-number_of_feature_steps: 35
+number_of_feature_steps: 42
 feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances
 feature_step LogNumberOfInstances: LogNumberOfInstances
+feature_step NumberOfClasses: NumberOfClasses
 feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures
 feature_step LogNumberOfFeatures: LogNumberOfFeatures
 feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues
@@ -20,6 +21,11 @@ feature_step DatasetRatio: DatasetRatio, LogDatasetRatio
 feature_step LogDatasetRatio: LogDatasetRatio
 feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio
 feature_step LogInverseDatasetRatio: LogInverseDatasetRatio
+feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD
+feature_step ClassProbabilityMin: ClassProbabilityMin
+feature_step ClassProbabilityMax: ClassProbabilityMax
+feature_step ClassProbabilityMean: ClassProbabilityMean
+feature_step ClassProbabilitySTD: ClassProbabilitySTD
 feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum
 feature_step SymbolsMin: SymbolsMin
 feature_step SymbolsMax: SymbolsMax
@@ -36,11 +42,12 @@ feature_step SkewnessMin: SkewnessMin
 feature_step SkewnessMax: SkewnessMax
 feature_step SkewnessMean: SkewnessMean
 feature_step SkewnessSTD: SkewnessSTD
-features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
+feature_step ClassEntropy: ClassEntropy
+features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy
 features_stochastic: 
-default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
+default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy
 
-algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92
+algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235,236,237,238,239,240,241,242,243,244,245,246,247,248,249,250,251,252,253,254,255,256,257,258,259,260,261,262,263,264,265,266,267,268,269,270,271,272,273,274,275,276,277,278,279,280,281,282,283,284,285,286,287,288,289,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,312,313,314,315,316,317,318,319,320,321,322,323,324,325,326,327,328,329,330,331,332,333,334,335,336,337,338,339,340,341,342,343,344,345,346,347,348,349,350,351,352,353,354,355,356,357,358,359,360,361,362,363,364,365,366,367,368,369,370,371,372
 algorithms_stochastic: 
 performance_measures: roc_auc
 performance_type: solution_quality
diff --git a/autosklearn/metalearning/files/roc_auc_multiclass.classification_sparse/feature_costs.arff b/autosklearn/metalearning/files/roc_auc_multiclass.classification_sparse/feature_costs.arff
index 0e17e20d22..8512fd9275 100644
--- a/autosklearn/metalearning/files/roc_auc_multiclass.classification_sparse/feature_costs.arff
+++ b/autosklearn/metalearning/files/roc_auc_multiclass.classification_sparse/feature_costs.arff
@@ -4,6 +4,7 @@
 @ATTRIBUTE repetition NUMERIC
 @ATTRIBUTE NumberOfInstances NUMERIC
 @ATTRIBUTE LogNumberOfInstances NUMERIC
+@ATTRIBUTE NumberOfClasses NUMERIC
 @ATTRIBUTE NumberOfFeatures NUMERIC
 @ATTRIBUTE LogNumberOfFeatures NUMERIC
 @ATTRIBUTE MissingValues NUMERIC
@@ -21,6 +22,11 @@
 @ATTRIBUTE LogDatasetRatio NUMERIC
 @ATTRIBUTE InverseDatasetRatio NUMERIC
 @ATTRIBUTE LogInverseDatasetRatio NUMERIC
+@ATTRIBUTE ClassOccurences NUMERIC
+@ATTRIBUTE ClassProbabilityMin NUMERIC
+@ATTRIBUTE ClassProbabilityMax NUMERIC
+@ATTRIBUTE ClassProbabilityMean NUMERIC
+@ATTRIBUTE ClassProbabilitySTD NUMERIC
 @ATTRIBUTE NumSymbols NUMERIC
 @ATTRIBUTE SymbolsMin NUMERIC
 @ATTRIBUTE SymbolsMax NUMERIC
@@ -37,162 +43,138 @@
 @ATTRIBUTE SkewnessMax NUMERIC
 @ATTRIBUTE SkewnessMean NUMERIC
 @ATTRIBUTE SkewnessSTD NUMERIC
+@ATTRIBUTE ClassEntropy NUMERIC
 
 @DATA
-75123,1.0,1e-05,1e-05,1e-05,1e-05,0.00253,0.00221,1e-05,0.00024,3e-05,0.00011,0.0,2e-05,2e-05,5e-05,8e-05,2e-05,1e-05,7e-05,1e-05,0.00072,1e-05,1e-05,0.00019,0.00041,0.00011,0.00061,0.00012,9e-05,0.00015,0.00025,0.00067,0.00014,0.00011,0.00016,0.00027
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-75101,1.0,1e-05,1e-05,1e-05,1e-05,0.0418,0.03546,1e-05,0.00389,1e-05,0.00248,0.0,2e-05,2e-05,4e-05,6e-05,2e-05,1e-05,5e-05,2e-05,0.00045,1e-05,0.0,0.00011,0.00023,0.0001,0.00041,6e-05,8e-05,0.0001,0.00018,0.00047,6e-05,0.0001,8e-05,0.00023
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-75187,1.0,1e-05,1e-05,2e-05,1e-05,0.00548,0.00473,1e-05,0.0005,2e-05,0.00029,1e-05,2e-05,2e-05,7e-05,9e-05,3e-05,1e-05,6e-05,1e-05,0.00062,1e-05,1e-05,0.00014,0.00033,0.00013,0.00038,7e-05,7e-05,8e-05,0.00016,0.00047,6e-05,9e-05,9e-05,0.00023
-266,1.0,1e-05,1e-05,1e-05,1e-05,0.0018,0.00146,1e-05,0.00023,2e-05,0.00015,2e-05,2e-05,2e-05,6e-05,8e-05,3e-05,1e-05,6e-05,1e-05,0.00059,1e-05,1e-05,0.00014,0.00031,0.00013,0.0004,5e-05,7e-05,8e-05,0.0002,0.00044,8e-05,8e-05,8e-05,0.0002
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-75217,1.0,1e-05,1e-05,2e-05,1e-05,0.00196,0.00158,1e-05,0.00026,2e-05,0.00016,1e-05,3e-05,3e-05,7e-05,0.0001,3e-05,1e-05,7e-05,2e-05,0.00089,1e-05,1e-05,0.00022,0.00053,0.00013,0.00074,9e-05,0.0001,0.00019,0.00036,0.00065,0.00013,0.0001,0.00011,0.00031
-75176,1.0,1e-05,1e-05,2e-05,1e-05,0.01406,0.01285,1e-05,0.00094,2e-05,0.00031,1e-05,3e-05,3e-05,6e-05,9e-05,3e-05,1e-05,7e-05,2e-05,0.00069,1e-05,1e-05,0.00017,0.00035,0.00015,0.00085,0.00018,0.00011,0.0002,0.00036,0.0011,0.00022,0.00023,0.00023,0.00042
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-75148,1.0,1e-05,1e-05,2e-05,2e-05,0.00129,0.00115,1e-05,0.00012,1e-05,4e-05,0.0,2e-05,2e-05,4e-05,5e-05,2e-05,1e-05,4e-05,1e-05,0.0004,1e-05,0.0,9e-05,0.00021,9e-05,0.00036,7e-05,7e-05,8e-05,0.00015,0.00041,6e-05,8e-05,8e-05,0.00019
-75103,1.0,1e-05,1e-05,1e-05,1e-05,0.01302,0.00819,3e-05,0.00234,3e-05,0.00255,0.0,2e-05,2e-05,6e-05,0.00012,5e-05,1e-05,0.00011,1e-05,0.0005,1e-05,0.0,9e-05,0.00029,0.00011,0.00054,8e-05,0.00011,0.00011,0.00024,0.00066,7e-05,0.00015,0.00011,0.00032
-75168,1.0,1e-05,0.0,1e-05,1e-05,0.02353,0.00601,0.00016,0.01754,0.00018,0.00031,0.0,0.00013,0.00013,0.00027,0.0003,0.00032,1e-05,0.00042,1e-05,0.0004,1e-05,0.0,8e-05,0.00021,0.0001,0.0011,0.00022,0.00025,0.00026,0.00037,0.00124,0.00022,0.00025,0.00028,0.00049
-75157,1.0,1e-05,1e-05,1e-05,1e-05,0.00125,0.00106,1e-05,0.00015,2e-05,7e-05,0.0,2e-05,2e-05,5e-05,6e-05,2e-05,1e-05,4e-05,1e-05,0.00048,2e-05,0.0,0.0001,0.00026,0.0001,0.00039,6e-05,7e-05,8e-05,0.00019,0.00043,6e-05,9e-05,8e-05,0.0002
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-75213,1.0,1e-05,1e-05,1e-05,1e-05,0.00096,0.00079,1e-05,0.00013,1e-05,7e-05,1e-05,2e-05,2e-05,5e-05,7e-05,2e-05,1e-05,6e-05,1e-05,0.00056,1e-05,1e-05,0.00013,0.00029,0.00012,0.00056,6e-05,8e-05,9e-05,0.00033,0.00063,8e-05,0.00014,0.00013,0.00029
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diff --git a/autosklearn/metalearning/files/roc_auc_multiclass.classification_sparse/feature_runstatus.arff b/autosklearn/metalearning/files/roc_auc_multiclass.classification_sparse/feature_runstatus.arff
index 0034ad2bd6..5fff2dfed3 100644
--- a/autosklearn/metalearning/files/roc_auc_multiclass.classification_sparse/feature_runstatus.arff
+++ b/autosklearn/metalearning/files/roc_auc_multiclass.classification_sparse/feature_runstatus.arff
@@ -4,6 +4,7 @@
 @ATTRIBUTE repetition NUMERIC
 @ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other}
@@ -21,6 +22,11 @@
 @ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other}
@@ -37,162 +43,138 @@
 @ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other}
 @ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other}
 
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+75249,1.0,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,other,ok,ok,ok,ok,ok
+75250,1.0,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,other,ok,ok,ok,ok,ok
diff --git a/autosklearn/metalearning/files/roc_auc_multiclass.classification_sparse/feature_values.arff b/autosklearn/metalearning/files/roc_auc_multiclass.classification_sparse/feature_values.arff
index a60400e3ce..736dc64321 100644
--- a/autosklearn/metalearning/files/roc_auc_multiclass.classification_sparse/feature_values.arff
+++ b/autosklearn/metalearning/files/roc_auc_multiclass.classification_sparse/feature_values.arff
@@ -13,7 +13,6 @@
 @ATTRIBUTE KurtosisMean NUMERIC
 @ATTRIBUTE KurtosisMin NUMERIC
 @ATTRIBUTE KurtosisSTD NUMERIC
-@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC
 @ATTRIBUTE LogDatasetRatio NUMERIC
 @ATTRIBUTE LogInverseDatasetRatio NUMERIC
 @ATTRIBUTE LogNumberOfFeatures NUMERIC
@@ -42,160 +41,135 @@
 @ATTRIBUTE SymbolsSum NUMERIC
 
 @DATA
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diff --git a/autosklearn/metalearning/files/root_mean_squared_error_regression_dense/algorithm_runs.arff b/autosklearn/metalearning/files/root_mean_squared_error_regression_dense/algorithm_runs.arff
new file mode 100644
index 0000000000..7a879d8934
--- /dev/null
+++ b/autosklearn/metalearning/files/root_mean_squared_error_regression_dense/algorithm_runs.arff
@@ -0,0 +1,34 @@
+@RELATION auto-sklearn_ALGORITHM_RUNS
+
+@ATTRIBUTE instance_id STRING
+@ATTRIBUTE repetition NUMERIC
+@ATTRIBUTE algorithm STRING
+@ATTRIBUTE root_mean_squared_error NUMERIC
+@ATTRIBUTE runstatus {ok, timeout, memout, not_applicable, crash, other}
+
+@DATA
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diff --git a/autosklearn/metalearning/files/root_mean_squared_error_regression_dense/configurations.csv b/autosklearn/metalearning/files/root_mean_squared_error_regression_dense/configurations.csv
new file mode 100644
index 0000000000..c300d7f3da
--- /dev/null
+++ b/autosklearn/metalearning/files/root_mean_squared_error_regression_dense/configurations.csv
@@ -0,0 +1,26 @@
+idx,data_preprocessing:categorical_transformer:categorical_encoding:__choice__,data_preprocessing:categorical_transformer:category_coalescence:__choice__,data_preprocessing:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction,data_preprocessing:numerical_transformer:imputation:strategy,data_preprocessing:numerical_transformer:rescaling:__choice__,data_preprocessing:numerical_transformer:rescaling:quantile_transformer:n_quantiles,data_preprocessing:numerical_transformer:rescaling:quantile_transformer:output_distribution,data_preprocessing:numerical_transformer:rescaling:robust_scaler:q_max,data_preprocessing:numerical_transformer:rescaling:robust_scaler:q_min,feature_preprocessor:__choice__,feature_preprocessor:extra_trees_preproc_for_regression:bootstrap,feature_preprocessor:extra_trees_preproc_for_regression:criterion,feature_preprocessor:extra_trees_preproc_for_regression:max_depth,feature_preprocessor:extra_trees_preproc_for_regression:max_features,feature_preprocessor:extra_trees_preproc_for_regression:max_leaf_nodes,feature_preprocessor:extra_trees_preproc_for_regression:min_samples_leaf,feature_preprocessor:extra_trees_preproc_for_regression:min_samples_split,feature_preprocessor:extra_trees_preproc_for_regression:min_weight_fraction_leaf,feature_preprocessor:extra_trees_preproc_for_regression:n_estimators,feature_preprocessor:fast_ica:algorithm,feature_preprocessor:fast_ica:fun,feature_preprocessor:fast_ica:n_components,feature_preprocessor:fast_ica:whiten,feature_preprocessor:feature_agglomeration:affinity,feature_preprocessor:feature_agglomeration:linkage,feature_preprocessor:feature_agglomeration:n_clusters,feature_preprocessor:feature_agglomeration:pooling_func,feature_preprocessor:kernel_pca:coef0,feature_preprocessor:kernel_pca:degree,feature_preprocessor:kernel_pca:gamma,feature_preprocessor:kernel_pca:kernel,feature_preprocessor:kernel_pca:n_components,feature_preprocessor:kitchen_sinks:gamma,feature_preprocessor:kitchen_sinks:n_components,feature_preprocessor:nystroem_sampler:coef0,feature_preprocessor:nystroem_sampler:degree,feature_preprocessor:nystroem_sampler:gamma,feature_preprocessor:nystroem_sampler:kernel,feature_preprocessor:nystroem_sampler:n_components,feature_preprocessor:pca:keep_variance,feature_preprocessor:pca:whiten,feature_preprocessor:polynomial:degree,feature_preprocessor:polynomial:include_bias,feature_preprocessor:polynomial:interaction_only,feature_preprocessor:random_trees_embedding:bootstrap,feature_preprocessor:random_trees_embedding:max_depth,feature_preprocessor:random_trees_embedding:max_leaf_nodes,feature_preprocessor:random_trees_embedding:min_samples_leaf,feature_preprocessor:random_trees_embedding:min_samples_split,feature_preprocessor:random_trees_embedding:min_weight_fraction_leaf,feature_preprocessor:random_trees_embedding:n_estimators,feature_preprocessor:select_percentile_regression:percentile,feature_preprocessor:select_percentile_regression:score_func,feature_preprocessor:select_rates_regression:alpha,feature_preprocessor:select_rates_regression:mode,feature_preprocessor:select_rates_regression:score_func,regressor:__choice__,regressor:adaboost:learning_rate,regressor:adaboost:loss,regressor:adaboost:max_depth,regressor:adaboost:n_estimators,regressor:ard_regression:alpha_1,regressor:ard_regression:alpha_2,regressor:ard_regression:fit_intercept,regressor:ard_regression:lambda_1,regressor:ard_regression:lambda_2,regressor:ard_regression:n_iter,regressor:ard_regression:threshold_lambda,regressor:ard_regression:tol,regressor:decision_tree:criterion,regressor:decision_tree:max_depth_factor,regressor:decision_tree:max_features,regressor:decision_tree:max_leaf_nodes,regressor:decision_tree:min_impurity_decrease,regressor:decision_tree:min_samples_leaf,regressor:decision_tree:min_samples_split,regressor:decision_tree:min_weight_fraction_leaf,regressor:extra_trees:bootstrap,regressor:extra_trees:criterion,regressor:extra_trees:max_depth,regressor:extra_trees:max_features,regressor:extra_trees:max_leaf_nodes,regressor:extra_trees:min_impurity_decrease,regressor:extra_trees:min_samples_leaf,regressor:extra_trees:min_samples_split,regressor:extra_trees:min_weight_fraction_leaf,regressor:gaussian_process:alpha,regressor:gaussian_process:thetaL,regressor:gaussian_process:thetaU,regressor:gradient_boosting:early_stop,regressor:gradient_boosting:l2_regularization,regressor:gradient_boosting:learning_rate,regressor:gradient_boosting:loss,regressor:gradient_boosting:max_bins,regressor:gradient_boosting:max_depth,regressor:gradient_boosting:max_leaf_nodes,regressor:gradient_boosting:min_samples_leaf,regressor:gradient_boosting:n_iter_no_change,regressor:gradient_boosting:scoring,regressor:gradient_boosting:tol,regressor:gradient_boosting:validation_fraction,regressor:k_nearest_neighbors:n_neighbors,regressor:k_nearest_neighbors:p,regressor:k_nearest_neighbors:weights,regressor:liblinear_svr:C,regressor:liblinear_svr:dual,regressor:liblinear_svr:epsilon,regressor:liblinear_svr:fit_intercept,regressor:liblinear_svr:intercept_scaling,regressor:liblinear_svr:loss,regressor:liblinear_svr:tol,regressor:libsvm_svr:C,regressor:libsvm_svr:coef0,regressor:libsvm_svr:degree,regressor:libsvm_svr:epsilon,regressor:libsvm_svr:gamma,regressor:libsvm_svr:kernel,regressor:libsvm_svr:max_iter,regressor:libsvm_svr:shrinking,regressor:libsvm_svr:tol,regressor:random_forest:bootstrap,regressor:random_forest:criterion,regressor:random_forest:max_depth,regressor:random_forest:max_features,regressor:random_forest:max_leaf_nodes,regressor:random_forest:min_impurity_decrease,regressor:random_forest:min_samples_leaf,regressor:random_forest:min_samples_split,regressor:random_forest:min_weight_fraction_leaf,regressor:sgd:alpha,regressor:sgd:average,regressor:sgd:epsilon,regressor:sgd:eta0,regressor:sgd:fit_intercept,regressor:sgd:l1_ratio,regressor:sgd:learning_rate,regressor:sgd:loss,regressor:sgd:penalty,regressor:sgd:power_t,regressor:sgd:tol
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+22,one_hot_encoding,no_coalescense,,most_frequent,standardize,,,,,fast_ica,,,,,,,,,,parallel,logcosh,215,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,off,4.242786820644205e-10,0.0953131401479951,least_squares,255,None,16,17,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+24,no_encoding,no_coalescense,,median,quantile_transformer,1150,uniform,,,kitchen_sinks,,,,,,,,,,,,,,,,,,,,,,,1.1605111479672479,5499,,,,,,,,,,,,,,,,,,,,,,,libsvm_svr,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,272.83550277553246,0.1471721071594263,2,0.18104912091393513,0.09926116813777154,poly,-1,True,0.06084368714049917,,,,,,,,,,,,,,,,,,,,
+30,no_encoding,no_coalescense,,most_frequent,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,friedman_mse,None,0.2961138557020546,None,0.0,14,12,0.0,,,,,,,,,,,
+34,no_encoding,no_coalescense,,most_frequent,robust_scaler,,,0.7049491520337817,0.23602880130851764,fast_ica,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,off,4.696295762197504e-10,0.03269357604116943,least_squares,255,None,32,14,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+36,one_hot_encoding,no_coalescense,,most_frequent,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,mse,None,0.1686847993740384,None,0.0,1,2,0.0,,,,,,,,,,,
+38,no_encoding,no_coalescense,,median,none,,,,,select_rates_regression,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.13294870400704156,fwe,f_regression,adaboost,0.47982456668237294,square,10,143,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+42,no_encoding,minority_coalescer,0.0074923422486941615,mean,quantile_transformer,919,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,mse,None,0.7102443468613595,None,0.0,20,4,0.0,,,,,,,,,,,
+45,no_encoding,minority_coalescer,0.00010476041523839592,median,standardize,,,,,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,-0.8964384900187328,,0.015021411517772181,sigmoid,7415,,,,,,,,,,,,,,,,,,ard_regression,,,,,4.544163312418526e-05,0.0003398091634662838,True,6.154987146709e-06,1.2041910604531783e-05,300,21462.056617690938,0.07078995384093524,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+48,one_hot_encoding,no_coalescense,,mean,quantile_transformer,148,uniform,,,select_rates_regression,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.10789818795901182,fwe,f_regression,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,off,0.0002841741346637058,0.024253735132716756,least_squares,255,None,36,1,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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+52,no_encoding,no_coalescense,,most_frequent,quantile_transformer,1425,uniform,,,feature_agglomeration,,,,,,,,,,,,,,cosine,complete,276,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,off,0.00018459550741867383,0.04757728173371449,least_squares,255,None,16,6,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+57,one_hot_encoding,minority_coalescer,0.00924813449669181,mean,quantile_transformer,987,uniform,,,select_rates_regression,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fwe,f_regression,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,off,2.5833231171101403e-10,0.10682575320034993,least_squares,255,None,31,12,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+59,no_encoding,minority_coalescer,0.010000000000000004,most_frequent,robust_scaler,,,0.7414290235319753,0.2631696868735969,extra_trees_preproc_for_regression,True,mse,None,0.8529069417035176,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,extra_trees,,,,,,,,,,,,,,,,,,,,,True,mse,None,0.9698708516736436,None,0.0,4,2,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+62,one_hot_encoding,minority_coalescer,0.027537836751886195,median,robust_scaler,,,0.9003944797470034,0.2566610073208164,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,5,None,1,2,1.0,10,,,,,,decision_tree,,,,,,,,,,,,,friedman_mse,0.23884067765544847,1.0,None,0.0,11,13,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+66,no_encoding,minority_coalescer,0.006136459298464629,mean,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,extra_trees,,,,,,,,,,,,,,,,,,,,,False,friedman_mse,None,0.9490464657381125,None,0.0,1,6,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+68,one_hot_encoding,minority_coalescer,0.00924813449669181,mean,quantile_transformer,987,uniform,,,select_rates_regression,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fwe,f_regression,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,off,2.5833231171101403e-10,0.10682575320034993,least_squares,255,None,31,12,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+70,no_encoding,no_coalescense,,median,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,mse,None,0.9234161475923298,None,0.0,6,13,0.0,,,,,,,,,,,
diff --git a/autosklearn/metalearning/files/root_mean_squared_error_regression_dense/description.txt b/autosklearn/metalearning/files/root_mean_squared_error_regression_dense/description.txt
new file mode 100644
index 0000000000..e00c21265b
--- /dev/null
+++ b/autosklearn/metalearning/files/root_mean_squared_error_regression_dense/description.txt
@@ -0,0 +1,51 @@
+features_cutoff_time: 3600
+features_cutoff_memory: 3072
+number_of_feature_steps: 35
+feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances
+feature_step LogNumberOfInstances: LogNumberOfInstances
+feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures
+feature_step LogNumberOfFeatures: LogNumberOfFeatures
+feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues
+feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues
+feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues
+feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues
+feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues
+feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues
+feature_step PercentageOfMissingValues: PercentageOfMissingValues
+feature_step NumberOfNumericFeatures: NumberOfNumericFeatures
+feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures
+feature_step RatioNumericalToNominal: RatioNumericalToNominal
+feature_step RatioNominalToNumerical: RatioNominalToNumerical
+feature_step DatasetRatio: DatasetRatio, LogDatasetRatio
+feature_step LogDatasetRatio: LogDatasetRatio
+feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio
+feature_step LogInverseDatasetRatio: LogInverseDatasetRatio
+feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum
+feature_step SymbolsMin: SymbolsMin
+feature_step SymbolsMax: SymbolsMax
+feature_step SymbolsMean: SymbolsMean
+feature_step SymbolsSTD: SymbolsSTD
+feature_step SymbolsSum: SymbolsSum
+feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD
+feature_step KurtosisMin: KurtosisMin
+feature_step KurtosisMax: KurtosisMax
+feature_step KurtosisMean: KurtosisMean
+feature_step KurtosisSTD: KurtosisSTD
+feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
+feature_step SkewnessMin: SkewnessMin
+feature_step SkewnessMax: SkewnessMax
+feature_step SkewnessMean: SkewnessMean
+feature_step SkewnessSTD: SkewnessSTD
+features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
+features_stochastic: 
+default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
+
+algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73
+algorithms_stochastic: 
+performance_measures: root_mean_squared_error
+performance_type: solution_quality
+
+scenario_id: auto-sklearn
+maximize: false
+algorithm_cutoff_time: 1800
+algorithm_cutoff_memory: 3072
diff --git a/autosklearn/metalearning/files/root_mean_squared_error_regression_dense/feature_costs.arff b/autosklearn/metalearning/files/root_mean_squared_error_regression_dense/feature_costs.arff
new file mode 100644
index 0000000000..2c1c95a875
--- /dev/null
+++ b/autosklearn/metalearning/files/root_mean_squared_error_regression_dense/feature_costs.arff
@@ -0,0 +1,66 @@
+@RELATION auto-sklearn_FEATURE_COSTS
+
+@ATTRIBUTE instance_id STRING
+@ATTRIBUTE repetition NUMERIC
+@ATTRIBUTE NumberOfInstances NUMERIC
+@ATTRIBUTE LogNumberOfInstances NUMERIC
+@ATTRIBUTE NumberOfFeatures NUMERIC
+@ATTRIBUTE LogNumberOfFeatures NUMERIC
+@ATTRIBUTE MissingValues NUMERIC
+@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC
+@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC
+@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC
+@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC
+@ATTRIBUTE NumberOfMissingValues NUMERIC
+@ATTRIBUTE PercentageOfMissingValues NUMERIC
+@ATTRIBUTE NumberOfNumericFeatures NUMERIC
+@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC
+@ATTRIBUTE RatioNumericalToNominal NUMERIC
+@ATTRIBUTE RatioNominalToNumerical NUMERIC
+@ATTRIBUTE DatasetRatio NUMERIC
+@ATTRIBUTE LogDatasetRatio NUMERIC
+@ATTRIBUTE InverseDatasetRatio NUMERIC
+@ATTRIBUTE LogInverseDatasetRatio NUMERIC
+@ATTRIBUTE NumSymbols NUMERIC
+@ATTRIBUTE SymbolsMin NUMERIC
+@ATTRIBUTE SymbolsMax NUMERIC
+@ATTRIBUTE SymbolsMean NUMERIC
+@ATTRIBUTE SymbolsSTD NUMERIC
+@ATTRIBUTE SymbolsSum NUMERIC
+@ATTRIBUTE Kurtosisses NUMERIC
+@ATTRIBUTE KurtosisMin NUMERIC
+@ATTRIBUTE KurtosisMax NUMERIC
+@ATTRIBUTE KurtosisMean NUMERIC
+@ATTRIBUTE KurtosisSTD NUMERIC
+@ATTRIBUTE Skewnesses NUMERIC
+@ATTRIBUTE SkewnessMin NUMERIC
+@ATTRIBUTE SkewnessMax NUMERIC
+@ATTRIBUTE SkewnessMean NUMERIC
+@ATTRIBUTE SkewnessSTD NUMERIC
+
+@DATA
+2280,1.0,2e-05,2e-05,3e-05,2e-05,0.01525,0.01381,3e-05,0.0012,3e-05,0.00031,1e-05,5e-05,5e-05,0.00013,0.00016,6e-05,2e-05,8e-05,3e-05,0.00118,1e-05,1e-05,0.00034,0.00064,0.00018,0.00052,7e-05,8e-05,0.0001,0.00027,0.00052,6e-05,0.0001,0.00012,0.00025
+2288,1.0,2e-05,1e-05,3e-05,2e-05,0.00788,0.00641,3e-05,0.00097,5e-05,0.00058,1e-05,3e-05,3e-05,7e-05,0.0001,4e-05,2e-05,8e-05,5e-05,0.00076,0.0,1e-05,0.0002,0.00041,0.00013,0.00089,0.00015,0.00019,0.00018,0.00037,0.00205,0.00025,0.0004,0.00035,0.00105
+2289,1.0,2e-05,1e-05,3e-05,2e-05,0.01106,0.01019,2e-05,0.00074,4e-05,0.0002,1e-05,4e-05,6e-05,8e-05,0.00011,5e-05,2e-05,9e-05,5e-05,0.00094,1e-05,1e-05,0.00031,0.00044,0.00017,0.00093,0.00012,0.00021,0.00016,0.00044,0.00091,0.00015,0.00014,0.00022,0.00039
+2292,1.0,2e-05,1e-05,2e-05,1e-05,0.01294,0.00992,1e-05,0.00179,2e-05,0.00127,1e-05,4e-05,2e-05,7e-05,0.0001,3e-05,1e-05,5e-05,2e-05,0.00064,0.0,0.0,0.00014,0.00035,0.00014,0.00045,6e-05,7e-05,8e-05,0.00024,0.00046,6e-05,7e-05,0.0001,0.00022
+2300,1.0,1e-05,1e-05,1e-05,1e-05,0.00147,0.00133,1e-05,0.00013,1e-05,4e-05,1e-05,2e-05,2e-05,5e-05,7e-05,2e-05,1e-05,4e-05,2e-05,0.00049,0.0,0.0,0.00013,0.00026,9e-05,0.00129,0.00017,0.00018,0.00031,0.00062,0.00075,0.00014,0.00012,0.00017,0.00033
+2306,1.0,1e-05,1e-05,2e-05,1e-05,0.02421,0.02168,1e-05,0.00183,2e-05,0.00074,1e-05,2e-05,2e-05,8e-05,7e-05,2e-05,1e-05,5e-05,3e-05,0.0005,0.0,0.0,0.00013,0.00028,8e-05,0.00074,0.0001,0.00012,0.00016,0.00037,0.00064,9e-05,0.00011,0.00014,0.00029
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+2313,1.0,3e-05,1e-05,1e-05,1e-05,0.00476,0.00419,1e-05,0.00043,1e-05,0.00016,0.0,2e-05,2e-05,5e-05,7e-05,2e-05,1e-05,4e-05,2e-05,0.00056,0.0,0.0,0.00019,0.00027,9e-05,0.00083,0.00011,0.00015,0.00016,0.0004,0.00103,0.00016,0.00016,0.00025,0.00047
+2315,1.0,1e-05,1e-05,2e-05,1e-05,0.00814,0.00711,2e-05,0.00073,2e-05,0.00035,1e-05,3e-05,3e-05,0.0001,0.00013,4e-05,1e-05,6e-05,3e-05,0.00078,1e-05,1e-05,0.00022,0.00043,0.00012,0.00076,0.00011,0.00012,0.00016,0.00037,0.00098,0.00011,0.00013,0.00018,0.00055
+4768,1.0,3e-05,1e-05,6e-05,4e-05,0.00863,0.00731,3e-05,0.00103,5e-05,0.00039,2e-05,9e-05,9e-05,0.00019,0.00024,9e-05,5e-05,0.00011,5e-05,0.00115,1e-05,1e-05,0.00036,0.00064,0.00013,0.00101,0.00027,0.00018,0.00017,0.0004,0.00071,0.00011,0.00012,0.00016,0.00032
+4769,1.0,2e-05,1e-05,2e-05,1e-05,0.01789,0.01374,2e-05,0.00222,2e-05,0.00197,1e-05,6e-05,4e-05,0.00011,0.00017,5e-05,1e-05,0.00011,3e-05,0.00141,1e-05,1e-05,0.00058,0.00067,0.00014,0.00066,0.0001,0.00011,0.00013,0.00033,0.00041,6e-05,7e-05,9e-05,0.00019
+4772,1.0,2e-05,1e-05,2e-05,1e-05,0.00978,0.00782,2e-05,0.00109,2e-05,0.00092,1e-05,4e-05,4e-05,8e-05,0.00011,4e-05,1e-05,6e-05,2e-05,0.00078,1e-05,1e-05,0.00022,0.00043,0.00012,0.00077,0.00014,0.00013,0.00016,0.00034,0.0009,0.00012,0.00016,0.00023,0.00038
+4774,1.0,1e-05,1e-05,2e-05,1e-05,0.02342,0.02131,1e-05,0.00151,2e-05,0.00064,1e-05,2e-05,2e-05,6e-05,6e-05,2e-05,1e-05,4e-05,2e-05,0.00024,1e-05,1e-05,5e-05,0.00013,5e-05,0.00065,0.0001,0.00012,0.00014,0.00029,0.00075,0.00015,0.00012,0.00016,0.00033
+4779,1.0,1e-05,1e-05,1e-05,1e-05,0.00696,0.00386,2e-05,0.00165,2e-05,0.00149,1e-05,5e-05,3e-05,7e-05,0.0001,3e-05,1e-05,6e-05,2e-05,0.00047,0.0,0.0,0.00012,0.00027,8e-05,0.00055,8e-05,0.00013,0.00013,0.0002,0.00081,0.00013,0.00014,0.00018,0.00035
+4790,1.0,1e-05,1e-05,2e-05,1e-05,0.02423,0.0123,3e-05,0.00628,6e-05,0.00575,1e-05,8e-05,6e-05,0.00024,0.00016,7e-05,1e-05,0.0001,2e-05,0.00082,1e-05,1e-05,0.00023,0.00044,0.00014,0.00103,0.00014,0.00017,0.00031,0.00042,0.00099,0.00014,0.00018,0.0002,0.00048
+4796,1.0,2e-05,1e-05,2e-05,1e-05,0.03581,0.0194,4e-05,0.00921,5e-05,0.00729,1e-05,8e-05,0.0001,0.00013,0.00016,8e-05,2e-05,0.00012,3e-05,0.00083,1e-05,1e-05,0.00023,0.00045,0.00015,0.00103,0.00015,0.00019,0.0003,0.00039,0.0009,0.00016,0.00015,0.00019,0.0004
+4835,1.0,2e-05,2e-05,3e-05,2e-05,0.00597,0.00541,3e-05,0.0005,3e-05,0.00014,1e-05,5e-05,5e-05,0.00014,0.00022,7e-05,2e-05,0.0001,3e-05,0.0031,1e-05,1e-05,0.00113,0.00174,0.0002,0.00113,0.00022,0.00019,0.00023,0.00049,0.00086,0.00013,0.00015,0.00026,0.00033
+4881,1.0,2e-05,1e-05,3e-05,1e-05,0.01016,0.00811,2e-05,0.00138,2e-05,0.00073,1e-05,4e-05,4e-05,8e-05,0.00011,4e-05,2e-05,6e-05,3e-05,0.00083,1e-05,1e-05,0.00022,0.00043,0.00017,0.00079,0.00011,0.00012,0.00016,0.00039,0.00104,0.00012,0.00013,0.00022,0.00058
+4883,1.0,1e-05,1e-05,3e-05,3e-05,0.00448,0.00395,1e-05,0.0004,1e-05,0.00017,1e-05,2e-05,2e-05,7e-05,8e-05,3e-05,1e-05,4e-05,2e-05,0.00042,0.0,0.0,0.00011,0.00023,7e-05,0.00179,0.00025,0.00035,0.00044,0.00075,0.00072,0.00011,0.00012,0.00016,0.00033
+4885,1.0,2e-05,1e-05,3e-05,1e-05,0.04525,0.04018,2e-05,0.00396,2e-05,0.00116,1e-05,5e-05,7e-05,9e-05,0.00015,6e-05,1e-05,0.00013,7e-05,0.00092,1e-05,1e-05,0.00032,0.00045,0.00014,0.00082,0.00012,0.00013,0.00017,0.0004,0.00076,0.00011,0.00012,0.00016,0.00036
+4892,1.0,1e-05,1e-05,2e-05,1e-05,0.00802,0.00665,2e-05,0.00093,2e-05,0.00048,1e-05,3e-05,3e-05,7e-05,0.0001,6e-05,1e-05,5e-05,2e-05,0.00112,0.0,1e-05,0.00034,0.00066,0.00011,0.00041,8e-05,7e-05,8e-05,0.00018,0.00046,6e-05,7e-05,9e-05,0.00025
+4893,1.0,3e-05,2e-05,4e-05,2e-05,0.02994,0.02648,3e-05,0.00238,4e-05,0.00116,1e-05,4e-05,5e-05,8e-05,0.00011,5e-05,2e-05,0.0001,4e-05,0.00089,1e-05,1e-05,0.00026,0.00049,0.00012,0.00077,0.00013,0.00012,0.00016,0.00036,0.00092,0.00019,0.00013,0.00017,0.00042
+5022,1.0,3e-05,2e-05,4e-05,3e-05,0.00228,0.00193,3e-05,0.00033,3e-05,0.0001,1e-05,7e-05,6e-05,0.00015,0.00018,7e-05,2e-05,9e-05,3e-05,0.00126,1e-05,1e-05,0.00037,0.00068,0.0002,0.00076,0.00011,0.00013,0.00018,0.00034,0.00094,0.00017,0.0002,0.00019,0.00039
+5024,1.0,2e-05,2e-05,1e-05,1e-05,0.00067,0.00056,1e-05,0.0001,1e-05,3e-05,0.0,5e-05,2e-05,4e-05,6e-05,2e-05,1e-05,4e-05,1e-05,0.00044,0.0,0.0,0.00013,0.00023,7e-05,0.00146,0.00027,0.0002,0.00037,0.00062,0.0012,0.00018,0.00025,0.00042,0.00035
diff --git a/autosklearn/metalearning/files/root_mean_squared_error_regression_dense/feature_runstatus.arff b/autosklearn/metalearning/files/root_mean_squared_error_regression_dense/feature_runstatus.arff
new file mode 100644
index 0000000000..0166848554
--- /dev/null
+++ b/autosklearn/metalearning/files/root_mean_squared_error_regression_dense/feature_runstatus.arff
@@ -0,0 +1,66 @@
+@RELATION auto-sklearn_FEATURE_RUNSTATUS
+
+@ATTRIBUTE instance_id STRING
+@ATTRIBUTE repetition NUMERIC
+@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other}
+
+@DATA
+2280,1.0,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,other,ok,ok,ok,ok
+2288,1.0,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,other,ok,ok,ok,ok
+2289,1.0,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,other,ok,ok,ok,ok
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+2306,1.0,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,other,ok,ok,ok,ok
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+2313,1.0,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,other,ok,ok,ok,ok
+2315,1.0,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,other,ok,ok,ok,ok
+4768,1.0,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,other,ok,ok,ok,ok
+4769,1.0,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,other,ok,ok,ok,ok
+4772,1.0,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,other,ok,ok,ok,ok
+4774,1.0,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,other,ok,ok,ok,ok
+4779,1.0,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,other,ok,ok,ok,ok
+4790,1.0,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,other,ok,ok,ok,ok
+4796,1.0,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,other,ok,ok,ok,ok
+4835,1.0,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,other,ok,ok,ok,ok
+4881,1.0,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,other,ok,ok,ok,ok
+4883,1.0,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,other,ok,ok,ok,ok
+4885,1.0,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,other,ok,ok,ok,ok
+4892,1.0,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,other,ok,ok,ok,ok
+4893,1.0,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,other,ok,ok,ok,ok
+5022,1.0,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,other,ok,ok,ok,ok
+5024,1.0,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,other,ok,ok,ok,ok
diff --git a/autosklearn/metalearning/files/root_mean_squared_error_regression_dense/feature_values.arff b/autosklearn/metalearning/files/root_mean_squared_error_regression_dense/feature_values.arff
new file mode 100644
index 0000000000..ee955516b5
--- /dev/null
+++ b/autosklearn/metalearning/files/root_mean_squared_error_regression_dense/feature_values.arff
@@ -0,0 +1,62 @@
+@RELATION auto-sklearn_FEATURE_VALUES
+
+@ATTRIBUTE instance_id STRING
+@ATTRIBUTE repetition NUMERIC
+@ATTRIBUTE DatasetRatio NUMERIC
+@ATTRIBUTE InverseDatasetRatio NUMERIC
+@ATTRIBUTE KurtosisMax NUMERIC
+@ATTRIBUTE KurtosisMean NUMERIC
+@ATTRIBUTE KurtosisMin NUMERIC
+@ATTRIBUTE KurtosisSTD NUMERIC
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diff --git a/test/test_ensemble_builder/data/.auto-sklearn/done/0_3 b/autosklearn/metalearning/files/root_mean_squared_error_regression_dense/readme.txt
similarity index 100%
rename from test/test_ensemble_builder/data/.auto-sklearn/done/0_3
rename to autosklearn/metalearning/files/root_mean_squared_error_regression_dense/readme.txt
diff --git a/autosklearn/metalearning/files/root_mean_squared_error_regression_sparse/algorithm_runs.arff b/autosklearn/metalearning/files/root_mean_squared_error_regression_sparse/algorithm_runs.arff
new file mode 100644
index 0000000000..7a879d8934
--- /dev/null
+++ b/autosklearn/metalearning/files/root_mean_squared_error_regression_sparse/algorithm_runs.arff
@@ -0,0 +1,34 @@
+@RELATION auto-sklearn_ALGORITHM_RUNS
+
+@ATTRIBUTE instance_id STRING
+@ATTRIBUTE repetition NUMERIC
+@ATTRIBUTE algorithm STRING
+@ATTRIBUTE root_mean_squared_error NUMERIC
+@ATTRIBUTE runstatus {ok, timeout, memout, not_applicable, crash, other}
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diff --git a/autosklearn/metalearning/files/root_mean_squared_error_regression_sparse/configurations.csv b/autosklearn/metalearning/files/root_mean_squared_error_regression_sparse/configurations.csv
new file mode 100644
index 0000000000..c300d7f3da
--- /dev/null
+++ b/autosklearn/metalearning/files/root_mean_squared_error_regression_sparse/configurations.csv
@@ -0,0 +1,26 @@
+idx,data_preprocessing:categorical_transformer:categorical_encoding:__choice__,data_preprocessing:categorical_transformer:category_coalescence:__choice__,data_preprocessing:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction,data_preprocessing:numerical_transformer:imputation:strategy,data_preprocessing:numerical_transformer:rescaling:__choice__,data_preprocessing:numerical_transformer:rescaling:quantile_transformer:n_quantiles,data_preprocessing:numerical_transformer:rescaling:quantile_transformer:output_distribution,data_preprocessing:numerical_transformer:rescaling:robust_scaler:q_max,data_preprocessing:numerical_transformer:rescaling:robust_scaler:q_min,feature_preprocessor:__choice__,feature_preprocessor:extra_trees_preproc_for_regression:bootstrap,feature_preprocessor:extra_trees_preproc_for_regression:criterion,feature_preprocessor:extra_trees_preproc_for_regression:max_depth,feature_preprocessor:extra_trees_preproc_for_regression:max_features,feature_preprocessor:extra_trees_preproc_for_regression:max_leaf_nodes,feature_preprocessor:extra_trees_preproc_for_regression:min_samples_leaf,feature_preprocessor:extra_trees_preproc_for_regression:min_samples_split,feature_preprocessor:extra_trees_preproc_for_regression:min_weight_fraction_leaf,feature_preprocessor:extra_trees_preproc_for_regression:n_estimators,feature_preprocessor:fast_ica:algorithm,feature_preprocessor:fast_ica:fun,feature_preprocessor:fast_ica:n_components,feature_preprocessor:fast_ica:whiten,feature_preprocessor:feature_agglomeration:affinity,feature_preprocessor:feature_agglomeration:linkage,feature_preprocessor:feature_agglomeration:n_clusters,feature_preprocessor:feature_agglomeration:pooling_func,feature_preprocessor:kernel_pca:coef0,feature_preprocessor:kernel_pca:degree,feature_preprocessor:kernel_pca:gamma,feature_preprocessor:kernel_pca:kernel,feature_preprocessor:kernel_pca:n_components,feature_preprocessor:kitchen_sinks:gamma,feature_preprocessor:kitchen_sinks:n_components,feature_preprocessor:nystroem_sampler:coef0,feature_preprocessor:nystroem_sampler:degree,feature_preprocessor:nystroem_sampler:gamma,feature_preprocessor:nystroem_sampler:kernel,feature_preprocessor:nystroem_sampler:n_components,feature_preprocessor:pca:keep_variance,feature_preprocessor:pca:whiten,feature_preprocessor:polynomial:degree,feature_preprocessor:polynomial:include_bias,feature_preprocessor:polynomial:interaction_only,feature_preprocessor:random_trees_embedding:bootstrap,feature_preprocessor:random_trees_embedding:max_depth,feature_preprocessor:random_trees_embedding:max_leaf_nodes,feature_preprocessor:random_trees_embedding:min_samples_leaf,feature_preprocessor:random_trees_embedding:min_samples_split,feature_preprocessor:random_trees_embedding:min_weight_fraction_leaf,feature_preprocessor:random_trees_embedding:n_estimators,feature_preprocessor:select_percentile_regression:percentile,feature_preprocessor:select_percentile_regression:score_func,feature_preprocessor:select_rates_regression:alpha,feature_preprocessor:select_rates_regression:mode,feature_preprocessor:select_rates_regression:score_func,regressor:__choice__,regressor:adaboost:learning_rate,regressor:adaboost:loss,regressor:adaboost:max_depth,regressor:adaboost:n_estimators,regressor:ard_regression:alpha_1,regressor:ard_regression:alpha_2,regressor:ard_regression:fit_intercept,regressor:ard_regression:lambda_1,regressor:ard_regression:lambda_2,regressor:ard_regression:n_iter,regressor:ard_regression:threshold_lambda,regressor:ard_regression:tol,regressor:decision_tree:criterion,regressor:decision_tree:max_depth_factor,regressor:decision_tree:max_features,regressor:decision_tree:max_leaf_nodes,regressor:decision_tree:min_impurity_decrease,regressor:decision_tree:min_samples_leaf,regressor:decision_tree:min_samples_split,regressor:decision_tree:min_weight_fraction_leaf,regressor:extra_trees:bootstrap,regressor:extra_trees:criterion,regressor:extra_trees:max_depth,regressor:extra_trees:max_features,regressor:extra_trees:max_leaf_nodes,regressor:extra_trees:min_impurity_decrease,regressor:extra_trees:min_samples_leaf,regressor:extra_trees:min_samples_split,regressor:extra_trees:min_weight_fraction_leaf,regressor:gaussian_process:alpha,regressor:gaussian_process:thetaL,regressor:gaussian_process:thetaU,regressor:gradient_boosting:early_stop,regressor:gradient_boosting:l2_regularization,regressor:gradient_boosting:learning_rate,regressor:gradient_boosting:loss,regressor:gradient_boosting:max_bins,regressor:gradient_boosting:max_depth,regressor:gradient_boosting:max_leaf_nodes,regressor:gradient_boosting:min_samples_leaf,regressor:gradient_boosting:n_iter_no_change,regressor:gradient_boosting:scoring,regressor:gradient_boosting:tol,regressor:gradient_boosting:validation_fraction,regressor:k_nearest_neighbors:n_neighbors,regressor:k_nearest_neighbors:p,regressor:k_nearest_neighbors:weights,regressor:liblinear_svr:C,regressor:liblinear_svr:dual,regressor:liblinear_svr:epsilon,regressor:liblinear_svr:fit_intercept,regressor:liblinear_svr:intercept_scaling,regressor:liblinear_svr:loss,regressor:liblinear_svr:tol,regressor:libsvm_svr:C,regressor:libsvm_svr:coef0,regressor:libsvm_svr:degree,regressor:libsvm_svr:epsilon,regressor:libsvm_svr:gamma,regressor:libsvm_svr:kernel,regressor:libsvm_svr:max_iter,regressor:libsvm_svr:shrinking,regressor:libsvm_svr:tol,regressor:random_forest:bootstrap,regressor:random_forest:criterion,regressor:random_forest:max_depth,regressor:random_forest:max_features,regressor:random_forest:max_leaf_nodes,regressor:random_forest:min_impurity_decrease,regressor:random_forest:min_samples_leaf,regressor:random_forest:min_samples_split,regressor:random_forest:min_weight_fraction_leaf,regressor:sgd:alpha,regressor:sgd:average,regressor:sgd:epsilon,regressor:sgd:eta0,regressor:sgd:fit_intercept,regressor:sgd:l1_ratio,regressor:sgd:learning_rate,regressor:sgd:loss,regressor:sgd:penalty,regressor:sgd:power_t,regressor:sgd:tol
+1,one_hot_encoding,no_coalescense,,mean,standardize,,,,,select_rates_regression,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.40612979303062824,fdr,f_regression,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,off,2.2742572602302616e-08,0.02539518548794612,least_squares,255,None,57,10,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+2,one_hot_encoding,minority_coalescer,0.010000000000000004,most_frequent,quantile_transformer,1818,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,mse,None,0.9744859278597818,None,0.0,11,2,0.0,,,,,,,,,,,
+5,no_encoding,minority_coalescer,0.06912558240513342,median,quantile_transformer,119,uniform,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,off,3.79271698524212e-09,0.025159826241956527,least_squares,255,None,42,29,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+9,no_encoding,minority_coalescer,0.014087234899150132,most_frequent,minmax,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,libsvm_svr,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,243.80018215429655,,5,0.0010313680328976054,0.12602136380125653,rbf,-1,True,0.0003015355281210108,,,,,,,,,,,,,,,,,,,,
+13,no_encoding,minority_coalescer,0.010000000000000004,median,quantile_transformer,780,uniform,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,,libsvm_svr,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,10.974102872028766,,4,0.09412187025168416,0.04653835917168057,rbf,-1,False,0.0010357973041304064,,,,,,,,,,,,,,,,,,,,
+15,no_encoding,no_coalescense,,most_frequent,none,,,,,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00025844019785412086,rbf,305,,,,,,,,,,,,,,,,,,liblinear_svr,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,18080.177016227895,False,0.0017155220175954669,True,1,squared_epsilon_insensitive,7.035440846539455e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+17,no_encoding,no_coalescense,,most_frequent,robust_scaler,,,0.8280417820125114,0.0781634653874277,kitchen_sinks,,,,,,,,,,,,,,,,,,,,,,,8.438432240830361e-05,2984,,,,,,,,,,,,,,,,,,,,,,,ard_regression,,,,,0.00044036509169446026,4.039147500668822e-10,True,8.922721154590444e-05,3.0105431227198885e-05,300,1899.168836704701,0.011611373742389547,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+19,one_hot_encoding,no_coalescense,,mean,quantile_transformer,148,uniform,,,select_rates_regression,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.10789818795901182,fwe,f_regression,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,off,0.0002841741346637058,0.024253735132716756,least_squares,255,None,36,1,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+22,one_hot_encoding,no_coalescense,,most_frequent,standardize,,,,,fast_ica,,,,,,,,,,parallel,logcosh,215,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,off,4.242786820644205e-10,0.0953131401479951,least_squares,255,None,16,17,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+24,no_encoding,no_coalescense,,median,quantile_transformer,1150,uniform,,,kitchen_sinks,,,,,,,,,,,,,,,,,,,,,,,1.1605111479672479,5499,,,,,,,,,,,,,,,,,,,,,,,libsvm_svr,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,272.83550277553246,0.1471721071594263,2,0.18104912091393513,0.09926116813777154,poly,-1,True,0.06084368714049917,,,,,,,,,,,,,,,,,,,,
+30,no_encoding,no_coalescense,,most_frequent,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,friedman_mse,None,0.2961138557020546,None,0.0,14,12,0.0,,,,,,,,,,,
+34,no_encoding,no_coalescense,,most_frequent,robust_scaler,,,0.7049491520337817,0.23602880130851764,fast_ica,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,off,4.696295762197504e-10,0.03269357604116943,least_squares,255,None,32,14,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+36,one_hot_encoding,no_coalescense,,most_frequent,minmax,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,mse,None,0.1686847993740384,None,0.0,1,2,0.0,,,,,,,,,,,
+38,no_encoding,no_coalescense,,median,none,,,,,select_rates_regression,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.13294870400704156,fwe,f_regression,adaboost,0.47982456668237294,square,10,143,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+42,no_encoding,minority_coalescer,0.0074923422486941615,mean,quantile_transformer,919,normal,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,mse,None,0.7102443468613595,None,0.0,20,4,0.0,,,,,,,,,,,
+45,no_encoding,minority_coalescer,0.00010476041523839592,median,standardize,,,,,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,-0.8964384900187328,,0.015021411517772181,sigmoid,7415,,,,,,,,,,,,,,,,,,ard_regression,,,,,4.544163312418526e-05,0.0003398091634662838,True,6.154987146709e-06,1.2041910604531783e-05,300,21462.056617690938,0.07078995384093524,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+48,one_hot_encoding,no_coalescense,,mean,quantile_transformer,148,uniform,,,select_rates_regression,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.10789818795901182,fwe,f_regression,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,off,0.0002841741346637058,0.024253735132716756,least_squares,255,None,36,1,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+51,one_hot_encoding,no_coalescense,,median,minmax,,,,,select_rates_regression,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.32764403627543237,fdr,f_regression,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,off,0.000153437102327148,0.01698754273672824,least_squares,255,None,47,76,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+52,no_encoding,no_coalescense,,most_frequent,quantile_transformer,1425,uniform,,,feature_agglomeration,,,,,,,,,,,,,,cosine,complete,276,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,off,0.00018459550741867383,0.04757728173371449,least_squares,255,None,16,6,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+57,one_hot_encoding,minority_coalescer,0.00924813449669181,mean,quantile_transformer,987,uniform,,,select_rates_regression,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fwe,f_regression,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,off,2.5833231171101403e-10,0.10682575320034993,least_squares,255,None,31,12,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+59,no_encoding,minority_coalescer,0.010000000000000004,most_frequent,robust_scaler,,,0.7414290235319753,0.2631696868735969,extra_trees_preproc_for_regression,True,mse,None,0.8529069417035176,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,extra_trees,,,,,,,,,,,,,,,,,,,,,True,mse,None,0.9698708516736436,None,0.0,4,2,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+62,one_hot_encoding,minority_coalescer,0.027537836751886195,median,robust_scaler,,,0.9003944797470034,0.2566610073208164,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,5,None,1,2,1.0,10,,,,,,decision_tree,,,,,,,,,,,,,friedman_mse,0.23884067765544847,1.0,None,0.0,11,13,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+66,no_encoding,minority_coalescer,0.006136459298464629,mean,standardize,,,,,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,extra_trees,,,,,,,,,,,,,,,,,,,,,False,friedman_mse,None,0.9490464657381125,None,0.0,1,6,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+68,one_hot_encoding,minority_coalescer,0.00924813449669181,mean,quantile_transformer,987,uniform,,,select_rates_regression,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fwe,f_regression,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,off,2.5833231171101403e-10,0.10682575320034993,least_squares,255,None,31,12,,loss,1e-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+70,no_encoding,no_coalescense,,median,none,,,,,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,mse,None,0.9234161475923298,None,0.0,6,13,0.0,,,,,,,,,,,
diff --git a/autosklearn/metalearning/files/root_mean_squared_error_regression_sparse/description.txt b/autosklearn/metalearning/files/root_mean_squared_error_regression_sparse/description.txt
new file mode 100644
index 0000000000..e00c21265b
--- /dev/null
+++ b/autosklearn/metalearning/files/root_mean_squared_error_regression_sparse/description.txt
@@ -0,0 +1,51 @@
+features_cutoff_time: 3600
+features_cutoff_memory: 3072
+number_of_feature_steps: 35
+feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances
+feature_step LogNumberOfInstances: LogNumberOfInstances
+feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures
+feature_step LogNumberOfFeatures: LogNumberOfFeatures
+feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues
+feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues
+feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues
+feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues
+feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues
+feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues
+feature_step PercentageOfMissingValues: PercentageOfMissingValues
+feature_step NumberOfNumericFeatures: NumberOfNumericFeatures
+feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures
+feature_step RatioNumericalToNominal: RatioNumericalToNominal
+feature_step RatioNominalToNumerical: RatioNominalToNumerical
+feature_step DatasetRatio: DatasetRatio, LogDatasetRatio
+feature_step LogDatasetRatio: LogDatasetRatio
+feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio
+feature_step LogInverseDatasetRatio: LogInverseDatasetRatio
+feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum
+feature_step SymbolsMin: SymbolsMin
+feature_step SymbolsMax: SymbolsMax
+feature_step SymbolsMean: SymbolsMean
+feature_step SymbolsSTD: SymbolsSTD
+feature_step SymbolsSum: SymbolsSum
+feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD
+feature_step KurtosisMin: KurtosisMin
+feature_step KurtosisMax: KurtosisMax
+feature_step KurtosisMean: KurtosisMean
+feature_step KurtosisSTD: KurtosisSTD
+feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
+feature_step SkewnessMin: SkewnessMin
+feature_step SkewnessMax: SkewnessMax
+feature_step SkewnessMean: SkewnessMean
+feature_step SkewnessSTD: SkewnessSTD
+features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
+features_stochastic: 
+default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD
+
+algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73
+algorithms_stochastic: 
+performance_measures: root_mean_squared_error
+performance_type: solution_quality
+
+scenario_id: auto-sklearn
+maximize: false
+algorithm_cutoff_time: 1800
+algorithm_cutoff_memory: 3072
diff --git a/autosklearn/metalearning/files/root_mean_squared_error_regression_sparse/feature_costs.arff b/autosklearn/metalearning/files/root_mean_squared_error_regression_sparse/feature_costs.arff
new file mode 100644
index 0000000000..2c1c95a875
--- /dev/null
+++ b/autosklearn/metalearning/files/root_mean_squared_error_regression_sparse/feature_costs.arff
@@ -0,0 +1,66 @@
+@RELATION auto-sklearn_FEATURE_COSTS
+
+@ATTRIBUTE instance_id STRING
+@ATTRIBUTE repetition NUMERIC
+@ATTRIBUTE NumberOfInstances NUMERIC
+@ATTRIBUTE LogNumberOfInstances NUMERIC
+@ATTRIBUTE NumberOfFeatures NUMERIC
+@ATTRIBUTE LogNumberOfFeatures NUMERIC
+@ATTRIBUTE MissingValues NUMERIC
+@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC
+@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC
+@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC
+@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC
+@ATTRIBUTE NumberOfMissingValues NUMERIC
+@ATTRIBUTE PercentageOfMissingValues NUMERIC
+@ATTRIBUTE NumberOfNumericFeatures NUMERIC
+@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC
+@ATTRIBUTE RatioNumericalToNominal NUMERIC
+@ATTRIBUTE RatioNominalToNumerical NUMERIC
+@ATTRIBUTE DatasetRatio NUMERIC
+@ATTRIBUTE LogDatasetRatio NUMERIC
+@ATTRIBUTE InverseDatasetRatio NUMERIC
+@ATTRIBUTE LogInverseDatasetRatio NUMERIC
+@ATTRIBUTE NumSymbols NUMERIC
+@ATTRIBUTE SymbolsMin NUMERIC
+@ATTRIBUTE SymbolsMax NUMERIC
+@ATTRIBUTE SymbolsMean NUMERIC
+@ATTRIBUTE SymbolsSTD NUMERIC
+@ATTRIBUTE SymbolsSum NUMERIC
+@ATTRIBUTE Kurtosisses NUMERIC
+@ATTRIBUTE KurtosisMin NUMERIC
+@ATTRIBUTE KurtosisMax NUMERIC
+@ATTRIBUTE KurtosisMean NUMERIC
+@ATTRIBUTE KurtosisSTD NUMERIC
+@ATTRIBUTE Skewnesses NUMERIC
+@ATTRIBUTE SkewnessMin NUMERIC
+@ATTRIBUTE SkewnessMax NUMERIC
+@ATTRIBUTE SkewnessMean NUMERIC
+@ATTRIBUTE SkewnessSTD NUMERIC
+
+@DATA
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diff --git a/autosklearn/metalearning/files/root_mean_squared_error_regression_sparse/feature_runstatus.arff b/autosklearn/metalearning/files/root_mean_squared_error_regression_sparse/feature_runstatus.arff
new file mode 100644
index 0000000000..0166848554
--- /dev/null
+++ b/autosklearn/metalearning/files/root_mean_squared_error_regression_sparse/feature_runstatus.arff
@@ -0,0 +1,66 @@
+@RELATION auto-sklearn_FEATURE_RUNSTATUS
+
+@ATTRIBUTE instance_id STRING
+@ATTRIBUTE repetition NUMERIC
+@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other}
+@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other}
+
+@DATA
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diff --git a/autosklearn/metalearning/files/root_mean_squared_error_regression_sparse/feature_values.arff b/autosklearn/metalearning/files/root_mean_squared_error_regression_sparse/feature_values.arff
new file mode 100644
index 0000000000..ee955516b5
--- /dev/null
+++ b/autosklearn/metalearning/files/root_mean_squared_error_regression_sparse/feature_values.arff
@@ -0,0 +1,62 @@
+@RELATION auto-sklearn_FEATURE_VALUES
+
+@ATTRIBUTE instance_id STRING
+@ATTRIBUTE repetition NUMERIC
+@ATTRIBUTE DatasetRatio NUMERIC
+@ATTRIBUTE InverseDatasetRatio NUMERIC
+@ATTRIBUTE KurtosisMax NUMERIC
+@ATTRIBUTE KurtosisMean NUMERIC
+@ATTRIBUTE KurtosisMin NUMERIC
+@ATTRIBUTE KurtosisSTD NUMERIC
+@ATTRIBUTE LogDatasetRatio NUMERIC
+@ATTRIBUTE LogInverseDatasetRatio NUMERIC
+@ATTRIBUTE LogNumberOfFeatures NUMERIC
+@ATTRIBUTE LogNumberOfInstances NUMERIC
+@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC
+@ATTRIBUTE NumberOfFeatures NUMERIC
+@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC
+@ATTRIBUTE NumberOfInstances NUMERIC
+@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC
+@ATTRIBUTE NumberOfMissingValues NUMERIC
+@ATTRIBUTE NumberOfNumericFeatures NUMERIC
+@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC
+@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC
+@ATTRIBUTE PercentageOfMissingValues NUMERIC
+@ATTRIBUTE RatioNominalToNumerical NUMERIC
+@ATTRIBUTE RatioNumericalToNominal NUMERIC
+@ATTRIBUTE SkewnessMax NUMERIC
+@ATTRIBUTE SkewnessMean NUMERIC
+@ATTRIBUTE SkewnessMin NUMERIC
+@ATTRIBUTE SkewnessSTD NUMERIC
+@ATTRIBUTE SymbolsMax NUMERIC
+@ATTRIBUTE SymbolsMean NUMERIC
+@ATTRIBUTE SymbolsMin NUMERIC
+@ATTRIBUTE SymbolsSTD NUMERIC
+@ATTRIBUTE SymbolsSum NUMERIC
+
+@DATA
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+2313,1.0,0.0010851871947911015,921.5,-1.1835296154022217,-1.1945379376411438,-1.2065609693527222,0.006796490696688237,-6.826002777109878,6.826002777109878,2.0794415416798357,8.905444318789714,0.0,8.0,0.0,7372.0,0.0,0.0,8.0,0.0,0.0,0.0,0.0,0.0,0.021982628852128983,-0.006175302842166275,-0.017840616405010223,0.012486298625528315,0.0,0.0,0.0,0.0,0.0
+2315,1.0,0.0016277807921866521,614.3333333333334,323.7693923069449,79.67254878426856,0.9639408588409424,105.42530803556463,-6.420537669001714,6.420537669001714,2.4849066497880004,8.905444318789714,0.0,12.0,0.0,7372.0,0.0,0.0,12.0,0.0,0.0,0.0,0.0,0.0,14.269277572631836,5.179579322536786,-0.7831974029541016,4.424204515765703,0.0,0.0,0.0,0.0,0.0
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+4769,1.0,0.0032323232323232323,309.375,7655.9724020822,873.8117624958829,-0.6266317367553711,1996.0150908399373,-5.734554133322955,5.734554133322955,3.6888794541139363,9.423433587436891,0.0,40.0,0.0,12375.0,0.0,0.0,40.0,0.0,0.0,0.0,0.0,0.0,2.352463722229004,-4.606731435842812,-84.36795043945312,17.29910265091775,0.0,0.0,0.0,0.0,0.0
+4772,1.0,0.004340748779164406,230.375,-1.1639093160629272,-1.1978388242423534,-1.2279666662216187,0.014321283460086303,-5.439708415989988,5.439708415989988,3.4657359027997265,8.905444318789714,0.0,32.0,0.0,7372.0,0.0,0.0,32.0,0.0,0.0,0.0,0.0,0.0,0.02432716079056263,-0.0018090461508109001,-0.03165235370397568,0.012199985987808441,0.0,0.0,0.0,0.0,0.0
+4774,1.0,0.00027254640102477445,3669.1,3.5124981893297544,-0.7548393935294911,-1.8211298967830503,1.2223250961372305,-8.2077016793668,8.2077016793668,2.302585092994046,10.510286772360844,3.0,10.0,0.0,36691.0,0.0,0.0,7.0,0.0,0.0,0.0,0.42857142857142855,2.3333333333333335,2.347870990776486,0.253761021503051,-1.1681448233357288,0.8913762857594408,3.0,2.3333333333333335,2.0,0.4714045207910317,7.0
+4779,1.0,0.050436953807740326,19.826732673267326,2023.5899658203125,78.89800691269137,-0.3645970821380615,269.96031674038045,-2.9870311621012546,2.9870311621012546,5.308267697401205,8.29529885950246,0.0,202.0,0.0,4005.0,0.0,0.0,202.0,0.0,0.0,0.0,0.0,0.0,41.17897415161133,2.253734525578963,-36.846744537353516,7.063697262194517,0.0,0.0,0.0,0.0,0.0
+4790,1.0,0.03139069534767384,31.856573705179283,7991.003811837606,642.1047528984591,-0.8172917366027832,1337.1404483968322,-3.4612437564885066,3.461243756488507,5.5254529391317835,8.98669669562029,0.0,251.0,0.0,7996.0,0.0,0.0,251.0,0.0,0.0,0.0,0.0,0.0,89.40361785888672,15.124316601668054,-44.16343307495117,19.170129417009754,0.0,0.0,0.0,0.0,0.0
+4796,1.0,0.03326663331665833,30.06015037593985,7991.003811837606,463.5706836305616,-0.18612432479858398,1325.5833172784367,-3.403200386838592,3.4032003868385914,5.583496308781699,8.98669669562029,0.0,266.0,0.0,7996.0,0.0,0.0,266.0,0.0,0.0,0.0,0.0,0.0,89.40361785888672,9.274567123598837,-2.195953845977783,17.747106637392935,0.0,0.0,0.0,0.0,0.0
+4835,1.0,0.002145922746781116,466.0,0.8862950801849365,0.45250606536865234,-0.651503324508667,0.5315639913043118,-6.144185634125646,6.144185634125646,1.791759469228055,7.935945103353701,0.0,6.0,0.0,2796.0,0.0,0.0,6.0,0.0,0.0,0.0,0.0,0.0,0.5836177468299866,0.19801302642251054,-0.7145333290100098,0.45392537178033815,0.0,0.0,0.0,0.0,0.0
+4881,1.0,0.004340748779164406,230.375,8.398560523986816,0.8951264098286629,-1.3067413568496704,3.2451089643474926,-5.439708415989988,5.439708415989988,3.4657359027997265,8.905444318789714,0.0,32.0,0.0,7372.0,0.0,0.0,32.0,0.0,0.0,0.0,0.0,0.0,2.1736176013946533,0.617036843628739,-0.022607745602726936,0.8860650474358256,0.0,0.0,0.0,0.0,0.0
+4883,1.0,0.0016277807921866521,614.3333333333334,323.7693923069449,79.67254878426856,0.9639408588409424,105.42530803556463,-6.420537669001714,6.420537669001714,2.4849066497880004,8.905444318789714,0.0,12.0,0.0,7372.0,0.0,0.0,12.0,0.0,0.0,0.0,0.0,0.0,14.269277572631836,5.179579322536786,-0.7831974029541016,4.424204515765703,0.0,0.0,0.0,0.0,0.0
+4885,1.0,0.00027254640102477445,3669.1,-1.188598230219335,-1.1964564993963673,-1.2026724815368652,0.003664290259953897,-8.2077016793668,8.2077016793668,2.302585092994046,10.510286772360844,0.0,10.0,0.0,36691.0,0.0,0.0,10.0,0.0,0.0,0.0,0.0,0.0,0.018026534467935562,0.0004419666947796941,-0.011050965636968613,0.007399082491716184,0.0,0.0,0.0,0.0,0.0
+4892,1.0,0.0028486163863266412,351.04761904761904,576.329345703125,81.51044419461924,0.9639408588409424,139.3485299654942,-5.860921881066291,5.860921881066291,3.044522437723423,8.905444318789714,0.0,21.0,0.0,7372.0,0.0,0.0,21.0,0.0,0.0,0.0,0.0,0.0,20.96137046813965,5.398353062924885,-0.7831974029541016,4.943832847158382,0.0,0.0,0.0,0.0,0.0
+4893,1.0,0.0007802974884174591,1281.5625,6719.606795643044,437.862979925658,-1.0920182685895878,1622.03517764269,-7.155835315586378,7.155835315586378,2.772588722239781,9.92842403782616,0.0,16.0,0.0,20505.0,0.0,0.0,16.0,0.0,0.0,0.0,0.0,0.0,70.76852416992188,5.9781048079021275,-5.867455959320068,16.97724081800652,0.0,0.0,0.0,0.0,0.0
+5022,1.0,0.011111111111111112,90.0,-1.0762021359915683,-1.3324955659833357,-1.9520033311864657,0.2528537963602448,-4.499809670330265,4.499809670330265,2.302585092994046,6.802394763324311,0.0,10.0,0.0,900.0,0.0,0.0,10.0,0.0,0.0,0.0,0.0,0.0,0.33015167713165283,0.019484431575983764,-0.31836414337158203,0.19549899975606772,0.0,0.0,0.0,0.0,0.0
+5024,1.0,0.0044444444444444444,225.0,-0.866234540939331,-1.0928374249016086,-1.3201078176498413,0.16201579691665904,-5.41610040220442,5.41610040220442,1.3862943611198906,6.802394763324311,0.0,4.0,0.0,900.0,0.0,0.0,4.0,0.0,0.0,0.0,0.0,0.0,0.28722548484802246,0.05653903912752867,-0.35667797923088074,0.256064936877358,0.0,0.0,0.0,0.0,0.0
diff --git a/test/test_ensemble_builder/data/.auto-sklearn/models/0.1.0.0.model b/autosklearn/metalearning/files/root_mean_squared_error_regression_sparse/readme.txt
similarity index 100%
rename from test/test_ensemble_builder/data/.auto-sklearn/models/0.1.0.0.model
rename to autosklearn/metalearning/files/root_mean_squared_error_regression_sparse/readme.txt
diff --git a/autosklearn/metrics/__init__.py b/autosklearn/metrics/__init__.py
index 51928fb3c5..62ed48e799 100644
--- a/autosklearn/metrics/__init__.py
+++ b/autosklearn/metrics/__init__.py
@@ -127,12 +127,28 @@ def __call__(
         score : float
             Score function applied to prediction of estimator on X.
         """
+
+        if self._score_func is sklearn.metrics.log_loss:
+            n_labels_pred = np.array(y_pred).reshape((len(y_pred), -1)).shape[1]
+            n_labels_test = len(np.unique(y_true))
+            if n_labels_pred != n_labels_test:
+                labels = list(range(n_labels_pred))
+                if sample_weight is not None:
+                    return self._sign * self._score_func(y_true, y_pred,
+                                                         sample_weight=sample_weight,
+                                                         labels=labels,
+                                                         **self._kwargs)
+                else:
+                    return self._sign * self._score_func(y_true, y_pred,
+                                                         labels=labels, **self._kwargs)
+
         if sample_weight is not None:
             return self._sign * self._score_func(y_true, y_pred,
                                                  sample_weight=sample_weight,
                                                  **self._kwargs)
         else:
-            return self._sign * self._score_func(y_true, y_pred, **self._kwargs)
+            return self._sign * self._score_func(y_true, y_pred,
+                                                 **self._kwargs)
 
 
 class _ThresholdScorer(Scorer):
@@ -339,7 +355,15 @@ def calculate_score(
             metric_dict[metric.name] = metric
             for metric_ in REGRESSION_METRICS:
                 func = REGRESSION_METRICS[metric_]
-                score_dict[func.name] = func(solution, cprediction)
+                try:
+                    score_dict[func.name] = func(solution, cprediction)
+                except ValueError as e:
+                    print(e, e.args[0])
+                    if e.args[0] == "Mean Squared Logarithmic Error cannot be used when " \
+                                    "targets contain negative values.":
+                        continue
+                    else:
+                        raise e
 
         else:
             metric_dict = copy.copy(CLASSIFICATION_METRICS)
diff --git a/autosklearn/smbo.py b/autosklearn/smbo.py
index 93b13b8bc8..316c2fa880 100644
--- a/autosklearn/smbo.py
+++ b/autosklearn/smbo.py
@@ -3,6 +3,7 @@
 import os
 import time
 import traceback
+import typing
 import warnings
 
 import dask.distributed
@@ -23,6 +24,7 @@
     BINARY_CLASSIFICATION, TASK_TYPES_TO_STRING, CLASSIFICATION_TASKS, \
     REGRESSION_TASKS, MULTICLASS_CLASSIFICATION, REGRESSION, \
     MULTIOUTPUT_REGRESSION
+from autosklearn.ensemble_builder import EnsembleBuilderManager
 from autosklearn.metalearning.mismbo import suggest_via_metalearning
 from autosklearn.data.abstract_data_manager import AbstractDataManager
 from autosklearn.evaluation import ExecuteTaFuncWithQueue, get_cost_of_crash
@@ -37,10 +39,11 @@
     'LandmarkDecisionTree',
     'LandmarkLDA',
     'LandmarkNaiveBayes',
+    'LandmarkRandomNodeLearner',
     'PCAFractionOfComponentsFor95PercentVariance',
     'PCAKurtosisFirstPC',
     'PCASkewnessFirstPC',
-    'PCA'
+    'PCA',
 }
 
 EXCLUDE_META_FEATURES_REGRESSION = {
@@ -98,15 +101,18 @@ def _calculate_metafeatures(data_feat_type, data_info_task, basename,
     return result
 
 
-def _calculate_metafeatures_encoded(basename, x_train, y_train, watcher,
+def _calculate_metafeatures_encoded(data_feat_type, basename, x_train, y_train, watcher,
                                     task, logger):
     EXCLUDE_META_FEATURES = EXCLUDE_META_FEATURES_CLASSIFICATION \
         if task in CLASSIFICATION_TASKS else EXCLUDE_META_FEATURES_REGRESSION
 
     task_name = 'CalculateMetafeaturesEncoded'
     watcher.start_task(task_name)
+    categorical = [True if feat_type.lower() in ['categorical'] else False
+                   for feat_type in data_feat_type]
+
     result = calculate_all_metafeatures_encoded_labels(
-        x_train, y_train, categorical=[False] * x_train.shape[1],
+        x_train, y_train, categorical=categorical,
         dataset_name=basename, dont_calculate=EXCLUDE_META_FEATURES)
     for key in list(result.metafeature_values.keys()):
         if result.metafeature_values[key].type_ != 'METAFEATURE':
@@ -214,7 +220,9 @@ def __init__(self, config_space, dataset_name,
                  exclude_preprocessors=None,
                  disable_file_output=False,
                  smac_scenario_args=None,
-                 get_smac_object_callback=None):
+                 get_smac_object_callback=None,
+                 ensemble_callback: typing.Optional[EnsembleBuilderManager] = None,
+                 ):
         super(AutoMLSMBO, self).__init__()
         # data related
         self.dataset_name = dataset_name
@@ -256,6 +264,8 @@ def __init__(self, config_space, dataset_name,
         self.smac_scenario_args = smac_scenario_args
         self.get_smac_object_callback = get_smac_object_callback
 
+        self.ensemble_callback = ensemble_callback
+
         dataset_name_ = "" if dataset_name is None else dataset_name
         logger_name = '%s(%d):%s' % (self.__class__.__name__, self.seed, ":" + dataset_name_)
         self.logger = get_logger(logger_name)
@@ -331,6 +341,7 @@ def _calculate_metafeatures_encoded(self):
             warnings.showwarning = self._send_warnings_to_log
 
             meta_features_encoded = _calculate_metafeatures_encoded(
+                self.datamanager.feat_type,
                 self.dataset_name,
                 self.datamanager.data['X_train'],
                 self.datamanager.data['Y_train'],
@@ -414,11 +425,8 @@ def run_smbo(self):
             else:
                 raise ValueError(self.task)
 
-        backend_copy = copy.deepcopy(self.backend)
-        backend_copy.context.delete_output_folder_after_terminate = False
-        backend_copy.context.delete_tmp_folder_after_terminate = False
         ta_kwargs = dict(
-            backend=backend_copy,
+            backend=copy.deepcopy(self.backend),
             autosklearn_seed=seed,
             resampling_strategy=self.resampling_strategy,
             initial_num_run=num_run,
@@ -489,6 +497,9 @@ def run_smbo(self):
         else:
             smac = get_smac_object(**smac_args)
 
+        if self.ensemble_callback is not None:
+            smac.register_callback(self.ensemble_callback)
+
         smac.optimize()
 
         self.runhistory = smac.solver.runhistory
@@ -581,7 +592,6 @@ def get_metalearning_suggestions(self):
                 else:
                     with warnings.catch_warnings():
                         warnings.showwarning = self._send_warnings_to_log
-                        self.datamanager.perform1HotEncoding()
                     meta_features_encoded = \
                         self._calculate_metafeatures_encoded_with_limits(
                             metafeature_calculation_time_limit)
diff --git a/autosklearn/util/backend.py b/autosklearn/util/backend.py
index 19f027fb29..9606c17642 100644
--- a/autosklearn/util/backend.py
+++ b/autosklearn/util/backend.py
@@ -25,7 +25,7 @@
 
 def create(
     temporary_directory: str,
-    output_directory: str,
+    output_directory: Optional[str],
     delete_tmp_folder_after_terminate: bool = True,
     delete_output_folder_after_terminate: bool = True,
 ) -> 'Backend':
@@ -38,10 +38,7 @@ def create(
     return backend
 
 
-def get_randomized_directory_names(
-    temporary_directory: Optional[str] = None,
-    output_directory: Optional[str] = None,
-) -> Tuple[str, str]:
+def get_randomized_directory_name(temporary_directory: Optional[str] = None) -> str:
     uuid_str = str(uuid.uuid1(clock_seq=os.getpid()))
 
     temporary_directory = (
@@ -55,25 +52,14 @@ def get_randomized_directory_names(
         )
     )
 
-    output_directory = (
-        output_directory
-        if output_directory
-        else os.path.join(
-            tempfile.gettempdir(),
-            "autosklearn_output_{}".format(
-                uuid_str,
-            ),
-        )
-    )
-
-    return temporary_directory, output_directory
+    return temporary_directory
 
 
 class BackendContext(object):
 
     def __init__(self,
                  temporary_directory: str,
-                 output_directory: str,
+                 output_directory: Optional[str],
                  delete_tmp_folder_after_terminate: bool,
                  delete_output_folder_after_terminate: bool,
                  ):
@@ -89,19 +75,22 @@ def __init__(self,
         self._tmp_dir_created = False
         self._output_dir_created = False
 
-        self._temporary_directory, self._output_directory = (
-            get_randomized_directory_names(
+        self._temporary_directory = (
+            get_randomized_directory_name(
                 temporary_directory=temporary_directory,
-                output_directory=output_directory,
             )
         )
+        self._output_directory = output_directory
         self._logger = logging.get_logger(__name__)
         self.create_directories()
 
     @property
-    def output_directory(self) -> str:
-        # make sure that tilde does not appear on the path.
-        return os.path.expanduser(os.path.expandvars(self._output_directory))
+    def output_directory(self) -> Optional[str]:
+        if self._output_directory is not None:
+            # make sure that tilde does not appear on the path.
+            return os.path.expanduser(os.path.expandvars(self._output_directory))
+        else:
+            return None
 
     @property
     def temporary_directory(self) -> str:
@@ -114,14 +103,12 @@ def create_directories(self) -> None:
         self._tmp_dir_created = True
 
         # Exception is raised if self.output_directory already exists.
-        os.makedirs(self.output_directory)
-        self._output_dir_created = True
-
-    def __del__(self) -> None:
-        self.delete_directories(force=False)
+        if self.output_directory is not None:
+            os.makedirs(self.output_directory)
+            self._output_dir_created = True
 
     def delete_directories(self, force: bool = True) -> None:
-        if self.delete_output_folder_after_terminate or force:
+        if self.output_directory and (self.delete_output_folder_after_terminate or force):
             if self._output_dir_created is False:
                 raise ValueError("Failed to delete output dir: %s because auto-sklearn did not "
                                  "create it. Please make sure that the specified output dir does "
@@ -130,12 +117,14 @@ def delete_directories(self, force: bool = True) -> None:
             try:
                 shutil.rmtree(self.output_directory)
             except Exception:
-                if self._logger is not None:
-                    self._logger.warning("Could not delete output dir: %s" %
-                                         self.output_directory)
-                else:
-                    print("Could not delete output dir: %s" %
-                          self.output_directory)
+                try:
+                    if self._logger is not None:
+                        self._logger.warning("Could not delete output dir: %s" %
+                                             self.output_directory)
+                    else:
+                        print("Could not delete output dir: %s" % self.output_directory)
+                except Exception:
+                    print("Could not delete output dir: %s" % self.output_directory)
 
         if self.delete_tmp_folder_after_terminate or force:
             if self._tmp_dir_created is False:
@@ -146,9 +135,13 @@ def delete_directories(self, force: bool = True) -> None:
             try:
                 shutil.rmtree(self.temporary_directory)
             except Exception:
-                if self._logger is not None:
-                    self._logger.warning("Could not delete tmp dir: %s" % self.temporary_directory)
-                else:
+                try:
+                    if self._logger is not None:
+                        self._logger.warning(
+                            "Could not delete tmp dir: %s" % self.temporary_directory)
+                    else:
+                        print("Could not delete tmp dir: %s" % self.temporary_directory)
+                except Exception:
                     print("Could not delete tmp dir: %s" % self.temporary_directory)
 
 
@@ -178,7 +171,7 @@ def __init__(self, context: BackendContext):
         self._make_internals_directory()
 
     @property
-    def output_directory(self) -> str:
+    def output_directory(self) -> Optional[str]:
         return self.context.output_directory
 
     @property
@@ -190,7 +183,10 @@ def _make_internals_directory(self) -> None:
             os.makedirs(self.internals_directory)
         except Exception as e:
             self.logger.debug("_make_internals_directory: %s" % e)
-            pass
+        try:
+            os.makedirs(self.get_runs_directory())
+        except Exception as e:
+            self.logger.debug("_make_internals_directory: %s" % e)
 
     def _get_start_time_filename(self, seed: Union[str, int]) -> str:
         if isinstance(seed, str):
@@ -233,13 +229,6 @@ def get_smac_output_directory_for_run(self, seed: int) -> str:
             'run_%d' % seed
         )
 
-    def get_smac_output_glob(self, smac_run_id: Union[str, int] = 1) -> str:
-        return os.path.join(
-            glob.escape(self.temporary_directory),
-            'smac3-output',
-            'run_%s' % str(smac_run_id),
-        )
-
     def _get_targets_ensemble_filename(self) -> str:
         return os.path.join(self.internals_directory,
                             "true_targets_ensemble.npy")
@@ -315,79 +304,25 @@ def load_datamanager(self) -> AbstractDataManager:
             with open(filepath, 'rb') as fh:
                 return pickle.load(fh)
 
-    def get_done_directory(self) -> str:
-        return os.path.join(self.internals_directory, 'done')
+    def get_runs_directory(self) -> str:
+        return os.path.join(self.internals_directory, 'runs')
 
-    def note_numrun_as_done(self, seed: int, num_run: int) -> None:
-        done_directory = self.get_done_directory()
-        os.makedirs(done_directory, exist_ok=True)
-        done_path = os.path.join(done_directory, '%d_%d' % (seed, num_run))
-        with open(done_path, 'w'):
-            pass
+    def get_numrun_directory(self, seed: int, num_run: int, budget: float) -> str:
+        return os.path.join(self.internals_directory, 'runs', '%d_%d_%s' % (seed, num_run, budget))
 
-    def get_model_dir(self) -> str:
-        return os.path.join(self.internals_directory, 'models')
-
-    def get_cv_model_dir(self) -> str:
-        return os.path.join(self.internals_directory, 'cv_models')
-
-    def get_model_path(self, seed: int, idx: int, budget: float) -> str:
-        return os.path.join(self.get_model_dir(),
-                            '%s.%s.%s.model' % (seed, idx, budget))
-
-    def get_cv_model_path(self, seed: int, idx: int, budget: float) -> str:
-        return os.path.join(self.get_cv_model_dir(),
-                            '%s.%s.%s.model' % (seed, idx, budget))
-
-    def save_model(self, model: Pipeline, filepath: str) -> None:
-        with tempfile.NamedTemporaryFile('wb', dir=os.path.dirname(
-                filepath), delete=False) as fh:
-            pickle.dump(model, fh, -1)
-            tempname = fh.name
+    def get_model_filename(self, seed: int, idx: int, budget: float) -> str:
+        return '%s.%s.%s.model' % (seed, idx, budget)
 
-        os.rename(tempname, filepath)
+    def get_cv_model_filename(self, seed: int, idx: int, budget: float) -> str:
+        return '%s.%s.%s.cv_model' % (seed, idx, budget)
 
     def list_all_models(self, seed: int) -> List[str]:
-        model_directory = self.get_model_dir()
-        if seed >= 0:
-            model_files = glob.glob(
-                os.path.join(glob.escape(model_directory), '%s.*.*.model' % seed)
-            )
-        else:
-            model_files = os.listdir(model_directory)
-            model_files = [os.path.join(model_directory, model_file)
-                           for model_file in model_files]
-
+        runs_directory = self.get_runs_directory()
+        model_files = glob.glob(
+            os.path.join(glob.escape(runs_directory), '%d_*' % seed, '%s.*.*.model' % seed)
+        )
         return model_files
 
-    def load_all_models(self, seed: int) -> List:
-        model_files = self.list_all_models(seed)
-        models = self.load_models_by_file_names(model_files)
-        return models
-
-    def load_models_by_file_names(self, model_file_names: List[str]) -> Pipeline:
-        models = dict()
-
-        for model_file in model_file_names:
-            # File names are like: {seed}.{index}.{budget}.model
-            if model_file.endswith('/'):
-                model_file = model_file[:-1]
-            if not model_file.endswith('.model') and \
-                    not model_file.endswith('.model'):
-                continue
-
-            basename = os.path.basename(model_file)
-
-            basename_parts = basename.split('.')
-            seed = int(basename_parts[0])
-            idx = int(basename_parts[1])
-            budget = float(basename_parts[2])
-
-            models[(seed, idx, budget)] = self.load_model_by_seed_and_id_and_budget(
-                seed, idx, budget)
-
-        return models
-
     def load_models_by_identifiers(self, identifiers: List[Tuple[int, int, float]]
                                    ) -> Dict:
         models = dict()
@@ -403,7 +338,7 @@ def load_model_by_seed_and_id_and_budget(self, seed: int,
                                              idx: int,
                                              budget: float
                                              ) -> Pipeline:
-        model_directory = self.get_model_dir()
+        model_directory = self.get_numrun_directory(seed, idx, budget)
 
         model_file_name = '%s.%s.%s.model' % (seed, idx, budget)
         model_file_path = os.path.join(model_directory, model_file_name)
@@ -426,13 +361,51 @@ def load_cv_model_by_seed_and_id_and_budget(self,
                                                 idx: int,
                                                 budget: float
                                                 ) -> Pipeline:
-        model_directory = self.get_cv_model_dir()
+        model_directory = self.get_numrun_directory(seed, idx, budget)
 
-        model_file_name = '%s.%s.%s.model' % (seed, idx, budget)
+        model_file_name = '%s.%s.%s.cv_model' % (seed, idx, budget)
         model_file_path = os.path.join(model_directory, model_file_name)
         with open(model_file_path, 'rb') as fh:
             return pickle.load(fh)
 
+    def save_numrun_to_dir(
+        self, seed: int, idx: int, budget: float, model: Optional[Pipeline],
+        cv_model: Optional[Pipeline], ensemble_predictions: Optional[np.ndarray],
+        valid_predictions: Optional[np.ndarray], test_predictions: Optional[np.ndarray],
+    ) -> None:
+        runs_directory = self.get_runs_directory()
+        tmpdir = tempfile.mkdtemp(dir=runs_directory)
+        if model is not None:
+            file_path = os.path.join(tmpdir, self.get_model_filename(seed, idx, budget))
+            with open(file_path, 'wb') as fh:
+                pickle.dump(model, fh, -1)
+
+        if cv_model is not None:
+            file_path = os.path.join(tmpdir, self.get_cv_model_filename(seed, idx, budget))
+            with open(file_path, 'wb') as fh:
+                pickle.dump(cv_model, fh, -1)
+
+        for preds, subset in (
+            (ensemble_predictions, 'ensemble'),
+            (valid_predictions, 'valid'),
+            (test_predictions, 'test')
+        ):
+            if preds is not None:
+                file_path = os.path.join(
+                    tmpdir,
+                    self.get_prediction_filename(subset, seed, idx, budget)
+                )
+                with open(file_path, 'wb') as fh:
+                    pickle.dump(preds.astype(np.float32), fh, -1)
+        try:
+            os.rename(tmpdir, self.get_numrun_directory(seed, idx, budget))
+        except OSError:
+            if os.path.exists(self.get_numrun_directory(seed, idx, budget)):
+                os.rename(self.get_numrun_directory(seed, idx, budget),
+                          os.path.join(runs_directory, tmpdir + '.old'))
+                os.rename(tmpdir, self.get_numrun_directory(seed, idx, budget))
+                shutil.rmtree(os.path.join(runs_directory, tmpdir + '.old'))
+
     def get_ensemble_dir(self) -> str:
         return os.path.join(self.internals_directory, 'ensembles')
 
@@ -474,38 +447,20 @@ def save_ensemble(self, ensemble: AbstractEnsemble, idx: int, seed: int) -> None
             tempname = fh.name
         os.rename(tempname, filepath)
 
-    def _get_prediction_output_dir(self, subset: str) -> str:
-        return os.path.join(self.internals_directory,
-                            'predictions_%s' % subset)
-
-    def get_prediction_output_path(self, subset: str,
-                                   automl_seed: Union[str, int],
-                                   idx: int,
-                                   budget: float
-                                   ) -> str:
-        output_dir = self._get_prediction_output_dir(subset)
-        # Make sure an output directory exists
-        if not os.path.exists(output_dir):
-            os.makedirs(output_dir)
-
-        return os.path.join(output_dir, 'predictions_%s_%s_%s_%s.npy' %
-                            (subset, automl_seed, idx, budget))
-
-    def save_predictions_as_npy(self,
-                                predictions: np.ndarray,
-                                filepath: str
-                                ) -> None:
-        with tempfile.NamedTemporaryFile('wb', dir=os.path.dirname(
-                filepath), delete=False) as fh:
-            pickle.dump(predictions.astype(np.float32), fh, -1)
-            tempname = fh.name
-        os.rename(tempname, filepath)
+    def get_prediction_filename(self, subset: str,
+                                automl_seed: Union[str, int],
+                                idx: int,
+                                budget: float
+                                ) -> str:
+        return 'predictions_%s_%s_%s_%s.npy' % (subset, automl_seed, idx, budget)
 
     def save_predictions_as_txt(self,
                                 predictions: np.ndarray,
                                 subset: str,
                                 idx: int, precision: int,
                                 prefix: Optional[str] = None) -> None:
+        if not self.output_directory:
+            return
         # Write prediction scores in prescribed format
         filepath = os.path.join(
             self.output_directory,
diff --git a/autosklearn/util/dependencies.py b/autosklearn/util/dependencies.py
index f3e31416b4..d213000871 100644
--- a/autosklearn/util/dependencies.py
+++ b/autosklearn/util/dependencies.py
@@ -72,7 +72,8 @@ def __init__(self, package_name: str):
 
 
 class IncorrectPackageVersionError(Exception):
-    error_message = "'{name} {installed_version}' version mismatch ({operation}{required_version})"
+    error_message = "found '{name}' version {installed_version} but requires {name} version " \
+                    "{operation}{required_version}"
 
     def __init__(self,
                  package_name: str,
diff --git a/doc/releases.rst b/doc/releases.rst
index ed6d6aefc6..65cd78c2f4 100644
--- a/doc/releases.rst
+++ b/doc/releases.rst
@@ -11,6 +11,29 @@
 Releases
 ========
 
+Version 0.11.0
+==============
+
+* ADD #992: Move ensemble building from being a separate process to a job submitted to the dask
+  cluster. This allows for better control of the memory used in multiprocessing settings.
+* FIX #905: Make ``AutoSklearn2Classifier`` picklable.
+* FIX #970: Fix a bug where Auto-sklearn would fail if categorical features are passed as a
+  Pandas Dataframe.
+* MAINT #772: Improve error message in case of dummy prediction failure.
+* MAINT #948: Finally use Pandas >= 1.0.
+* MAINT #973: Improve meta-data by running meta-data generation for more time and separately for
+  important metrics.
+* MAINT #997: Improve memory handling in the ensemble building process. This allows building
+  ensembles for larger datasets.
+
+Contributors v0.11.0
+********************
+
+* Matthias Feurer
+* Francisco Rivera
+* Karl Leswing
+* ROHIT AGARWAL
+
 Version 0.10.0
 ==============
 
diff --git a/examples/60_search/example_parallel_manual_spawning.py b/examples/60_search/example_parallel_manual_spawning.py
index fac9426035..a7e8a2709c 100644
--- a/examples/60_search/example_parallel_manual_spawning.py
+++ b/examples/60_search/example_parallel_manual_spawning.py
@@ -130,7 +130,7 @@ def start_cli_worker(scheduler_address):
         automl = AutoSklearnClassifier(
             time_left_for_this_task=30,
             per_run_time_limit=10,
-            ml_memory_limit=1024,
+            memory_limit=1024,
             tmp_folder=tmp_folder,
             output_folder=output_folder,
             seed=777,
diff --git a/examples/60_search/example_parallel_n_jobs.py b/examples/60_search/example_parallel_n_jobs.py
index 2e6d3be20e..5ff2a58602 100644
--- a/examples/60_search/example_parallel_n_jobs.py
+++ b/examples/60_search/example_parallel_n_jobs.py
@@ -44,7 +44,7 @@
         output_folder='/tmp/autosklearn_parallel_1_example_out',
         n_jobs=4,
         # Each one of the 4 jobs is allocated 3GB
-        ml_memory_limit=3072,
+        memory_limit=3072,
         seed=5,
     )
     automl.fit(X_train, y_train, dataset_name='breast_cancer')
diff --git a/examples/60_search/example_random_search.py b/examples/60_search/example_random_search.py
index c0a15e9b68..0624b6cdd8 100644
--- a/examples/60_search/example_random_search.py
+++ b/examples/60_search/example_random_search.py
@@ -45,7 +45,7 @@ def get_roar_object_callback(
 ):
     """Random online adaptive racing."""
 
-    if n_jobs > 1 or dask_client:
+    if n_jobs > 1 or (dask_client and len(dask_client.nthreads()) > 1):
         raise ValueError("Please make sure to guard the code invoking Auto-sklearn by "
                          "`if __name__ == '__main__'` and remove this exception.")
 
@@ -95,7 +95,7 @@ def get_random_search_object_callback(
 ):
     """Random search."""
 
-    if n_jobs > 1 or dask_client:
+    if n_jobs > 1 or (dask_client and len(dask_client.nthreads()) > 1):
         raise ValueError("Please make sure to guard the code invoking Auto-sklearn by "
                          "`if __name__ == '__main__'` and remove this exception.")
 
diff --git a/examples/60_search/example_successive_halving.py b/examples/60_search/example_successive_halving.py
index 8ad253f156..7fd2924e52 100644
--- a/examples/60_search/example_successive_halving.py
+++ b/examples/60_search/example_successive_halving.py
@@ -37,7 +37,7 @@ def get_smac_object(
         from smac.runhistory.runhistory2epm import RunHistory2EPM4LogCost
         from smac.scenario.scenario import Scenario
 
-        if n_jobs > 1 or dask_client:
+        if n_jobs > 1 or (dask_client and len(dask_client.nthreads()) > 1):
             raise ValueError("Please make sure to guard the code invoking Auto-sklearn by "
                              "`if __name__ == '__main__'` and remove this exception.")
 
@@ -84,8 +84,8 @@ def get_smac_object(
 # ==========================
 
 automl = autosklearn.classification.AutoSklearnClassifier(
-    time_left_for_this_task=30,
-    per_run_time_limit=5,
+    time_left_for_this_task=40,
+    per_run_time_limit=10,
     tmp_folder='/tmp/autosklearn_sh_example_tmp',
     output_folder='/tmp/autosklearn_sh_example_out',
     disable_evaluator_output=False,
@@ -117,10 +117,10 @@ def get_smac_object(
     sklearn.model_selection.train_test_split(X, y, random_state=1, shuffle=True)
 
 automl = autosklearn.classification.AutoSklearnClassifier(
-    time_left_for_this_task=30,
-    per_run_time_limit=5,
-    tmp_folder='/tmp/autosklearn_sh_example_tmp',
-    output_folder='/tmp/autosklearn_sh_example_out',
+    time_left_for_this_task=40,
+    per_run_time_limit=10,
+    tmp_folder='/tmp/autosklearn_sh_example_tmp_01',
+    output_folder='/tmp/autosklearn_sh_example_out_01',
     disable_evaluator_output=False,
     resampling_strategy='cv',
     include_estimators=['extra_trees', 'gradient_boosting', 'random_forest', 'sgd',
@@ -148,10 +148,10 @@ def get_smac_object(
     sklearn.model_selection.train_test_split(X, y, random_state=1, shuffle=True)
 
 automl = autosklearn.classification.AutoSklearnClassifier(
-    time_left_for_this_task=30,
-    per_run_time_limit=5,
-    tmp_folder='/tmp/autosklearn_sh_example_tmp',
-    output_folder='/tmp/autosklearn_sh_example_out',
+    time_left_for_this_task=40,
+    per_run_time_limit=10,
+    tmp_folder='/tmp/autosklearn_sh_example_tmp_cv_02',
+    output_folder='/tmp/autosklearn_sh_example_out_cv_02',
     disable_evaluator_output=False,
     resampling_strategy='cv-iterative-fit',
     include_estimators=['extra_trees', 'gradient_boosting', 'random_forest', 'sgd',
@@ -179,10 +179,10 @@ def get_smac_object(
     sklearn.model_selection.train_test_split(X, y, random_state=1, shuffle=True)
 
 automl = autosklearn.classification.AutoSklearnClassifier(
-    time_left_for_this_task=30,
-    per_run_time_limit=5,
-    tmp_folder='/tmp/autosklearn_sh_example_tmp',
-    output_folder='/tmp/autosklearn_sh_example_out',
+    time_left_for_this_task=40,
+    per_run_time_limit=10,
+    tmp_folder='/tmp/autosklearn_sh_example_tmp_03',
+    output_folder='/tmp/autosklearn_sh_example_out_03',
     disable_evaluator_output=False,
     # 'holdout' with 'train_size'=0.67 is the default argument setting
     # for AutoSklearnClassifier. It is explicitly specified in this example
@@ -212,10 +212,10 @@ def get_smac_object(
     sklearn.model_selection.train_test_split(X, y, random_state=1, shuffle=True)
 
 automl = autosklearn.classification.AutoSklearnClassifier(
-    time_left_for_this_task=30,
-    per_run_time_limit=5,
-    tmp_folder='/tmp/autosklearn_sh_example_tmp',
-    output_folder='/tmp/autosklearn_sh_example_out',
+    time_left_for_this_task=40,
+    per_run_time_limit=10,
+    tmp_folder='/tmp/autosklearn_sh_example_tmp_04',
+    output_folder='/tmp/autosklearn_sh_example_out_04',
     disable_evaluator_output=False,
     # 'holdout' with 'train_size'=0.67 is the default argument setting
     # for AutoSklearnClassifier. It is explicitly specified in this example
diff --git a/examples/80_extending/example_extending_classification.py b/examples/80_extending/example_extending_classification.py
index 466194664e..888e55dbbd 100644
--- a/examples/80_extending/example_extending_classification.py
+++ b/examples/80_extending/example_extending_classification.py
@@ -136,7 +136,7 @@ def get_hyperparameter_search_space(dataset_properties=None):
     # Bellow two flags are provided to speed up calculations
     # Not recommended for a real implementation
     initial_configurations_via_metalearning=0,
-    smac_scenario_args={'runcount_limit': 1},
+    smac_scenario_args={'runcount_limit': 5},
 )
 clf.fit(X_train, y_train)
 
diff --git a/examples/80_extending/example_extending_preprocessor.py b/examples/80_extending/example_extending_preprocessor.py
index 16182595bc..1bedf95a2a 100644
--- a/examples/80_extending/example_extending_preprocessor.py
+++ b/examples/80_extending/example_extending_preprocessor.py
@@ -123,7 +123,7 @@ def get_hyperparameter_search_space(dataset_properties=None):
     # Bellow two flags are provided to speed up calculations
     # Not recommended for a real implementation
     initial_configurations_via_metalearning=0,
-    smac_scenario_args={'runcount_limit': 1},
+    smac_scenario_args={'runcount_limit': 5},
 )
 clf.fit(X_train, y_train)
 
diff --git a/examples/80_extending/example_extending_regression.py b/examples/80_extending/example_extending_regression.py
index bf985c79d8..e6ee7edd1f 100644
--- a/examples/80_extending/example_extending_regression.py
+++ b/examples/80_extending/example_extending_regression.py
@@ -10,6 +10,7 @@
 from ConfigSpace.configuration_space import ConfigurationSpace
 from ConfigSpace.hyperparameters import UniformFloatHyperparameter, \
     UniformIntegerHyperparameter, CategoricalHyperparameter
+from ConfigSpace.conditions import EqualsCondition
 
 import sklearn.metrics
 import autosklearn.regression
@@ -27,11 +28,12 @@
 # ============================================================
 
 class KernelRidgeRegression(AutoSklearnRegressionAlgorithm):
-    def __init__(self, alpha, kernel, gamma, degree, random_state=None):
+    def __init__(self, alpha, kernel, gamma, degree, coef0, random_state=None):
         self.alpha = alpha
         self.kernel = kernel
         self.gamma = gamma
         self.degree = degree
+        self.coef0 = coef0
         self.random_state = random_state
         self.estimator = None
 
@@ -39,12 +41,14 @@ def fit(self, X, y):
         self.alpha = float(self.alpha)
         self.gamma = float(self.gamma)
         self.degree = int(self.degree)
+        self.coef0 = float(self.coef0)
 
         import sklearn.kernel_ridge
         self.estimator = sklearn.kernel_ridge.KernelRidge(alpha=self.alpha,
                                                           kernel=self.kernel,
                                                           gamma=self.gamma,
                                                           degree=self.degree,
+                                                          coef0=self.coef0,
                                                           )
         self.estimator.fit(X, y)
         return self
@@ -71,15 +75,12 @@ def get_properties(dataset_properties=None):
     def get_hyperparameter_search_space(dataset_properties=None):
         cs = ConfigurationSpace()
         alpha = UniformFloatHyperparameter(
-            name='alpha', lower=10 ** -5, upper=1, log=True, default_value=0.1)
+            name='alpha', lower=10 ** -5, upper=1, log=True, default_value=1.0)
         kernel = CategoricalHyperparameter(
             name='kernel',
-            choices=['linear',
-                     'rbf',
-                     'sigmoid',
-                     'polynomial',
-                     ],
-            default_value='linear'
+            # We restrict ourselves to two possible kernels for this example
+            choices=['polynomial', 'rbf'],
+            default_value='polynomial'
         )
         gamma = UniformFloatHyperparameter(
             name='gamma', lower=0.00001, upper=1, default_value=0.1, log=True
@@ -87,7 +88,13 @@ def get_hyperparameter_search_space(dataset_properties=None):
         degree = UniformIntegerHyperparameter(
             name='degree', lower=2, upper=5, default_value=3
         )
-        cs.add_hyperparameters([alpha, kernel, gamma, degree])
+        coef0 = UniformFloatHyperparameter(
+            name='coef0', lower=1e-2, upper=1e2, log=True, default_value=1,
+        )
+        cs.add_hyperparameters([alpha, kernel, gamma, degree, coef0])
+        degree_condition = EqualsCondition(degree, kernel, 'polynomial')
+        coef0_condition = EqualsCondition(coef0, kernel, 'polynomial')
+        cs.add_conditions([degree_condition, coef0_condition])
         return cs
 
 
@@ -114,7 +121,7 @@ def get_hyperparameter_search_space(dataset_properties=None):
     # Bellow two flags are provided to speed up calculations
     # Not recommended for a real implementation
     initial_configurations_via_metalearning=0,
-    smac_scenario_args={'runcount_limit': 1},
+    smac_scenario_args={'runcount_limit': 5},
 )
 reg.fit(X_train, y_train)
 
diff --git a/requirements.txt b/requirements.txt
index 45573d0eaf..6b4a85e4ce 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -10,10 +10,10 @@ dask
 distributed
 lockfile
 pyyaml
-pandas<1.0
+pandas>=1.0
 liac-arff
 
 ConfigSpace>=0.4.14,<0.5
-pynisher>=0.4.2
+pynisher>=0.6.1
 pyrfr>=0.7,<0.9
-smac>=0.13,<0.14
+smac>=0.13.1,<0.14
diff --git a/scripts/01_create_commands.py b/scripts/01_create_commands.py
index a542d0a33e..b56652d2d6 100644
--- a/scripts/01_create_commands.py
+++ b/scripts/01_create_commands.py
@@ -3,6 +3,8 @@
 import os
 import sys
 
+import openml
+
 sys.path.append('.')
 from update_metadata_util import classification_tasks, regression_tasks
 
@@ -23,6 +25,13 @@
 commands = []
 for task_id in (classification_tasks if not test else (75222, 233, 258)):
     for metric in ('accuracy', 'balanced_accuracy', 'roc_auc', 'logloss'):
+
+        if (
+            len(openml.tasks.get_task(task_id, download_data=False).class_labels) > 2
+            and metric == 'roc_auc'
+        ):
+            continue
+
         command = ('python3 %s --working-directory %s --time-limit 86400 '
                    '--per-run-time-limit 1800 --task-id %d -s 1 --metric %s' %
                    (absolute_script_name, working_directory, task_id, metric))
diff --git a/scripts/02_retrieve_metadata.py b/scripts/02_retrieve_metadata.py
index caebac5a89..6e05f5ec0a 100644
--- a/scripts/02_retrieve_metadata.py
+++ b/scripts/02_retrieve_metadata.py
@@ -29,25 +29,15 @@ def retrieve_matadata(validation_directory, metric, configuration_space,
     configurations_to_ids = dict()
 
     try:
-        possible_experiment_directories = glob.glob(os.path.join(
-            validation_directory, '*', '*'
+        validation_trajectory_files = glob.glob(os.path.join(
+            validation_directory, '*', '*', 'validation_trajectory_*.json'
         ))
     except FileNotFoundError:
         return {}, {}
 
-    for ped in possible_experiment_directories:
+    for validation_trajectory_file in validation_trajectory_files:
         task_name = None
 
-        ped = os.path.join(validation_directory, ped)
-        if not os.path.exists(ped) or not os.path.isdir(ped):
-            continue
-        print("Going through directory %s" % ped)
-
-        validation_trajectory_file = os.path.join(ped, 'smac3-output', 'run_1',
-                                                  'validation_trajectory.json')
-        if not os.path.exists(validation_trajectory_file):
-            print('Could not find output file', validation_trajectory_file)
-            continue
         with open(validation_trajectory_file) as fh:
             validation_trajectory = json.load(fh)
 
diff --git a/scripts/03_calculate_metafeatures.py b/scripts/03_calculate_metafeatures.py
index 4357733535..6808a21399 100644
--- a/scripts/03_calculate_metafeatures.py
+++ b/scripts/03_calculate_metafeatures.py
@@ -1,63 +1,52 @@
 from argparse import ArgumentParser
 from collections import defaultdict, OrderedDict
 import copy
+import logging
 import os
 import sys
-import time
+import unittest.mock
 
 import arff
 import joblib
 import numpy as np
 import pandas as pd
-import pynisher
-import scipy.sparse
 
-from autosklearn.data.abstract_data_manager import perform_one_hot_encoding
-from autosklearn.metalearning.metafeatures import metafeatures, metafeature
-from autosklearn.smbo import EXCLUDE_META_FEATURES_CLASSIFICATION, EXCLUDE_META_FEATURES_REGRESSION
+from autosklearn.constants import BINARY_CLASSIFICATION, MULTICLASS_CLASSIFICATION, REGRESSION
+from autosklearn.metalearning.metafeatures import metafeatures
+from autosklearn.smbo import _calculate_metafeatures, _calculate_metafeatures_encoded, \
+    EXCLUDE_META_FEATURES_REGRESSION, EXCLUDE_META_FEATURES_CLASSIFICATION
+from autosklearn.util.stopwatch import StopWatch
 
 sys.path.append('.')
 from update_metadata_util import load_task, classification_tasks, \
     regression_tasks
 
+logger = logging.getLogger("03_calculate_metafeatures")
 
-def calculate_metafeatures(task_id, exclude):
-    print(task_id)
-    X_train, y_train, X_test, y_test, cat, _ = load_task(task_id)
-    categorical = [True if 'categorical' == c else False for c in cat]
-
-    _metafeatures_labels = metafeatures.calculate_all_metafeatures_with_labels(
-        X_train, y_train, [False] * X_train.shape[1], task_id, dont_calculate=exclude)
-
-    X_train, sparse = perform_one_hot_encoding(scipy.sparse.issparse(X_train),
-                                               categorical, [X_train])
-    X_train = X_train[0]
-    categorical = [False] * X_train.shape[1]
-
-    start_time = time.time()
-    obj = pynisher.enforce_limits(mem_in_mb=3072)(
-        metafeatures.calculate_all_metafeatures_encoded_labels)
-    _metafeatures_encoded_labels = obj(X_train, y_train,
-                                       categorical, task_id, dont_calculate=exclude)
-    end_time = time.time()
-
-    if obj.exit_status == pynisher.MemorylimitException:
-        # During the conversion of the dataset (rescaling, etc...), it can
-        # happen that we run out of memory.
-        _metafeatures_encoded_labels = \
-            metafeature.DatasetMetafeatures(task_id, dict())
-
-        metafeature_calculation_time = (end_time - start_time) / \
-                                       len(metafeatures.npy_metafeatures)
-
-        for metafeature_name in metafeatures.npy_metafeatures:
-            type_ = "HELPERFUNCTION" if metafeature_name not in \
-                                        metafeatures.metafeatures.functions \
-                else "METAFEATURE"
-            _metafeatures_encoded_labels.metafeature_values[metafeature_name] = \
-                metafeature.MetaFeatureValue(metafeature_name, type_, 0, 0,
-                                             np.NaN, metafeature_calculation_time,
-                                             "Memory error during dataset scaling.")
+
+def calculate_metafeatures(task_id):
+    X_train, y_train, X_test, y_test, cat, task_type = load_task(task_id)
+    watch = StopWatch()
+
+    if task_type == 'classification':
+        if len(np.unique(y_train)) == 2:
+            task_type = BINARY_CLASSIFICATION
+        else:
+            task_type = MULTICLASS_CLASSIFICATION
+    else:
+        task_type = REGRESSION
+
+    _metafeatures_labels = _calculate_metafeatures(
+        x_train=X_train, y_train=y_train, data_feat_type=cat,
+        data_info_task=task_type, basename=str(task_id), logger=logger,
+        watcher=watch,
+    )
+
+    _metafeatures_encoded_labels = _calculate_metafeatures_encoded(
+        x_train=X_train, y_train=y_train, data_feat_type=cat,
+        task=task_type, basename=str(task_id), logger=logger,
+        watcher=watch,
+    )
 
     mf = _metafeatures_labels
     mf.metafeature_values.update(
@@ -70,24 +59,20 @@ def calculate_metafeatures(task_id, exclude):
     parser = ArgumentParser()
     parser.add_argument("--working-directory", type=str, required=True)
     parser.add_argument("--memory-limit", type=int, default=3072)
-    parser.add_argument("--n-jobs",
-                        help="Compute metafeatures in parallel if possible.",
-                        type=int, default=1)
     parser.add_argument("--test-mode", action='store_true')
 
     args = parser.parse_args()
     working_directory = args.working_directory
     memory_limit = args.memory_limit
-    n_jobs = args.n_jobs
     test_mode = args.test_mode
 
-    output_directory = os.path.join(working_directory, 'metafeatures')
-    try:
-        os.makedirs(output_directory)
-    except:
-        pass
-
     for task_type in ('classification', 'regression'):
+        output_directory = os.path.join(working_directory, 'metafeatures', task_type)
+        try:
+            os.makedirs(output_directory)
+        except:
+            pass
+
         all_metafeatures = {}
 
         if task_type == 'classification':
@@ -98,9 +83,6 @@ def calculate_metafeatures(task_id, exclude):
         if test_mode:
             tasks = [tasks[0]]
 
-        EXCLUDE_META_FEATURES = EXCLUDE_META_FEATURES_CLASSIFICATION \
-            if task_type == 'classification' else EXCLUDE_META_FEATURES_REGRESSION
-
         tasks = copy.deepcopy(tasks)
         np.random.shuffle(tasks)
 
@@ -110,9 +92,10 @@ def producer():
 
         memory = joblib.Memory(location='/tmp/joblib', verbose=10)
         cached_calculate_metafeatures = memory.cache(calculate_metafeatures)
-        mfs = joblib.Parallel(n_jobs=args.n_jobs) \
-            (joblib.delayed(cached_calculate_metafeatures)(task_id, EXCLUDE_META_FEATURES)
-             for task_id in producer())
+        mfs = [
+            cached_calculate_metafeatures(task_id)
+            for task_id in producer()
+        ]
 
         for mf in mfs:
             if mf is not None:
@@ -140,12 +123,14 @@ def producer():
                         metafeature_value.value
 
         calculation_times = pd.DataFrame(calculation_times).transpose()
+        calculation_times = calculation_times.sort_index()
         with open(os.path.join(output_directory, "calculation_times.csv"),
                   "w") as fh:
             fh.write(calculation_times.to_csv())
 
         # Write all metafeatures in the aslib1.0 format
-        metafeature_values = pd.DataFrame(metafeature_values).transpose()
+        metafeature_values = metafeature_values = pd.DataFrame(metafeature_values).transpose()
+        metafeature_values = metafeature_values.sort_index()
         arff_object = dict()
         arff_object['attributes'] = [('instance_id', 'STRING'),
                                      ('repetition', 'NUMERIC')] + \
@@ -169,9 +154,15 @@ def producer():
         # Feature steps and runtimes according to the aslib1.0 format
         feature_steps = defaultdict(list)
         metafeature_names = list()
+
+        exclude_metafeatures = (
+            EXCLUDE_META_FEATURES_CLASSIFICATION
+            if task_type == 'classification' else EXCLUDE_META_FEATURES_REGRESSION
+        )
+
         for metafeature_name in metafeatures.metafeatures.functions:
 
-            if metafeature_name in EXCLUDE_META_FEATURES:
+            if metafeature_name in exclude_metafeatures:
                 continue
 
             dependency = metafeatures.metafeatures.get_dependency(metafeature_name)
diff --git a/scripts/04_create_aslib_files.py b/scripts/04_create_aslib_files.py
index bd929bdd30..d5e10a9c15 100644
--- a/scripts/04_create_aslib_files.py
+++ b/scripts/04_create_aslib_files.py
@@ -36,16 +36,15 @@
             metadata_sets = itertools.product(
                 [0, 1], [BINARY_CLASSIFICATION, MULTICLASS_CLASSIFICATION],
                 CLASSIFICATION_METRICS)
-            input_directory = os.path.join(working_directory, 'configuration',
-                                           'classification')
         elif task_type == 'regression':
             metadata_sets = itertools.product(
                 [0, 1], [REGRESSION], REGRESSION_METRICS)
-            input_directory = os.path.join(working_directory, 'configuration',
-                                           'regression')
         else:
             raise ValueError(task_type)
 
+        input_directory = os.path.join(working_directory, 'configuration', task_type)
+        metafeatures_dir_for_task = os.path.join(metafeatures_dir, task_type)
+
         for sparse, task, metric in metadata_sets:
             print(TASK_TYPES_TO_STRING[task], metric, sparse)
 
@@ -68,7 +67,7 @@
                 pass
 
             # Create description.txt
-            with open(os.path.join(metafeatures_dir,
+            with open(os.path.join(metafeatures_dir_for_task,
                                    "description.features.txt")) as fh:
                 description_metafeatures = fh.read()
 
@@ -90,7 +89,7 @@
                     fh.write("\n")
 
             # Copy feature values and add instance id
-            with open(os.path.join(metafeatures_dir,
+            with open(os.path.join(metafeatures_dir_for_task,
                                    "feature_values.arff")) as fh:
                 feature_values = arff.load(fh)
 
@@ -102,7 +101,7 @@
                 arff.dump(feature_values, fh)
 
             # Copy feature runstatus and add instance id
-            with open(os.path.join(metafeatures_dir,
+            with open(os.path.join(metafeatures_dir_for_task,
                                    "feature_runstatus.arff")) as fh:
                 feature_runstatus = arff.load(fh)
 
@@ -115,7 +114,7 @@
 
             # Copy feature runstatus and add instance id
             with open(
-                    os.path.join(metafeatures_dir, "feature_costs.arff")) as fh:
+                    os.path.join(metafeatures_dir_for_task, "feature_costs.arff")) as fh:
                 feature_costs = arff.load(fh)
 
             feature_costs['relation'] = scenario_id + "_" + feature_costs[
diff --git a/scripts/2015_nips_paper/run/run_auto_sklearn.py b/scripts/2015_nips_paper/run/run_auto_sklearn.py
index 279fee7806..366280692e 100644
--- a/scripts/2015_nips_paper/run/run_auto_sklearn.py
+++ b/scripts/2015_nips_paper/run/run_auto_sklearn.py
@@ -78,7 +78,7 @@ def run_experiment(working_directory,
             'initial_configurations_via_metalearning': 25,
             'ensemble_size': 0,
             'seed': seed,
-            'ml_memory_limit': 3072,
+            'memory_limit': 3072,
             'resampling_strategy': 'holdout',
             'resampling_strategy_arguments': {'train_size': 0.67},
             'tmp_folder': tmp_dir,
@@ -95,7 +95,7 @@ def run_experiment(working_directory,
             'initial_configurations_via_metalearning': 0,
             'ensemble_size': 0,
             'seed': seed,
-            'ml_memory_limit': 3072,
+            'memory_limit': 3072,
             'resampling_strategy': 'holdout',
             'resampling_strategy_arguments': {'train_size': 0.67},
             'tmp_folder': tmp_dir,
diff --git a/scripts/run_auto-sklearn_for_metadata_generation.py b/scripts/run_auto-sklearn_for_metadata_generation.py
index a5043dfe26..f9354fb88d 100644
--- a/scripts/run_auto-sklearn_for_metadata_generation.py
+++ b/scripts/run_auto-sklearn_for_metadata_generation.py
@@ -43,11 +43,9 @@
 
 configuration_output_dir = os.path.join(working_directory, 'configuration',
                                         task_type)
-try:
-    os.makedirs(configuration_output_dir)
-except:
-    pass
+os.makedirs(configuration_output_dir, exist_ok=True)
 tmp_dir = os.path.join(configuration_output_dir, str(task_id), metric)
+os.makedirs(tmp_dir, exist_ok=True)
 
 tempdir = tempfile.mkdtemp()
 autosklearn_directory = os.path.join(tempdir, "dir")
@@ -59,7 +57,7 @@
     'ensemble_size': 0,
     'ensemble_nbest': 0,
     'seed': seed,
-    'ml_memory_limit': 3072,
+    'memory_limit': 3072,
     'resampling_strategy': 'partial-cv',
     'delete_tmp_folder_after_terminate': False,
     'tmp_folder': autosklearn_directory,
@@ -101,14 +99,7 @@
     raise ValueError(task_type)
 
 automl.fit(X_train, y_train, dataset_name=str(task_id),
-           feat_type=cat)
-data = automl.automl_._backend.load_datamanager()
-# Data manager can't be replaced with save_datamanager, it has to be deleted
-# first
-os.remove(automl.automl_._backend._get_datamanager_pickle_filename())
-data.data['X_test'] = X_test
-data.data['Y_test'] = y_test
-automl.automl_._backend.save_datamanager(data)
+           feat_type=cat, X_test=X_test, y_test=y_test)
 trajectory = automl.trajectory_
 
 incumbent_id_to_model = {}
@@ -120,7 +111,9 @@
 else:
     memory_limit_factor = 2
 
-for entry in trajectory:
+print('Starting to validate configurations')
+for i, entry in enumerate(trajectory):
+    print('Starting to validate configuration %d/%d' % (i + 1, len(trajectory)))
     incumbent_id = entry.incumbent_id
     train_performance = entry.train_perf
     if incumbent_id not in incumbent_id_to_model:
@@ -137,7 +130,7 @@
         # To avoid the output "first run crashed"...
         stats.submitted_ta_runs += 1
         stats.finished_ta_runs += 1
-        memory_lim = memory_limit_factor * automl_arguments['ml_memory_limit']
+        memory_lim = memory_limit_factor * automl_arguments['memory_limit']
         ta = ExecuteTaFuncWithQueue(backend=automl.automl_._backend,
                                     autosklearn_seed=seed,
                                     resampling_strategy='test',
@@ -168,18 +161,34 @@
 
         validated_trajectory.append(list(entry) + [task_id] +
                                     [run_value.additional_info])
+    print('Finished validating configuration %d/%d' % (i + 1, len(trajectory)))
+print('Finished to validate configurations')
 
+print('Starting to copy data to configuration directory', flush=True)
 validated_trajectory = [entry[:2] + [entry[2].get_dictionary()] + entry[3:]
                         for entry in validated_trajectory]
-validated_trajectory_file = os.path.join(autosklearn_directory,
-                                         'smac3-output',
-                                         'run_%d' % seed,
-                                         'validation_trajectory.json')
+validated_trajectory_file = os.path.join(tmp_dir, 'validation_trajectory_%d.json' % seed)
 with open(validated_trajectory_file, 'w') as fh:
     json.dump(validated_trajectory, fh, indent=4)
 
-shutil.copytree(autosklearn_directory, tmp_dir)
+
+for dirpath, dirnames, filenames in os.walk(autosklearn_directory, topdown=False):
+    print(dirpath, dirnames, filenames)
+    for filename in filenames:
+        if filename == 'datamanager.pkl':
+            os.remove(os.path.join(dirpath, filename))
+        elif filename == 'configspace.pcs':
+            os.remove(os.path.join(dirpath, filename))
+    for dirname in dirnames:
+        if dirname in ('models', 'cv_models'):
+            os.rmdir(os.path.join(dirpath, dirname))
+
+
+print('Going to copy the configuration directory')
+shutil.copytree(autosklearn_directory, os.path.join(tmp_dir, 'auto-sklearn-output'))
+print('Finished copying the configuration directory')
 try:
     shutil.rmtree(tempdir)
 except:
     pass
+print('Finished configuring')
diff --git a/setup.py b/setup.py
index f035b0e868..9617112e3e 100644
--- a/setup.py
+++ b/setup.py
@@ -33,7 +33,7 @@
         "pytest-timeout",
         "flaky",
         "pytest-cov",
-
+        "openml",
     ]
 }
 
diff --git a/test/conftest.py b/test/conftest.py
new file mode 100644
index 0000000000..992d8ce372
--- /dev/null
+++ b/test/conftest.py
@@ -0,0 +1,188 @@
+import os
+import shutil
+import time
+import unittest.mock
+
+import dask
+from dask.distributed import Client, get_client
+import psutil
+import pytest
+
+from autosklearn.util.backend import create, Backend
+from autosklearn.automl import AutoML
+
+
+class AutoMLStub(AutoML):
+    def __init__(self):
+        self.__class__ = AutoML
+        self._task = None
+        self._dask_client = None
+        self._is_dask_client_internally_created = False
+
+    def __del__(self):
+        pass
+
+
+@pytest.fixture(scope="function")
+def automl_stub(request):
+    automl = AutoMLStub()
+    automl._seed = 42
+    automl._backend = unittest.mock.Mock(spec=Backend)
+    automl._backend.context = unittest.mock.Mock()
+    automl._delete_output_directories = lambda: 0
+    return automl
+
+
+@pytest.fixture(scope="function")
+def backend(request):
+
+    test_dir = os.path.dirname(__file__)
+    tmp = os.path.join(test_dir, '.tmp__%s__%s' % (request.module.__name__, request.node.name))
+    output = os.path.join(
+        test_dir, '.output__%s__%s' % (request.module.__name__, request.node.name)
+    )
+
+    for dir in (tmp, output):
+        for i in range(10):
+            if os.path.exists(dir):
+                try:
+                    shutil.rmtree(dir)
+                    break
+                except OSError:
+                    time.sleep(1)
+
+    # Make sure the folders we wanna create do not already exist.
+    backend = create(
+        tmp,
+        output,
+        delete_tmp_folder_after_terminate=True,
+        delete_output_folder_after_terminate=True,
+    )
+
+    def get_finalizer(tmp_dir, output_dir):
+        def session_run_at_end():
+            for dir in (tmp_dir, output_dir):
+                for i in range(10):
+                    if os.path.exists(dir):
+                        try:
+                            shutil.rmtree(dir)
+                            break
+                        except OSError:
+                            time.sleep(1)
+        return session_run_at_end
+    request.addfinalizer(get_finalizer(tmp, output))
+
+    return backend
+
+
+@pytest.fixture(scope="function")
+def tmp_dir(request):
+    return _dir_fixture('tmp', request)
+
+
+@pytest.fixture(scope="function")
+def output_dir(request):
+    return _dir_fixture('output', request)
+
+
+def _dir_fixture(dir_type, request):
+
+    test_dir = os.path.dirname(__file__)
+    dir = os.path.join(
+        test_dir, '.%s__%s__%s' % (dir_type, request.module.__name__, request.node.name)
+    )
+
+    for i in range(10):
+        if os.path.exists(dir):
+            try:
+                shutil.rmtree(dir)
+                break
+            except OSError:
+                pass
+
+    def get_finalizer(dir):
+        def session_run_at_end():
+            for i in range(10):
+                if os.path.exists(dir):
+                    try:
+                        shutil.rmtree(dir)
+                        break
+                    except OSError:
+                        time.sleep(1)
+
+        return session_run_at_end
+
+    request.addfinalizer(get_finalizer(dir))
+
+    return dir
+
+
+@pytest.fixture(scope="function")
+def dask_client(request):
+    """
+    This fixture is meant to be called one per pytest session.
+
+    The goal of this function is to create a global client at the start
+    of the testing phase. We can create clients at the start of the
+    session (this case, as above scope is session), module, class or function
+    level.
+
+    The overhead of creating a dask client per class/module/session is something
+    that travis cannot handle, so we rely on the following execution flow:
+
+    1- At the start of the pytest session, session_run_at_beginning fixture is called
+    to create a global client on port 4567.
+    2- Any test that needs a client, would query the global scheduler that allows
+    communication through port 4567.
+    3- At the end of the test, we shutdown any remaining work being done by any worker
+    in the client. This has a maximum 10 seconds timeout. The client object will afterwards
+    be empty and when pytest closes, it can safely delete the object without hanging.
+
+    More info on this file can be found on:
+    https://docs.pytest.org/en/stable/writing_plugins.html#conftest-py-plugins
+    """
+    dask.config.set({'distributed.worker.daemon': False})
+
+    client = Client(n_workers=2, threads_per_worker=1, processes=False)
+    print("Started Dask client={}\n".format(client))
+
+    def get_finalizer(address):
+        def session_run_at_end():
+            client = get_client(address)
+            print("Closed Dask client={}\n".format(client))
+            client.shutdown()
+            client.close()
+            del client
+        return session_run_at_end
+    request.addfinalizer(get_finalizer(client.scheduler_info()['address']))
+
+    return client
+
+
+@pytest.fixture(scope="function")
+def dask_client_single_worker(request):
+    """
+    Same as above, but only with a single worker.
+    """
+    dask.config.set({'distributed.worker.daemon': False})
+
+    client = Client(n_workers=1, threads_per_worker=1, processes=False)
+    print("Started Dask client={}\n".format(client))
+
+    def get_finalizer(address):
+        def session_run_at_end():
+            client = get_client(address)
+            print("Closed Dask client={}\n".format(client))
+            client.shutdown()
+            client.close()
+            del client
+        return session_run_at_end
+    request.addfinalizer(get_finalizer(client.scheduler_info()['address']))
+
+    return client
+
+
+def pytest_sessionfinish(session, exitstatus):
+    proc = psutil.Process()
+    for child in proc.children(recursive=True):
+        print(child, child.cmdline())
diff --git a/test/test_automl/automl_utils.py b/test/test_automl/automl_utils.py
new file mode 100644
index 0000000000..1d1d6e2827
--- /dev/null
+++ b/test/test_automl/automl_utils.py
@@ -0,0 +1,32 @@
+# -*- encoding: utf-8 -*-
+import os
+
+import numpy as np
+
+
+def extract_msg_from_log(log_file):
+    include_messages = ['INFO', 'DEBUG', 'WARN',
+                        'CRITICAL', 'ERROR', 'FATAL']
+
+    # There is a lot of content in the log files. Only
+    # parsing the main message and ignore the metalearning
+    # messages
+    try:
+        with open(log_file) as logfile:
+            content = logfile.readlines()
+
+        # Get the messages to debug easier!
+        content = [x for x in content if any(
+            msg in x for msg in include_messages
+        ) and 'metalearning' not in x]
+
+    except Exception as e:
+        return str(e)
+    return os.linesep.join(content)
+
+
+def count_succeses(cv_results):
+    return np.sum(
+        [status in ['Success', 'Success (but do not advance to higher budget)']
+         for status in cv_results['status']]
+    )
diff --git a/test/test_automl/base.py b/test/test_automl/base.py
deleted file mode 100644
index 39ef73b87b..0000000000
--- a/test/test_automl/base.py
+++ /dev/null
@@ -1,67 +0,0 @@
-# -*- encoding: utf-8 -*-
-import os
-import shutil
-import time
-import unittest
-
-import numpy as np
-
-from autosklearn.util.backend import create
-
-
-class Base(unittest.TestCase):
-    _multiprocess_can_split_ = True
-    """All tests which are a subclass of this must define their own output
-    directory and call self._setUp."""
-
-    def setUp(self):
-        self.test_dir = os.path.dirname(__file__)
-
-        try:
-            os.environ['TRAVIS']
-            self.travis = True
-        except Exception:
-            self.travis = False
-
-    def _setUp(self, dir):
-        if os.path.exists(dir):
-            for i in range(10):
-                try:
-                    shutil.rmtree(dir)
-                    break
-                except OSError:
-                    time.sleep(1)
-
-    def _create_backend(self, test_name, delete_tmp_folder_after_terminate=True,
-                        delete_output_folder_after_terminate=True):
-        tmp = os.path.join(self.test_dir, '..', '.tmp._%s' % test_name)
-        output = os.path.join(self.test_dir, '..', '.output._%s' % test_name)
-        # Make sure the folders we wanna create do not already exist.
-        self._setUp(tmp)
-        self._setUp(output)
-        backend = create(
-            tmp,
-            output,
-            delete_tmp_folder_after_terminate=delete_tmp_folder_after_terminate,
-            delete_output_folder_after_terminate=delete_output_folder_after_terminate,
-            )
-        return backend
-
-    def _tearDown(self, dir):
-        """
-        Delete the temporary and the output directories manually
-        in case they are not deleted.
-        """
-        if os.path.exists(dir):
-            for i in range(10):
-                try:
-                    shutil.rmtree(dir)
-                    break
-                except OSError:
-                    time.sleep(1)
-
-    def _count_succeses(self, cv_results):
-        return np.sum(
-            [status in ['Success', 'Success (but do not advance to higher budget)']
-             for status in cv_results['status']]
-        )
diff --git a/test/test_automl/test_automl.py b/test/test_automl/test_automl.py
index ff708cdd92..6cc701976a 100644
--- a/test/test_automl/test_automl.py
+++ b/test/test_automl/test_automl.py
@@ -9,11 +9,11 @@
 
 import numpy as np
 import pandas as pd
+import pytest
 import sklearn.datasets
 from smac.scenario.scenario import Scenario
 from smac.facade.roar_facade import ROAR
 
-from autosklearn.util.backend import Backend
 from autosklearn.automl import AutoML
 import autosklearn.automl
 from autosklearn.data.xy_data_manager import XYDataManager
@@ -24,579 +24,580 @@
 from smac.tae import StatusType
 
 sys.path.append(os.path.dirname(__file__))
-from base import Base  # noqa (E402: module level import not at top of file)
+from automl_utils import extract_msg_from_log, count_succeses  # noqa (E402: module level import not at top of file)
 
 
 class AutoMLStub(AutoML):
     def __init__(self):
         self.__class__ = AutoML
         self._task = None
-
-
-class AutoMLTest(Base, unittest.TestCase):
-    _multiprocess_can_split_ = True
-
-    def setUp(self):
-        super().setUp()
-
-        self.automl = AutoMLStub()
-
-        self.automl._seed = 42
-        self.automl._backend = unittest.mock.Mock(spec=Backend)
-        self.automl._delete_output_directories = lambda: 0
-
-    def test_refit_shuffle_on_fail(self):
-        backend_api = self._create_backend('test_refit_shuffle_on_fail')
-
-        failing_model = unittest.mock.Mock()
-        failing_model.fit.side_effect = [ValueError(), ValueError(), None]
-        failing_model.fit_transformer.side_effect = [
-            ValueError(), ValueError(), (None, {})]
-        failing_model.get_max_iter.return_value = 100
-
-        auto = AutoML(backend_api, 20, 5)
-        ensemble_mock = unittest.mock.Mock()
-        ensemble_mock.get_selected_model_identifiers.return_value = [(1, 1, 50.0)]
-        auto.ensemble_ = ensemble_mock
-        for budget_type in [None, 'iterations']:
-            auto._budget_type = budget_type
-
-            auto.models_ = {(1, 1, 50.0): failing_model}
-
-            # Make sure a valid 2D array is given to automl
-            X = np.array([1, 2, 3]).reshape(-1, 1)
-            y = np.array([1, 2, 3])
-            auto.refit(X, y)
-
-            self.assertEqual(failing_model.fit.call_count, 3)
-        self.assertEqual(failing_model.fit_transformer.call_count, 3)
-
-        del auto
-        self._tearDown(backend_api.temporary_directory)
-        self._tearDown(backend_api.output_directory)
-
-    def test_only_loads_ensemble_models(self):
-
-        def side_effect(ids, *args, **kwargs):
-            return models if ids is identifiers else {}
-
-        # Add a resampling strategy as this is required by load_models
-        self.automl._resampling_strategy = 'holdout'
-        identifiers = [(1, 2), (3, 4)]
-
-        models = [42]
-        load_ensemble_mock = unittest.mock.Mock()
-        load_ensemble_mock.get_selected_model_identifiers.return_value = identifiers
-        self.automl._backend.load_ensemble.return_value = load_ensemble_mock
-        self.automl._backend.load_models_by_identifiers.side_effect = side_effect
-
-        self.automl._load_models()
-        self.assertEqual(models, self.automl.models_)
-        self.assertIsNone(self.automl.cv_models_)
-
-        self.automl._resampling_strategy = 'cv'
-
-        models = [42]
-        self.automl._backend.load_cv_models_by_identifiers.side_effect = side_effect
-
-        self.automl._load_models()
-        self.assertEqual(models, self.automl.cv_models_)
-
-    def test_check_for_models_if_no_ensemble(self):
-        models = [42]
-        self.automl._backend.load_ensemble.return_value = None
-        self.automl._backend.list_all_models.return_value = models
-        self.automl._disable_evaluator_output = False
-
-        self.automl._load_models()
-
-    def test_raises_if_no_models(self):
-        self.automl._backend.load_ensemble.return_value = None
-        self.automl._backend.list_all_models.return_value = []
-        self.automl._resampling_strategy = 'holdout'
-
-        self.automl._disable_evaluator_output = False
-        self.assertRaises(ValueError, self.automl._load_models)
-
-        self.automl._disable_evaluator_output = True
-        self.automl._load_models()
-
-    def test_fit(self):
-        backend_api = self._create_backend('test_fit')
-
-        X_train, Y_train, X_test, Y_test = putil.get_dataset('iris')
-        automl = autosklearn.automl.AutoML(
-            backend=backend_api,
-            time_left_for_this_task=20,
-            per_run_time_limit=5,
-            metric=accuracy,
-        )
-        automl.fit(
-            X_train, Y_train, task=MULTICLASS_CLASSIFICATION,
-        )
-        score = automl.score(X_test, Y_test)
-        self.assertGreaterEqual(score, 0.8)
-        self.assertGreater(self._count_succeses(automl.cv_results_), 0)
-        self.assertEqual(automl._task, MULTICLASS_CLASSIFICATION)
-
-        del automl
-        self._tearDown(backend_api.temporary_directory)
-        self._tearDown(backend_api.output_directory)
-
-    def test_delete_non_candidate_models(self):
-        backend_api = self._create_backend(
-            'test_delete', delete_tmp_folder_after_terminate=False)
-
-        seed = 555
-        X, Y, _, _ = putil.get_dataset('iris')
-        automl = autosklearn.automl.AutoML(
-            backend_api,
-            time_left_for_this_task=30,
-            per_run_time_limit=5,
-            ensemble_nbest=3,
-            seed=seed,
-            initial_configurations_via_metalearning=0,
-            resampling_strategy='holdout',
-            include_estimators=['sgd'],
-            include_preprocessors=['no_preprocessing'],
-            metric=accuracy,
+        self._dask_client = None
+        self._is_dask_client_internally_created = False
+
+    def __del__(self):
+        pass
+
+
+def test_fit(dask_client, backend):
+
+    X_train, Y_train, X_test, Y_test = putil.get_dataset('iris')
+    automl = autosklearn.automl.AutoML(
+        backend=backend,
+        time_left_for_this_task=30,
+        per_run_time_limit=5,
+        metric=accuracy,
+        dask_client=dask_client,
+    )
+    automl.fit(
+        X_train, Y_train, task=MULTICLASS_CLASSIFICATION,
+    )
+    score = automl.score(X_test, Y_test)
+    assert score > 0.8
+    assert count_succeses(automl.cv_results_) > 0
+    assert automl._task == MULTICLASS_CLASSIFICATION
+
+    del automl
+
+
+def test_fit_roar(dask_client_single_worker, backend):
+    def get_roar_object_callback(
+            scenario_dict,
+            seed,
+            ta,
+            ta_kwargs,
+            dask_client,
+            n_jobs,
+            **kwargs
+    ):
+        """Random online adaptive racing.
+
+        http://ml.informatik.uni-freiburg.de/papers/11-LION5-SMAC.pdf"""
+        scenario = Scenario(scenario_dict)
+        return ROAR(
+            scenario=scenario,
+            rng=seed,
+            tae_runner=ta,
+            tae_runner_kwargs=ta_kwargs,
+            dask_client=dask_client,
+            n_jobs=n_jobs,
         )
 
-        automl.fit(X, Y, task=MULTICLASS_CLASSIFICATION,
-                   X_test=X, y_test=Y)
-
-        # Assert at least one model file has been deleted and that there were no
-        # deletion errors
-        log_file_path = glob.glob(os.path.join(
-            backend_api.temporary_directory, 'AutoML(' + str(seed) + '):*.log'))
-        with open(log_file_path[0]) as log_file:
-            log_content = log_file.read()
-            self.assertIn('Deleted files of non-candidate model', log_content)
-            self.assertNotIn('Failed to delete files of non-candidate model', log_content)
-            self.assertNotIn('Failed to lock model', log_content)
-
-        # Assert that the files of the models used by the ensemble weren't deleted
-        model_files = backend_api.list_all_models(seed=seed)
-        model_files_idx = set()
-        for m_file in model_files:
-            # Extract the model identifiers from the filename
-            m_file = os.path.split(m_file)[1].replace('.model', '').split('.', 2)
-            model_files_idx.add((int(m_file[0]), int(m_file[1]), float(m_file[2])))
-        ensemble_members_idx = set(automl.ensemble_.identifiers_)
-        self.assertTrue(ensemble_members_idx.issubset(model_files_idx))
-
-        del automl
-        self._tearDown(backend_api.temporary_directory)
-        self._tearDown(backend_api.output_directory)
-
-    def test_fit_roar(self):
-        def get_roar_object_callback(
-                scenario_dict,
-                seed,
-                ta,
-                ta_kwargs,
-                **kwargs
-        ):
-            """Random online adaptive racing.
-
-            http://ml.informatik.uni-freiburg.de/papers/11-LION5-SMAC.pdf"""
-            scenario = Scenario(scenario_dict)
-            return ROAR(
-                scenario=scenario,
-                rng=seed,
-                tae_runner=ta,
-                tae_runner_kwargs=ta_kwargs,
-            )
+    X_train, Y_train, X_test, Y_test = putil.get_dataset('iris')
+    automl = autosklearn.automl.AutoML(
+        backend=backend,
+        time_left_for_this_task=30,
+        per_run_time_limit=5,
+        initial_configurations_via_metalearning=0,
+        get_smac_object_callback=get_roar_object_callback,
+        metric=accuracy,
+        dask_client=dask_client_single_worker,
+    )
+    setup_logger()
+    automl._logger = get_logger('test_fit_roar')
+    automl.fit(
+        X_train, Y_train, task=MULTICLASS_CLASSIFICATION,
+    )
+    score = automl.score(X_test, Y_test)
+    assert score > 0.8
+    assert count_succeses(automl.cv_results_) > 0
+    assert automl._task == MULTICLASS_CLASSIFICATION
+
+    del automl
 
-        backend_api = self._create_backend('test_fit_roar')
 
-        X_train, Y_train, X_test, Y_test = putil.get_dataset('iris')
-        automl = autosklearn.automl.AutoML(
-            backend=backend_api,
-            time_left_for_this_task=20,
-            per_run_time_limit=5,
-            initial_configurations_via_metalearning=0,
-            get_smac_object_callback=get_roar_object_callback,
-            metric=accuracy,
-        )
-        setup_logger()
-        automl._logger = get_logger('test_fit_roar')
-        automl.fit(
-            X_train, Y_train, task=MULTICLASS_CLASSIFICATION,
-        )
-        score = automl.score(X_test, Y_test)
-        self.assertGreaterEqual(score, 0.8)
-        self.assertGreater(self._count_succeses(automl.cv_results_), 0)
-        self.assertEqual(automl._task, MULTICLASS_CLASSIFICATION)
-
-        del automl
-        self._tearDown(backend_api.temporary_directory)
-        self._tearDown(backend_api.output_directory)
-
-    def test_binary_score_and_include(self):
-        """
-        Test fix for binary classification prediction
-        taking the index 1 of second dimension in prediction matrix
-        """
-        backend_api = self._create_backend('test_binary_score_and_include')
-
-        data = sklearn.datasets.make_classification(
-            n_samples=400, n_features=10, n_redundant=1, n_informative=3,
-            n_repeated=1, n_clusters_per_class=2, random_state=1)
-        X_train = data[0][:200]
-        Y_train = data[1][:200]
-        X_test = data[0][200:]
-        Y_test = data[1][200:]
-
-        automl = autosklearn.automl.AutoML(
-            backend_api, 20, 5,
-            include_estimators=['sgd'],
-            include_preprocessors=['no_preprocessing'],
-            metric=accuracy,
-        )
-        automl.fit(X_train, Y_train, task=BINARY_CLASSIFICATION)
-        self.assertEqual(automl._task, BINARY_CLASSIFICATION)
-
-        # TODO, the assumption from above is not really tested here
-        # Also, the score method should be removed, it only makes little sense
-        score = automl.score(X_test, Y_test)
-        self.assertGreaterEqual(score, 0.4)
-
-        del automl
-        self._tearDown(backend_api.temporary_directory)
-        self._tearDown(backend_api.output_directory)
-
-    def test_automl_outputs(self):
-        backend_api = self._create_backend('test_automl_outputs')
-
-        X_train, Y_train, X_test, Y_test = putil.get_dataset('iris')
-        name = 'iris'
-        data_manager_file = os.path.join(
-            backend_api.temporary_directory,
-            '.auto-sklearn',
-            'datamanager.pkl'
+def test_refit_shuffle_on_fail(backend, dask_client):
+
+    failing_model = unittest.mock.Mock()
+    failing_model.fit.side_effect = [ValueError(), ValueError(), None]
+    failing_model.fit_transformer.side_effect = [
+        ValueError(), ValueError(), (None, {})]
+    failing_model.get_max_iter.return_value = 100
+
+    auto = AutoML(backend, 30, 5, dask_client=dask_client)
+    ensemble_mock = unittest.mock.Mock()
+    ensemble_mock.get_selected_model_identifiers.return_value = [(1, 1, 50.0)]
+    auto.ensemble_ = ensemble_mock
+    for budget_type in [None, 'iterations']:
+        auto._budget_type = budget_type
+
+        auto.models_ = {(1, 1, 50.0): failing_model}
+
+        # Make sure a valid 2D array is given to automl
+        X = np.array([1, 2, 3]).reshape(-1, 1)
+        y = np.array([1, 2, 3])
+        auto.refit(X, y)
+
+        assert failing_model.fit.call_count == 3
+    assert failing_model.fit_transformer.call_count == 3
+
+    del auto
+
+
+def test_only_loads_ensemble_models(automl_stub):
+
+    def side_effect(ids, *args, **kwargs):
+        return models if ids is identifiers else {}
+
+    # Add a resampling strategy as this is required by load_models
+    automl_stub._resampling_strategy = 'holdout'
+    identifiers = [(1, 2), (3, 4)]
+
+    models = [42]
+    load_ensemble_mock = unittest.mock.Mock()
+    load_ensemble_mock.get_selected_model_identifiers.return_value = identifiers
+    automl_stub._backend.load_ensemble.return_value = load_ensemble_mock
+    automl_stub._backend.load_models_by_identifiers.side_effect = side_effect
+
+    automl_stub._load_models()
+    assert models == automl_stub.models_
+    assert automl_stub.cv_models_ is None
+
+    automl_stub._resampling_strategy = 'cv'
+
+    models = [42]
+    automl_stub._backend.load_cv_models_by_identifiers.side_effect = side_effect
+
+    automl_stub._load_models()
+    assert models == automl_stub.cv_models_
+
+
+def test_check_for_models_if_no_ensemble(automl_stub):
+    models = [42]
+    automl_stub._backend.load_ensemble.return_value = None
+    automl_stub._backend.list_all_models.return_value = models
+    automl_stub._disable_evaluator_output = False
+
+    automl_stub._load_models()
+
+
+def test_raises_if_no_models(automl_stub):
+    automl_stub._backend.load_ensemble.return_value = None
+    automl_stub._backend.list_all_models.return_value = []
+    automl_stub._resampling_strategy = 'holdout'
+
+    automl_stub._disable_evaluator_output = False
+    with pytest.raises(ValueError):
+        automl_stub._load_models()
+
+    automl_stub._disable_evaluator_output = True
+    automl_stub._load_models()
+
+
+def test_delete_non_candidate_models(backend, dask_client):
+
+    seed = 555
+    X, Y, _, _ = putil.get_dataset('iris')
+    automl = autosklearn.automl.AutoML(
+        backend,
+        time_left_for_this_task=60,
+        per_run_time_limit=5,
+        ensemble_nbest=3,
+        seed=seed,
+        initial_configurations_via_metalearning=0,
+        resampling_strategy='holdout',
+        include_estimators=['sgd'],
+        include_preprocessors=['no_preprocessing'],
+        metric=accuracy,
+        dask_client=dask_client,
+        # Force model to be deleted. That is, from 50 which is the
+        # default to 3 to make sure we delete models.
+        max_models_on_disc=3,
+    )
+
+    automl.fit(X, Y, task=MULTICLASS_CLASSIFICATION,
+               X_test=X, y_test=Y)
+
+    # Assert at least one model file has been deleted and that there were no
+    # deletion errors
+    log_file_path = glob.glob(os.path.join(
+        backend.temporary_directory, 'AutoML(' + str(seed) + '):*.log'))
+    with open(log_file_path[0]) as log_file:
+        log_content = log_file.read()
+        assert 'Deleted files of non-candidate model' in log_content, log_content
+        assert 'Failed to delete files of non-candidate model' not in log_content, log_content
+        assert 'Failed to lock model' not in log_content, log_content
+
+    # Assert that the files of the models used by the ensemble weren't deleted
+    model_files = backend.list_all_models(seed=seed)
+    model_files_idx = set()
+    for m_file in model_files:
+        # Extract the model identifiers from the filename
+        m_file = os.path.split(m_file)[1].replace('.model', '').split('.', 2)
+        model_files_idx.add((int(m_file[0]), int(m_file[1]), float(m_file[2])))
+    ensemble_members_idx = set(automl.ensemble_.identifiers_)
+    assert ensemble_members_idx.issubset(model_files_idx), (ensemble_members_idx, model_files_idx)
+
+    del automl
+
+
+def test_binary_score_and_include(backend, dask_client):
+    """
+    Test fix for binary classification prediction
+    taking the index 1 of second dimension in prediction matrix
+    """
+
+    data = sklearn.datasets.make_classification(
+        n_samples=400, n_features=10, n_redundant=1, n_informative=3,
+        n_repeated=1, n_clusters_per_class=2, random_state=1)
+    X_train = data[0][:200]
+    Y_train = data[1][:200]
+    X_test = data[0][200:]
+    Y_test = data[1][200:]
+
+    automl = autosklearn.automl.AutoML(
+        backend, 20, 5,
+        include_estimators=['sgd'],
+        include_preprocessors=['no_preprocessing'],
+        metric=accuracy,
+        dask_client=dask_client,
+    )
+    automl.fit(X_train, Y_train, task=BINARY_CLASSIFICATION)
+    assert automl._task == BINARY_CLASSIFICATION
+
+    # TODO, the assumption from above is not really tested here
+    # Also, the score method should be removed, it only makes little sense
+    score = automl.score(X_test, Y_test)
+    assert score >= 0.4
+
+    del automl
+
+
+def test_automl_outputs(backend, dask_client):
+
+    X_train, Y_train, X_test, Y_test = putil.get_dataset('iris')
+    name = 'iris'
+    data_manager_file = os.path.join(
+        backend.temporary_directory,
+        '.auto-sklearn',
+        'datamanager.pkl'
+    )
+
+    auto = autosklearn.automl.AutoML(
+        backend, 30, 5,
+        initial_configurations_via_metalearning=0,
+        seed=100,
+        metric=accuracy,
+        dask_client=dask_client,
+    )
+    setup_logger()
+    auto._logger = get_logger('test_automl_outputs')
+    auto.fit(
+        X=X_train,
+        y=Y_train,
+        X_test=X_test,
+        y_test=Y_test,
+        dataset_name=name,
+        task=MULTICLASS_CLASSIFICATION,
+    )
+
+    # Log file path
+    log_file_path = glob.glob(os.path.join(
+        backend.temporary_directory, 'AutoML*.log'))[0]
+
+    # pickled data manager (without one hot encoding!)
+    with open(data_manager_file, 'rb') as fh:
+        D = pickle.load(fh)
+        assert np.allclose(D.data['X_train'], X_train)
+
+    # Check that all directories are there
+    fixture = [
+        'true_targets_ensemble.npy',
+        'start_time_100',
+        'datamanager.pkl',
+        'ensemble_read_preds.pkl',
+        'ensemble_read_scores.pkl',
+        'runs',
+        'ensembles',
+    ]
+    assert (
+        sorted(os.listdir(os.path.join(backend.temporary_directory, '.auto-sklearn')))
+        == sorted(fixture)
+    )
+
+    # At least one ensemble, one validation, one test prediction and one
+    # model and one ensemble
+    fixture = glob.glob(os.path.join(
+        backend.temporary_directory,
+        '.auto-sklearn', 'runs', '*', 'predictions_ensemble*npy',
+    ))
+    assert len(fixture) > 0
+
+    fixture = glob.glob(os.path.join(backend.temporary_directory, '.auto-sklearn',
+                                     'runs', '*', '100.*.model'))
+    assert len(fixture) > 0
+
+    fixture = os.listdir(os.path.join(backend.temporary_directory,
+                                      '.auto-sklearn', 'ensembles'))
+    assert '100.0000000000.ensemble' in fixture
+
+    # Start time
+    start_time_file_path = os.path.join(backend.temporary_directory,
+                                        '.auto-sklearn', "start_time_100")
+    with open(start_time_file_path, 'r') as fh:
+        start_time = float(fh.read())
+    assert time.time() - start_time >= 10, extract_msg_from_log(log_file_path)
+
+    del auto
+
+
+def test_do_dummy_prediction(backend, dask_client):
+    datasets = {
+        'breast_cancer': BINARY_CLASSIFICATION,
+        'wine': MULTICLASS_CLASSIFICATION,
+        'diabetes': REGRESSION,
+    }
+
+    for name, task in datasets.items():
+
+        X_train, Y_train, X_test, Y_test = putil.get_dataset(name)
+        datamanager = XYDataManager(
+            X_train, Y_train,
+            X_test, Y_test,
+            task=task,
+            dataset_name=name,
+            feat_type=None,
         )
 
         auto = autosklearn.automl.AutoML(
-            backend_api, 20, 5,
-            initial_configurations_via_metalearning=0,
-            seed=100,
+            backend, 20, 5,
+            initial_configurations_via_metalearning=25,
             metric=accuracy,
+            dask_client=dask_client,
         )
         setup_logger()
-        auto._logger = get_logger('test_automl_outputs')
-        auto.fit(
-            X=X_train,
-            y=Y_train,
-            X_test=X_test,
-            y_test=Y_test,
-            dataset_name=name,
-            task=MULTICLASS_CLASSIFICATION,
+        auto._logger = get_logger('test_do_dummy_predictions')
+        auto._backend.save_datamanager(datamanager)
+        D = backend.load_datamanager()
+
+        # Check if data manager is correcly loaded
+        assert D.info['task'] == datamanager.info['task']
+        auto._do_dummy_prediction(D, 1)
+
+        # Ensure that the dummy predictions are not in the current working
+        # directory, but in the temporary directory.
+        assert not os.path.exists(os.path.join(os.getcwd(), '.auto-sklearn'))
+        assert os.path.exists(os.path.join(
+            backend.temporary_directory, '.auto-sklearn', 'runs', '1_1_0.0',
+            'predictions_ensemble_1_1_0.0.npy')
         )
 
-        # pickled data manager (without one hot encoding!)
-        with open(data_manager_file, 'rb') as fh:
-            D = pickle.load(fh)
-            self.assertTrue(np.allclose(D.data['X_train'], X_train))
-
-        # Check that all directories are there
-        fixture = ['cv_models', 'true_targets_ensemble.npy',
-                   'start_time_100', 'datamanager.pkl',
-                   'predictions_ensemble',
-                   'done', 'ensembles', 'predictions_test', 'models']
-        self.assertEqual(sorted(os.listdir(os.path.join(backend_api.temporary_directory,
-                                                        '.auto-sklearn'))),
-                         sorted(fixture))
-
-        # At least one ensemble, one validation, one test prediction and one
-        # model and one ensemble
-        fixture = os.listdir(os.path.join(backend_api.temporary_directory,
-                                          '.auto-sklearn', 'predictions_ensemble'))
-        self.assertGreater(len(fixture), 0)
-
-        fixture = glob.glob(os.path.join(backend_api.temporary_directory, '.auto-sklearn',
-                                         'models', '100.*.model'))
-        self.assertGreater(len(fixture), 0)
-
-        fixture = os.listdir(os.path.join(backend_api.temporary_directory,
-                                          '.auto-sklearn', 'ensembles'))
-        self.assertIn('100.0000000001.ensemble', fixture)
-
-        # Start time
-        start_time_file_path = os.path.join(backend_api.temporary_directory,
-                                            '.auto-sklearn', "start_time_100")
-        with open(start_time_file_path, 'r') as fh:
-            start_time = float(fh.read())
-        self.assertGreaterEqual(time.time() - start_time, 10)
-
         del auto
-        self._tearDown(backend_api.temporary_directory)
-        self._tearDown(backend_api.output_directory)
-
-    def test_do_dummy_prediction(self):
-        datasets = {
-            'breast_cancer': BINARY_CLASSIFICATION,
-            'wine': MULTICLASS_CLASSIFICATION,
-            'diabetes': REGRESSION,
-        }
-
-        for name, task in datasets.items():
-            backend_api = self._create_backend('test_do_dummy_prediction')
-
-            X_train, Y_train, X_test, Y_test = putil.get_dataset(name)
-            datamanager = XYDataManager(
-                X_train, Y_train,
-                X_test, Y_test,
-                task=task,
-                dataset_name=name,
-                feat_type=None,
-            )
 
-            auto = autosklearn.automl.AutoML(
-                backend_api, 20, 5,
-                initial_configurations_via_metalearning=25,
-                metric=accuracy,
-            )
-            setup_logger()
-            auto._logger = get_logger('test_do_dummy_predictions')
-            auto._backend.save_datamanager(datamanager)
-            D = backend_api.load_datamanager()
-
-            # Check if data manager is correcly loaded
-            self.assertEqual(D.info['task'], datamanager.info['task'])
 
-            auto._do_dummy_prediction(D, 1)
+@unittest.mock.patch('autosklearn.evaluation.ExecuteTaFuncWithQueue.run')
+def test_fail_if_dummy_prediction_fails(ta_run_mock, backend, dask_client):
+
+    X_train, Y_train, X_test, Y_test = putil.get_dataset('iris')
+    datamanager = XYDataManager(
+        X_train, Y_train,
+        X_test, Y_test,
+        task=2,
+        feat_type=['Numerical' for i in range(X_train.shape[1])],
+        dataset_name='iris',
+    )
+
+    time_for_this_task = 30
+    per_run_time = 10
+    auto = autosklearn.automl.AutoML(backend,
+                                     time_for_this_task,
+                                     per_run_time,
+                                     initial_configurations_via_metalearning=25,
+                                     metric=accuracy,
+                                     dask_client=dask_client,
+                                     )
+    setup_logger()
+    auto._logger = get_logger('test_fail_if_dummy_prediction_fails')
+    auto._backend._make_internals_directory()
+    auto._backend.save_datamanager(datamanager)
+
+    # First of all, check that ta.run() is actually called.
+    ta_run_mock.return_value = StatusType.SUCCESS, None, None, {}
+    auto._do_dummy_prediction(datamanager, 1)
+    ta_run_mock.assert_called_once_with(1, cutoff=time_for_this_task)
+
+    # Case 1. Check that function raises no error when statustype == success.
+    # ta.run() returns status, cost, runtime, and additional info.
+    ta_run_mock.return_value = StatusType.SUCCESS, None, None, {}
+    raised = False
+    try:
+        auto._do_dummy_prediction(datamanager, 1)
+    except ValueError:
+        raised = True
+    assert not raised, 'Exception raised'
+
+    # Case 2. Check that if statustype returned by ta.run() != success,
+    # the function raises error.
+    ta_run_mock.return_value = StatusType.CRASHED, None, None, {}
+    with pytest.raises(
+        ValueError,
+        match='Dummy prediction failed with run state StatusType.CRASHED and additional output: {}.'  # noqa
+    ):
+        auto._do_dummy_prediction(datamanager, 1)
 
-            # Ensure that the dummy predictions are not in the current working
-            # directory, but in the temporary directory.
-            self.assertFalse(os.path.exists(os.path.join(os.getcwd(),
-                                                         '.auto-sklearn')))
-            self.assertTrue(os.path.exists(os.path.join(
-                backend_api.temporary_directory, '.auto-sklearn', 'predictions_ensemble',
-                'predictions_ensemble_1_1_0.0.npy')))
+    ta_run_mock.return_value = StatusType.ABORT, None, None, {}
+    with pytest.raises(
+        ValueError,
+        match='Dummy prediction failed with run state StatusType.ABORT '
+              'and additional output: {}.',
+    ):
+        auto._do_dummy_prediction(datamanager, 1)
+    ta_run_mock.return_value = StatusType.TIMEOUT, None, None, {}
+    with pytest.raises(
+        ValueError,
+        match='Dummy prediction failed with run state StatusType.TIMEOUT '
+              'and additional output: {}.'
+    ):
+        auto._do_dummy_prediction(datamanager, 1)
+    ta_run_mock.return_value = StatusType.MEMOUT, None, None, {}
+    with pytest.raises(
+        ValueError,
+        match='Dummy prediction failed with run state StatusType.MEMOUT '
+              'and additional output: {}.',
+    ):
+        auto._do_dummy_prediction(datamanager, 1)
+    ta_run_mock.return_value = StatusType.CAPPED, None, None, {}
+    with pytest.raises(
+        ValueError,
+        match='Dummy prediction failed with run state StatusType.CAPPED '
+              'and additional output: {}.'
+    ):
+        auto._do_dummy_prediction(datamanager, 1)
 
-            del auto
-            self._tearDown(backend_api.temporary_directory)
-            self._tearDown(backend_api.output_directory)
+    ta_run_mock.return_value = StatusType.CRASHED, None, None, {'exitcode': -6}
+    with pytest.raises(
+        ValueError,
+        match='The error suggests that the provided memory limits were too tight.',
+    ):
+        auto._do_dummy_prediction(datamanager, 1)
 
-    @unittest.mock.patch('autosklearn.evaluation.ExecuteTaFuncWithQueue.run')
-    def test_fail_if_dummy_prediction_fails(self, ta_run_mock):
-        backend_api = self._create_backend('test_fail_if_dummy_prediction_fails')
 
-        X_train, Y_train, X_test, Y_test = putil.get_dataset('iris')
-        datamanager = XYDataManager(
-            X_train, Y_train,
-            X_test, Y_test,
-            task=2,
-            feat_type=['Numerical' for i in range(X_train.shape[1])],
-            dataset_name='iris',
-        )
+@unittest.mock.patch('autosklearn.smbo.AutoMLSMBO.run_smbo')
+def test_exceptions_inside_log_in_smbo(smbo_run_mock, backend, dask_client):
 
-        time_for_this_task = 30
-        per_run_time = 10
-        auto = autosklearn.automl.AutoML(backend_api,
-                                         time_for_this_task,
-                                         per_run_time,
-                                         initial_configurations_via_metalearning=25,
-                                         metric=accuracy,
-                                         )
-        setup_logger()
-        auto._logger = get_logger('test_fail_if_dummy_prediction_fails')
-        auto._backend._make_internals_directory()
-        auto._backend.save_datamanager(datamanager)
+    automl = autosklearn.automl.AutoML(
+        backend,
+        20,
+        5,
+        metric=accuracy,
+        dask_client=dask_client,
+    )
 
-        # First of all, check that ta.run() is actually called.
-        ta_run_mock.return_value = StatusType.SUCCESS, None, None, "test"
-        auto._do_dummy_prediction(datamanager, 1)
-        ta_run_mock.assert_called_once_with(1, cutoff=time_for_this_task)
-
-        # Case 1. Check that function raises no error when statustype == success.
-        # ta.run() returns status, cost, runtime, and additional info.
-        ta_run_mock.return_value = StatusType.SUCCESS, None, None, "test"
-        raised = False
-        try:
-            auto._do_dummy_prediction(datamanager, 1)
-        except ValueError:
-            raised = True
-        self.assertFalse(raised, 'Exception raised')
-
-        # Case 2. Check that if statustype returned by ta.run() != success,
-        # the function raises error.
-        ta_run_mock.return_value = StatusType.CRASHED, None, None, "test"
-        self.assertRaisesRegex(ValueError,
-                               'Dummy prediction failed with run state StatusType.CRASHED '
-                               'and additional output: test.',
-                               auto._do_dummy_prediction,
-                               datamanager, 1,
-                               )
-        ta_run_mock.return_value = StatusType.ABORT, None, None, "test"
-        self.assertRaisesRegex(ValueError,
-                               'Dummy prediction failed with run state StatusType.ABORT '
-                               'and additional output: test.',
-                               auto._do_dummy_prediction,
-                               datamanager, 1,
-                               )
-        ta_run_mock.return_value = StatusType.TIMEOUT, None, None, "test"
-        self.assertRaisesRegex(ValueError,
-                               'Dummy prediction failed with run state StatusType.TIMEOUT '
-                               'and additional output: test.',
-                               auto._do_dummy_prediction,
-                               datamanager, 1,
-                               )
-        ta_run_mock.return_value = StatusType.MEMOUT, None, None, "test"
-        self.assertRaisesRegex(ValueError,
-                               'Dummy prediction failed with run state StatusType.MEMOUT '
-                               'and additional output: test.',
-                               auto._do_dummy_prediction,
-                               datamanager, 1,
-                               )
-        ta_run_mock.return_value = StatusType.CAPPED, None, None, "test"
-        self.assertRaisesRegex(ValueError,
-                               'Dummy prediction failed with run state StatusType.CAPPED '
-                               'and additional output: test.',
-                               auto._do_dummy_prediction,
-                               datamanager, 1,
-                               )
-
-        self._tearDown(backend_api.temporary_directory)
-        self._tearDown(backend_api.output_directory)
-
-    @unittest.mock.patch('autosklearn.smbo.AutoMLSMBO.run_smbo')
-    def test_exceptions_inside_log_in_smbo(self, smbo_run_mock):
-
-        # Make sure that any exception during the AutoML fit due to
-        # SMAC are properly captured in a log file
-        backend_api = self._create_backend('test_exceptions_inside_log')
-        self._tearDown(backend_api.temporary_directory)
-        self._tearDown(backend_api.output_directory)
-
-        automl = autosklearn.automl.AutoML(
-            backend_api,
-            20,
-            5,
-            metric=accuracy,
-        )
+    output_file = 'test_exceptions_inside_log.log'
+    setup_logger(output_file=output_file)
+    logger = get_logger('test_exceptions_inside_log')
 
-        output_file = 'test_exceptions_inside_log.log'
-        setup_logger(output_file=output_file)
-        logger = get_logger('test_exceptions_inside_log')
-
-        # Create a custom exception to prevent other errors to slip in
-        class MyException(Exception):
-            pass
-
-        X_train, Y_train, X_test, Y_test = putil.get_dataset('iris')
-        # The first call is on dummy predictor failure
-        message = str(np.random.randint(100)) + '_run_smbo'
-        smbo_run_mock.side_effect = MyException(message)
-
-        with unittest.mock.patch('autosklearn.automl.AutoML._get_logger') as mock:
-            mock.return_value = logger
-            with self.assertRaises(MyException):
-                automl.fit(
-                    X_train,
-                    Y_train,
-                    task=MULTICLASS_CLASSIFICATION,
-                )
-            with open(output_file) as f:
-                self.assertTrue(message in f.read())
-
-        # Cleanup
-        os.unlink(output_file)
-        self._tearDown(backend_api.temporary_directory)
-        self._tearDown(backend_api.output_directory)
-
-    def test_load_best_individual_model(self):
-
-        backend_api = self._create_backend('test_fit')
-
-        for metric in [log_loss, balanced_accuracy]:
-            X_train, Y_train, X_test, Y_test = putil.get_dataset('iris')
-            automl = autosklearn.automl.AutoML(
-                backend=backend_api,
-                time_left_for_this_task=20,
-                per_run_time_limit=5,
-                metric=metric,
-            )
+    # Create a custom exception to prevent other errors to slip in
+    class MyException(Exception):
+        pass
 
-            with unittest.mock.patch(
-                'autosklearn.ensemble_builder.EnsembleBuilder.run'
-            ) as mock_ensemble_run:
-                mock_ensemble_run.side_effect = MemoryError
-                automl.fit(
-                    X_train, Y_train, task=MULTICLASS_CLASSIFICATION,
-                )
-
-            # A memory error occurs in the ensemble construction
-            self.assertIsNone(automl._backend.load_ensemble(automl._seed))
-
-            # The load model is robust to this and loads the best model
-            automl._load_models()
-            self.assertIsNotNone(automl.ensemble_)
-
-            # Just 1 model is there for ensemble and all weight must be on it
-            get_models_with_weights = automl.get_models_with_weights()
-            self.assertEqual(len(get_models_with_weights), 1)
-            self.assertEqual(get_models_with_weights[0][0], 1.0)
-
-            # Match a toy dataset
-            if metric._sign < 0:
-                self.assertLessEqual(automl.score(X_test, Y_test), 0.2)
-            else:
-                self.assertGreaterEqual(automl.score(X_test, Y_test), 0.8)
-
-            del automl
-            self._tearDown(backend_api.temporary_directory)
-            self._tearDown(backend_api.output_directory)
-
-    def test_fail_if_feat_type_on_pandas_input(self):
-        """We do not support feat type when pandas
-        is provided as an input
-        """
-        backend_api = self._create_backend('test_fail_feat_pandas')
-        automl = autosklearn.automl.AutoML(
-            backend=backend_api,
-            time_left_for_this_task=20,
-            per_run_time_limit=5,
-            metric=accuracy,
-        )
+    X_train, Y_train, X_test, Y_test = putil.get_dataset('iris')
+    # The first call is on dummy predictor failure
+    message = str(np.random.randint(100)) + '_run_smbo'
+    smbo_run_mock.side_effect = MyException(message)
 
-        X_train = pd.DataFrame({'a': [1, 1], 'c': [1, 2]})
-        y_train = [1, 0]
-        with self.assertRaisesRegex(ValueError,
-                                    "feat_type cannot be provided when using pandas"):
+    with unittest.mock.patch('autosklearn.automl.AutoML._get_logger') as mock:
+        mock.return_value = logger
+        with pytest.raises(MyException):
             automl.fit(
-                X_train, y_train,
-                task=BINARY_CLASSIFICATION,
-                feat_type=['Categorical', 'Numerical'],
+                X_train,
+                Y_train,
+                task=MULTICLASS_CLASSIFICATION,
             )
-        self._tearDown(backend_api.temporary_directory)
-        self._tearDown(backend_api.output_directory)
-
-    def test_fail_if_dtype_changes_automl(self):
-        """We do not support changes in the input type.
-        Once a estimator is fitted, it should not change data type
-        """
-        backend_api = self._create_backend('test_fail_feat_typechange')
-        automl = autosklearn.automl.AutoML(
-            backend=backend_api,
-            time_left_for_this_task=20,
-            per_run_time_limit=5,
-            metric=accuracy,
+        with open(output_file) as f:
+            assert message in f.read()
+
+    # Cleanup
+    os.unlink(output_file)
+
+
+@pytest.mark.parametrize("metric", [log_loss, balanced_accuracy])
+def test_load_best_individual_model(metric, backend, dask_client):
+
+    X_train, Y_train, X_test, Y_test = putil.get_dataset('iris')
+    automl = autosklearn.automl.AutoML(
+        backend=backend,
+        time_left_for_this_task=30,
+        per_run_time_limit=5,
+        metric=metric,
+        dask_client=dask_client,
+    )
+
+    # We cannot easily mock a function sent to dask
+    # so for this test we create the whole set of models/ensembles
+    # but prevent it to be loaded
+    automl.fit(
+        X_train, Y_train, task=MULTICLASS_CLASSIFICATION,
+    )
+    automl._backend.load_ensemble = unittest.mock.MagicMock(return_value=None)
+
+    # A memory error occurs in the ensemble construction
+    assert automl._backend.load_ensemble(automl._seed) is None
+
+    # The load model is robust to this and loads the best model
+    automl._load_models()
+    assert automl.ensemble_ is not None
+
+    # Just 1 model is there for ensemble and all weight must be on it
+    get_models_with_weights = automl.get_models_with_weights()
+    assert len(get_models_with_weights) == 1
+    assert get_models_with_weights[0][0] == 1.0
+
+    # Match a toy dataset
+    if metric.name == 'balanced_accuracy':
+        assert automl.score(X_test, Y_test) > 0.9
+    elif metric.name == 'log_loss':
+        assert automl.score(X_test, Y_test) <= 0.2
+    else:
+        raise ValueError(metric.name)
+
+    del automl
+
+
+def test_fail_if_feat_type_on_pandas_input(backend, dask_client):
+    """We do not support feat type when pandas
+    is provided as an input
+    """
+    automl = autosklearn.automl.AutoML(
+        backend=backend,
+        time_left_for_this_task=30,
+        per_run_time_limit=5,
+        metric=accuracy,
+        dask_client=dask_client,
+    )
+
+    X_train = pd.DataFrame({'a': [1, 1], 'c': [1, 2]})
+    y_train = [1, 0]
+    with pytest.raises(
+        ValueError,
+        match="feat_type cannot be provided when using pandas"
+    ):
+        automl.fit(
+            X_train, y_train,
+            task=BINARY_CLASSIFICATION,
+            feat_type=['Categorical', 'Numerical'],
         )
 
-        X_train = pd.DataFrame({'a': [1, 1], 'c': [1, 2]})
-        y_train = [1, 0]
-        automl.InputValidator.validate(X_train, y_train, is_classification=True)
-        with self.assertRaisesRegex(ValueError,
-                                    "Auto-sklearn previously received features of type"):
-            automl.fit(
-                X_train.to_numpy(), y_train,
-                task=BINARY_CLASSIFICATION,
-            )
-        self._tearDown(backend_api.temporary_directory)
-        self._tearDown(backend_api.output_directory)
-
 
-if __name__ == "__main__":
-    unittest.main()
+def test_fail_if_dtype_changes_automl(backend, dask_client):
+    """We do not support changes in the input type.
+    Once a estimator is fitted, it should not change data type
+    """
+    automl = autosklearn.automl.AutoML(
+        backend=backend,
+        time_left_for_this_task=30,
+        per_run_time_limit=5,
+        metric=accuracy,
+        dask_client=dask_client,
+    )
+
+    X_train = pd.DataFrame({'a': [1, 1], 'c': [1, 2]})
+    y_train = [1, 0]
+    automl.InputValidator.validate(X_train, y_train, is_classification=True)
+    with pytest.raises(
+        ValueError,
+        match="Auto-sklearn previously received features of type"
+    ):
+        automl.fit(
+            X_train.to_numpy(), y_train,
+            task=BINARY_CLASSIFICATION,
+        )
diff --git a/test/test_automl/test_estimators.py b/test/test_automl/test_estimators.py
index 2c06d37222..4db354dbe7 100644
--- a/test/test_automl/test_estimators.py
+++ b/test/test_automl/test_estimators.py
@@ -1,9 +1,11 @@
+import glob
 import os
 import pickle
 import re
 import sys
 import unittest
 import unittest.mock
+import pytest
 
 import joblib
 from joblib import cpu_count
@@ -26,635 +28,593 @@
 from autosklearn.smbo import get_smac_object
 
 sys.path.append(os.path.dirname(__file__))
-from base import Base  # noqa (E402: module level import not at top of file)
-
-
-class ArrayReturningDummyPredictor(sklearn.dummy.DummyClassifier):
-    def __init__(self, test):
-        self.arr = test
-        self.fitted_ = True
-
-    def predict_proba(self, X, *args, **kwargs):
-        return self.arr
-
-
-class EstimatorTest(Base, unittest.TestCase):
-    _multiprocess_can_split_ = True
-
-    # def test_fit_partial_cv(self):
-    #
-    #     output = os.path.join(self.test_dir, '..', '.tmp_estimator_fit_partial_cv')
-    #     self._setUp(output)
-    #
-    #     X_train, Y_train, X_test, Y_test = putil.get_dataset('iris')
-    #     automl = AutoSklearnClassifier(time_left_for_this_task=20,
-    #                                    per_run_time_limit=5,
-    #                                    tmp_folder=output,
-    #                                    output_folder=output,
-    #                                    resampling_strategy='partial-cv',
-    #                                    ensemble_size=0,
-    #                                    delete_tmp_folder_after_terminate=False)
-    #     automl.fit(X_train, Y_train)
-    #
-    #     del automl
-    #     self._tearDown(output)
-
-    def test_feat_type_wrong_arguments(self):
-        cls = AutoSklearnClassifier()
-        X = np.zeros((100, 100))
-        y = np.zeros((100, ))
-        self.assertRaisesRegex(
-            ValueError,
-            'Array feat_type does not have same number of '
-            'variables as X has features. 1 vs 100.',
-            cls.fit,
-            X=X, y=y, feat_type=[True]
-        )
-
-        self.assertRaisesRegex(
-            ValueError,
-            'Array feat_type must only contain strings.',
-            cls.fit,
-            X=X, y=y, feat_type=[True]*100
-        )
-
-        self.assertRaisesRegex(
-            ValueError,
-            'Only `Categorical` and `Numerical` are '
-            'valid feature types, you passed `Car`',
-            cls.fit,
-            X=X, y=y, feat_type=['Car']*100
-        )
-
-    # Mock AutoSklearnEstimator.fit so the test doesn't actually run fit().
-    @unittest.mock.patch('autosklearn.estimators.AutoSklearnEstimator.fit')
-    def test_type_of_target(self, mock_estimator):
-        # Test that classifier raises error for illegal target types.
-        X = np.array([[1, 2],
-                      [2, 3],
-                      [3, 4],
-                      [4, 5],
-                      ])
-        # Possible target types
-        y_binary = np.array([0, 0, 1, 1])
-        y_continuous = np.array([0.1, 1.3, 2.1, 4.0])
-        y_multiclass = np.array([0, 1, 2, 0])
-        y_multilabel = np.array([[0, 1],
-                                 [1, 1],
-                                 [1, 0],
-                                 [0, 0],
-                                 ])
-        y_multiclass_multioutput = np.array([[0, 1],
-                                             [1, 3],
-                                             [2, 2],
-                                             [5, 3],
-                                             ])
-        y_continuous_multioutput = np.array([[0.1, 1.5],
-                                             [1.2, 3.5],
-                                             [2.7, 2.7],
-                                             [5.5, 3.9],
-                                             ])
-
-        cls = AutoSklearnClassifier()
-        # Illegal target types for classification: continuous,
-        # multiclass-multioutput, continuous-multioutput.
-        self.assertRaisesRegex(ValueError,
-                               "Classification with data of type"
-                               " multiclass-multioutput is not supported",
-                               cls.fit,
-                               X=X,
-                               y=y_multiclass_multioutput,
-                               )
-
-        self.assertRaisesRegex(ValueError,
-                               "Classification with data of type"
-                               " continuous is not supported",
-                               cls.fit,
-                               X=X,
-                               y=y_continuous,
-                               )
-
-        self.assertRaisesRegex(ValueError,
-                               "Classification with data of type"
-                               " continuous-multioutput is not supported",
-                               cls.fit,
-                               X=X,
-                               y=y_continuous_multioutput,
-                               )
-
-        # Legal target types for classification: binary, multiclass,
-        # multilabel-indicator.
-        try:
-            cls.fit(X, y_binary)
-        except ValueError:
-            self.fail("cls.fit() raised ValueError while fitting "
-                      "binary targets")
-
-        try:
-            cls.fit(X, y_multiclass)
-        except ValueError:
-            self.fail("cls.fit() raised ValueError while fitting "
-                      "multiclass targets")
-
-        try:
-            cls.fit(X, y_multilabel)
-        except ValueError:
-            self.fail("cls.fit() raised ValueError while fitting "
-                      "multilabel-indicator targets")
-
-        # Test that regressor raises error for illegal target types.
-        reg = AutoSklearnRegressor()
-        # Illegal target types for regression: multilabel-indicator
-        # multiclass-multioutput
-        self.assertRaisesRegex(
-            ValueError,
-            "Regression with data of type"
-            " multilabel-indicator is not supported",
-            reg.fit,
-            X=X,
-            y=y_multilabel,
-        )
-
-        self.assertRaisesRegex(
-            ValueError,
-            "Regression with data of type"
-            " multiclass-multioutput is not supported",
-            reg.fit,
-            X=X,
-            y=y_multiclass_multioutput,
-        )
-
-        # Legal target types: continuous, multiclass,
-        # continuous-multioutput,
-        # binary
-        try:
-            reg.fit(X, y_continuous)
-        except ValueError:
-            self.fail("reg.fit() raised ValueError while fitting "
-                      "continuous targets")
-
-        try:
-            reg.fit(X, y_multiclass)
-        except ValueError:
-            self.fail("reg.fit() raised ValueError while fitting "
-                      "multiclass targets")
-
-        try:
-            reg.fit(X, y_continuous_multioutput)
-        except ValueError:
-            self.fail("reg.fit() raised ValueError while fitting "
-                      "continuous_multioutput targets")
-
-        try:
-            reg.fit(X, y_binary)
-        except ValueError:
-            self.fail("reg.fit() raised ValueError while fitting "
-                      "binary targets")
-
-    def test_cv_results(self):
-        # TODO restructure and actually use real SMAC output from a long run
-        # to do this unittest!
-        tmp = os.path.join(self.test_dir, '..', '.tmp_cv_results')
-        output = os.path.join(self.test_dir, '..', '.out_cv_results')
-        self._setUp(tmp)
-        self._setUp(output)
-        X_train, Y_train, X_test, Y_test = putil.get_dataset('iris')
-
-        cls = AutoSklearnClassifier(time_left_for_this_task=30,
-                                    per_run_time_limit=5,
-                                    output_folder=output,
-                                    tmp_folder=tmp,
-                                    seed=1,
-                                    initial_configurations_via_metalearning=0,
-                                    ensemble_size=0)
-        cls.fit(X_train, Y_train)
-        cv_results = cls.cv_results_
-        self.assertIsInstance(cv_results, dict)
-        self.assertIsInstance(cv_results['mean_test_score'], np.ndarray)
-        self.assertIsInstance(cv_results['mean_fit_time'], np.ndarray)
-        self.assertIsInstance(cv_results['params'], list)
-        self.assertIsInstance(cv_results['rank_test_scores'], np.ndarray)
-        self.assertTrue([isinstance(val, npma.MaskedArray) for key, val in
-                         cv_results.items() if key.startswith('param_')])
-        del cls
-        self._tearDown(tmp)
-        self._tearDown(output)
-
-    def test_fit_n_jobs(self):
-        tmp = os.path.join(self.test_dir, '..', '.tmp_estimator_fit_n_jobs')
-        output = os.path.join(self.test_dir, '..', '.out_estimator_fit_n_jobs')
-        self._setUp(tmp)
-        self._setUp(output)
-
-        X_train, Y_train, X_test, Y_test = putil.get_dataset('breast_cancer')
-
-        # test parallel Classifier to predict classes, not only indices
-        Y_train += 1
-        Y_test += 1
-
-        class get_smac_object_wrapper:
-
-            def __call__(self, *args, **kwargs):
-                self.n_jobs = kwargs['n_jobs']
-                smac = get_smac_object(*args, **kwargs)
-                self.dask_n_jobs = smac.solver.tae_runner.n_workers
-                self.dask_client_n_jobs = len(
-                    smac.solver.tae_runner.client.scheduler_info()['workers']
-                )
-                return smac
-        get_smac_object_wrapper_instance = get_smac_object_wrapper()
-
-        automl = AutoSklearnClassifier(
-            time_left_for_this_task=30,
-            per_run_time_limit=5,
-            output_folder=output,
-            tmp_folder=tmp,
-            seed=1,
-            initial_configurations_via_metalearning=0,
-            ensemble_size=5,
-            n_jobs=2,
-            include_estimators=['sgd'],
-            include_preprocessors=['no_preprocessing'],
-            get_smac_object_callback=get_smac_object_wrapper_instance,
-            max_models_on_disc=None,
-        )
-        automl.fit(X_train, Y_train)
-
-        # Test that the argument is correctly passed to SMAC
-        self.assertEqual(getattr(get_smac_object_wrapper_instance, 'n_jobs'), 2)
-        self.assertEqual(getattr(get_smac_object_wrapper_instance, 'dask_n_jobs'), 2)
-        self.assertEqual(getattr(get_smac_object_wrapper_instance, 'dask_client_n_jobs'), 2)
-
-        available_num_runs = set()
-        for run_key, run_value in automl.automl_.runhistory_.data.items():
-            if run_value.additional_info is not None and 'num_run' in run_value.additional_info:
-                available_num_runs.add(run_value.additional_info['num_run'])
-        predictions_dir = automl.automl_._backend._get_prediction_output_dir(
-            'ensemble'
-        )
-        available_predictions = set()
-        predictions = os.listdir(predictions_dir)
-        seeds = set()
-        for prediction in predictions:
-            match = re.match(MODEL_FN_RE, prediction.replace("predictions_ensemble", ""))
-            print(prediction, match)
-            if match:
-                num_run = int(match.group(2))
-                available_predictions.add(num_run)
-                seed = int(match.group(1))
-                seeds.add(seed)
-
-        # Remove the dummy prediction, it is not part of the runhistory
-        available_predictions.remove(1)
-        self.assertSetEqual(available_predictions, available_num_runs)
-
-        self.assertEqual(len(seeds), 1)
-
-        ensemble_dir = automl.automl_._backend.get_ensemble_dir()
-        ensembles = os.listdir(ensemble_dir)
-
-        seeds = set()
-        for ensemble_file in ensembles:
-            seeds.add(int(ensemble_file.split('.')[0].split('_')[0]))
-        self.assertEqual(len(seeds), 1)
-
-        self.assertGreater(self._count_succeses(automl.cv_results_), 0)
-
-        self._tearDown(tmp)
-        self._tearDown(output)
-
-    @unittest.mock.patch('autosklearn.estimators.AutoSklearnEstimator.build_automl')
-    def test_fit_n_jobs_negative(self, build_automl_patch):
-        n_cores = cpu_count()
-        cls = AutoSklearnEstimator(n_jobs=-1)
-        cls.fit()
-        self.assertEqual(cls._n_jobs, n_cores)
-
-    def test_get_number_of_available_cores(self):
-        n_cores = cpu_count()
-        self.assertGreaterEqual(n_cores, 1)
-
-
-class AutoMLClassifierTest(Base, unittest.TestCase):
-    @unittest.mock.patch('autosklearn.automl.AutoML.predict')
-    def test_multiclass_prediction(self, predict_mock):
-        predicted_probabilities = [[0, 0, 0.99], [0, 0.99, 0], [0.99, 0, 0],
-                                   [0, 0.99, 0], [0, 0, 0.99]]
-        predicted_indexes = [2, 1, 0, 1, 2]
-        expected_result = ['c', 'b', 'a', 'b', 'c']
-
-        predict_mock.return_value = np.array(predicted_probabilities)
-
-        classifier = AutoMLClassifier(
-            time_left_for_this_task=1,
-            per_run_time_limit=1,
-            backend=None,
-        )
-        classifier.InputValidator.validate_target(
-            pd.DataFrame(expected_result, dtype='category'),
-            is_classification=True,
-        )
-
-        actual_result = classifier.predict([None] * len(predicted_indexes))
-
-        np.testing.assert_array_equal(expected_result, actual_result)
-
-    @unittest.mock.patch('autosklearn.automl.AutoML.predict')
-    def test_multilabel_prediction(self, predict_mock):
-        predicted_probabilities = [[0.99, 0],
-                                   [0.99, 0],
-                                   [0, 0.99],
-                                   [0.99, 0.99],
-                                   [0.99, 0.99]]
-        predicted_indexes = np.array([[1, 0], [1, 0], [0, 1], [1, 1], [1, 1]])
-        expected_result = np.array([[2, 13], [2, 13], [1, 17], [2, 17], [2, 17]])
-
-        predict_mock.return_value = np.array(predicted_probabilities)
-
-        classifier = AutoMLClassifier(
-            time_left_for_this_task=1,
-            per_run_time_limit=1,
-            backend=None,
-        )
-        classifier.InputValidator.validate_target(
-            pd.DataFrame(expected_result, dtype='int64'),
-            is_classification=True,
-        )
-
-        actual_result = classifier.predict([None] * len(predicted_indexes))
-
-        np.testing.assert_array_equal(expected_result, actual_result)
-
-    def test_can_pickle_classifier(self):
-        tmp = os.path.join(self.test_dir, '..', '.tmp_can_pickle')
-        output = os.path.join(self.test_dir, '..', '.out_can_pickle')
-        self._setUp(tmp)
-        self._setUp(output)
-
-        X_train, Y_train, X_test, Y_test = putil.get_dataset('iris')
-        automl = AutoSklearnClassifier(time_left_for_this_task=30,
-                                       per_run_time_limit=5,
-                                       tmp_folder=tmp,
-                                       output_folder=output)
-        automl.fit(X_train, Y_train)
-
-        initial_predictions = automl.predict(X_test)
-        initial_accuracy = sklearn.metrics.accuracy_score(Y_test,
-                                                          initial_predictions)
-        self.assertGreaterEqual(initial_accuracy, 0.75)
-        self.assertGreater(self._count_succeses(automl.cv_results_), 0)
-
-        # Test pickle
-        dump_file = os.path.join(output, 'automl.dump.pkl')
-
-        with open(dump_file, 'wb') as f:
-            pickle.dump(automl, f)
-
-        with open(dump_file, 'rb') as f:
-            restored_automl = pickle.load(f)
-
-        restored_predictions = restored_automl.predict(X_test)
-        restored_accuracy = sklearn.metrics.accuracy_score(Y_test,
-                                                           restored_predictions)
-        self.assertGreaterEqual(restored_accuracy, 0.75)
-
-        self.assertEqual(initial_accuracy, restored_accuracy)
-
-        # Test joblib
-        dump_file = os.path.join(output, 'automl.dump.joblib')
-
-        joblib.dump(automl, dump_file)
-
-        restored_automl = joblib.load(dump_file)
-
-        restored_predictions = restored_automl.predict(X_test)
-        restored_accuracy = sklearn.metrics.accuracy_score(Y_test,
-                                                           restored_predictions)
-        self.assertGreaterEqual(restored_accuracy, 0.75)
-
-        self.assertEqual(initial_accuracy, restored_accuracy)
-
-    def test_multilabel(self):
-        tmp = os.path.join(self.test_dir, '..', '.tmp_multilabel_fit')
-        output = os.path.join(self.test_dir, '..', '.out_multilabel_fit')
-        self._setUp(tmp)
-        self._setUp(output)
-
-        X_train, Y_train, X_test, Y_test = putil.get_dataset(
-            'iris', make_multilabel=True)
-        automl = AutoSklearnClassifier(time_left_for_this_task=30,
-                                       per_run_time_limit=5,
-                                       tmp_folder=tmp,
-                                       output_folder=output)
-
-        automl.fit(X_train, Y_train)
-        predictions = automl.predict(X_test)
-        self.assertEqual(predictions.shape, (50, 3))
-        self.assertGreater(self._count_succeses(automl.cv_results_), 0)
-        score = f1_macro(Y_test, predictions)
-        self.assertGreaterEqual(score, 0.9)
-        probs = automl.predict_proba(X_train)
-        self.assertAlmostEqual(np.mean(probs), 0.33, places=1)
-
-    def test_binary(self):
-        tmp = os.path.join(self.test_dir, '..', '.out_binary_fit')
-        output = os.path.join(self.test_dir, '..', '.tmp_binary_fit')
-        self._setUp(output)
-        self._setUp(tmp)
-
-        X_train, Y_train, X_test, Y_test = putil.get_dataset(
-            'iris', make_binary=True)
-        automl = AutoSklearnClassifier(time_left_for_this_task=30,
-                                       per_run_time_limit=5,
-                                       tmp_folder=tmp,
-                                       output_folder=output)
-
-        automl.fit(X_train, Y_train, X_test=X_test, y_test=Y_test,
-                   dataset_name='binary_test_dataset')
-        predictions = automl.predict(X_test)
-        self.assertEqual(predictions.shape, (50, ))
-        score = accuracy(Y_test, predictions)
-        self.assertGreaterEqual(score, 0.9)
-        self.assertGreater(self._count_succeses(automl.cv_results_), 0)
-
-        output_files = os.listdir(output)
-        self.assertIn('binary_test_dataset_test_1.predict', output_files)
-
-    def test_classification_pandas_support(self):
-        X, y = sklearn.datasets.fetch_openml(
-            data_id=2,  # cat/num dataset
-            return_X_y=True,
-            as_frame=True,
-        )
-
-        # Drop NAN!!
-        X = X.dropna('columns')
-
-        # This test only make sense if input is dataframe
-        self.assertTrue(isinstance(X, pd.DataFrame))
-        self.assertTrue(isinstance(y, pd.Series))
-        automl = AutoSklearnClassifier(
-            time_left_for_this_task=30,
-            per_run_time_limit=5,
-            exclude_estimators=['libsvm_svc'],
-            seed=5,
-        )
-
-        automl.fit(X, y)
-
-        # Make sure that at least better than random.
-        # We use same X_train==X_test to test code quality
-        self.assertTrue(automl.score(X, y) > 0.555)
-
-        automl.refit(X, y)
-
-        # Make sure that at least better than random.
-        # accuracy in sklearn needs valid data
-        # It should be 0.555 as the dataset is unbalanced.
-        y = automl.automl_.InputValidator.encode_target(y)
-        prediction = automl.automl_.InputValidator.encode_target(automl.predict(X))
-        self.assertTrue(accuracy(y, prediction) > 0.555)
-        self.assertGreater(self._count_succeses(automl.cv_results_), 0)
-
-
-class AutoMLRegressorTest(Base, unittest.TestCase):
-    def test_regression(self):
-        tmp = os.path.join(self.test_dir, '..', '.tmp_regression_fit')
-        output = os.path.join(self.test_dir, '..', '.out_regression_fit')
-        self._setUp(tmp)
-        self._setUp(output)
-
-        X_train, Y_train, X_test, Y_test = putil.get_dataset('boston')
-        automl = AutoSklearnRegressor(time_left_for_this_task=30,
-                                      per_run_time_limit=5,
-                                      tmp_folder=tmp,
-                                      output_folder=output)
-
-        automl.fit(X_train, Y_train)
-        predictions = automl.predict(X_test)
-        self.assertEqual(predictions.shape, (356,))
-        score = mean_squared_error(Y_test, predictions)
-        # On average np.sqrt(30) away from the target -> ~5.5 on average
-        # Results with select rates drops avg score to a range of -32.40 to -37, on 30 seconds
-        # constraint. With more time_left_for_this_task this is no longer an issue
-        self.assertGreaterEqual(score, -37)
-        self.assertGreater(self._count_succeses(automl.cv_results_), 0)
-
-        self._tearDown(tmp)
-        self._tearDown(output)
-
-    def test_cv_regression(self):
-        """
-        Makes sure that when using a cv strategy, we are able to fit
-        a regressor
-        """
-        tmp = os.path.join(self.test_dir, '..', '.tmp_regression_fit_cv')
-        output = os.path.join(self.test_dir, '..', '.out_regression_fit_cv')
-        self._setUp(tmp)
-        self._setUp(output)
-
-        X_train, Y_train, X_test, Y_test = putil.get_dataset('boston', train_size_maximum=300)
-        automl = AutoSklearnRegressor(time_left_for_this_task=60,
-                                      per_run_time_limit=10,
-                                      resampling_strategy='cv',
-                                      tmp_folder=tmp,
-                                      output_folder=output)
-
-        automl.fit(X_train, Y_train)
-        predictions = automl.predict(X_test)
-        self.assertEqual(predictions.shape, (206,))
-        score = r2(Y_test, predictions)
-        print(Y_test)
-        print(predictions)
-        self.assertGreaterEqual(score, 0.1)
-        self.assertGreater(self._count_succeses(automl.cv_results_), 0)
-
-        self._tearDown(tmp)
-        self._tearDown(output)
-
-    def test_regression_pandas_support(self):
-        X, y = sklearn.datasets.fetch_openml(
-            data_id=41514,  # diabetes
-            return_X_y=True,
-            as_frame=True,
-        )
-        # This test only make sense if input is dataframe
-        self.assertTrue(isinstance(X, pd.DataFrame))
-        self.assertTrue(isinstance(y, pd.Series))
-        automl = AutoSklearnRegressor(
-            time_left_for_this_task=30,
-            per_run_time_limit=5,
-        )
-
-        # Make sure we error out because y is not encoded
-        automl.fit(X, y)
-
-        # Make sure that at least better than random.
-        # We use same X_train==X_test to test code quality
-        self.assertTrue(automl.score(X, y) > 0.5)
-
-        automl.refit(X, y)
-
-        # Make sure that at least better than random.
-        self.assertTrue(r2(y, automl.predict(X)) > 0.5)
-        self.assertGreater(self._count_succeses(automl.cv_results_), 0)
-
-
-class AutoSklearnClassifierTest(unittest.TestCase):
-    # Currently this class only tests that the methods of AutoSklearnClassifier
-    # which should return self actually return self.
-    def test_classification_methods_returns_self(self):
-        X_train, y_train, X_test, y_test = putil.get_dataset('iris')
-        automl = AutoSklearnClassifier(time_left_for_this_task=60,
-                                       per_run_time_limit=10,
-                                       ensemble_size=0,
-                                       exclude_preprocessors=['fast_ica'])
-
-        automl_fitted = automl.fit(X_train, y_train)
-        self.assertIs(automl, automl_fitted)
-
-        automl_ensemble_fitted = automl.fit_ensemble(y_train, ensemble_size=5)
-        self.assertIs(automl, automl_ensemble_fitted)
-
-        automl_refitted = automl.refit(X_train.copy(), y_train.copy())
-        self.assertIs(automl, automl_refitted)
-
-
-class AutoSklearnRegressorTest(unittest.TestCase):
-    # Currently this class only tests that the methods of AutoSklearnRegressor
-    # that should return self actually return self.
-    def test_regression_methods_returns_self(self):
-        X_train, y_train, X_test, y_test = putil.get_dataset('boston')
-        automl = AutoSklearnRegressor(time_left_for_this_task=30,
-                                      per_run_time_limit=5,
-                                      ensemble_size=0)
-
-        automl_fitted = automl.fit(X_train, y_train)
-        self.assertIs(automl, automl_fitted)
-
-        automl_ensemble_fitted = automl.fit_ensemble(y_train, ensemble_size=5)
-        self.assertIs(automl, automl_ensemble_fitted)
-
-        automl_refitted = automl.refit(X_train.copy(), y_train.copy())
-        self.assertIs(automl, automl_refitted)
-
-
-class AutoSklearn2ClassifierTest(unittest.TestCase):
-    # Currently this class only tests that the methods of AutoSklearnClassifier
-    # which should return self actually return self.
-    def test_classification_methods_returns_self(self):
-        X_train, y_train, X_test, y_test = putil.get_dataset('iris')
-        automl = AutoSklearn2Classifier(time_left_for_this_task=60, ensemble_size=0,)
-
-        automl_fitted = automl.fit(X_train, y_train)
-        self.assertIs(automl, automl_fitted)
-
-        automl_ensemble_fitted = automl.fit_ensemble(y_train, ensemble_size=5)
-        self.assertIs(automl, automl_ensemble_fitted)
-
-        automl_refitted = automl.refit(X_train.copy(), y_train.copy())
-        self.assertIs(automl, automl_refitted)
-
-
-if __name__ == "__main__":
-    unittest.main()
+from automl_utils import extract_msg_from_log, count_succeses  # noqa (E402: module level import not at top of file)
+
+
+def test_fit_n_jobs(tmp_dir, output_dir):
+
+    X_train, Y_train, X_test, Y_test = putil.get_dataset('breast_cancer')
+
+    # test parallel Classifier to predict classes, not only indices
+    Y_train += 1
+    Y_test += 1
+
+    class get_smac_object_wrapper:
+
+        def __call__(self, *args, **kwargs):
+            self.n_jobs = kwargs['n_jobs']
+            smac = get_smac_object(*args, **kwargs)
+            self.dask_n_jobs = smac.solver.tae_runner.n_workers
+            self.dask_client_n_jobs = len(
+                smac.solver.tae_runner.client.scheduler_info()['workers']
+            )
+            return smac
+    get_smac_object_wrapper_instance = get_smac_object_wrapper()
+
+    automl = AutoSklearnClassifier(
+        time_left_for_this_task=30,
+        per_run_time_limit=5,
+        output_folder=output_dir,
+        tmp_folder=tmp_dir,
+        seed=1,
+        initial_configurations_via_metalearning=0,
+        ensemble_size=5,
+        n_jobs=2,
+        include_estimators=['sgd'],
+        include_preprocessors=['no_preprocessing'],
+        get_smac_object_callback=get_smac_object_wrapper_instance,
+        max_models_on_disc=None,
+    )
+    automl.fit(X_train, Y_train)
+
+    # Test that the argument is correctly passed to SMAC
+    assert getattr(get_smac_object_wrapper_instance, 'n_jobs') == 2
+    assert getattr(get_smac_object_wrapper_instance, 'dask_n_jobs') == 2
+    assert getattr(get_smac_object_wrapper_instance, 'dask_client_n_jobs') == 2
+
+    available_num_runs = set()
+    for run_key, run_value in automl.automl_.runhistory_.data.items():
+        if run_value.additional_info is not None and 'num_run' in run_value.additional_info:
+            available_num_runs.add(run_value.additional_info['num_run'])
+    available_predictions = set()
+    predictions = glob.glob(
+        os.path.join(automl.automl_._backend.get_runs_directory(), '*', 'predictions_ensemble*.npy')
+    )
+    seeds = set()
+    for prediction in predictions:
+        prediction = os.path.split(prediction)[1]
+        match = re.match(MODEL_FN_RE, prediction.replace("predictions_ensemble", ""))
+        if match:
+            num_run = int(match.group(2))
+            available_predictions.add(num_run)
+            seed = int(match.group(1))
+            seeds.add(seed)
+
+    # Remove the dummy prediction, it is not part of the runhistory
+    available_predictions.remove(1)
+    assert available_num_runs.issubset(available_predictions)
+
+    assert len(seeds) == 1
+
+    ensemble_dir = automl.automl_._backend.get_ensemble_dir()
+    ensembles = os.listdir(ensemble_dir)
+
+    seeds = set()
+    for ensemble_file in ensembles:
+        seeds.add(int(ensemble_file.split('.')[0].split('_')[0]))
+    assert len(seeds) == 1
+
+    assert count_succeses(automl.cv_results_) > 0
+    # For travis-ci it is important that the client no longer exists
+    assert automl.automl_._dask_client is None
+
+
+def test_feat_type_wrong_arguments():
+    cls = AutoSklearnClassifier(ensemble_size=0)
+    X = np.zeros((100, 100))
+    y = np.zeros((100, ))
+
+    expected_msg = r".*Array feat_type does not have same number of "
+    "variables as X has features. 1 vs 100.*"
+    with pytest.raises(ValueError, match=expected_msg):
+        cls.fit(X=X, y=y, feat_type=[True])
+
+    expected_msg = r".*Array feat_type must only contain strings.*"
+    with pytest.raises(ValueError, match=expected_msg):
+        cls.fit(X=X, y=y, feat_type=[True]*100)
+
+    expected_msg = r".*Only `Categorical` and `Numerical` are"
+    "valid feature types, you passed `Car`.*"
+    with pytest.raises(ValueError, match=expected_msg):
+        cls.fit(X=X, y=y, feat_type=['Car']*100)
+
+
+# Mock AutoSklearnEstimator.fit so the test doesn't actually run fit().
+@unittest.mock.patch('autosklearn.estimators.AutoSklearnEstimator.fit')
+def test_type_of_target(mock_estimator):
+    # Test that classifier raises error for illegal target types.
+    X = np.array([[1, 2],
+                  [2, 3],
+                  [3, 4],
+                  [4, 5],
+                  ])
+    # Possible target types
+    y_binary = np.array([0, 0, 1, 1])
+    y_continuous = np.array([0.1, 1.3, 2.1, 4.0])
+    y_multiclass = np.array([0, 1, 2, 0])
+    y_multilabel = np.array([[0, 1],
+                             [1, 1],
+                             [1, 0],
+                             [0, 0],
+                             ])
+    y_multiclass_multioutput = np.array([[0, 1],
+                                         [1, 3],
+                                         [2, 2],
+                                         [5, 3],
+                                         ])
+    y_continuous_multioutput = np.array([[0.1, 1.5],
+                                         [1.2, 3.5],
+                                         [2.7, 2.7],
+                                         [5.5, 3.9],
+                                         ])
+
+    cls = AutoSklearnClassifier(ensemble_size=0)
+    # Illegal target types for classification: continuous,
+    # multiclass-multioutput, continuous-multioutput.
+    expected_msg = r".*Classification with data of type"
+    " multiclass-multioutput is not supported.*"
+    with pytest.raises(ValueError, match=expected_msg):
+        cls.fit(X=X, y=y_multiclass_multioutput)
+
+    expected_msg = r".*Classification with data of type"
+    " continuous is not supported.*"
+    with pytest.raises(ValueError, match=expected_msg):
+        cls.fit(X=X, y=y_continuous)
+
+    expected_msg = r".*Classification with data of type"
+    " continuous-multioutput is not supported.*"
+    with pytest.raises(ValueError, match=expected_msg):
+        cls.fit(X=X, y=y_continuous_multioutput)
+
+    # Legal target types for classification: binary, multiclass,
+    # multilabel-indicator.
+    try:
+        cls.fit(X, y_binary)
+    except ValueError:
+        pytest.fail("cls.fit() raised ValueError while fitting "
+                    "binary targets")
+
+    try:
+        cls.fit(X, y_multiclass)
+    except ValueError:
+        pytest.fail("cls.fit() raised ValueError while fitting "
+                    "multiclass targets")
+
+    try:
+        cls.fit(X, y_multilabel)
+    except ValueError:
+        pytest.fail("cls.fit() raised ValueError while fitting "
+                    "multilabel-indicator targets")
+
+    # Test that regressor raises error for illegal target types.
+    reg = AutoSklearnRegressor(ensemble_size=0)
+    # Illegal target types for regression: multilabel-indicator
+    # multiclass-multioutput
+    expected_msg = r".*Regression with data of type"
+    " multilabel-indicator is not supported.*"
+    with pytest.raises(ValueError, match=expected_msg):
+        reg.fit(X=X, y=y_multilabel,)
+
+    expected_msg = r".*Regression with data of type"
+    " multiclass-multioutput is not supported.*"
+    with pytest.raises(ValueError, match=expected_msg):
+        reg.fit(X=X, y=y_multiclass_multioutput,)
+
+    # Legal target types: continuous, multiclass,
+    # continuous-multioutput,
+    # binary
+    try:
+        reg.fit(X, y_continuous)
+    except ValueError:
+        pytest.fail("reg.fit() raised ValueError while fitting "
+                    "continuous targets")
+
+    try:
+        reg.fit(X, y_multiclass)
+    except ValueError:
+        pytest.fail("reg.fit() raised ValueError while fitting "
+                    "multiclass targets")
+
+    try:
+        reg.fit(X, y_continuous_multioutput)
+    except ValueError:
+        pytest.fail("reg.fit() raised ValueError while fitting "
+                    "continuous_multioutput targets")
+
+    try:
+        reg.fit(X, y_binary)
+    except ValueError:
+        pytest.fail("reg.fit() raised ValueError while fitting "
+                    "binary targets")
+
+
+def test_cv_results(tmp_dir, output_dir):
+    # TODO restructure and actually use real SMAC output from a long run
+    # to do this unittest!
+    X_train, Y_train, X_test, Y_test = putil.get_dataset('iris')
+
+    cls = AutoSklearnClassifier(time_left_for_this_task=30,
+                                per_run_time_limit=5,
+                                tmp_folder=tmp_dir,
+                                output_folder=output_dir,
+                                seed=1,
+                                initial_configurations_via_metalearning=0,
+                                ensemble_size=0)
+    cls.fit(X_train, Y_train)
+    cv_results = cls.cv_results_
+    assert isinstance(cv_results, dict), type(cv_results)
+    assert isinstance(cv_results['mean_test_score'], np.ndarray), type(
+        cv_results['mean_test_score'])
+    assert isinstance(cv_results['mean_fit_time'], np.ndarray), type(
+        cv_results['mean_fit_time']
+    )
+    assert isinstance(cv_results['params'], list), type(cv_results['params'])
+    assert isinstance(cv_results['rank_test_scores'], np.ndarray), type(
+        cv_results['rank_test_scores']
+    )
+    cv_result_items = [isinstance(val, npma.MaskedArray) for key, val in
+                       cv_results.items() if key.startswith('param_')]
+    assert all(cv_result_items), cv_results.items()
+
+
+@unittest.mock.patch('autosklearn.estimators.AutoSklearnEstimator.build_automl')
+def test_fit_n_jobs_negative(build_automl_patch):
+    n_cores = cpu_count()
+    cls = AutoSklearnEstimator(n_jobs=-1, ensemble_size=0)
+    cls.fit()
+    assert cls._n_jobs == n_cores
+
+
+def test_get_number_of_available_cores():
+    n_cores = cpu_count()
+    assert n_cores >= 1, n_cores
+
+
+@unittest.mock.patch('autosklearn.automl.AutoML.predict')
+def test_multiclass_prediction(predict_mock, backend, dask_client):
+    predicted_probabilities = [[0, 0, 0.99], [0, 0.99, 0], [0.99, 0, 0],
+                               [0, 0.99, 0], [0, 0, 0.99]]
+    predicted_indexes = [2, 1, 0, 1, 2]
+    expected_result = ['c', 'b', 'a', 'b', 'c']
+
+    predict_mock.return_value = np.array(predicted_probabilities)
+
+    classifier = AutoMLClassifier(
+        time_left_for_this_task=1,
+        per_run_time_limit=1,
+        backend=backend,
+        dask_client=dask_client,
+    )
+    classifier.InputValidator.validate_target(
+        pd.DataFrame(expected_result, dtype='category'),
+        is_classification=True,
+    )
+
+    actual_result = classifier.predict([None] * len(predicted_indexes))
+
+    np.testing.assert_array_equal(expected_result, actual_result)
+
+
+@unittest.mock.patch('autosklearn.automl.AutoML.predict')
+def test_multilabel_prediction(predict_mock, backend, dask_client):
+    predicted_probabilities = [[0.99, 0],
+                               [0.99, 0],
+                               [0, 0.99],
+                               [0.99, 0.99],
+                               [0.99, 0.99]]
+    predicted_indexes = np.array([[1, 0], [1, 0], [0, 1], [1, 1], [1, 1]])
+    expected_result = np.array([[2, 13], [2, 13], [1, 17], [2, 17], [2, 17]])
+
+    predict_mock.return_value = np.array(predicted_probabilities)
+
+    classifier = AutoMLClassifier(
+        time_left_for_this_task=1,
+        per_run_time_limit=1,
+        backend=backend,
+        dask_client=dask_client,
+    )
+    classifier.InputValidator.validate_target(
+        pd.DataFrame(expected_result, dtype='int64'),
+        is_classification=True,
+    )
+
+    actual_result = classifier.predict([None] * len(predicted_indexes))
+
+    np.testing.assert_array_equal(expected_result, actual_result)
+
+
+def test_can_pickle_classifier(tmp_dir, output_dir, dask_client):
+    X_train, Y_train, X_test, Y_test = putil.get_dataset('iris')
+    automl = AutoSklearnClassifier(time_left_for_this_task=30,
+                                   per_run_time_limit=5,
+                                   tmp_folder=tmp_dir,
+                                   dask_client=dask_client,
+                                   output_folder=output_dir)
+    automl.fit(X_train, Y_train)
+
+    initial_predictions = automl.predict(X_test)
+    initial_accuracy = sklearn.metrics.accuracy_score(Y_test,
+                                                      initial_predictions)
+    assert initial_accuracy >= 0.75
+    assert count_succeses(automl.cv_results_) > 0
+
+    # Test pickle
+    dump_file = os.path.join(output_dir, 'automl.dump.pkl')
+
+    with open(dump_file, 'wb') as f:
+        pickle.dump(automl, f)
+
+    with open(dump_file, 'rb') as f:
+        restored_automl = pickle.load(f)
+
+    restored_predictions = restored_automl.predict(X_test)
+    restored_accuracy = sklearn.metrics.accuracy_score(Y_test,
+                                                       restored_predictions)
+    assert restored_accuracy >= 0.75
+    assert initial_accuracy == restored_accuracy
+
+    # Test joblib
+    dump_file = os.path.join(output_dir, 'automl.dump.joblib')
+
+    joblib.dump(automl, dump_file)
+
+    restored_automl = joblib.load(dump_file)
+
+    restored_predictions = restored_automl.predict(X_test)
+    restored_accuracy = sklearn.metrics.accuracy_score(Y_test,
+                                                       restored_predictions)
+    assert restored_accuracy >= 0.75
+    assert initial_accuracy == restored_accuracy
+
+
+def test_multilabel(tmp_dir, output_dir, dask_client):
+
+    X_train, Y_train, X_test, Y_test = putil.get_dataset(
+        'iris', make_multilabel=True)
+    automl = AutoSklearnClassifier(time_left_for_this_task=30,
+                                   per_run_time_limit=5,
+                                   tmp_folder=tmp_dir,
+                                   dask_client=dask_client,
+                                   output_folder=output_dir)
+
+    automl.fit(X_train, Y_train)
+    # Log file path
+    log_file_path = glob.glob(os.path.join(
+        tmp_dir, 'AutoML*.log'))[0]
+    predictions = automl.predict(X_test)
+    assert predictions.shape == (50, 3), extract_msg_from_log(log_file_path)
+    assert count_succeses(automl.cv_results_) > 0,  extract_msg_from_log(log_file_path)
+
+    score = f1_macro(Y_test, predictions)
+    assert score >= 0.9, extract_msg_from_log(log_file_path)
+
+    probs = automl.predict_proba(X_train)
+    assert np.mean(probs) == pytest.approx(0.33, rel=1e-1)
+
+
+def test_binary(tmp_dir, output_dir, dask_client):
+
+    X_train, Y_train, X_test, Y_test = putil.get_dataset(
+        'iris', make_binary=True)
+    automl = AutoSklearnClassifier(time_left_for_this_task=40,
+                                   per_run_time_limit=10,
+                                   tmp_folder=tmp_dir,
+                                   dask_client=dask_client,
+                                   output_folder=output_dir)
+
+    automl.fit(X_train, Y_train, X_test=X_test, y_test=Y_test,
+               dataset_name='binary_test_dataset')
+    log_file_path = glob.glob(os.path.join(
+        tmp_dir, 'AutoML*.log'))[0]
+    predictions = automl.predict(X_test)
+    assert predictions.shape == (50, ), extract_msg_from_log(log_file_path)
+
+    score = accuracy(Y_test, predictions)
+    assert score > 0.9, extract_msg_from_log(log_file_path)
+    assert count_succeses(automl.cv_results_) > 0, extract_msg_from_log(log_file_path)
+
+    output_files = glob.glob(os.path.join(output_dir, 'binary_test_dataset_test_*.predict'))
+    assert len(output_files) > 0, (output_files, extract_msg_from_log(log_file_path))
+
+
+def test_classification_pandas_support(tmp_dir, output_dir, dask_client):
+
+    X, y = sklearn.datasets.fetch_openml(
+        data_id=2,  # cat/num dataset
+        return_X_y=True,
+        as_frame=True,
+    )
+
+    # Drop NAN!!
+    X = X.dropna('columns')
+
+    # This test only make sense if input is dataframe
+    assert isinstance(X, pd.DataFrame)
+    assert isinstance(y, pd.Series)
+    automl = AutoSklearnClassifier(
+        time_left_for_this_task=30,
+        per_run_time_limit=5,
+        exclude_estimators=['libsvm_svc'],
+        dask_client=dask_client,
+        seed=5,
+        tmp_folder=tmp_dir,
+        output_folder=output_dir,
+    )
+
+    automl.fit(X, y)
+
+    log_file_path = glob.glob(os.path.join(
+        tmp_dir, 'AutoML*.log'))[0]
+
+    # Make sure that at least better than random.
+    # We use same X_train==X_test to test code quality
+    assert automl.score(X, y) > 0.555, extract_msg_from_log(log_file_path)
+
+    automl.refit(X, y)
+
+    # Make sure that at least better than random.
+    # accuracy in sklearn needs valid data
+    # It should be 0.555 as the dataset is unbalanced.
+    y = automl.automl_.InputValidator.encode_target(y)
+    prediction = automl.automl_.InputValidator.encode_target(automl.predict(X))
+    assert accuracy(y, prediction) > 0.555
+    assert count_succeses(automl.cv_results_) > 0
+
+
+def test_regression(tmp_dir, output_dir, dask_client):
+
+    X_train, Y_train, X_test, Y_test = putil.get_dataset('boston')
+    automl = AutoSklearnRegressor(time_left_for_this_task=30,
+                                  per_run_time_limit=5,
+                                  tmp_folder=tmp_dir,
+                                  dask_client=dask_client,
+                                  output_folder=output_dir)
+
+    automl.fit(X_train, Y_train)
+
+    # Log file path
+    log_file_path = glob.glob(os.path.join(
+        tmp_dir, 'AutoML*.log'))[0]
+
+    predictions = automl.predict(X_test)
+    assert predictions.shape == (356,)
+    score = mean_squared_error(Y_test, predictions)
+
+    # On average np.sqrt(30) away from the target -> ~5.5 on average
+    # Results with select rates drops avg score to a range of -32.40 to -37, on 30 seconds
+    # constraint. With more time_left_for_this_task this is no longer an issue
+    assert score >= -37, extract_msg_from_log(log_file_path)
+    assert count_succeses(automl.cv_results_) > 0
+
+
+def test_cv_regression(tmp_dir, output_dir, dask_client):
+    """
+    Makes sure that when using a cv strategy, we are able to fit
+    a regressor
+    """
+
+    X_train, Y_train, X_test, Y_test = putil.get_dataset('boston', train_size_maximum=300)
+    automl = AutoSklearnRegressor(time_left_for_this_task=60,
+                                  per_run_time_limit=10,
+                                  resampling_strategy='cv',
+                                  tmp_folder=tmp_dir,
+                                  dask_client=dask_client,
+                                  output_folder=output_dir)
+
+    automl.fit(X_train, Y_train)
+
+    # Log file path
+    log_file_path = glob.glob(os.path.join(
+        tmp_dir, 'AutoML*.log'))[0]
+
+    predictions = automl.predict(X_test)
+    assert predictions.shape == (206,)
+    score = r2(Y_test, predictions)
+    assert score >= 0.1, extract_msg_from_log(log_file_path)
+    assert count_succeses(automl.cv_results_) > 0, extract_msg_from_log(log_file_path)
+
+
+def test_regression_pandas_support(tmp_dir, output_dir, dask_client):
+
+    X, y = sklearn.datasets.fetch_openml(
+        data_id=41514,  # diabetes
+        return_X_y=True,
+        as_frame=True,
+    )
+    # This test only make sense if input is dataframe
+    assert isinstance(X, pd.DataFrame)
+    assert isinstance(y, pd.Series)
+    automl = AutoSklearnRegressor(
+        time_left_for_this_task=40,
+        per_run_time_limit=5,
+        dask_client=dask_client,
+        tmp_folder=tmp_dir,
+        output_folder=output_dir,
+    )
+
+    # Make sure we error out because y is not encoded
+    automl.fit(X, y)
+
+    log_file_path = glob.glob(os.path.join(
+        tmp_dir, 'AutoML*.log'))[0]
+
+    # Make sure that at least better than random.
+    # We use same X_train==X_test to test code quality
+    assert automl.score(X, y) >= 0.5, extract_msg_from_log(log_file_path)
+
+    automl.refit(X, y)
+
+    # Make sure that at least better than random.
+    assert r2(y, automl.predict(X)) > 0.5, extract_msg_from_log(log_file_path)
+    assert count_succeses(automl.cv_results_) > 0, extract_msg_from_log(log_file_path)
+
+
+# Currently this class only tests that the methods of AutoSklearnClassifier
+# which should return self actually return self.
+def test_autosklearn_classification_methods_returns_self(dask_client):
+    X_train, y_train, X_test, y_test = putil.get_dataset('iris')
+    automl = AutoSklearnClassifier(time_left_for_this_task=60,
+                                   per_run_time_limit=10,
+                                   ensemble_size=0,
+                                   dask_client=dask_client,
+                                   exclude_preprocessors=['fast_ica'])
+
+    automl_fitted = automl.fit(X_train, y_train)
+    assert automl is automl_fitted
+
+    automl_ensemble_fitted = automl.fit_ensemble(y_train, ensemble_size=5)
+    assert automl is automl_ensemble_fitted
+
+    automl_refitted = automl.refit(X_train.copy(), y_train.copy())
+    assert automl is automl_refitted
+
+
+# Currently this class only tests that the methods of AutoSklearnRegressor
+# that should return self actually return self.
+def test_autosklearn_regression_methods_returns_self(dask_client):
+    X_train, y_train, X_test, y_test = putil.get_dataset('boston')
+    automl = AutoSklearnRegressor(time_left_for_this_task=30,
+                                  per_run_time_limit=5,
+                                  dask_client=dask_client,
+                                  ensemble_size=0)
+
+    automl_fitted = automl.fit(X_train, y_train)
+    assert automl is automl_fitted
+
+    automl_ensemble_fitted = automl.fit_ensemble(y_train, ensemble_size=5)
+    assert automl is automl_ensemble_fitted
+
+    automl_refitted = automl.refit(X_train.copy(), y_train.copy())
+    assert automl is automl_refitted
+
+
+# Currently this class only tests that the methods of AutoSklearnClassifier
+# which should return self actually return self.
+def test_autosklearn2_classification_methods_returns_self(dask_client):
+    X_train, y_train, X_test, y_test = putil.get_dataset('iris')
+    automl = AutoSklearn2Classifier(time_left_for_this_task=60, ensemble_size=0,
+                                    dask_client=dask_client)
+
+    automl_fitted = automl.fit(X_train, y_train)
+    assert automl is automl_fitted
+
+    automl_ensemble_fitted = automl.fit_ensemble(y_train, ensemble_size=5)
+    assert automl is automl_ensemble_fitted
+
+    automl_refitted = automl.refit(X_train.copy(), y_train.copy())
+    assert automl is automl_refitted
+
+    predictions = automl_fitted.predict(X_test)
+    assert sklearn.metrics.accuracy_score(y_test, predictions) >= 2 / 3
+
+    pickle.dumps(automl_fitted)
diff --git a/test/test_data/test_data_manager.py b/test/test_data/test_data_manager.py
deleted file mode 100644
index ccc538ec4a..0000000000
--- a/test/test_data/test_data_manager.py
+++ /dev/null
@@ -1,94 +0,0 @@
-import unittest
-
-import numpy as np
-
-from autosklearn.data.abstract_data_manager import AbstractDataManager
-
-
-dataset_train = [[2.5, 3.3, 2, 5, 1, 1],
-                 [1.0, 0.7, 1, 5, 1, 0],
-                 [1.3, 0.8, 1, 4, 1, 1]]
-dataset_train = np.array(dataset_train)
-dataset_valid = [[1.5, 1.7, 1, 4, 1, 1],
-                 [2.0, 2.1, 1, 5, 1, 0],
-                 [1.9, 1.8, 2, 4, 0, 1]]
-dataset_valid = np.array(dataset_valid)
-dataset_test = [[0.9, 2.2, 2, 4, 1, 1],
-                [0.7, 3.1, 1, 5, 1, 1],
-                [2.4, 2.6, 2, 5, 0, 1]]
-dataset_test = np.array(dataset_test)
-
-N = "Numerical"
-B = "Binary"
-C = "Categorical"
-
-
-class InitFreeDataManager(AbstractDataManager):
-    def __init__(self):
-        pass
-
-
-class CompetitionDataManagerTest(unittest.TestCase):
-    _multiprocess_can_split_ = True
-
-    def setUp(self):
-        self.D = InitFreeDataManager()
-        self.D._data = {}
-        self.D._data['X_train'] = dataset_train.copy()
-        self.D._data['X_valid'] = dataset_valid.copy()
-        self.D._data['X_test'] = dataset_test.copy()
-
-    def test_perform1HotEncoding(self):
-        self.D.feat_type = [N, N, N, N, N, N]
-        self.D._info = {'is_sparse': 0, 'has_missing': 0}
-        self.D.perform1HotEncoding()
-
-        np.testing.assert_array_almost_equal(dataset_train, self.D.data['X_train'])
-        np.testing.assert_array_almost_equal(dataset_valid, self.D.data['X_valid'])
-        np.testing.assert_array_almost_equal(dataset_test, self.D.data['X_test'])
-        self.assertIsInstance(self.D.data['X_train'], np.ndarray)
-        self.assertIsInstance(self.D.data['X_valid'], np.ndarray)
-        self.assertIsInstance(self.D.data['X_test'], np.ndarray)
-
-    def test_perform1HotEncoding_binary_data(self):
-        self.D.feat_type = [N, N, N, N, B, B]
-        self.D._info = {'is_sparse': 0, 'has_missing': 0}
-        self.D.perform1HotEncoding()
-
-        # Nothing should have happened to the array...
-        np.testing.assert_array_almost_equal(dataset_train, self.D.data['X_train'])
-        np.testing.assert_array_almost_equal(dataset_valid, self.D.data['X_valid'])
-        np.testing.assert_array_almost_equal(dataset_test, self.D.data['X_test'])
-        self.assertIsInstance(self.D.data['X_train'], np.ndarray)
-        self.assertIsInstance(self.D.data['X_valid'], np.ndarray)
-        self.assertIsInstance(self.D.data['X_test'], np.ndarray)
-
-    def test_perform1HotEncoding_categorical_data(self):
-        self.D.feat_type = [N, N, C, C, B, B]
-        self.D._info = {'is_sparse': 0, 'has_missing': 0}
-        self.D.perform1HotEncoding()
-
-        # Check if converted back to dense array
-        self.assertIsInstance(self.D.data['X_train'], np.ndarray)
-        self.assertIsInstance(self.D.data['X_valid'], np.ndarray)
-        self.assertIsInstance(self.D.data['X_test'], np.ndarray)
-        # Check if the dimensions are correct
-        # Two new cols are created (one hot enc.) and one column is dropped
-        # by the variancethreshold
-        self.assertEqual((3, 7), self.D.data['X_train'].shape)
-        self.assertEqual((3, 7), self.D.data['X_valid'].shape)
-        self.assertEqual((3, 7), self.D.data['X_test'].shape)
-        # Some tests if encoding works
-        self.assertEqual(self.D.data['X_train'][:, :4].max(), 1)
-        self.assertEqual(self.D.data['X_valid'][:, :4].min(), 0)
-        self.assertEqual(self.D.data['X_test'][:, :4].min(), 0)
-        # Test that other stuff is not encoded
-        # It was 2.5, but after rescaling it became 1.38
-        self.assertEqual(self.D.data['X_train'][0, 4], 1.3887301496588276)
-
-    def test_perform1HotEncoding_binary_data_with_missing_values(self):
-        # self.D.feat_type = [N, N, N, N, B, B]
-        # self.D.info = {'is_sparse': 0, 'has_missing': 1}
-        # self.D.perform1HotEncoding()
-        # self.assertEqual((3, 8), self.D.data['X_train'].shape)
-        pass
diff --git a/test/test_data/test_validation.py b/test/test_data/test_validation.py
index 25a3f36144..c0c673755a 100644
--- a/test/test_data/test_validation.py
+++ b/test/test_data/test_validation.py
@@ -538,6 +538,14 @@ def test_big_dataset_encoding(self):
         # No change to numerical columns
         np.testing.assert_array_equal(x['carbon'].to_numpy(), x_t[:, 3])
 
+        # Categorical columns are sorted to the beginning
+        self.assertEqual(
+            validator.feature_types,
+            (['categorical'] * 3) + (['numerical'] * 7)
+        )
+        self.assertEqual(x.iloc[0, 6], 610)
+        np.testing.assert_array_equal(x_t[0], [0, 0, 0, 8, 0, 0, 0.7, 610, 0, np.NaN])
+
         return
 
     def test_join_and_check(self):
diff --git a/test/test_ensemble_builder/data/.auto-sklearn/predictions_ensemble/predictions_ensemble_true.npy b/test/test_ensemble_builder/data/.auto-sklearn/predictions_ensemble_true.npy
similarity index 100%
rename from test/test_ensemble_builder/data/.auto-sklearn/predictions_ensemble/predictions_ensemble_true.npy
rename to test/test_ensemble_builder/data/.auto-sklearn/predictions_ensemble_true.npy
diff --git a/test/test_ensemble_builder/data/.auto-sklearn/models/0.2.0.0.model b/test/test_ensemble_builder/data/.auto-sklearn/runs/0_1_0.0/0.1.0.0.model
similarity index 100%
rename from test/test_ensemble_builder/data/.auto-sklearn/models/0.2.0.0.model
rename to test/test_ensemble_builder/data/.auto-sklearn/runs/0_1_0.0/0.1.0.0.model
diff --git a/test/test_ensemble_builder/data/.auto-sklearn/predictions_ensemble/predictions_ensemble_0_1_0.0.npy b/test/test_ensemble_builder/data/.auto-sklearn/runs/0_1_0.0/predictions_ensemble_0_1_0.0.npy
similarity index 100%
rename from test/test_ensemble_builder/data/.auto-sklearn/predictions_ensemble/predictions_ensemble_0_1_0.0.npy
rename to test/test_ensemble_builder/data/.auto-sklearn/runs/0_1_0.0/predictions_ensemble_0_1_0.0.npy
diff --git a/test/test_ensemble_builder/data/.auto-sklearn/predictions_test/predictions_test_0_1_0.0.npy b/test/test_ensemble_builder/data/.auto-sklearn/runs/0_1_0.0/predictions_test_0_1_0.0.npy
similarity index 100%
rename from test/test_ensemble_builder/data/.auto-sklearn/predictions_test/predictions_test_0_1_0.0.npy
rename to test/test_ensemble_builder/data/.auto-sklearn/runs/0_1_0.0/predictions_test_0_1_0.0.npy
diff --git a/test/test_ensemble_builder/data/.auto-sklearn/predictions_valid/predictions_valid_0_1_0.0.npy b/test/test_ensemble_builder/data/.auto-sklearn/runs/0_1_0.0/predictions_valid_0_1_0.0.npy
similarity index 100%
rename from test/test_ensemble_builder/data/.auto-sklearn/predictions_valid/predictions_valid_0_1_0.0.npy
rename to test/test_ensemble_builder/data/.auto-sklearn/runs/0_1_0.0/predictions_valid_0_1_0.0.npy
diff --git a/test/test_ensemble_builder/data/.auto-sklearn/models/0.3.0.0.model b/test/test_ensemble_builder/data/.auto-sklearn/runs/0_2_0.0/0.2.0.0.model
similarity index 100%
rename from test/test_ensemble_builder/data/.auto-sklearn/models/0.3.0.0.model
rename to test/test_ensemble_builder/data/.auto-sklearn/runs/0_2_0.0/0.2.0.0.model
diff --git a/test/test_ensemble_builder/data/.auto-sklearn/predictions_ensemble/predictions_ensemble_0_2_0.0.npy b/test/test_ensemble_builder/data/.auto-sklearn/runs/0_2_0.0/predictions_ensemble_0_2_0.0.npy
similarity index 100%
rename from test/test_ensemble_builder/data/.auto-sklearn/predictions_ensemble/predictions_ensemble_0_2_0.0.npy
rename to test/test_ensemble_builder/data/.auto-sklearn/runs/0_2_0.0/predictions_ensemble_0_2_0.0.npy
diff --git a/test/test_ensemble_builder/data/.auto-sklearn/predictions_test/predictions_test_0_2_0.0.np b/test/test_ensemble_builder/data/.auto-sklearn/runs/0_2_0.0/predictions_test_0_2_0.0.np
similarity index 100%
rename from test/test_ensemble_builder/data/.auto-sklearn/predictions_test/predictions_test_0_2_0.0.np
rename to test/test_ensemble_builder/data/.auto-sklearn/runs/0_2_0.0/predictions_test_0_2_0.0.np
diff --git a/test/test_ensemble_builder/data/.auto-sklearn/predictions_test/predictions_test_0_2_0.0.npy b/test/test_ensemble_builder/data/.auto-sklearn/runs/0_2_0.0/predictions_test_0_2_0.0.npy
similarity index 100%
rename from test/test_ensemble_builder/data/.auto-sklearn/predictions_test/predictions_test_0_2_0.0.npy
rename to test/test_ensemble_builder/data/.auto-sklearn/runs/0_2_0.0/predictions_test_0_2_0.0.npy
diff --git a/test/test_ensemble_builder/data/.auto-sklearn/predictions_valid/predictions_valid_0_2_0.0.npy b/test/test_ensemble_builder/data/.auto-sklearn/runs/0_2_0.0/predictions_valid_0_2_0.0.npy
similarity index 100%
rename from test/test_ensemble_builder/data/.auto-sklearn/predictions_valid/predictions_valid_0_2_0.0.npy
rename to test/test_ensemble_builder/data/.auto-sklearn/runs/0_2_0.0/predictions_valid_0_2_0.0.npy
diff --git a/test/test_ensemble_builder/data/.auto-sklearn/models/0.3.100.0.model b/test/test_ensemble_builder/data/.auto-sklearn/runs/0_3_100.0/0.3.0.0.model
similarity index 100%
rename from test/test_ensemble_builder/data/.auto-sklearn/models/0.3.100.0.model
rename to test/test_ensemble_builder/data/.auto-sklearn/runs/0_3_100.0/0.3.0.0.model
diff --git a/test/test_ensemble_builder/data/.auto-sklearn/predictions_ensemble/predictions_ensemble_0_4_0.0.npy.gz b/test/test_ensemble_builder/data/.auto-sklearn/runs/0_3_100.0/0.3.100.0.model
similarity index 100%
rename from test/test_ensemble_builder/data/.auto-sklearn/predictions_ensemble/predictions_ensemble_0_4_0.0.npy.gz
rename to test/test_ensemble_builder/data/.auto-sklearn/runs/0_3_100.0/0.3.100.0.model
diff --git a/test/test_ensemble_builder/data/.auto-sklearn/predictions_ensemble/predictions_ensemble_0_3_100.0.npy b/test/test_ensemble_builder/data/.auto-sklearn/runs/0_3_100.0/predictions_ensemble_0_3_100.0.npy
similarity index 100%
rename from test/test_ensemble_builder/data/.auto-sklearn/predictions_ensemble/predictions_ensemble_0_3_100.0.npy
rename to test/test_ensemble_builder/data/.auto-sklearn/runs/0_3_100.0/predictions_ensemble_0_3_100.0.npy
diff --git a/test/test_ensemble_builder/data/.auto-sklearn/predictions_test/predictions_test_0_3_100.0.npy b/test/test_ensemble_builder/data/.auto-sklearn/runs/0_3_100.0/predictions_test_0_3_100.0.npy
similarity index 100%
rename from test/test_ensemble_builder/data/.auto-sklearn/predictions_test/predictions_test_0_3_100.0.npy
rename to test/test_ensemble_builder/data/.auto-sklearn/runs/0_3_100.0/predictions_test_0_3_100.0.npy
diff --git a/test/test_ensemble_builder/data/.auto-sklearn/predictions_valid/predictions_valid_0_3_100.0.npy b/test/test_ensemble_builder/data/.auto-sklearn/runs/0_3_100.0/predictions_valid_0_3_100.0.npy
similarity index 100%
rename from test/test_ensemble_builder/data/.auto-sklearn/predictions_valid/predictions_valid_0_3_100.0.npy
rename to test/test_ensemble_builder/data/.auto-sklearn/runs/0_3_100.0/predictions_valid_0_3_100.0.npy
diff --git a/test/test_ensemble_builder/ensemble_utils.py b/test/test_ensemble_builder/ensemble_utils.py
new file mode 100644
index 0000000000..653c5455ca
--- /dev/null
+++ b/test/test_ensemble_builder/ensemble_utils.py
@@ -0,0 +1,102 @@
+import os
+import shutil
+import unittest
+
+import numpy as np
+
+from autosklearn.metrics import make_scorer
+from autosklearn.ensemble_builder import (
+    EnsembleBuilder, AbstractEnsemble
+)
+
+
+def scorer_function(a, b):
+    return 0.9
+
+
+MockMetric = make_scorer('mock', scorer_function)
+
+
+class BackendMock(object):
+
+    def __init__(self, target_directory):
+        this_directory = os.path.abspath(
+            os.path.dirname(__file__)
+        )
+        shutil.copytree(os.path.join(this_directory, 'data'), os.path.join(target_directory))
+        self.temporary_directory = target_directory
+        self.internals_directory = os.path.join(self.temporary_directory, '.auto-sklearn')
+
+    def load_datamanager(self):
+        manager = unittest.mock.Mock()
+        manager.__reduce__ = lambda self: (unittest.mock.MagicMock, ())
+        array = np.load(os.path.join(
+            self.temporary_directory,
+            '.auto-sklearn',
+            'runs', '0_3_100.0',
+            'predictions_test_0_3_100.0.npy'
+        ))
+        manager.data.get.return_value = array
+        return manager
+
+    def load_targets_ensemble(self):
+        with open(os.path.join(
+            self.temporary_directory,
+            ".auto-sklearn",
+            "predictions_ensemble_true.npy"
+        ), "rb") as fp:
+            y = np.load(fp, allow_pickle=True)
+        return y
+
+    def save_ensemble(self, ensemble, index_run, seed):
+        return
+
+    def save_predictions_as_txt(self, predictions, subset, idx, prefix, precision):
+        return
+
+    def get_runs_directory(self) -> str:
+        return os.path.join(self.temporary_directory, '.auto-sklearn', 'runs')
+
+    def get_numrun_directory(self, seed: int, num_run: int, budget: float) -> str:
+        return os.path.join(self.get_runs_directory(), '%d_%d_%s' % (seed, num_run, budget))
+
+    def get_model_filename(self, seed: int, idx: int, budget: float) -> str:
+        return '%s.%s.%s.model' % (seed, idx, budget)
+
+
+def compare_read_preds(read_preds1, read_preds2):
+    """
+    compares read_preds attribute. An alternative to
+    assert Dict Equal as it contains np arrays, so we have
+    to use np testing utilities accordingly
+    """
+
+    # Both arrays should have the same splits
+    assert set(read_preds1.keys()) == set(read_preds2.keys())
+
+    for k, v in read_preds1.items():
+
+        # Each split should have the same elements
+        assert set(read_preds1[k].keys()) == set(read_preds2[k].keys())
+
+        # This level contains the scores/ensmebles/etc
+        for actual_k, actual_v in read_preds1[k].items():
+
+            # If it is a numpy array, make sure it is the same
+            if type(actual_v) is np.ndarray:
+                np.testing.assert_array_equal(actual_v, read_preds2[k][actual_k])
+            else:
+                assert actual_v == read_preds2[k][actual_k]
+
+
+class EnsembleBuilderMemMock(EnsembleBuilder):
+
+    def fit_ensemble(self, selected_keys):
+        return True
+
+    def predict(self, set_: str,
+                ensemble: AbstractEnsemble,
+                selected_keys: list,
+                n_preds: int,
+                index_run: int):
+        np.ones([10000000, 1000000])
diff --git a/test/test_ensemble_builder/test_ensemble.py b/test/test_ensemble_builder/test_ensemble.py
index 2a69926f30..300932bda6 100644
--- a/test/test_ensemble_builder/test_ensemble.py
+++ b/test/test_ensemble_builder/test_ensemble.py
@@ -2,588 +2,791 @@
 import sys
 import time
 import unittest.mock
+import pickle
+import pytest
+import shutil
 
+import dask.distributed
 import numpy as np
-
 import pandas as pd
-
 from smac.runhistory.runhistory import RunValue, RunKey, RunHistory
 
+from autosklearn.constants import MULTILABEL_CLASSIFICATION, BINARY_CLASSIFICATION
 from autosklearn.metrics import roc_auc, accuracy, log_loss
 from autosklearn.ensembles.ensemble_selection import EnsembleSelection
-from autosklearn.ensemble_builder import EnsembleBuilder, Y_VALID, Y_TEST
+from autosklearn.ensemble_builder import (
+    EnsembleBuilder,
+    EnsembleBuilderManager,
+    Y_ENSEMBLE,
+    Y_VALID,
+    Y_TEST,
+)
 from autosklearn.ensembles.singlebest_ensemble import SingleBest
 
 this_directory = os.path.dirname(__file__)
 sys.path.append(this_directory)
+from ensemble_utils import BackendMock, compare_read_preds, EnsembleBuilderMemMock, MockMetric  # noqa (E402: module level import not   at top of file)
 
 
-class BackendMock(object):
-
-    def __init__(self):
-        this_directory = os.path.abspath(
-            os.path.dirname(__file__)
-        )
-        self.temporary_directory = os.path.join(
-            this_directory, 'data',
-        )
-
-    def load_datamanager(self):
-        manager = unittest.mock.Mock()
-        array = np.load(os.path.join(
-            this_directory, 'data',
-            '.auto-sklearn',
-            'predictions_test',
-            'predictions_test_0_3_100.0.npy'
-        ))
-        manager.data.get.return_value = array
-        return manager
-
-    def load_targets_ensemble(self):
-        with open(os.path.join(
-            self.temporary_directory,
-            ".auto-sklearn",
-            "predictions_ensemble",
-            "predictions_ensemble_true.npy"
-        ), "rb") as fp:
-            y = np.load(fp, allow_pickle=True)
-        return y
-
-    def get_done_directory(self):
-        return os.path.join(this_directory, 'data', '.auto-sklearn', 'done')
-
-
-class EnsembleBuilderMemMock(EnsembleBuilder):
-
-    def fit_ensemble(self, selected_keys):
-        np.ones([10000000, 1000000])
-
-
-class EnsembleTest(unittest.TestCase):
-    def setUp(self):
-        self.backend = BackendMock()
+@pytest.fixture(scope="function")
+def ensemble_backend(request):
+    test_id = '%s_%s' % (request.module.__name__, request.node.name)
+    test_dir = os.path.join(this_directory, test_id)
 
-    def tearDown(self):
+    try:
+        shutil.rmtree(test_dir)
+    except:  # noqa E722
         pass
 
-    def testRead(self):
-
-        ensbuilder = EnsembleBuilder(
-            backend=self.backend,
-            dataset_name="TEST",
-            task_type=1,  # Binary Classification
-            metric=roc_auc,
-            limit=-1,  # not used,
-            seed=0,  # important to find the test files
-        )
-
-        success = ensbuilder.score_ensemble_preds()
-        self.assertTrue(success, str(ensbuilder.read_preds))
-        self.assertEqual(len(ensbuilder.read_preds), 3)
-
-        filename = os.path.join(
-            self.backend.temporary_directory,
-            ".auto-sklearn/predictions_ensemble/predictions_ensemble_0_1_0.0.npy"
-        )
-        self.assertEqual(ensbuilder.read_preds[filename]["ens_score"], 0.5)
-
-        filename = os.path.join(
-            self.backend.temporary_directory,
-            ".auto-sklearn/predictions_ensemble/predictions_ensemble_0_2_0.0.npy"
-        )
-        self.assertEqual(ensbuilder.read_preds[filename]["ens_score"], 1.0)
-
-    def testNBest(self):
-        for ensemble_nbest, models_on_disc, exp in (
-                (1, None, 1),
-                (1.0, None, 2),
-                (0.1, None, 1),
-                (0.9, None, 1),
-                (1, 2, 1),
-                (2, 1, 1),
-        ):
-            ensbuilder = EnsembleBuilder(
-                backend=self.backend,
-                dataset_name="TEST",
-                task_type=1,  # Binary Classification
-                metric=roc_auc,
-                limit=-1,  # not used,
-                seed=0,  # important to find the test files
-                ensemble_nbest=ensemble_nbest,
-                max_models_on_disc=models_on_disc,
-            )
-
-            ensbuilder.score_ensemble_preds()
-            sel_keys = ensbuilder.get_n_best_preds()
-
-            self.assertEqual(len(sel_keys), exp)
-
-            fixture = os.path.join(
-                self.backend.temporary_directory,
-                ".auto-sklearn/predictions_ensemble/predictions_ensemble_0_2_0.0.npy"
-            )
-            self.assertEqual(sel_keys[0], fixture)
-
-    def testMaxModelsOnDisc(self):
-
-        ensemble_nbest = 4
-        for (test_case, exp) in [
-                # If None, no reduction
-                (None, 2),
-                # If Int, limit only on exceed
-                (4, 2),
-                (1, 1),
-                # If Float, translate float to # models.
-                # below, mock of each file is 100 Mb and
-                # 4 files .model and .npy (test/val/pred) exist
-                (700.0, 1),
-                (800.0, 2),
-                (9999.0, 2),
-        ]:
-            ensbuilder = EnsembleBuilder(
-                backend=self.backend,
-                dataset_name="TEST",
-                task_type=1,  # Binary Classification
-                metric=roc_auc,
-                limit=-1,  # not used,
-                seed=0,  # important to find the test files
-                ensemble_nbest=ensemble_nbest,
-                max_models_on_disc=test_case,
-            )
-
-            with unittest.mock.patch('os.path.getsize') as mock:
-                mock.return_value = 100*1024*1024
-                ensbuilder.score_ensemble_preds()
-                sel_keys = ensbuilder.get_n_best_preds()
-                self.assertEqual(len(sel_keys), exp)
-
-        # Test for Extreme scenarios
-        # Make sure that the best predictions are kept
-        ensbuilder = EnsembleBuilder(
-            backend=self.backend,
-            dataset_name="TEST",
-            task_type=1,  # Binary Classification
-            metric=roc_auc,
-            limit=-1,  # not used,
-            seed=0,  # important to find the test files
-            ensemble_nbest=50,
-            max_models_on_disc=10000.0,
-        )
-        ensbuilder.read_preds = {}
-        for i in range(50):
-            ensbuilder.read_preds['pred'+str(i)] = {
-                'ens_score': i*10,
-                'num_run': i,
-                0: True,
-                'loaded': 1,
-                "seed": 1,
-                "disc_space_cost_mb": 50*i,
-            }
-        sel_keys = ensbuilder.get_n_best_preds()
-        self.assertListEqual(['pred49', 'pred48', 'pred47', 'pred46'], sel_keys)
-
-        # Make sure at least one model is kept alive
-        ensbuilder.max_models_on_disc = 0.0
-        sel_keys = ensbuilder.get_n_best_preds()
-        self.assertListEqual(['pred49'], sel_keys)
-
-    def testPerformanceRangeThreshold(self):
-        to_test = ((0.0, 4), (0.1, 4), (0.3, 3), (0.5, 2), (0.6, 2), (0.8, 1),
-                   (1.0, 1), (1, 1))
-        for performance_range_threshold, exp in to_test:
-            ensbuilder = EnsembleBuilder(
-                backend=self.backend,
-                dataset_name="TEST",
-                task_type=1,  # Binary Classification
-                metric=roc_auc,
-                limit=-1,  # not used,
-                seed=0,  # important to find the test files
-                ensemble_nbest=100,
-                performance_range_threshold=performance_range_threshold
-            )
-            ensbuilder.read_preds = {
-                'A': {'ens_score': 1, 'num_run': 1, 0: True, 'loaded': -1, "seed": 1},
-                'B': {'ens_score': 2, 'num_run': 2, 0: True, 'loaded': -1, "seed": 1},
-                'C': {'ens_score': 3, 'num_run': 3, 0: True, 'loaded': -1, "seed": 1},
-                'D': {'ens_score': 4, 'num_run': 4, 0: True, 'loaded': -1, "seed": 1},
-                'E': {'ens_score': 5, 'num_run': 5, 0: True, 'loaded': -1, "seed": 1},
-            }
-            sel_keys = ensbuilder.get_n_best_preds()
-
-            self.assertEqual(len(sel_keys), exp)
-
-    def testPerformanceRangeThresholdMaxBest(self):
-        to_test = ((0.0, 1, 1), (0.0, 1.0, 4), (0.1, 2, 2), (0.3, 4, 3),
-                   (0.5, 1, 1), (0.6, 10, 2), (0.8, 0.5, 1), (1, 1.0, 1))
-        for performance_range_threshold, ensemble_nbest, exp in to_test:
-            ensbuilder = EnsembleBuilder(
-                backend=self.backend,
-                dataset_name="TEST",
-                task_type=1,  # Binary Classification
-                metric=roc_auc,
-                limit=-1,  # not used,
-                seed=0,  # important to find the test files
-                ensemble_nbest=ensemble_nbest,
-                performance_range_threshold=performance_range_threshold,
-                max_models_on_disc=None,
-            )
-            ensbuilder.read_preds = {
-                'A': {'ens_score': 1, 'num_run': 1, 0: True, 'loaded': -1, "seed": 1},
-                'B': {'ens_score': 2, 'num_run': 2, 0: True, 'loaded': -1, "seed": 1},
-                'C': {'ens_score': 3, 'num_run': 3, 0: True, 'loaded': -1, "seed": 1},
-                'D': {'ens_score': 4, 'num_run': 4, 0: True, 'loaded': -1, "seed": 1},
-                'E': {'ens_score': 5, 'num_run': 5, 0: True, 'loaded': -1, "seed": 1},
-            }
-            sel_keys = ensbuilder.get_n_best_preds()
-
-            self.assertEqual(len(sel_keys), exp)
-
-    def testFallBackNBest(self):
-
-        ensbuilder = EnsembleBuilder(backend=self.backend,
-                                     dataset_name="TEST",
-                                     task_type=1,  # Binary Classification
-                                     metric=roc_auc,
-                                     limit=-1,  # not used,
-                                     seed=0,  # important to find the test files
-                                     ensemble_nbest=1
-                                     )
-
-        ensbuilder.score_ensemble_preds()
-
-        filename = os.path.join(
-            self.backend.temporary_directory,
-            ".auto-sklearn/predictions_ensemble/predictions_ensemble_0_2_0.0.npy"
-        )
-        ensbuilder.read_preds[filename]["ens_score"] = -1
-
-        filename = os.path.join(
-            self.backend.temporary_directory,
-            ".auto-sklearn/predictions_ensemble/predictions_ensemble_0_3_100.0.npy"
-        )
-        ensbuilder.read_preds[filename]["ens_score"] = -1
-
-        filename = os.path.join(
-            self.backend.temporary_directory,
-            ".auto-sklearn/predictions_ensemble/predictions_ensemble_0_1_0.0.npy"
-        )
-        ensbuilder.read_preds[filename]["ens_score"] = -1
-
-        sel_keys = ensbuilder.get_n_best_preds()
-
-        fixture = os.path.join(
-            self.backend.temporary_directory,
-            ".auto-sklearn/predictions_ensemble/predictions_ensemble_0_1_0.0.npy"
-        )
-        self.assertEqual(len(sel_keys), 1)
-        self.assertEqual(sel_keys[0], fixture)
-
-    def testGetValidTestPreds(self):
-
-        ensbuilder = EnsembleBuilder(backend=self.backend,
-                                     dataset_name="TEST",
-                                     task_type=1,  # Binary Classification
-                                     metric=roc_auc,
-                                     limit=-1,  # not used,
-                                     seed=0,  # important to find the test files
-                                     ensemble_nbest=1
-                                     )
-
+    # Make sure the folders we wanna create do not already exist.
+    backend = BackendMock(test_dir)
+
+    def get_finalizer(ensemble_backend):
+        def session_run_at_end():
+            try:
+                shutil.rmtree(test_dir)
+            except:  # noqa E722
+                pass
+        return session_run_at_end
+    request.addfinalizer(get_finalizer(backend))
+
+    return backend
+
+
+@pytest.fixture(scope="function")
+def ensemble_run_history(request):
+
+    run_history = RunHistory()
+    run_history._add(
+        RunKey(
+            config_id=3,
+            instance_id='{"task_id": "breast_cancer"}',
+            seed=1,
+            budget=3.0
+        ),
+        RunValue(
+            cost=0.11347517730496459,
+            time=0.21858787536621094,
+            status=None,
+            starttime=time.time(),
+            endtime=time.time(),
+            additional_info={
+                'duration': 0.20323538780212402,
+                'num_run': 3,
+                'configuration_origin': 'Random Search'}
+        ),
+        status=None,
+        origin=None,
+    )
+    run_history._add(
+        RunKey(
+            config_id=6,
+            instance_id='{"task_id": "breast_cancer"}',
+            seed=1,
+            budget=6.0
+        ),
+        RunValue(
+            cost=2*0.11347517730496459,
+            time=2*0.21858787536621094,
+            status=None,
+            starttime=time.time(),
+            endtime=time.time(),
+            additional_info={
+                'duration': 0.20323538780212402,
+                'num_run': 6,
+                'configuration_origin': 'Random Search'}
+        ),
+        status=None,
+        origin=None,
+    )
+    return run_history
+
+
+def testRead(ensemble_backend):
+
+    ensbuilder = EnsembleBuilder(
+        backend=ensemble_backend,
+        dataset_name="TEST",
+        task_type=BINARY_CLASSIFICATION,
+        metric=roc_auc,
+        seed=0,  # important to find the test files
+    )
+
+    success = ensbuilder.score_ensemble_preds()
+    assert success, str(ensbuilder.read_preds)
+    assert len(ensbuilder.read_preds) == 3, ensbuilder.read_preds.keys()
+    assert len(ensbuilder.read_scores) == 3, ensbuilder.read_scores.keys()
+
+    filename = os.path.join(
+        ensemble_backend.temporary_directory,
+        ".auto-sklearn/runs/0_1_0.0/predictions_ensemble_0_1_0.0.npy"
+    )
+    assert ensbuilder.read_scores[filename]["ens_score"] == 0.5
+
+    filename = os.path.join(
+        ensemble_backend.temporary_directory,
+        ".auto-sklearn/runs/0_2_0.0/predictions_ensemble_0_2_0.0.npy"
+    )
+    assert ensbuilder.read_scores[filename]["ens_score"] == 1.0
+
+
+@pytest.mark.parametrize(
+    "ensemble_nbest,max_models_on_disc,exp",
+    (
+            (1, None, 1),
+            (1.0, None, 2),
+            (0.1, None, 1),
+            (0.9, None, 1),
+            (1, 2, 1),
+            (2, 1, 1),
+    )
+)
+def testNBest(ensemble_backend, ensemble_nbest, max_models_on_disc, exp):
+    ensbuilder = EnsembleBuilder(
+        backend=ensemble_backend,
+        dataset_name="TEST",
+        task_type=BINARY_CLASSIFICATION,
+        metric=roc_auc,
+        seed=0,  # important to find the test files
+        ensemble_nbest=ensemble_nbest,
+        max_models_on_disc=max_models_on_disc,
+    )
+
+    ensbuilder.score_ensemble_preds()
+    sel_keys = ensbuilder.get_n_best_preds()
+
+    assert len(sel_keys) == exp
+
+    fixture = os.path.join(
+        ensemble_backend.temporary_directory,
+        ".auto-sklearn/runs/0_2_0.0/predictions_ensemble_0_2_0.0.npy"
+    )
+    assert sel_keys[0] == fixture
+
+
+@pytest.mark.parametrize("test_case,exp", [
+    # If None, no reduction
+    (None, 2),
+    # If Int, limit only on exceed
+    (4, 2),
+    (1, 1),
+    # If Float, translate float to # models.
+    # below, mock of each file is 100 Mb and 4 files .model and .npy (test/val/pred) exist
+    # per run (except for run3, there they are 5). Now, it takes 500MB for run 3 and
+    # another 500 MB of slack because we keep as much space as the largest model
+    # available as slack
+    (1499.0, 1),
+    (1500.0, 2),
+    (9999.0, 2),
+])
+def testMaxModelsOnDisc(ensemble_backend, test_case, exp):
+    ensemble_nbest = 4
+    ensbuilder = EnsembleBuilder(
+        backend=ensemble_backend,
+        dataset_name="TEST",
+        task_type=BINARY_CLASSIFICATION,
+        metric=roc_auc,
+        seed=0,  # important to find the test files
+        ensemble_nbest=ensemble_nbest,
+        max_models_on_disc=test_case,
+    )
+
+    with unittest.mock.patch('os.path.getsize') as mock:
+        mock.return_value = 100*1024*1024
         ensbuilder.score_ensemble_preds()
-
-        d1 = os.path.join(
-            self.backend.temporary_directory,
-            ".auto-sklearn/predictions_ensemble/predictions_ensemble_0_1_0.0.npy"
-        )
-        d2 = os.path.join(
-            self.backend.temporary_directory,
-            ".auto-sklearn/predictions_ensemble/predictions_ensemble_0_2_0.0.npy"
-        )
-        d3 = os.path.join(
-            self.backend.temporary_directory,
-            ".auto-sklearn/predictions_ensemble/predictions_ensemble_0_3_100.0.npy"
-        )
-
         sel_keys = ensbuilder.get_n_best_preds()
-        self.assertEqual(len(sel_keys), 1)
-        ensbuilder.get_valid_test_preds(selected_keys=sel_keys)
-
-        # Number of read files should be three and
-        # predictions_ensemble_0_4_0.0.npy must not be in there
-        self.assertEqual(len(ensbuilder.read_preds), 3)
-        self.assertNotIn(
-            os.path.join(
-                self.backend.temporary_directory,
-                ".auto-sklearn/predictions_ensemble/predictions_ensemble_0_4_0.0.npy"
-            ),
-            ensbuilder.read_preds
-        )
-
-        # not selected --> should still be None
-        self.assertIsNone(ensbuilder.read_preds[d1][Y_VALID])
-        self.assertIsNone(ensbuilder.read_preds[d1][Y_TEST])
-        self.assertIsNone(ensbuilder.read_preds[d3][Y_VALID])
-        self.assertIsNone(ensbuilder.read_preds[d3][Y_TEST])
-
-        # selected --> read valid and test predictions
-        self.assertIsNotNone(ensbuilder.read_preds[d2][Y_VALID])
-        self.assertIsNotNone(ensbuilder.read_preds[d2][Y_TEST])
-
-    def testEntireEnsembleBuilder(self):
-
-        ensbuilder = EnsembleBuilder(
-            backend=self.backend,
-            dataset_name="TEST",
-            task_type=1,  # Binary Classification
-            metric=roc_auc,
-            limit=-1,  # not used,
-            seed=0,  # important to find the test files
-            ensemble_nbest=2,
-        )
-        ensbuilder.SAVE2DISC = False
-
-        ensbuilder.score_ensemble_preds()
-
-        d2 = os.path.join(
-            self.backend.temporary_directory,
-            ".auto-sklearn/predictions_ensemble/predictions_ensemble_0_2_0.0.npy"
+        assert len(sel_keys) == exp, test_case
+
+
+def testMaxModelsOnDisc2(ensemble_backend):
+    # Test for Extreme scenarios
+    # Make sure that the best predictions are kept
+    ensbuilder = EnsembleBuilder(
+        backend=ensemble_backend,
+        dataset_name="TEST",
+        task_type=BINARY_CLASSIFICATION,
+        metric=roc_auc,
+        seed=0,  # important to find the test files
+        ensemble_nbest=50,
+        max_models_on_disc=10000.0,
+    )
+    ensbuilder.read_preds = {}
+    for i in range(50):
+        ensbuilder.read_scores['pred'+str(i)] = {
+            'ens_score': i*10,
+            'num_run': i,
+            'loaded': 1,
+            "seed": 1,
+            "disc_space_cost_mb": 50*i,
+        }
+        ensbuilder.read_preds['pred'+str(i)] = {Y_ENSEMBLE: True}
+    sel_keys = ensbuilder.get_n_best_preds()
+    assert ['pred49', 'pred48', 'pred47'] == sel_keys
+
+    # Make sure at least one model is kept alive
+    ensbuilder.max_models_on_disc = 0.0
+    sel_keys = ensbuilder.get_n_best_preds()
+    assert ['pred49'] == sel_keys
+
+
+@pytest.mark.parametrize(
+    "performance_range_threshold,exp",
+    ((0.0, 4), (0.1, 4), (0.3, 3), (0.5, 2), (0.6, 2), (0.8, 1), (1.0, 1), (1, 1))
+)
+def testPerformanceRangeThreshold(ensemble_backend, performance_range_threshold, exp):
+    ensbuilder = EnsembleBuilder(
+        backend=ensemble_backend,
+        dataset_name="TEST",
+        task_type=BINARY_CLASSIFICATION,
+        metric=roc_auc,
+        seed=0,  # important to find the test files
+        ensemble_nbest=100,
+        performance_range_threshold=performance_range_threshold
+    )
+    ensbuilder.read_scores = {
+        'A': {'ens_score': 1, 'num_run': 1, 'loaded': -1, "seed": 1},
+        'B': {'ens_score': 2, 'num_run': 2, 'loaded': -1, "seed": 1},
+        'C': {'ens_score': 3, 'num_run': 3, 'loaded': -1, "seed": 1},
+        'D': {'ens_score': 4, 'num_run': 4, 'loaded': -1, "seed": 1},
+        'E': {'ens_score': 5, 'num_run': 5, 'loaded': -1, "seed": 1},
+    }
+    ensbuilder.read_preds = {
+        key: {key_2: True for key_2 in (Y_ENSEMBLE, Y_VALID, Y_TEST)}
+        for key in ensbuilder.read_scores
+    }
+    sel_keys = ensbuilder.get_n_best_preds()
+
+    assert len(sel_keys) == exp
+
+
+@pytest.mark.parametrize(
+    "performance_range_threshold,ensemble_nbest,exp",
+    (
+        (0.0, 1, 1), (0.0, 1.0, 4), (0.1, 2, 2), (0.3, 4, 3),
+        (0.5, 1, 1), (0.6, 10, 2), (0.8, 0.5, 1), (1, 1.0, 1)
+    )
+)
+def testPerformanceRangeThresholdMaxBest(ensemble_backend, performance_range_threshold,
+                                         ensemble_nbest, exp):
+    ensbuilder = EnsembleBuilder(
+        backend=ensemble_backend,
+        dataset_name="TEST",
+        task_type=BINARY_CLASSIFICATION,
+        metric=roc_auc,
+        seed=0,  # important to find the test files
+        ensemble_nbest=ensemble_nbest,
+        performance_range_threshold=performance_range_threshold,
+        max_models_on_disc=None,
+    )
+    ensbuilder.read_scores = {
+        'A': {'ens_score': 1, 'num_run': 1, 'loaded': -1, "seed": 1},
+        'B': {'ens_score': 2, 'num_run': 2, 'loaded': -1, "seed": 1},
+        'C': {'ens_score': 3, 'num_run': 3, 'loaded': -1, "seed": 1},
+        'D': {'ens_score': 4, 'num_run': 4, 'loaded': -1, "seed": 1},
+        'E': {'ens_score': 5, 'num_run': 5, 'loaded': -1, "seed": 1},
+    }
+    ensbuilder.read_preds = {
+        key: {key_2: True for key_2 in (Y_ENSEMBLE, Y_VALID, Y_TEST)}
+        for key in ensbuilder.read_scores
+    }
+    sel_keys = ensbuilder.get_n_best_preds()
+
+    assert len(sel_keys) == exp
+
+
+def testFallBackNBest(ensemble_backend):
+
+    ensbuilder = EnsembleBuilder(backend=ensemble_backend,
+                                 dataset_name="TEST",
+                                 task_type=BINARY_CLASSIFICATION,
+                                 metric=roc_auc,
+                                 seed=0,  # important to find the test files
+                                 ensemble_nbest=1
+                                 )
+
+    ensbuilder.score_ensemble_preds()
+    print()
+    print(ensbuilder.read_preds.keys())
+    print(ensbuilder.read_scores.keys())
+    print(ensemble_backend.temporary_directory)
+
+    filename = os.path.join(
+        ensemble_backend.temporary_directory,
+        ".auto-sklearn/runs/0_2_0.0/predictions_ensemble_0_2_0.0.npy"
+    )
+    ensbuilder.read_scores[filename]["ens_score"] = -1
+
+    filename = os.path.join(
+        ensemble_backend.temporary_directory,
+        ".auto-sklearn/runs/0_3_100.0/predictions_ensemble_0_3_100.0.npy"
+    )
+    ensbuilder.read_scores[filename]["ens_score"] = -1
+
+    filename = os.path.join(
+        ensemble_backend.temporary_directory,
+        ".auto-sklearn/runs/0_1_0.0/predictions_ensemble_0_1_0.0.npy"
+    )
+    ensbuilder.read_scores[filename]["ens_score"] = -1
+
+    sel_keys = ensbuilder.get_n_best_preds()
+
+    fixture = os.path.join(
+        ensemble_backend.temporary_directory,
+        ".auto-sklearn/runs/0_1_0.0/predictions_ensemble_0_1_0.0.npy"
+    )
+    assert len(sel_keys) == 1
+    assert sel_keys[0] == fixture
+
+
+def testGetValidTestPreds(ensemble_backend):
+
+    ensbuilder = EnsembleBuilder(backend=ensemble_backend,
+                                 dataset_name="TEST",
+                                 task_type=BINARY_CLASSIFICATION,
+                                 metric=roc_auc,
+                                 seed=0,  # important to find the test files
+                                 ensemble_nbest=1
+                                 )
+
+    ensbuilder.score_ensemble_preds()
+
+    d1 = os.path.join(
+        ensemble_backend.temporary_directory,
+        ".auto-sklearn/runs/0_1_0.0/predictions_ensemble_0_1_0.0.npy"
+    )
+    d2 = os.path.join(
+        ensemble_backend.temporary_directory,
+        ".auto-sklearn/runs/0_2_0.0/predictions_ensemble_0_2_0.0.npy"
+    )
+    d3 = os.path.join(
+        ensemble_backend.temporary_directory,
+        ".auto-sklearn/runs/0_3_100.0/predictions_ensemble_0_3_100.0.npy"
+    )
+
+    sel_keys = ensbuilder.get_n_best_preds()
+    assert len(sel_keys) == 1
+    ensbuilder.get_valid_test_preds(selected_keys=sel_keys)
+
+    # Number of read files should be three and
+    # predictions_ensemble_0_4_0.0.npy must not be in there
+    assert len(ensbuilder.read_preds) == 3
+    assert os.path.join(
+            ensemble_backend.temporary_directory,
+            ".auto-sklearn/runs/0_4_0.0/predictions_ensemble_0_4_0.0.npy"
+    ) not in ensbuilder.read_preds
+
+    # not selected --> should still be None
+    assert ensbuilder.read_preds[d1][Y_VALID] is None
+    assert ensbuilder.read_preds[d1][Y_TEST] is None
+    assert ensbuilder.read_preds[d3][Y_VALID] is None
+    assert ensbuilder.read_preds[d3][Y_TEST] is None
+
+    # selected --> read valid and test predictions
+    assert ensbuilder.read_preds[d2][Y_VALID] is not None
+    assert ensbuilder.read_preds[d2][Y_TEST] is not None
+
+
+def testEntireEnsembleBuilder(ensemble_backend):
+
+    ensbuilder = EnsembleBuilder(
+        backend=ensemble_backend,
+        dataset_name="TEST",
+        task_type=BINARY_CLASSIFICATION,
+        metric=roc_auc,
+        seed=0,  # important to find the test files
+        ensemble_nbest=2,
+    )
+    ensbuilder.SAVE2DISC = False
+
+    ensbuilder.score_ensemble_preds()
+
+    d2 = os.path.join(
+        ensemble_backend.temporary_directory,
+        ".auto-sklearn/runs/0_2_0.0/predictions_ensemble_0_2_0.0.npy"
+    )
+
+    sel_keys = ensbuilder.get_n_best_preds()
+    assert len(sel_keys) > 0
+
+    ensemble = ensbuilder.fit_ensemble(selected_keys=sel_keys)
+    print(ensemble, sel_keys)
+
+    n_sel_valid, n_sel_test = ensbuilder.get_valid_test_preds(selected_keys=sel_keys)
+
+    # both valid and test prediction files are available
+    assert len(n_sel_valid) > 0
+    assert n_sel_valid == n_sel_test
+
+    y_valid = ensbuilder.predict(
+        set_="valid",
+        ensemble=ensemble,
+        selected_keys=n_sel_valid,
+        n_preds=len(sel_keys),
+        index_run=1,
+    )
+    y_test = ensbuilder.predict(
+        set_="test",
+        ensemble=ensemble,
+        selected_keys=n_sel_test,
+        n_preds=len(sel_keys),
+        index_run=1,
+    )
+
+    # predictions for valid and test are the same
+    # --> should results in the same predictions
+    np.testing.assert_array_almost_equal(y_valid, y_test)
+
+    # since d2 provides perfect predictions
+    # it should get a higher weight
+    # so that y_valid should be exactly y_valid_d2
+    y_valid_d2 = ensbuilder.read_preds[d2][Y_VALID][:, 1]
+    np.testing.assert_array_almost_equal(y_valid, y_valid_d2)
+
+
+def test_main(ensemble_backend):
+
+    ensbuilder = EnsembleBuilder(
+        backend=ensemble_backend,
+        dataset_name="TEST",
+        task_type=MULTILABEL_CLASSIFICATION,  # Multilabel Classification
+        metric=roc_auc,
+        seed=0,  # important to find the test files
+        ensemble_nbest=2,
+        max_models_on_disc=None,
         )
+    ensbuilder.SAVE2DISC = False
 
-        sel_keys = ensbuilder.get_n_best_preds()
-        self.assertGreater(len(sel_keys), 0)
+    run_history, ensemble_nbest, _, _, _ = ensbuilder.main(
+        time_left=np.inf, iteration=1, return_predictions=False,
+    )
 
-        ensemble = ensbuilder.fit_ensemble(selected_keys=sel_keys)
-        print(ensemble, sel_keys)
+    assert len(ensbuilder.read_preds) == 3
+    assert ensbuilder.last_hash is not None
+    assert ensbuilder.y_true_ensemble is not None
 
-        n_sel_valid, n_sel_test = ensbuilder.get_valid_test_preds(selected_keys=sel_keys)
+    # Make sure the run history is ok
 
-        # both valid and test prediction files are available
-        self.assertGreater(len(n_sel_valid), 0)
-        self.assertEqual(n_sel_valid, n_sel_test)
+    # We expect at least 1 element to be in the ensemble
+    assert len(run_history) > 0
 
-        y_valid = ensbuilder.predict(
-            set_="valid",
-            ensemble=ensemble,
-            selected_keys=n_sel_valid,
-            n_preds=len(sel_keys),
-            index_run=1,
-        )
-        y_test = ensbuilder.predict(
-            set_="test",
-            ensemble=ensemble,
-            selected_keys=n_sel_test,
-            n_preds=len(sel_keys),
-            index_run=1,
-        )
+    # As the data loader loads the same val/train/test
+    # we expect 1.0 as score and all keys available
+    expected_performance = {
+        'ensemble_val_score': 1.0,
+        'ensemble_test_score': 1.0,
+        'ensemble_optimization_score': 1.0,
+    }
 
-        # predictions for valid and test are the same
-        # --> should results in the same predictions
-        np.testing.assert_array_almost_equal(y_valid, y_test)
+    # Make sure that expected performance is a subset of the run history
+    assert all(item in run_history[0].items() for item in expected_performance.items())
+    assert 'Timestamp' in run_history[0]
+    assert isinstance(run_history[0]['Timestamp'], pd.Timestamp)
 
-        # since d2 provides perfect predictions
-        # it should get a higher weight
-        # so that y_valid should be exactly y_valid_d2
-        y_valid_d2 = ensbuilder.read_preds[d2][Y_VALID][:, 1]
-        np.testing.assert_array_almost_equal(y_valid, y_valid_d2)
+    assert os.path.exists(
+        os.path.join(ensemble_backend.internals_directory, 'ensemble_read_preds.pkl')
+    ), os.listdir(ensemble_backend.internals_directory)
+    assert os.path.exists(
+        os.path.join(ensemble_backend.internals_directory, 'ensemble_read_scores.pkl')
+    ), os.listdir(ensemble_backend.internals_directory)
 
-    def testMain(self):
 
+def test_run_end_at(ensemble_backend):
+    with unittest.mock.patch('pynisher.enforce_limits') as pynisher_mock:
         ensbuilder = EnsembleBuilder(
-            backend=self.backend,
+            backend=ensemble_backend,
             dataset_name="TEST",
-            task_type=3,  # Multilabel Classification
+            task_type=MULTILABEL_CLASSIFICATION,  # Multilabel Classification
             metric=roc_auc,
-            limit=-1,  # not used,
             seed=0,  # important to find the test files
             ensemble_nbest=2,
-            max_iterations=1,  # prevents infinite loop
             max_models_on_disc=None,
             )
         ensbuilder.SAVE2DISC = False
 
-        ensbuilder.main()
-
-        self.assertEqual(len(ensbuilder.read_preds), 3)
-        self.assertIsNotNone(ensbuilder.last_hash)
-        self.assertIsNotNone(ensbuilder.y_true_ensemble)
-
-        # Make sure the run history is ok
-        run_history = ensbuilder.get_ensemble_history()
-
-        # We expect 1 element to be the ensemble
-        self.assertEqual(len(run_history), 1)
-
-        # As the data loader loads the same val/train/test
-        # we expect 1.0 as score and all keys available
-        expected_performance = {
-            'ensemble_val_score': 1.0,
-            'ensemble_test_score': 1.0,
-            'ensemble_optimization_score': 1.0,
-        }
-        self.assertDictContainsSubset(expected_performance, run_history[0])
-        self.assertIn('Timestamp', run_history[0])
-        self.assertIsInstance(run_history[0]['Timestamp'], pd.Timestamp)
-
-    def testLimit(self):
-        ensbuilder = EnsembleBuilderMemMock(backend=self.backend,
-                                            dataset_name="TEST",
-                                            task_type=1,  # Binary Classification
-                                            metric=roc_auc,
-                                            limit=1000,  # not used,
-                                            seed=0,  # important to find the test files
-                                            ensemble_nbest=10,
-                                            max_iterations=1,  # prevents infinite loop
-                                            # small to trigger MemoryException
-                                            memory_limit=10
-                                            )
-        ensbuilder.SAVE2DISC = False
-
-        ensbuilder.run()
+        current_time = time.time()
+
+        ensbuilder.run(end_at=current_time + 10, iteration=1)
+        # 4 seconds left because: 10 seconds - 5 seconds overhead - very little overhead,
+        # but then rounded to an integer
+        assert pynisher_mock.call_args_list[0][1]["wall_time_in_s"], 4
+
+
+def testLimit(ensemble_backend):
+    ensbuilder = EnsembleBuilderMemMock(backend=ensemble_backend,
+                                        dataset_name="TEST",
+                                        task_type=BINARY_CLASSIFICATION,
+                                        metric=roc_auc,
+                                        seed=0,  # important to find the test files
+                                        ensemble_nbest=10,
+                                        # small to trigger MemoryException
+                                        memory_limit=100,
+                                        )
+    ensbuilder.SAVE2DISC = False
+
+    read_scores_file = os.path.join(
+        ensemble_backend.internals_directory,
+        'ensemble_read_scores.pkl'
+    )
+    read_preds_file = os.path.join(
+        ensemble_backend.internals_directory,
+        'ensemble_read_preds.pkl'
+    )
+
+    with unittest.mock.patch('logging.getLogger') as get_logger_mock, \
+            unittest.mock.patch('logging.config.dictConfig') as _:
+        logger_mock = unittest.mock.Mock()
+        get_logger_mock.return_value = logger_mock
+
+        ensbuilder.run(time_left=1000, iteration=0)
+        assert os.path.exists(read_scores_file)
+        assert not os.path.exists(read_preds_file)
+        assert logger_mock.warning.call_count == 1
+        ensbuilder.run(time_left=1000, iteration=0)
+        assert os.path.exists(read_scores_file)
+        assert not os.path.exists(read_preds_file)
+        assert logger_mock.warning.call_count == 2
+        ensbuilder.run(time_left=1000, iteration=0)
+        assert os.path.exists(read_scores_file)
+        assert not os.path.exists(read_preds_file)
+        assert logger_mock.warning.call_count == 3
 
         # it should try to reduce ensemble_nbest until it also failed at 2
-        self.assertEqual(ensbuilder.ensemble_nbest, 1)
-
-
-class EnsembleSelectionTest(unittest.TestCase):
-    def testPredict(self):
-        # Test that ensemble prediction applies weights correctly to given
-        # predictions. There are two possible cases:
-        # 1) predictions.shape[0] == len(self.weights_). In this case,
-        # predictions include those made by zero-weighted models. Therefore,
-        # we simply apply each weights to the corresponding model preds.
-        # 2) predictions.shape[0] < len(self.weights_). In this case,
-        # predictions exclude those made by zero-weighted models. Therefore,
-        # we first exclude all occurrences of zero in self.weights_, and then
-        # apply the weights.
-        # If none of the above is the case, predict() raises Error.
-        ensemble = EnsembleSelection(ensemble_size=3,
-                                     task_type=1,
-                                     random_state=np.random.RandomState(0),
-                                     metric=accuracy,
-                                     )
-        # Test for case 1. Create (3, 2, 2) predictions.
-        per_model_pred = np.array([
-            [[0.9, 0.1],
-             [0.4, 0.6]],
-            [[0.8, 0.2],
-             [0.3, 0.7]],
-            [[1.0, 0.0],
-             [0.1, 0.9]]
-        ])
-        # Weights of 3 hypothetical models
-        ensemble.weights_ = [0.7, 0.2, 0.1]
-        pred = ensemble.predict(per_model_pred)
-        truth = np.array([[0.89, 0.11],  # This should be the true prediction.
-                          [0.35, 0.65]])
-        self.assertTrue(np.allclose(pred, truth))
-
-        # Test for case 2.
-        per_model_pred = np.array([
-            [[0.9, 0.1],
-             [0.4, 0.6]],
-            [[0.8, 0.2],
-             [0.3, 0.7]],
-            [[1.0, 0.0],
-             [0.1, 0.9]]
-        ])
-        # The third model now has weight of zero.
-        ensemble.weights_ = [0.7, 0.2, 0.0, 0.1]
-        pred = ensemble.predict(per_model_pred)
-        truth = np.array([[0.89, 0.11],
-                          [0.35, 0.65]])
-        self.assertTrue(np.allclose(pred, truth))
-
-        # Test for error case.
-        per_model_pred = np.array([
-            [[0.9, 0.1],
-             [0.4, 0.6]],
-            [[0.8, 0.2],
-             [0.3, 0.7]],
-            [[1.0, 0.0],
-             [0.1, 0.9]]
-        ])
-        # Now the weights have 2 zero weights and 2 non-zero weights,
-        # which is incompatible.
-        ensemble.weights_ = [0.6, 0.0, 0.0, 0.4]
-
-        with self.assertRaises(ValueError):
-            ensemble.predict(per_model_pred)
-
-
-class SingleBestTest(unittest.TestCase):
-    def setUp(self):
-        self.run_history = RunHistory()
-        self.run_history._add(
-            RunKey(
-                config_id=3,
-                instance_id='{"task_id": "breast_cancer"}',
-                seed=1,
-                budget=3.0
-            ),
-            RunValue(
-                cost=0.11347517730496459,
-                time=0.21858787536621094,
-                status=None,
-                starttime=time.time(),
-                endtime=time.time(),
-                additional_info={
-                    'duration': 0.20323538780212402,
-                    'num_run': 3,
-                    'configuration_origin': 'Random Search'}
-            ),
-            status=None,
-            origin=None,
-        )
-        self.run_history._add(
-            RunKey(
-                config_id=6,
-                instance_id='{"task_id": "breast_cancer"}',
-                seed=1,
-                budget=6.0
-            ),
-            RunValue(
-                cost=2*0.11347517730496459,
-                time=2*0.21858787536621094,
-                status=None,
-                starttime=time.time(),
-                endtime=time.time(),
-                additional_info={
-                    'duration': 0.20323538780212402,
-                    'num_run': 6,
-                    'configuration_origin': 'Random Search'}
-            ),
-            status=None,
-            origin=None,
-        )
-
-    def test_get_identifiers_from_run_history_accuracy(self):
-        ensemble = SingleBest(
-             metric=accuracy,
-             random_state=1,
-             run_history=self.run_history,
+        assert ensbuilder.ensemble_nbest == 1
+
+        ensbuilder.run(time_left=1000, iteration=0)
+        assert os.path.exists(read_scores_file)
+        assert not os.path.exists(read_preds_file)
+        assert logger_mock.warning.call_count == 4
+
+        # it should next reduce the number of models to read at most
+        assert ensbuilder.read_at_most == 1
+
+        # And then it still runs, but basically won't do anything any more except for raising error
+        # messages via the logger
+        ensbuilder.run(time_left=1000, iteration=0)
+        assert os.path.exists(read_scores_file)
+        assert not os.path.exists(read_preds_file)
+        assert logger_mock.warning.call_count == 4
+        assert logger_mock.error.call_count == 1
+
+
+def test_read_pickle_read_preds(ensemble_backend):
+    """
+    This procedure test that we save the read predictions before
+    destroying the ensemble builder and that we are able to read
+    them safely after
+    """
+    ensbuilder = EnsembleBuilder(
+        backend=ensemble_backend,
+        dataset_name="TEST",
+        task_type=MULTILABEL_CLASSIFICATION,  # Multilabel Classification
+        metric=roc_auc,
+        seed=0,  # important to find the test files
+        ensemble_nbest=2,
+        max_models_on_disc=None,
         )
-
-        # Just one model
-        self.assertEqual(len(ensemble.identifiers_), 1)
-
-        # That model must be the best
-        seed, num_run, budget = ensemble.identifiers_[0]
-        self.assertEqual(num_run, 3)
-        self.assertEqual(seed, 1)
-        self.assertEqual(budget, 3.0)
-
-    def test_get_identifiers_from_run_history_log_loss(self):
-        ensemble = SingleBest(
-             metric=log_loss,
-             random_state=1,
-             run_history=self.run_history,
+    ensbuilder.SAVE2DISC = False
+
+    ensbuilder.main(time_left=np.inf, iteration=1, return_predictions=False)
+
+    # Check that the memory was created
+    ensemble_memory_file = os.path.join(
+        ensemble_backend.internals_directory,
+        'ensemble_read_preds.pkl'
+    )
+    assert os.path.exists(ensemble_memory_file)
+
+    # Make sure we pickle the correct read preads and hash
+    with (open(ensemble_memory_file, "rb")) as memory:
+        read_preds, last_hash = pickle.load(memory)
+
+    compare_read_preds(read_preds, ensbuilder.read_preds)
+    assert last_hash == ensbuilder.last_hash
+
+    ensemble_memory_file = os.path.join(
+        ensemble_backend.internals_directory,
+        'ensemble_read_scores.pkl'
+    )
+    assert os.path.exists(ensemble_memory_file)
+
+    # Make sure we pickle the correct read scores
+    with (open(ensemble_memory_file, "rb")) as memory:
+        read_scores = pickle.load(memory)
+
+    compare_read_preds(read_scores, ensbuilder.read_scores)
+
+    # Then create a new instance, which should automatically read this file
+    ensbuilder2 = EnsembleBuilder(
+        backend=ensemble_backend,
+        dataset_name="TEST",
+        task_type=MULTILABEL_CLASSIFICATION,  # Multilabel Classification
+        metric=roc_auc,
+        seed=0,  # important to find the test files
+        ensemble_nbest=2,
+        max_models_on_disc=None,
         )
-
-        # Just one model
-        self.assertEqual(len(ensemble.identifiers_), 1)
-
-        # That model must be the best
-        seed, num_run, budget = ensemble.identifiers_[0]
-        self.assertEqual(num_run, 3)
-        self.assertEqual(seed, 1)
-        self.assertEqual(budget, 3.0)
+    compare_read_preds(ensbuilder2.read_preds, ensbuilder.read_preds)
+    compare_read_preds(ensbuilder2.read_scores, ensbuilder.read_scores)
+    assert ensbuilder2.last_hash == ensbuilder.last_hash
+
+
+def testPredict():
+    # Test that ensemble prediction applies weights correctly to given
+    # predictions. There are two possible cases:
+    # 1) predictions.shape[0] == len(self.weights_). In this case,
+    # predictions include those made by zero-weighted models. Therefore,
+    # we simply apply each weights to the corresponding model preds.
+    # 2) predictions.shape[0] < len(self.weights_). In this case,
+    # predictions exclude those made by zero-weighted models. Therefore,
+    # we first exclude all occurrences of zero in self.weights_, and then
+    # apply the weights.
+    # If none of the above is the case, predict() raises Error.
+    ensemble = EnsembleSelection(ensemble_size=3,
+                                 task_type=BINARY_CLASSIFICATION,
+                                 random_state=np.random.RandomState(0),
+                                 metric=accuracy,
+                                 )
+    # Test for case 1. Create (3, 2, 2) predictions.
+    per_model_pred = np.array([
+        [[0.9, 0.1],
+         [0.4, 0.6]],
+        [[0.8, 0.2],
+         [0.3, 0.7]],
+        [[1.0, 0.0],
+         [0.1, 0.9]]
+    ])
+    # Weights of 3 hypothetical models
+    ensemble.weights_ = [0.7, 0.2, 0.1]
+    pred = ensemble.predict(per_model_pred)
+    truth = np.array([[0.89, 0.11],  # This should be the true prediction.
+                      [0.35, 0.65]])
+    assert np.allclose(pred, truth)
+
+    # Test for case 2.
+    per_model_pred = np.array([
+        [[0.9, 0.1],
+         [0.4, 0.6]],
+        [[0.8, 0.2],
+         [0.3, 0.7]],
+        [[1.0, 0.0],
+         [0.1, 0.9]]
+    ])
+    # The third model now has weight of zero.
+    ensemble.weights_ = [0.7, 0.2, 0.0, 0.1]
+    pred = ensemble.predict(per_model_pred)
+    truth = np.array([[0.89, 0.11],
+                      [0.35, 0.65]])
+    assert np.allclose(pred, truth)
+
+    # Test for error case.
+    per_model_pred = np.array([
+        [[0.9, 0.1],
+         [0.4, 0.6]],
+        [[0.8, 0.2],
+         [0.3, 0.7]],
+        [[1.0, 0.0],
+         [0.1, 0.9]]
+    ])
+    # Now the weights have 2 zero weights and 2 non-zero weights,
+    # which is incompatible.
+    ensemble.weights_ = [0.6, 0.0, 0.0, 0.4]
+
+    with pytest.raises(ValueError):
+        ensemble.predict(per_model_pred)
+
+
+@pytest.mark.parametrize("metric", [log_loss, accuracy])
+@unittest.mock.patch('os.path.exists')
+def test_get_identifiers_from_run_history(exists, metric, ensemble_run_history, ensemble_backend):
+    exists.return_value = True
+    ensemble = SingleBest(
+         metric=log_loss,
+         seed=1,
+         run_history=ensemble_run_history,
+         backend=ensemble_backend,
+    )
+
+    # Just one model
+    assert len(ensemble.identifiers_) == 1
+
+    # That model must be the best
+    seed, num_run, budget = ensemble.identifiers_[0]
+    assert num_run == 3
+    assert seed == 1
+    assert budget == 3.0
+
+
+def test_ensemble_builder_process_realrun(dask_client, ensemble_backend):
+    manager = EnsembleBuilderManager(
+        start_time=time.time(),
+        time_left_for_ensembles=1000,
+        backend=ensemble_backend,
+        dataset_name='Test',
+        task=BINARY_CLASSIFICATION,
+        metric=MockMetric,
+        ensemble_size=50,
+        ensemble_nbest=10,
+        max_models_on_disc=None,
+        seed=0,
+        precision=32,
+        max_iterations=1,
+        read_at_most=np.inf,
+        ensemble_memory_limit=None,
+        random_state=0,
+        logger_name='Ensemblebuilder',
+    )
+    manager.build_ensemble(dask_client)
+    future = manager.futures.pop()
+    dask.distributed.wait([future])  # wait for the ensemble process to finish
+    result = future.result()
+    history, _, _, _, _ = result
+
+    assert 'ensemble_optimization_score' in history[0]
+    assert history[0]['ensemble_optimization_score'] == 0.9
+    assert 'ensemble_val_score' in history[0]
+    assert history[0]['ensemble_val_score'] == 0.9
+    assert 'ensemble_test_score' in history[0]
+    assert history[0]['ensemble_test_score'] == 0.9
+
+
+@unittest.mock.patch('autosklearn.ensemble_builder.EnsembleBuilder.fit_ensemble')
+def test_ensemble_builder_nbest_remembered(fit_ensemble, ensemble_backend, dask_client):
+    """
+    Makes sure ensemble builder returns the size of the ensemble that pynisher allowed
+    This way, we can remember it and not waste more time trying big ensemble sizes
+    """
+
+    fit_ensemble.side_effect = MemoryError
+
+    manager = EnsembleBuilderManager(
+        start_time=time.time(),
+        time_left_for_ensembles=1000,
+        backend=ensemble_backend,
+        dataset_name='Test',
+        task=MULTILABEL_CLASSIFICATION,
+        metric=roc_auc,
+        ensemble_size=50,
+        ensemble_nbest=10,
+        max_models_on_disc=None,
+        seed=0,
+        precision=32,
+        read_at_most=np.inf,
+        ensemble_memory_limit=1000,
+        random_state=0,
+        logger_name='Ensemblebuilder',
+        max_iterations=None,
+    )
+
+    manager.build_ensemble(dask_client)
+    future = manager.futures[0]
+    dask.distributed.wait([future])  # wait for the ensemble process to finish
+    assert future.result() == ([], 5, None, None, None)
+    file_path = os.path.join(ensemble_backend.internals_directory, 'ensemble_read_preds.pkl')
+    assert not os.path.exists(file_path)
+
+    manager.build_ensemble(dask_client)
+
+    future = manager.futures[0]
+    dask.distributed.wait([future])  # wait for the ensemble process to finish
+    assert not os.path.exists(file_path)
+    assert future.result() == ([], 2, None, None, None)
diff --git a/test/test_evaluation/test_abstract_evaluator.py b/test/test_evaluation/test_abstract_evaluator.py
index e43cac595d..c033b69531 100644
--- a/test/test_evaluation/test_abstract_evaluator.py
+++ b/test/test_evaluation/test_abstract_evaluator.py
@@ -43,7 +43,10 @@ def setUp(self):
 
     def tearDown(self):
         if os.path.exists(self.ev_path):
-            os.rmdir(self.ev_path)
+            try:
+                os.rmdir(self.ev_path)
+            except:  # noqa E722
+                pass
 
     def test_finish_up_model_predicts_NaN(self):
         '''Tests by handing in predictions which contain NaNs'''
@@ -116,8 +119,7 @@ def test_finish_up_model_predicts_NaN(self):
 
         self.assertEqual(self.backend_mock.save_predictions_as_npy.call_count, 0)
 
-    @unittest.mock.patch('os.path.exists')
-    def test_disable_file_output(self, exists_mock):
+    def test_disable_file_output(self):
         queue_mock = unittest.mock.Mock()
 
         rs = np.random.RandomState(1)
@@ -143,32 +145,54 @@ def test_disable_file_output(self, exists_mock):
 
         self.assertIsNone(loss_)
         self.assertEqual(additional_run_info_, {})
-        # This function is not guarded by an if statement
-        self.assertEqual(self.backend_mock.save_predictions_as_npy.call_count, 0)
-        self.assertEqual(self.backend_mock.save_model.call_count, 0)
+        # This function is never called as there is a return before
+        self.assertEqual(self.backend_mock.save_numrun_to_dir.call_count, 0)
 
-        ae = AbstractEvaluator(
-            backend=self.backend_mock,
-            output_y_hat_optimization=False,
-            queue=queue_mock,
-            disable_file_output=['model'],
-            metric=accuracy,
-        )
-        ae.Y_optimization = predictions_ensemble
-
-        loss_, additional_run_info_ = (
-            ae.file_output(
-                predictions_ensemble,
-                predictions_valid,
-                predictions_test,
+        for call_count, disable in enumerate(['model', 'cv_model'], start=1):
+            ae = AbstractEvaluator(
+                backend=self.backend_mock,
+                output_y_hat_optimization=False,
+                queue=queue_mock,
+                disable_file_output=[disable],
+                metric=accuracy,
+            )
+            ae.Y_optimization = predictions_ensemble
+            ae.model = unittest.mock.Mock()
+            ae.models = [unittest.mock.Mock()]
+
+            loss_, additional_run_info_ = (
+                ae.file_output(
+                    predictions_ensemble,
+                    predictions_valid,
+                    predictions_test,
+                )
             )
-        )
 
-        self.assertIsNone(loss_)
-        self.assertEqual(additional_run_info_, {})
-        # This function is not guarded by an if statement
-        self.assertEqual(self.backend_mock.save_predictions_as_npy.call_count, 3)
-        self.assertEqual(self.backend_mock.save_model.call_count, 0)
+            self.assertIsNone(loss_)
+            self.assertEqual(additional_run_info_, {})
+            self.assertEqual(self.backend_mock.save_numrun_to_dir.call_count, call_count)
+            if disable == 'model':
+                self.assertIsNone(
+                    self.backend_mock.save_numrun_to_dir.call_args_list[-1][1]['model'])
+                self.assertIsNotNone(
+                    self.backend_mock.save_numrun_to_dir.call_args_list[-1][1]['cv_model'])
+            else:
+                self.assertIsNotNone(
+                    self.backend_mock.save_numrun_to_dir.call_args_list[-1][1]['model'])
+                self.assertIsNone(
+                    self.backend_mock.save_numrun_to_dir.call_args_list[-1][1]['cv_model'])
+            self.assertIsNotNone(
+                self.backend_mock.save_numrun_to_dir.call_args_list[-1][1][
+                    'ensemble_predictions']
+            )
+            self.assertIsNotNone(
+                self.backend_mock.save_numrun_to_dir.call_args_list[-1][1][
+                    'valid_predictions']
+            )
+            self.assertIsNotNone(
+                self.backend_mock.save_numrun_to_dir.call_args_list[-1][1][
+                    'test_predictions']
+            )
 
         ae = AbstractEvaluator(
             backend=self.backend_mock,
@@ -177,9 +201,9 @@ def test_disable_file_output(self, exists_mock):
             metric=accuracy,
             disable_file_output=['y_optimization'],
         )
-        exists_mock.return_value = True
         ae.Y_optimization = predictions_ensemble
         ae.model = 'model'
+        ae.models = [unittest.mock.Mock()]
 
         loss_, additional_run_info_ = (
             ae.file_output(
@@ -191,9 +215,19 @@ def test_disable_file_output(self, exists_mock):
 
         self.assertIsNone(loss_)
         self.assertEqual(additional_run_info_, {})
-        # This function is not guarded by an if statement
-        self.assertEqual(self.backend_mock.save_predictions_as_npy.call_count, 5)
-        self.assertEqual(self.backend_mock.save_model.call_count, 1)
+
+        self.assertIsNone(
+            self.backend_mock.save_numrun_to_dir.call_args_list[-1][1][
+                'ensemble_predictions']
+        )
+        self.assertIsNotNone(
+            self.backend_mock.save_numrun_to_dir.call_args_list[-1][1][
+                'valid_predictions']
+        )
+        self.assertIsNotNone(
+            self.backend_mock.save_numrun_to_dir.call_args_list[-1][1][
+                'test_predictions']
+        )
 
     def test_file_output(self):
         shutil.rmtree(self.working_directory, ignore_errors=True)
@@ -233,6 +267,6 @@ def test_file_output(self):
             )
 
             self.assertTrue(os.path.exists(os.path.join(self.working_directory, 'tmp',
-                                                        '.auto-sklearn', 'done', '1_0')))
+                                                        '.auto-sklearn', 'runs', '1_0_None')))
 
             shutil.rmtree(self.working_directory, ignore_errors=True)
diff --git a/test/test_evaluation/test_evaluation.py b/test/test_evaluation/test_evaluation.py
index acb515bb30..a7197b6ec6 100644
--- a/test/test_evaluation/test_evaluation.py
+++ b/test/test_evaluation/test_evaluation.py
@@ -7,7 +7,6 @@
 import unittest.mock
 
 import numpy as np
-
 import pynisher
 from smac.runhistory.runhistory import RunInfo
 from smac.stats.stats import Stats
@@ -142,6 +141,7 @@ def test_cutoff_lower_than_remaining_time(self, pynisher_mock):
     def test_eval_with_limits_holdout_fail_silent(self, pynisher_mock):
         pynisher_mock.return_value = None
         config = unittest.mock.Mock()
+        config.origin = 'MOCK'
         config.config_id = 198
         ta = ExecuteTaFuncWithQueue(backend=BackendMock(), autosklearn_seed=1,
                                     resampling_strategy='holdout',
@@ -159,9 +159,12 @@ def test_eval_with_limits_holdout_fail_silent(self, pynisher_mock):
         self.assertEqual(info[1].status, StatusType.CRASHED)
         self.assertEqual(info[1].cost, 1.0)
         self.assertIsInstance(info[1].time, float)
-        self.assertEqual(len(info[1].additional_info), 2)
-        self.assertIn('configuration_origin', info[1].additional_info)
-        self.assertEqual(info[1].additional_info['error'], "Result queue is empty")
+        self.assertEqual(info[1].additional_info, {'configuration_origin': 'MOCK',
+                                                   'error': "Result queue is empty",
+                                                   'exit_status': 0,
+                                                   'exitcode': 0,
+                                                   'subprocess_stdout': '',
+                                                   'subprocess_stderr': ''})
 
         self.stats.submitted_ta_runs += 1
         info = ta.run_wrapper(RunInfo(config=config, cutoff=30, instance=None,
@@ -169,9 +172,12 @@ def test_eval_with_limits_holdout_fail_silent(self, pynisher_mock):
         self.assertEqual(info[1].status, StatusType.CRASHED)
         self.assertEqual(info[1].cost, 1.0)
         self.assertIsInstance(info[1].time, float)
-        self.assertEqual(len(info[1].additional_info), 2)
-        self.assertIn('configuration_origin', info[1].additional_info)
-        self.assertEqual(info[1].additional_info['error'], "Result queue is empty")
+        self.assertEqual(info[1].additional_info, {'configuration_origin': 'MOCK',
+                                                   'error': "Result queue is empty",
+                                                   'exit_status': 0,
+                                                   'exitcode': 0,
+                                                   'subprocess_stdout': '',
+                                                   'subprocess_stderr': ''})
 
     @unittest.mock.patch('autosklearn.evaluation.train_evaluator.eval_holdout')
     def test_eval_with_limits_holdout_fail_memory_error(self, pynisher_mock):
@@ -195,6 +201,7 @@ def test_eval_with_limits_holdout_fail_memory_error(self, pynisher_mock):
         worst_possible_result = MAXINT
         self.assertEqual(info[1].cost, worst_possible_result)
         self.assertIsInstance(info[1].time, float)
+        self.assertNotIn('exitcode', info[1].additional_info)
 
     @unittest.mock.patch('pynisher.enforce_limits')
     def test_eval_with_limits_holdout_fail_timeout(self, pynisher_mock):
@@ -221,6 +228,7 @@ def test_eval_with_limits_holdout_fail_timeout(self, pynisher_mock):
         self.assertEqual(info[1].status, StatusType.TIMEOUT)
         self.assertEqual(info[1].cost, 1.0)
         self.assertIsInstance(info[1].time, float)
+        self.assertNotIn('exitcode', info[1].additional_info)
 
     @unittest.mock.patch('pynisher.enforce_limits')
     def test_eval_with_limits_holdout_timeout_with_results_in_queue(self, pynisher_mock):
@@ -255,6 +263,7 @@ def side_effect(**kwargs):
         self.assertEqual(info[1].status, StatusType.SUCCESS)
         self.assertEqual(info[1].cost, 0.5)
         self.assertIsInstance(info[1].time, float)
+        self.assertNotIn('exitcode', info[1].additional_info)
 
         # And a crashed run which is in the queue
         def side_effect(**kwargs):
@@ -277,6 +286,7 @@ def side_effect(**kwargs):
         self.assertEqual(info[1].status, StatusType.CRASHED)
         self.assertEqual(info[1].cost, 1.0)
         self.assertIsInstance(info[1].time, float)
+        self.assertNotIn('exitcode', info[1].additional_info)
 
     @unittest.mock.patch('autosklearn.evaluation.train_evaluator.eval_holdout')
     def test_eval_with_limits_holdout_2(self, eval_houldout_mock):
@@ -330,3 +340,40 @@ def test_exception_in_target_function(self, eval_holdout_mock):
         self.assertIsInstance(info[1].time, float)
         self.assertEqual(info[1].additional_info['error'], 'ValueError()')
         self.assertIn('traceback', info[1].additional_info)
+        self.assertNotIn('exitcode', info[1].additional_info)
+
+    def test_silent_exception_in_target_function(self):
+        config = unittest.mock.Mock()
+        config.config_id = 198
+
+        backend_mock = BackendMock()
+        ta = ExecuteTaFuncWithQueue(backend=backend_mock,
+                                    autosklearn_seed=1,
+                                    resampling_strategy='holdout',
+                                    logger=self.logger,
+                                    stats=self.stats,
+                                    memory_limit=3072,
+                                    metric=accuracy,
+                                    cost_for_crash=get_cost_of_crash(accuracy),
+                                    abort_on_first_run_crash=False,
+                                    iterative=False,
+                                    )
+        ta.pynisher_logger = unittest.mock.Mock()
+        self.stats.submitted_ta_runs += 1
+        info = ta.run_wrapper(RunInfo(config=config, cutoff=3000, instance=None,
+                                      instance_specific=None, seed=1, capped=False))
+        self.assertEqual(info[1].status, StatusType.CRASHED, msg=str(info[1].additional_info))
+        self.assertEqual(info[1].cost, 1.0)
+        self.assertIsInstance(info[1].time, float)
+        self.assertIn(
+            info[1].additional_info['error'],
+            (
+                """AttributeError("'BackendMock' object has no attribute """
+                """'save_targets_ensemble'",)""",
+                """AttributeError("'BackendMock' object has no attribute """
+                """'save_targets_ensemble'")""",
+            )
+        )
+        self.assertNotIn('exitcode', info[1].additional_info)
+        self.assertNotIn('exit_status', info[1].additional_info)
+        self.assertNotIn('traceback', info[1])
diff --git a/test/test_evaluation/test_test_evaluator.py b/test/test_evaluation/test_test_evaluator.py
index 5a7cfac96d..e01a0f75e6 100644
--- a/test/test_evaluation/test_test_evaluator.py
+++ b/test/test_evaluation/test_test_evaluator.py
@@ -43,7 +43,6 @@ def test_datasets(self):
 
             with self.subTest(testname):
                 backend_mock = unittest.mock.Mock(spec=Backend)
-                backend_mock.get_model_dir.return_value = 'dutirapbdxvltcrpbdlcatepdeau'
                 D = getter()
                 D_ = copy.deepcopy(D)
                 y = D.data['Y_train']
diff --git a/test/test_evaluation/test_train_evaluator.py b/test/test_evaluation/test_train_evaluator.py
index 37db05e329..b960fd31c5 100644
--- a/test/test_evaluation/test_train_evaluator.py
+++ b/test/test_evaluation/test_train_evaluator.py
@@ -70,9 +70,12 @@ def setUp(self):
         backend_mock.get_prediction_output_path.side_effect = dummy_pred_files
         self.backend_mock = backend_mock
 
+        self.tmp_dir = os.path.join(self.ev_path, 'tmp_dir')
+        self.output_dir = os.path.join(self.ev_path, 'out_dir')
+
     def tearDown(self):
         if os.path.exists(self.ev_path):
-            os.rmdir(self.ev_path)
+            shutil.rmtree(self.ev_path)
 
     @unittest.mock.patch('autosklearn.pipeline.classification.SimpleClassificationPipeline')
     def test_holdout(self, pipeline_mock):
@@ -82,16 +85,14 @@ def test_holdout(self, pipeline_mock):
         D.name = 'test'
 
         pipeline_mock.predict_proba.side_effect = \
-            lambda X, batch_size: np.tile([0.6, 0.4], (len(X), 1))
+            lambda X, batch_size=None: np.tile([0.6, 0.4], (len(X), 1))
         pipeline_mock.side_effect = lambda **kwargs: pipeline_mock
         pipeline_mock.get_additional_run_info.return_value = None
         pipeline_mock.get_max_iter.return_value = 1
         pipeline_mock.get_current_iter.return_value = 1
-        tmp_dir = os.path.join(os.getcwd(), '.out_test_holdout')
-        output_dir = os.path.join(os.getcwd(), '.tmp_test_holdout')
 
         configuration = unittest.mock.Mock(spec=Configuration)
-        backend_api = backend.create(tmp_dir, output_dir)
+        backend_api = backend.create(self.tmp_dir, self.output_dir)
         backend_api.load_datamanager = lambda: D
         queue_ = multiprocessing.Queue()
 
@@ -150,16 +151,14 @@ def configuration_fully_fitted(self):
             SideEffect().configuration_fully_fitted
         pipeline_mock.fit_transformer.return_value = Xt_fixture, {}
         pipeline_mock.predict_proba.side_effect = \
-            lambda X, batch_size: np.tile([0.6, 0.4], (len(X), 1))
+            lambda X, batch_size=None: np.tile([0.6, 0.4], (len(X), 1))
         pipeline_mock.get_additional_run_info.return_value = None
         pipeline_mock.side_effect = lambda **kwargs: pipeline_mock
         pipeline_mock.get_max_iter.return_value = 512
         pipeline_mock.get_current_iter.side_effect = (2, 4, 8, 16, 32, 64, 128, 256, 512)
-        tmp_dir = os.path.join(os.getcwd(), '.tmp_test_iterative_holdout')
-        output_dir = os.path.join(os.getcwd(), '.out_test_iterative_holdout')
 
         configuration = unittest.mock.Mock(spec=Configuration)
-        backend_api = backend.create(tmp_dir, output_dir)
+        backend_api = backend.create(self.tmp_dir, self.output_dir)
         backend_api.load_datamanager = lambda: D
         queue_ = multiprocessing.Queue()
 
@@ -249,16 +248,14 @@ def configuration_fully_fitted(self):
             SideEffect().configuration_fully_fitted
         pipeline_mock.fit_transformer.return_value = Xt_fixture, {}
         pipeline_mock.predict_proba.side_effect = \
-            lambda X, batch_size: np.tile([0.6, 0.4], (len(X), 1))
+            lambda X, batch_size=None: np.tile([0.6, 0.4], (len(X), 1))
         pipeline_mock.side_effect = lambda **kwargs: pipeline_mock
         pipeline_mock.get_additional_run_info.return_value = None
         pipeline_mock.get_max_iter.return_value = 512
         pipeline_mock.get_current_iter.side_effect = (2, 4, 8, 16, 32, 64, 128, 256, 512)
-        tmp_dir = os.path.join(os.getcwd(), '.tmp_test_iterative_holdout_interuption')
-        output_dir = os.path.join(os.getcwd(), '.out_test_iterative_holdout_interuption')
 
         configuration = unittest.mock.Mock(spec=Configuration)
-        backend_api = backend.create(tmp_dir, output_dir)
+        backend_api = backend.create(self.tmp_dir, self.output_dir)
         backend_api.load_datamanager = lambda: D
         queue_ = multiprocessing.Queue()
 
@@ -322,14 +319,12 @@ def test_iterative_holdout_not_iterative(self, pipeline_mock):
         pipeline_mock.estimator_supports_iterative_fit.return_value = False
         pipeline_mock.fit_transformer.return_value = Xt_fixture, {}
         pipeline_mock.predict_proba.side_effect = \
-            lambda X, batch_size: np.tile([0.6, 0.4], (len(X), 1))
+            lambda X, batch_size=None: np.tile([0.6, 0.4], (len(X), 1))
         pipeline_mock.side_effect = lambda **kwargs: pipeline_mock
         pipeline_mock.get_additional_run_info.return_value = None
-        tmp_dir = os.path.join(os.getcwd(), '.tmp_test_iterative_holdout_not_iterative')
-        output_dir = os.path.join(os.getcwd(), '.out_test_iterative_holdout_not_iterative')
 
         configuration = unittest.mock.Mock(spec=Configuration)
-        backend_api = backend.create(tmp_dir, output_dir)
+        backend_api = backend.create(self.tmp_dir, self.output_dir)
         backend_api.load_datamanager = lambda: D
         queue_ = multiprocessing.Queue()
 
@@ -365,14 +360,12 @@ def test_cv(self, pipeline_mock):
         D = get_binary_classification_datamanager()
 
         pipeline_mock.predict_proba.side_effect = \
-            lambda X, batch_size: np.tile([0.6, 0.4], (len(X), 1))
+            lambda X, batch_size=None: np.tile([0.6, 0.4], (len(X), 1))
         pipeline_mock.side_effect = lambda **kwargs: pipeline_mock
         pipeline_mock.get_additional_run_info.return_value = None
-        tmp_dir = os.path.join(os.getcwd(), '.tmp_test_cv')
-        output_dir = os.path.join(os.getcwd(), '.out_test_cv')
 
         configuration = unittest.mock.Mock(spec=Configuration)
-        backend_api = backend.create(tmp_dir, output_dir)
+        backend_api = backend.create(self.tmp_dir, self.output_dir)
         backend_api.load_datamanager = lambda: D
         queue_ = multiprocessing.Queue()
 
@@ -416,18 +409,16 @@ def test_partial_cv(self, pipeline_mock):
         D = get_binary_classification_datamanager()
 
         pipeline_mock.predict_proba.side_effect = \
-            lambda X, batch_size: np.tile([0.6, 0.4], (len(X), 1))
+            lambda X, batch_size=None: np.tile([0.6, 0.4], (len(X), 1))
         pipeline_mock.side_effect = lambda **kwargs: pipeline_mock
         pipeline_mock.get_additional_run_info.return_value = None
         pipeline_mock.get_max_iter.return_value = 1
         pipeline_mock.get_current_iter.return_value = 1
-        tmp_dir = os.path.join(os.getcwd(), '.tmp_test_partial_cv')
-        output_dir = os.path.join(os.getcwd(), '.out_test_partial_cv')
         D = get_binary_classification_datamanager()
         D.name = 'test'
 
         configuration = unittest.mock.Mock(spec=Configuration)
-        backend_api = backend.create(tmp_dir, output_dir)
+        backend_api = backend.create(self.tmp_dir, self.output_dir)
         backend_api.load_datamanager = lambda: D
         queue_ = multiprocessing.Queue()
 
@@ -479,16 +470,14 @@ def configuration_fully_fitted(self):
             SideEffect().configuration_fully_fitted
         pipeline_mock.fit_transformer.return_value = Xt_fixture, {}
         pipeline_mock.predict_proba.side_effect = \
-            lambda X, batch_size: np.tile([0.6, 0.4], (len(X), 1))
+            lambda X, batch_size=None: np.tile([0.6, 0.4], (len(X), 1))
         pipeline_mock.get_additional_run_info.return_value = None
         pipeline_mock.side_effect = lambda **kwargs: pipeline_mock
         pipeline_mock.get_max_iter.return_value = 512
         pipeline_mock.get_current_iter.side_effect = (2, 4, 8, 16, 32, 64, 128, 256, 512)
-        tmp_dir = os.path.join(os.getcwd(), '.tmp_test_iterative_partial_cv')
-        output_dir = os.path.join(os.getcwd(), '.out_test_iterative_partial_cv')
 
         configuration = unittest.mock.Mock(spec=Configuration)
-        backend_api = backend.create(tmp_dir, output_dir)
+        backend_api = backend.create(self.tmp_dir, self.output_dir)
         backend_api.load_datamanager = lambda: D
         queue_ = multiprocessing.Queue()
 
@@ -548,9 +537,8 @@ def side_effect(self, *args, **kwargs):
         # and a total of five calls because of five iterations of fitting
         self.assertEqual(pipeline_mock.predict_proba.call_count, 36)
 
-    @unittest.mock.patch('os.makedirs')
     @unittest.mock.patch.object(TrainEvaluator, '_loss')
-    def test_file_output(self, loss_mock, makedirs_mock):
+    def test_file_output(self, loss_mock):
 
         D = get_regression_datamanager()
         D.name = 'test'
@@ -578,9 +566,12 @@ def test_file_output(self, loss_mock, makedirs_mock):
 
         self.assertEqual(rval, (None, {}))
         self.assertEqual(self.backend_mock.save_targets_ensemble.call_count, 1)
-        self.assertEqual(self.backend_mock.save_predictions_as_npy.call_count, 3)
-        self.assertEqual(makedirs_mock.call_count, 1)
-        self.assertEqual(self.backend_mock.save_model.call_count, 1)
+        self.assertEqual(self.backend_mock.save_numrun_to_dir.call_count, 1)
+        self.assertEqual(self.backend_mock.save_numrun_to_dir.call_args_list[-1][1].keys(),
+                         {'seed', 'idx', 'budget', 'model', 'cv_model',
+                          'ensemble_predictions', 'valid_predictions', 'test_predictions'})
+        self.assertIsNotNone(self.backend_mock.save_numrun_to_dir.call_args_list[-1][1]['model'])
+        self.assertIsNone(self.backend_mock.save_numrun_to_dir.call_args_list[-1][1]['cv_model'])
 
         evaluator.models = ['model2', 'model2']
         rval = evaluator.file_output(
@@ -590,9 +581,12 @@ def test_file_output(self, loss_mock, makedirs_mock):
         )
         self.assertEqual(rval, (None, {}))
         self.assertEqual(self.backend_mock.save_targets_ensemble.call_count, 2)
-        self.assertEqual(self.backend_mock.save_predictions_as_npy.call_count, 6)
-        self.assertEqual(makedirs_mock.call_count, 2)
-        self.assertEqual(self.backend_mock.save_model.call_count, 3)
+        self.assertEqual(self.backend_mock.save_numrun_to_dir.call_count, 2)
+        self.assertEqual(self.backend_mock.save_numrun_to_dir.call_args_list[-1][1].keys(),
+                         {'seed', 'idx', 'budget', 'model', 'cv_model',
+                          'ensemble_predictions', 'valid_predictions', 'test_predictions'})
+        self.assertIsNotNone(self.backend_mock.save_numrun_to_dir.call_args_list[-1][1]['model'])
+        self.assertIsNotNone(self.backend_mock.save_numrun_to_dir.call_args_list[-1][1]['cv_model'])
 
         # Check for not containing NaNs - that the models don't predict nonsense
         # for unseen data
@@ -719,7 +713,7 @@ def test_subsample_indices_regression(self, mock, backend_mock):
     def test_predict_proba_binary_classification(self, mock):
         D = get_binary_classification_datamanager()
         self.backend_mock.load_datamanager.return_value = D
-        mock.predict_proba.side_effect = lambda y, batch_size: np.array(
+        mock.predict_proba.side_effect = lambda y, batch_size=None: np.array(
             [[0.1, 0.9]] * y.shape[0]
         )
         mock.side_effect = lambda **kwargs: mock
@@ -735,13 +729,12 @@ def test_predict_proba_binary_classification(self, mock):
                                    metric=accuracy)
 
         evaluator.fit_predict_and_loss()
-        Y_optimization_pred = self.backend_mock.save_predictions_as_npy.call_args_list[0][0][0]
+        Y_optimization_pred = self.backend_mock.save_numrun_to_dir.call_args_list[0][1][
+            'ensemble_predictions']
 
         for i in range(7):
             self.assertEqual(0.9, Y_optimization_pred[i][1])
 
-        self.assertEqual(self.backend_mock.save_model.call_count, 2)
-
     @unittest.mock.patch.object(TrainEvaluator, 'file_output')
     @unittest.mock.patch.object(TrainEvaluator, '_partial_fit_and_predict_standard')
     @unittest.mock.patch('autosklearn.util.backend.Backend')
@@ -999,8 +992,6 @@ def test_fit_predict_and_loss_budget_2_additional_run_info(
         self.assertEqual(finish_up_mock.call_args[1]['additional_run_info'], {'val': 14678})
 
     def test_get_results(self):
-        backend_mock = unittest.mock.Mock(spec=backend.Backend)
-        backend_mock.get_model_dir.return_value = 'dutirapbdxvltcrpbdlcatepdeau'
         queue_ = multiprocessing.Queue()
         for i in range(5):
             queue_.put((i * 1, 1 - (i * 0.2), 0, "", StatusType.SUCCESS))
diff --git a/test/test_metalearning/test_metalearning.py b/test/test_metalearning/test_metalearning.py
index c04142a5d4..a21745c4bb 100644
--- a/test/test_metalearning/test_metalearning.py
+++ b/test/test_metalearning/test_metalearning.py
@@ -104,10 +104,12 @@ def test_metadata_directory(self):
         # Test that metadata directory is set correctly (if user specifies,
         # Auto-sklearn should check that the directory exists. If not, it
         # should use the default directory.
+        dask_client = unittest.mock.Mock()
         automl1 = AutoSklearnClassifier(
             time_left_for_this_task=30,
             per_run_time_limit=5,
             metadata_directory="pyMetaLearn/metadata_dir",  # user specified metadata_dir
+            dask_client=dask_client,
         )
         self.assertEqual(automl1.metadata_directory,
                          "pyMetaLearn/metadata_dir")
@@ -115,6 +117,7 @@ def test_metadata_directory(self):
         automl2 = AutoSklearnClassifier(  # default metadata_dir
             time_left_for_this_task=30,
             per_run_time_limit=5,
+            dask_client=dask_client,
         )
         self.assertIsNone(automl2.metadata_directory)
 
@@ -123,6 +126,8 @@ def test_metadata_directory(self):
             time_left_for_this_task=30,
             per_run_time_limit=5,
             metadata_directory=nonexistent_dir,  # user specified metadata_dir
+            dask_client=dask_client,
+            ensemble_size=0,
         )
         X, y = load_breast_cancer(return_X_y=True)
         self.assertRaisesRegex(ValueError, "The specified metadata directory "
diff --git a/test/test_metric/test_metrics.py b/test/test_metric/test_metrics.py
index 95ef667695..39aa493539 100644
--- a/test/test_metric/test_metrics.py
+++ b/test/test_metric/test_metrics.py
@@ -407,6 +407,15 @@ def test_classification_multiclass(self):
             current_score = scorer(y_true, y_pred)
             self.assertLess(current_score, previous_score)
 
+            # less labels in the targets than in the predictions
+            y_true = np.array([0.0, 0.0, 1.0, 1.0])
+            y_pred = np.array([
+                [1.0, 0.0, 0.0], [1.0, 0.0, 0.0],
+                [0.0, 1.0, 0.0], [0.0, 0.0, 1.0]]
+            )
+            score = scorer(y_true, y_pred)
+            self.assertTrue(np.isfinite(score))
+
     def test_classification_multilabel(self):
 
         for metric, scorer in autosklearn.metrics.CLASSIFICATION_METRICS.items():
diff --git a/test/test_pipeline/test_classification.py b/test/test_pipeline/test_classification.py
index ab029a97c5..0a296b6f33 100644
--- a/test/test_pipeline/test_classification.py
+++ b/test/test_pipeline/test_classification.py
@@ -18,6 +18,7 @@
 import sklearn.svm
 from sklearn.utils.validation import check_is_fitted
 
+from ConfigSpace.exceptions import ForbiddenValueError
 from ConfigSpace.configuration_space import ConfigurationSpace
 from ConfigSpace.hyperparameters import CategoricalHyperparameter
 
@@ -182,7 +183,7 @@ def test_repr(self):
         self.assertIsInstance(cls, SimpleClassificationPipeline)
 
     def test_multilabel(self):
-        cache = Memory(cachedir=tempfile.gettempdir())
+        cache = Memory(location=tempfile.gettempdir())
         cached_func = cache.cache(
             sklearn.datasets.make_multilabel_classification
         )
@@ -830,7 +831,12 @@ def test_fit_instantiates_component(self):
         cs = cls.get_hyperparameter_search_space()
         self.assertIn('CrashPreprocessor', str(cs))
         config = cs.sample_configuration()
-        config['feature_preprocessor:__choice__'] = 'CrashPreprocessor'
+        while True:
+            try:
+                config['feature_preprocessor:__choice__'] = 'CrashPreprocessor'
+                break
+            except ForbiddenValueError:
+                continue
         cls.set_hyperparameters(config)
         with self.assertRaisesRegex(
             ValueError,
diff --git a/test/test_pipeline/test_regression.py b/test/test_pipeline/test_regression.py
index de730bc604..27e753bcb2 100644
--- a/test/test_pipeline/test_regression.py
+++ b/test/test_pipeline/test_regression.py
@@ -97,7 +97,7 @@ def test_configurations_sparse(self):
                                   dataset_properties=dataset_properties)
 
     def test_multioutput(self):
-        cache = Memory(cachedir=tempfile.gettempdir())
+        cache = Memory(location=tempfile.gettempdir())
         cached_func = cache.cache(
             sklearn.datasets.make_regression
         )
diff --git a/test/test_scripts/test_metadata_generation.py b/test/test_scripts/test_metadata_generation.py
index 707121360c..129d1f1513 100644
--- a/test/test_scripts/test_metadata_generation.py
+++ b/test/test_scripts/test_metadata_generation.py
@@ -4,10 +4,12 @@
 import shutil
 import socket
 import subprocess
-import sys
 import unittest
 
 import arff
+import numpy as np
+
+from autosklearn.metrics import CLASSIFICATION_METRICS, REGRESSION_METRICS
 
 
 class TestMetadataGeneration(unittest.TestCase):
@@ -21,8 +23,11 @@ def print_files(self):
         for dirpath, dirnames, filenames in os.walk(self.working_directory):
             print(dirpath, dirnames, filenames)
 
-    @unittest.skipIf(sys.version_info < (3, 6), 'This test requires up-to-date python')
-    def test_metadata_generation_classification(self):
+    def test_metadata_generation(self):
+
+        regression_task_id = 5022
+        classification_task_id = 75222
+
         current_directory = __file__
         scripts_directory = os.path.abspath(os.path.join(current_directory,
                                                          '..', '..', '..',
@@ -34,8 +39,6 @@ def test_metadata_generation_classification(self):
         except Exception as e:
             print(e)
 
-        task_type = 'classification'
-
         # 2. should be done by the person running the unit tests!
 
         # 3. create configuration commands
@@ -50,55 +53,75 @@ def test_metadata_generation_classification(self):
 
         with open(commands_output_file) as fh:
             cmds = fh.read().split('\n')
-            # 6 regression, 12 classification, 1 empty line
-            self.assertEqual(len(cmds), 19)
-
-        with open(commands_output_file) as fh:
-            while True:
-                cmd = fh.readline()
-                if 'task-id 75222' in cmd:
-                    break
-
-        self.assertIn('time-limit 86400', cmd)
-        self.assertIn('per-run-time-limit 1800', cmd)
-        cmd = cmd.replace('time-limit 86400', 'time-limit 60').replace(
-            'per-run-time-limit 1800', 'per-run-time-limit 7')
-        # This tells the script to use the same memory limit for testing as
-        # for training. In production, it would use twice as much!
-        cmd = cmd.replace('-s 1', '-s 1 --unittest')
-        print('COMMAND: %s' % cmd)
-        rval = subprocess.run(cmd, shell=True, stdout=subprocess.PIPE,
-                              stderr=subprocess.PIPE)
-        print('STDOUT: %s' % repr(rval.stdout), flush=True)
-        print('STDERR: %s' % repr(rval.stderr), flush=True)
-
-        self.print_files()
-
-        expected_output_directory = os.path.join(self.working_directory,
-                                                 'configuration',
-                                                 task_type,
-                                                 '75222', 'accuracy')
-        self.assertTrue(os.path.exists(expected_output_directory))
-        smac_log = os.path.join(expected_output_directory, 'AutoML(1):75222.log')
-        with open(smac_log) as fh:
-            smac_output = fh.read()
-        self.assertEqual(rval.returncode, 0, msg=str(rval) + '\n' + smac_output)
-        expected_validation_output = os.path.join(expected_output_directory,
-                                                  'smac3-output',
-                                                  'run_1',
-                                                  'validation_trajectory.json')
-        self.assertTrue(os.path.exists(expected_validation_output))
-        trajectory = os.path.join(expected_output_directory,
-                                  'smac3-output', 'run_1', 'trajectory.json')
-
-        with open(expected_validation_output) as fh_validation:
-            with open(trajectory) as fh_trajectory:
-                traj = json.load(fh_trajectory)
-                valid_traj = json.load(fh_validation)
-                print('Validation trajectory:')
-                print(valid_traj)
-                self.assertGreater(len(traj), 0)
-                self.assertEqual(len(traj), len(valid_traj))
+            # 6 regression, 11 classification (roc_auc + task 258 is illegal), 1 empty line
+            self.assertEqual(len(cmds), 18)
+
+        for task_id, task_type, metric in (
+                (classification_task_id, 'classification', 'accuracy'),
+                (regression_task_id, 'regression', 'r2')
+        ):
+            with open(commands_output_file) as fh:
+                while True:
+                    cmd = fh.readline()
+                    if 'task-id %d' % task_id in cmd:
+                        break
+
+            self.assertIn('time-limit 86400', cmd)
+            self.assertIn('per-run-time-limit 1800', cmd)
+            cmd = cmd.replace('time-limit 86400', 'time-limit 60').replace(
+                'per-run-time-limit 1800', 'per-run-time-limit 7')
+            # This tells the script to use the same memory limit for testing as
+            # for training. In production, it would use twice as much!
+            cmd = cmd.replace('-s 1', '-s 1 --unittest')
+            print('COMMAND: %s' % cmd)
+            rval = subprocess.run(cmd, shell=True, stdout=subprocess.PIPE,
+                                  stderr=subprocess.PIPE)
+            print('STDOUT: %s' % repr(rval.stdout), flush=True)
+            print('STDERR: %s' % repr(rval.stderr), flush=True)
+
+            self.print_files()
+
+            expected_output_directory = os.path.join(self.working_directory,
+                                                     'configuration',
+                                                     task_type,
+                                                     str(task_id), metric,
+                                                     'auto-sklearn-output')
+            self.assertTrue(os.path.exists(expected_output_directory),
+                            msg=expected_output_directory)
+            smac_log = os.path.join(expected_output_directory, 'AutoML(1):%d.log' % task_id)
+            with open(smac_log) as fh:
+                smac_output = fh.read()
+            self.assertEqual(rval.returncode, 0, msg=str(rval) + '\n' + smac_output)
+            expected_validation_output = os.path.join(expected_output_directory, '..',
+                                                      'validation_trajectory_1.json')
+            self.assertTrue(os.path.exists(expected_validation_output))
+            trajectory = os.path.join(expected_output_directory,
+                                      'smac3-output', 'run_1', 'trajectory.json')
+
+            with open(expected_validation_output) as fh_validation:
+                with open(trajectory) as fh_trajectory:
+                    traj = json.load(fh_trajectory)
+                    valid_traj = json.load(fh_validation)
+                    print('Validation trajectory:')
+                    print(valid_traj)
+                    self.assertGreater(len(traj), 2, msg=str(valid_traj))
+                    self.assertEqual(len(traj), len(valid_traj), msg=str(valid_traj))
+                    for entry in valid_traj:
+                        if task_type == 'classification':
+                            for metric in CLASSIFICATION_METRICS:
+                                # This is a multilabel metric
+                                if metric in ('precision_samples', 'recall_samples', 'f1_samples'):
+                                    continue
+                                self.assertIn(metric, entry[-1])
+                                self.assertIsInstance(entry[-1][metric], float)
+                                self.assertTrue(np.isfinite(entry[-1][metric]),
+                                                (metric, str(entry[-1][metric])))
+                        else:
+                            for metric in REGRESSION_METRICS:
+                                self.assertIn(metric, entry[-1])
+                                self.assertIsInstance(entry[-1][metric], float)
+                                self.assertTrue(np.isfinite(entry[-1][metric]),
+                                                (metric, str(entry[-1][metric])))
 
         # 5. Get the test performance of these configurations
         script_filename = os.path.join(scripts_directory, '02_retrieve_metadata.py')
@@ -110,8 +133,7 @@ def test_metadata_generation_classification(self):
         print('STDERR: %s' % repr(rval.stderr), flush=True)
         self.assertEqual(rval.returncode, 0, msg=str(rval))
 
-        for file in ['algorithm_runs.arff', 'configurations.csv',
-                     'description.results.txt']:
+        for file in ['algorithm_runs.arff', 'configurations.csv', 'description.results.txt']:
             for metric in ['accuracy', 'balanced_accuracy', 'log_loss', 'roc_auc']:
                 path = os.path.join(
                     self.working_directory,
@@ -121,149 +143,7 @@ def test_metadata_generation_classification(self):
                 )
                 self.assertTrue(os.path.exists(path), msg=path)
 
-        # 6. Calculate metafeatures
-        script_filename = os.path.join(scripts_directory, '03_calculate_metafeatures.py')
-        cmd = (
-            'python3 %s --working-directory %s --test-mode '
-            % (script_filename, self.working_directory)
-        )
-        rval = subprocess.run(cmd, shell=True, stdout=subprocess.PIPE,
-                              stderr=subprocess.PIPE)
-        self.assertEqual(rval.returncode, 0, msg=str(rval))
-        for file in ['calculation_times.csv', 'description.features.txt',
-                     'feature_costs.arff', 'feature_runstatus.arff',
-                     'feature_values.arff']:
-            self.assertTrue(os.path.exists(os.path.join(self.working_directory,
-                                                        'metafeatures',
-                                                        file)))
-
-        # 7. Create aslib files
-        script_filename = os.path.join(scripts_directory, '04_create_aslib_files.py')
-        cmd = 'python3 %s --working-directory %s ' % (
-            script_filename, self.working_directory)
-        rval = subprocess.run(cmd, shell=True, stdout=subprocess.PIPE,
-                              stderr=subprocess.PIPE)
-        self.assertEqual(rval.returncode, 0, msg=str(rval))
-
-        for file in ['algorithm_runs.arff', 'configurations.csv',
-                     'description.txt', 'feature_costs.arff',
-                     'feature_runstatus.arff', 'feature_values.arff',
-                     'readme.txt']:
-            self.assertTrue(os.path.exists(os.path.join(self.working_directory,
-                                                        'metadata',
-                                                        'accuracy_binary.classification_dense',
-                                                        file)))
-
-        with open(os.path.join(self.working_directory,
-                               'metadata',
-                               'accuracy_binary.classification_dense',
-                               'algorithm_runs.arff')) as fh:
-            algorithm_runs = arff.load(fh)
-            self.assertEqual(algorithm_runs['attributes'],
-                             [('instance_id', 'STRING'),
-                              ('repetition', 'NUMERIC'),
-                              ('algorithm', 'STRING'),
-                              ('accuracy', 'NUMERIC'),
-                              ('runstatus',
-                               ['ok', 'timeout', 'memout', 'not_applicable',
-                                'crash', 'other'])])
-            self.assertEqual(len(algorithm_runs['data']), 1)
-            self.assertEqual(len(algorithm_runs['data'][0]), 5)
-            self.assertLess(algorithm_runs['data'][0][3], 0.9)
-            self.assertEqual(algorithm_runs['data'][0][4], 'ok')
-
-    @unittest.skipIf(sys.version_info < (3, 6), 'This test requires up-to-date python')
-    def test_metadata_generation_regression(self):
-        current_directory = __file__
-        scripts_directory = os.path.abspath(os.path.join(current_directory,
-                                                         '..', '..', '..',
-                                                         'scripts'))
-
-        # 1. create working directory
-        try:
-            os.makedirs(self.working_directory)
-        except Exception as e:
-            print(e)
-
-        task_type = 'regression'
-
-        # 2. should be done by the person running the unit tests!
-
-        # 3. create configuration commands
-        script_filename = os.path.join(scripts_directory, '01_create_commands.py')
-        cmd = 'python3 %s --working-directory %s --test' % (script_filename, self.working_directory)
-        rval = subprocess.run(cmd, shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
-        self.assertEqual(rval.returncode, 0, msg=str(rval))
-
-        # 4. run one of the commands to get some data
-        commands_output_file = os.path.join(self.working_directory, 'metadata_commands.txt')
-        self.assertTrue(os.path.exists(commands_output_file))
-
-        with open(commands_output_file) as fh:
-            cmds = fh.read().split('\n')
-            self.assertEqual(len(cmds), 19)
-
-        with open(commands_output_file) as fh:
-            while True:
-                cmd = fh.readline()
-                if 'task-id 5022' in cmd:
-                    break
-
-        self.assertIn('time-limit 86400', cmd)
-        self.assertIn('per-run-time-limit 1800', cmd)
-        cmd = cmd.replace('time-limit 86400', 'time-limit 60').replace(
-            'per-run-time-limit 1800', 'per-run-time-limit 7')
-        # This tells the script to use the same memory limit for testing as
-        # for training. In production, it would use twice as much!
-        cmd = cmd.replace('-s 1', '-s 1 --unittest')
-        print('COMMAND: %s' % cmd)
-        rval = subprocess.run(cmd, shell=True, stdout=subprocess.PIPE,
-                              stderr=subprocess.PIPE)
-        print('STDOUT: %s' % repr(rval.stdout), flush=True)
-        print('STDERR: %s' % repr(rval.stderr), flush=True)
-
-        self.print_files()
-
-        expected_output_directory = os.path.join(self.working_directory,
-                                                 'configuration',
-                                                 task_type,
-                                                 '5022', 'r2')
-        self.assertTrue(os.path.exists(expected_output_directory))
-        smac_log = os.path.join(expected_output_directory, 'AutoML(1):5022.log')
-        with open(smac_log) as fh:
-            smac_output = fh.read()
-        self.assertEqual(rval.returncode, 0, msg=str(rval) + '\n' + smac_output)
-        expected_validation_output = os.path.join(expected_output_directory,
-                                                  'smac3-output',
-                                                  'run_1',
-                                                  'validation_trajectory.json')
-        self.assertTrue(os.path.exists(expected_validation_output))
-        trajectory = os.path.join(expected_output_directory,
-                                  'smac3-output', 'run_1', 'trajectory.json')
-
-        with open(expected_validation_output) as fh_validation:
-            with open(trajectory) as fh_trajectory:
-                traj = json.load(fh_trajectory)
-                valid_traj = json.load(fh_validation)
-                print('Validation trajectory:')
-                print(valid_traj)
-                self.assertGreater(len(traj), 0)
-                self.assertEqual(len(traj), len(valid_traj))
-
-        # 5. Get the test performance of these configurations
-        script_filename = os.path.join(scripts_directory, '02_retrieve_metadata.py')
-        cmd = 'python3 %s --working-directory %s ' % (script_filename, self.working_directory)
-        print('COMMAND: %s' % cmd)
-        rval = subprocess.run(cmd, shell=True, stdout=subprocess.PIPE,
-                              stderr=subprocess.PIPE)
-        print('STDOUT: %s' % repr(rval.stdout), flush=True)
-        print('STDERR: %s' % repr(rval.stderr), flush=True)
-        self.assertEqual(rval.returncode, 0, msg=str(rval))
-
-        self.print_files()
-
-        for file in ['algorithm_runs.arff', 'configurations.csv',
-                     'description.results.txt']:
+        for file in ['algorithm_runs.arff', 'configurations.csv', 'description.results.txt']:
             for metric in ['r2', 'mean_squared_error']:
                 path = os.path.join(
                     self.working_directory,
@@ -282,47 +162,81 @@ def test_metadata_generation_regression(self):
         rval = subprocess.run(cmd, shell=True, stdout=subprocess.PIPE,
                               stderr=subprocess.PIPE)
         self.assertEqual(rval.returncode, 0, msg=str(rval))
-        for file in ['calculation_times.csv', 'description.features.txt',
-                     'feature_costs.arff', 'feature_runstatus.arff',
-                     'feature_values.arff']:
-            self.assertTrue(os.path.exists(os.path.join(self.working_directory,
-                                                        'metafeatures',
-                                                        file)))
+        for task_type in ('classification', 'regression'):
+            for file in ['calculation_times.csv', 'description.features.txt',
+                         'feature_costs.arff', 'feature_runstatus.arff',
+                         'feature_values.arff']:
+                self.assertTrue(
+                    os.path.exists(os.path.join(
+                        self.working_directory,
+                        'metafeatures',
+                        task_type,
+                        file)
+                    )
+                )
+
+        with open(
+            os.path.join(
+                self.working_directory, 'metafeatures', 'regression', 'feature_values.arff'
+            )
+        ) as fh:
+            metafeatures_arff = fh.read().split('\n')
+            contains_regression_id = False
+            for line in metafeatures_arff:
+                if line.startswith('2280,'):
+                    contains_regression_id = True
+            self.assertTrue(contains_regression_id, msg=metafeatures_arff)
+
+        with open(
+                os.path.join(
+                    self.working_directory, 'metafeatures', 'classification', 'feature_values.arff'
+                )
+        ) as fh:
+            metafeatures_arff = fh.read().split('\n')
+            contains_classification_id = False
+            for line in metafeatures_arff:
+                if line.startswith('233,'):
+                    contains_classification_id = True
+            self.assertTrue(contains_classification_id, msg=metafeatures_arff)
 
         # 7. Create aslib files
         script_filename = os.path.join(scripts_directory, '04_create_aslib_files.py')
-        cmd = 'python3 %s --working-directory %s  ' % (
+        cmd = 'python3 %s --working-directory %s ' % (
             script_filename, self.working_directory)
         rval = subprocess.run(cmd, shell=True, stdout=subprocess.PIPE,
                               stderr=subprocess.PIPE)
         self.assertEqual(rval.returncode, 0, msg=str(rval))
 
-        for file in ['algorithm_runs.arff', 'configurations.csv',
-                     'description.txt', 'feature_costs.arff',
-                     'feature_runstatus.arff', 'feature_values.arff',
-                     'readme.txt']:
-            self.assertTrue(os.path.exists(os.path.join(self.working_directory,
-                                                        'metadata',
-                                                        'r2_%s_dense' % task_type,
-                                                        file)))
-
-        with open(os.path.join(self.working_directory,
-                               'metadata',
-                               'r2_regression_dense',
-                               'algorithm_runs.arff')) as fh:
-            algorithm_runs = arff.load(fh)
-            self.assertEqual(algorithm_runs['attributes'],
-                             [('instance_id', 'STRING'),
-                              ('repetition', 'NUMERIC'),
-                              ('algorithm', 'STRING'),
-                              ('r2', 'NUMERIC'),
-                              ('runstatus',
-                               ['ok', 'timeout', 'memout', 'not_applicable',
-                                'crash', 'other'])])
-            self.assertEqual(len(algorithm_runs['data']), 1, algorithm_runs)
-            self.assertEqual(len(algorithm_runs['data'][0]), 5, algorithm_runs)
-            self.assertLess(algorithm_runs['data'][0][3], 0.9, algorithm_runs)
-            self.assertEqual(algorithm_runs['data'][0][4], 'ok', algorithm_runs)
+        for metric, combination in (
+            ('accuracy', 'accuracy_binary.classification_dense'),
+            ('r2', 'r2_regression_dense'),
+        ):
+            for file in ['algorithm_runs.arff', 'configurations.csv',
+                         'description.txt', 'feature_costs.arff',
+                         'feature_runstatus.arff', 'feature_values.arff',
+                         'readme.txt']:
+                self.assertTrue(os.path.exists(os.path.join(self.working_directory,
+                                                            'metadata',
+                                                            combination,
+                                                            file)))
+
+            with open(os.path.join(self.working_directory,
+                                   'metadata',
+                                   combination,
+                                   'algorithm_runs.arff')) as fh:
+                algorithm_runs = arff.load(fh)
+                self.assertEqual(algorithm_runs['attributes'],
+                                 [('instance_id', 'STRING'),
+                                  ('repetition', 'NUMERIC'),
+                                  ('algorithm', 'STRING'),
+                                  (metric, 'NUMERIC'),
+                                  ('runstatus',
+                                   ['ok', 'timeout', 'memout', 'not_applicable',
+                                    'crash', 'other'])])
+                self.assertEqual(len(algorithm_runs['data']), 1)
+                self.assertEqual(len(algorithm_runs['data'][0]), 5)
+                self.assertLess(algorithm_runs['data'][0][3], 0.9)
+                self.assertEqual(algorithm_runs['data'][0][4], 'ok')
 
     def tearDown(self):
         for i in range(5):
diff --git a/test/test_util/test_backend.py b/test/test_util/test_backend.py
index 474c5d19c5..4a62589358 100644
--- a/test/test_util/test_backend.py
+++ b/test/test_util/test_backend.py
@@ -14,25 +14,8 @@ def __init__(self):
             self.__class__ = Backend
 
     def setUp(self):
-        self.model_directory = '/model_directory/'
         self.backend = self.BackendStub()
-        self.backend.get_model_dir = lambda: self.model_directory
-
-    def test_load_models_by_file_names(self):
-        self.backend.load_model_by_seed_and_id_and_budget = unittest.mock.Mock()
-        self.backend.load_model_by_seed_and_id_and_budget.side_effect = lambda *args: args
-        rval = self.backend.load_models_by_file_names(['1.2.0.0.model',
-                                                       '1.3.50.0.model',
-                                                       '1.4.100.0models',
-                                                       ])
-        self.assertEqual(rval, {(1, 2, 0.0): (1, 2, 0.0),
-                                (1, 3, 50.0): (1, 3, 50.0)})
-
-        with self.assertRaisesRegex(
-            ValueError,
-            "could not convert string to float: 'model'",
-        ):
-            self.backend.load_models_by_file_names(['1.5.model'])
+        self.backend.internals_directory = '/'
 
     @unittest.mock.patch('pickle.load')
     @unittest.mock.patch('os.path.exists')
@@ -74,7 +57,7 @@ def test_loads_models_by_identifiers(self, exists_mock, openMock, pickleLoadMock
         self.assertDictEqual(expected_dict, actual_dict)
 
     def _setup_load_model_mocks(self, openMock, pickleLoadMock, seed, idx, budget):
-        model_path = '/model_directory/%s.%s.%s.model' % (seed, idx, budget)
+        model_path = '/runs/%s_%s_%s/%s.%s.%s.model' % (seed, idx, budget, seed, idx, budget)
         file_handler = 'file_handler'
         expected_model = 'model'
 
diff --git a/test/test_util/test_dependencies.py b/test/test_util/test_dependencies.py
index 1d3ce079a9..53b2285750 100644
--- a/test/test_util/test_dependencies.py
+++ b/test/test_util/test_dependencies.py
@@ -68,7 +68,7 @@ def test_wrong_package_version(self, getDistributionMock):
 
         self.assertRaisesRegex(
             IncorrectPackageVersionError,
-            re.escape("'package 0.1.2' version mismatch (>0.1.2)"),
+            re.escape("found 'package' version 0.1.2 but requires package version >0.1.2"),
             verify_packages,
             requirement,
             )
@@ -82,7 +82,7 @@ def test_outdated_requirement(self, getDistributionMock):
 
         self.assertRaisesRegex(
             IncorrectPackageVersionError,
-            re.escape("'package 0.0.9' version mismatch (>=0.1)"),
+            re.escape("found 'package' version 0.0.9 but requires package version >=0.1"),
             verify_packages,
             requirement,
             )
@@ -96,7 +96,7 @@ def test_too_fresh_requirement(self, getDistributionMock):
 
         self.assertRaisesRegex(
             IncorrectPackageVersionError,
-            re.escape("'package 0.1.3' version mismatch (==0.1.2)"),
+            re.escape("found 'package' version 0.1.3 but requires package version ==0.1.2"),
             verify_packages,
             requirement,
             )
diff --git a/testcommand.sh b/testcommand.sh
index 4adfd70b56..f74bc42d86 100644
--- a/testcommand.sh
+++ b/testcommand.sh
@@ -1,2 +1,2 @@
 #!/usr/bin/env bash
-pytest -n 8 --durations=300 --timeout=1800 --timeout-method=thread -v $1
+pytest -n 8 --durations=300 --timeout=300 --dist load --timeout-method=thread -v $1