Module tree 

Tree-based models: Decision Tree, Random Forest, Gradient Boosting, and Histogram-based Gradient Boosting.

Structs

DecisionTreeClassifier

CART decision tree for classification.

DecisionTreeRegressor

CART decision tree for regression.

FeatureBinner

Quantile-based feature binner.

GradientBoostingClassifier

Gradient Boosting for classification (binary + multiclass).

GradientBoostingRegressor

Gradient Boosting for regression.

HistGradientBoostingClassifier

Histogram-based Gradient Boosting for classification (binary + multiclass).

HistGradientBoostingRegressor

Histogram-based Gradient Boosting for regression.

RandomForestClassifier

Random Forest for classification.

RandomForestRegressor

Random Forest for regression (mean of tree predictions).

Enums

HistNodeView

Public view of a HistNode for ONNX export, with bin thresholds converted to raw feature value thresholds.

MaxFeatures

Strategy for selecting the number of features per split.

RegressionLoss

Loss function for gradient boosting regression.

SplitCriterion

Split quality criterion.

TreeNode

A node in the decision tree (recursive representation).