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.
PackageModelsMaturityNote
Clustering.jlKMeans, KMedoidshigh
DecisionTree.jlDecisionTreeClassifier, DecisionTreeRegressor, AdaBoostStumpClassifier, RandomForestClassifier, RandomForestRegressorhigh
EvoTrees.jlEvoTreeRegressor, EvoTreeClassifier, EvoTreeCount, EvoTreeGaussianmediumgradient boosting models
GLM.jlLinearRegressor, LinearBinaryClassifier, LinearCountRegressormedium
LIBSVM.jlLinearSVC, SVC, NuSVC, NuSVR, EpsilonSVR, OneClassSVMhighalso via ScikitLearn.jl
LightGBM.jlLightGBMClassifier, LightGBMRegressorhigh
MLJFlux.jlNeuralNetworkRegressor, NeuralNetworkClassifier, MultitargetNeuralNetworkRegressor, ImageClassifierexperimental
MLJLinearModels.jlLinearRegressor, RidgeRegressor, LassoRegressor, ElasticNetRegressor, QuantileRegressor, HuberRegressor, RobustRegressor, LADRegressor, LogisticClassifier, MultinomialClassifierexperimental
MLJModels.jl (built-in)StaticTransformer, FeatureSelector, FillImputer, UnivariateStandardizer, Standardizer, UnivariateBoxCoxTransformer, OneHotEncoder, ContinuousEncoder, ConstantRegressor, ConstantClassifier, BinaryThreshholdPredictormedium
MultivariateStats.jlLinearRegressor, MultitargetLinearRegressor, RidgeRegressor, MultitargetRidgeRegressor, PCA, KernelPCA, ICA, LDA, BayesianLDA, SubspaceLDA, BayesianSubspaceLDA, FactorAnalysis, PPCAhigh
NaiveBayes.jlGaussianNBClassifier, MultinomialNBClassifier, HybridNBClassifierexperimental
NearestNeighborModels.jlKNNClassifier, KNNRegressor, MultitargetKNNClassifier, MultitargetKNNRegressorhigh
ParallelKMeans.jlKMeansexperimental
PartialLeastSquaresRegressor.jlPLSRegressor, KPLSRegressorexperimental
ScikitLearn.jlARDRegressor, 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, TheilSenRegressorhigh
XGBoost.jlXGBoostRegressor, XGBoostClassifier, XGBoostCounthigh
BetaML.jlDecisionTreeClassifier, DecisionTreeRegressor, KernelPerceptronClassifier, PegasosClassifier, PerceptronClassifier, RandomForestClassifiermedium

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!