Not throwing error for wrong parameters

[venkyyuvy]

import xgboost
from sklearn.datasets import make_regression

xgboost.__version__
# '1.0.2'
from sklearn.datasets import make_regression
X_train, y_train = make_regression()

xgboost.XGBRegressor(wrong_param=3).fit(X_train, y_train)

XGBRegressor(base_score=0.5, booster=None, colsample_bylevel=1,
#              colsample_bynode=1, colsample_bytree=1, gamma=0, gpu_id=-1,
#              importance_type='gain', interaction_constraints=None,
#              learning_rate=0.300000012, max_delta_step=0, max_depth=6,
#              min_child_weight=1, missing=nan, monotone_constraints=None,
#              n_estimators=100, n_jobs=0, num_parallel_tree=1,
#              objective='reg:squarederror', random_state=0, reg_alpha=0,
#              reg_lambda=1, scale_pos_weight=1, subsample=1, tree_method=None,
#              validate_parameters=False, verbosity=None, wrong_param=3)

[trivialfis]

Parameter validation is not yet enabled for scikit learn interface. But should be available for native interface:

xgboost.train({'foo': 'bar'}, ...)

Should throw a warning.