Supported Operators

scikit-learn

Tree-based operators

• DecisionTreeClassifier
• DecisionTreeRegressor
• ExtraTreesClassifier
• ExtraTreesRegressor
• GradientBoostingClassifier
• GradientBoostingRegressor
• HistGradientBoostingClassifier
• HistGradientBoostingRegressor
• IsolationForest
• RandomForestClassifier
• RandomForestRegressor

Linear methods

• LinearRegression
• LogisticRegression
• LogisticRegressionCV
• RidgeCV
• SGDClassifier

SVM

• LinearSVC
• LinearSVR
• NuSVC
• SVC

Clustering

• KMeans
• MeanShift

Classifiers: Other

• BernoulliNB
• GaussianNB
• KNeighborsClassifier
• KNeighborsRegressor
• MLPClassifier
• MLPRegressor
• MultinomialNB

Preprocessing

• Binarizer
• LabelEncoder
• Normalizer
• OneHotEncoder
• RobustScaler
• MaxAbsScaler
• MinMaxScaler
• StandardScaler

Matrix Decomposition

• PCA
• KernelPCA
• TruncatedSVD
• FastICA

Feature Selectors

• SelectPercentile
• SelectKBest
• VarianceThreshold

Feature Pre-processing: One-to-One

• SimpleImputer
• MissingIndicator

Feature Pre-processing: Other

• PolynomialFeatures

Other

• FeatureUnion
• ColumnTransformer
• Pipeline
• StackingClassifier
• StackingRegressor

LightGBM

• LGBMClassifier
• LGBMRanker
• LGBMRegressor

XGBoost

• XGBClassifier
• XBGRanker
• XGBRegressor

ONNX.ML

• Abs
• Add
• ArrayFeatureExtractor
• Binarizer
• Cast
• Concat
• Div
• Imputer
• LabelEncoder
• Less
• LinearClassifier
• LinearRegressor
• Mul
• Neg
• Normalizer
• OneHotEncoder
• Reshape
• Sum
• Scaler
• SVMClassifier
• TreeEnsembleClassifier
• TreeEnsembleRegressor

SparkML

• Bucketizer
• VectorAssembler
• LogisticRegressionModel