HistGradientBoostingClassifier

#include <Skigen/Ensemble>

template <typename Scalar = double>
class Skigen::HistGradientBoostingClassifier(loss=Loss::LogLoss, learning_rate=0.1, max_iter=100, max_leaf_nodes=31, max_depth=std::nullopt, min_samples_leaf=20, l2_regularization=0, max_bins=255, early_stopping=false, tol=1e-7, random_state=std::nullopt)

Histogram-based Gradient Boosting for binary classification.

Bins each feature into at most max_bins quantile-based buckets, then runs stage-wise additive log-odds gradient boosting (the same scheme as GradientBoostingClassifier) on the binned representation.

Mirrors sklearn.ensemble.HistGradientBoostingClassifier for the binary log-loss case.

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Attributes:

• loss : Loss

• learning_rate : Scalar

• max_iter : int

• max_bins : int

• init : Scalar

• classes : const Eigen::VectorXi

• n_classes : int

• n_iter : int

• bin_edges : const std::vector< std::vector< Scalar > > &

• train_score : VectorType

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Methods

SKIGEN_PARAMS()

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predict(X)

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decision_function(X)

Raw additive score (log-odds), shape (n_samples,).

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predict_proba(X)

Probability estimates, shape (n_samples, 2).

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