Forests and trees

Random forests, Extra Trees, single trees, bagging, AdaBoost, isolation forests and saved-model tree inference.

AdaBoostClassifier	An AdaBoost classifier (SAMME).
AdaBoostRegressor	An AdaBoost regressor (AdaBoost.R2).
BaggingClassifier	A bagging ensemble of classifiers.
BaggingRegressor	A bagging ensemble of regressors.
CalibratedClassifierCV	Calibrate a classifier's probabilities with cross-validation.
DARTClassifier	Gradient boosting with DART dropout, classification.
DARTRegressor	Gradient boosting with DART dropout, squared-error loss.
DecisionTreeClassifier	A single classification tree (CART).
DecisionTreeRegressor	A single regression tree (CART).
ExtraTreesClassifier	Extra-trees classifier with scikit-learn's defaults.
ExtraTreesRegressor	Extra-trees regressor with scikit-learn's defaults.
host_model	Load a saved model for prediction on the CPU.
host_predict	Predict on the CPU with a saved model: host_model(path).predict(X).
host_predict_proba	Class probabilities on the CPU from a saved classifier: host_model(path).predict_proba(X).
HostForest	A saved random forest or extra-trees model that predicts on the CPU.
HostGBDT	CPU inference for a saved gradient-boosting model.
IsolationForest	Isolation Forest anomaly detector with scikit-learn's interface.
KernelExplainer	Model-agnostic Kernel SHAP values.
MultiOutputClassifier	Fit one classifier per label column.
MultiOutputRegressor	Fit one regressor per target column.
OneVsRestClassifier	Multiclass classification with one binary classifier per class.
PermutationExplainer	Model-agnostic Permutation SHAP values.
RandomForestClassifier	Random forest classifier with cuML's defaults.
RandomForestRegressor	Random forest regressor with cuML's defaults.
RandomTreesEmbedding	An unsupervised embedding by totally random trees.
StackingClassifier	Stack classifiers and train a final classifier on their cross-validated outputs.
StackingRegressor	Stack regressors and train a final regressor on their cross-validated outputs.
TreeExplainer	Exact SHAP values for mojolearn tree models.
VotingClassifier	Combine classifiers by a weighted majority vote or averaged probabilities.
VotingRegressor	Average the predictions of several regressors.