Libraries Summary.md

Linear Regression & Classification

A generic notebook describing the methods can be found here.

	SparseRegression.jl	MultivariateStats.jl	OnlineStats.jl
Package works	yes	yes	yes
Deprecations warnings	No	No	No
Compatible with JuliaDB	If transformed into matrix	If transformed into matrix	If transformed into matrix
Documentation	lacking	very good	very good
Simplicity	good	good	High

SVM

Theory on support vector machines is found here

	KSVM.jl	LIBSVM.jl	SVM.jl
Package works	no	yes	no
Deprecations warnings	yes	No	yes
-	If transformed into matrix	yes	-
Documentation	none	yes, as inline documentation. Type ?Functionname	none
Simplicity	good	medium	good

Gradient Boosting

A notebook detailing boosting and gradient boosting is available here.

• LightGMB.jl a Julia interface for Microsoft's LightGBM
• XGBoost.jl an interface fo XGBoost (written in C)
• A Julia implementation of gradient boosting

	LightGBM.jl	XGBoost.jl	GradientBoost.jl
Packages works	no	Yes	No
Deprecation warnings	None	None	Several
Compatible with JuliaDB	Yes (transformation of tables to arrays required)	-
Documetation	None	Points to XGBoost docs	few examples

Decision Trees

A generic notebook discussing decision tree models is available here. Note:

• DecisionTrees.jl was found to be about one order of magnitude slower than the python version
• OnlineStats only has an implementation for a type of tree called FastTree (and its ensemble, the FastForest)

	DecisionTrees.jl	ScikitLearn.jl	OnlineStats.jl
Packages works	yes	Yes	Yes
Deprecation warnings	None	Some	None
Compatible with JuliaDB	Yes (transformation of tables to arrays required)	Yes (transformation of tables to arrays required)	Yes (transformation of tables to arrays required)
Documetation	None, but many examples	very good	None
Simplicity	Good, like sklearn	good	quite low

Utilities

• MLPreprocessing is used to do simple scaling/normalising
• MLLabelUtils provides functions to modify the labels to be compatible with whatever the algorithm requires. For instance transform into categorical, booleans, from text to number etc
• MLBase provides function for label encoding, classification from model scores, performance evaluation (ROC, F1 etc), cross-validation (Kfold, stratified Kfold, subsmapling), and grid search hyperparameter tuning.
• MLMetrics provides model evaluation functions for regression, classification, multilabel ranking, and clustering designed to follow python's sklearn.metrics closely.

	MLPreprocessing.jl	MLLabelUtils.jl	MLBase.jl	MLMetrics.jl
Packages works	yes	yes	yes	yes
Deprecation warnings	None	No	No	yes
Compatible with JuliaDB	If tables are converted to arrays or dataframes	If tables transformed into arrays	If tables transformed into arrays	yes
Documetation	None, but sufficient examples	good	good	none
Simplicity	Fair	good	good	good