List of Supported Models
MLJ provides access to to a wide variety of machine learning models. We are always looking for help adding new models or testing existing ones. Currently available models are listed below; for the most up-to-date list, run using MLJ; models().
- experimental: indicates the package is fairly new and/or is under active development; you can help by testing these packages and making them more robust,
- medium: indicates the package is fairly mature but may benefit from optimisations and/or extra features; you can help by suggesting either,
- high: indicates the package is very mature and functionalities are expected to have been fairly optimised and tested.
| Package | Models | Maturity | Note |
|---|---|---|---|
| Clustering.jl | KMeans, KMedoids | high | † |
| DecisionTree.jl | DecisionTreeClassifier, DecisionTreeRegressor, AdaBoostStumpClassifier, RandomForestClassifier, RandomForestRegressor | high | |
| EvoTrees.jl | EvoTreeRegressor, EvoTreeClassifier, EvoTreeCount, EvoTreeGaussian | medium | gradient boosting models |
| GLM.jl | LinearRegressor, LinearBinaryClassifier, LinearCountRegressor | medium | † |
| LIBSVM.jl | LinearSVC, SVC, NuSVC, NuSVR, EpsilonSVR, OneClassSVM | high | also via ScikitLearn.jl |
| LightGBM.jl | LightGBMClassifier, LightGBMRegressor | high | |
| MLJFlux.jl | NeuralNetworkRegressor, NeuralNetworkClassifier, MultitargetNeuralNetworkRegressor, ImageClassifier | experimental | |
| MLJLinearModels.jl | LinearRegressor, RidgeRegressor, LassoRegressor, ElasticNetRegressor, QuantileRegressor, HuberRegressor, RobustRegressor, LADRegressor, LogisticClassifier, MultinomialClassifier | experimental | |
| MLJModels.jl (built-in) | StaticTransformer, FeatureSelector, FillImputer, UnivariateStandardizer, Standardizer, UnivariateBoxCoxTransformer, OneHotEncoder, ContinuousEncoder, ConstantRegressor, ConstantClassifier, BinaryThreshholdPredictor | medium | |
| MultivariateStats.jl | LinearRegressor, RidgeRegressor, PCA, KernelPCA, ICA, LDA, BayesianLDA, SubspaceLDA, BayesianSubspaceLDA, FactorAnalysis, PPCA | high | |
| NaiveBayes.jl | GaussianNBClassifier, MultinomialNBClassifier, HybridNBClassifier | experimental | |
| NearestNeighbors.jl | KNNClassifier, KNNRegressor | high | |
| ParallelKMeans.jl | KMeans | experimental | |
| PartialLeastSquaresRegressor.jl | PLSRegressor, KPLSRegressor | experimental | |
| ScikitLearn.jl | ARDRegressor, AdaBoostClassifier, AdaBoostRegressor, AffinityPropagation, AgglomerativeClustering, BaggingClassifier, BaggingRegressor, BayesianLDA, BayesianQDA, BayesianRidgeRegressor, BernoulliNBClassifier, Birch, ComplementNBClassifier, DBSCAN, DummyClassifier, DummyRegressor, ElasticNetCVRegressor, ElasticNetRegressor, ExtraTreesClassifier, ExtraTreesRegressor, FeatureAgglomeration, GaussianNBClassifier, GaussianProcessClassifier, GaussianProcessRegressor, GradientBoostingClassifier, GradientBoostingRegressor, HuberRegressor, KMeans, KNeighborsClassifier, KNeighborsRegressor, LarsCVRegressor, LarsRegressor, LassoCVRegressor, LassoLarsCVRegressor, LassoLarsICRegressor, LassoLarsRegressor, LassoRegressor, LinearRegressor, LogisticCVClassifier, LogisticClassifier, MeanShift, MiniBatchKMeans, MultiTaskElasticNetCVRegressor, MultiTaskElasticNetRegressor, MultiTaskLassoCVRegressor, MultiTaskLassoRegressor, MultinomialNBClassifier, OPTICS, OrthogonalMatchingPursuitCVRegressor, OrthogonalMatchingPursuitRegressor, PassiveAggressiveClassifier, PassiveAggressiveRegressor, PerceptronClassifier, ProbabilisticSGDClassifier, RANSACRegressor, RandomForestClassifier, RandomForestRegressor, RidgeCVClassifier, RidgeCVRegressor, RidgeClassifier, RidgeRegressor, SGDClassifier, SGDRegressor, SVMClassifier, SVMLClassifier, SVMLRegressor, SVMNuClassifier, SVMNuRegressor, SVMRegressor, SpectralClustering, TheilSenRegressor | high | † |
| XGBoost.jl | XGBoostRegressor, XGBoostClassifier, XGBoostCount | high |
Note (†): Some models are missing and assistance is welcome to complete the interface. Post a message on the Julia #mlj Slack channel if you would like to help, thanks!