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, monotonic_cst=std::nullopt, categorical_features=std::nullopt, early_stopping=false, validation_fraction=0.1, n_iter_no_change=10, tol=1e-7, random_state=std::nullopt)
Histogram-based Gradient Boosting for classification.
Bins each feature into at most max_bins quantile-based buckets, then runs stage-wise additive gradient boosting on the binned representation with a native gradient/hessian histogram split finder. Binary problems boost a single log-odds score; multiclass problems boost one tree per class per iteration against the softmax cross-entropy gradient.
Mirrors sklearn.ensemble.HistGradientBoostingClassifier for the log-loss case.
Attributes:
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loss : Loss
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learning_rate : Scalar
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max_iter : int
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max_bins : int
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init : VectorType Per-class init scores (length 1 for binary, n_classes otherwise).
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classes : const Eigen::VectorXi
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n_classes : int
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n_iter : int
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bin_edges : const std::vector< std::vector< Scalar > > &
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train_score : VectorType
Methods
SKIGEN_PARAMS()
fit(X, y)
Fit natively from a sparse design matrix without densifying.
The sparse input is binned directly into the compact uint8 histogram representation; no dense double matrix is built.
fit_from_binned(X_binned, y)
predict(X)
decision_function(X)
Raw additive scores. Shape (n_samples,) for binary (log-odds), (n_samples, n_classes) for multiclass.
predict(X)
Predicted class labels from a sparse design matrix.
predict_proba(X)
Probability estimates from a sparse design matrix.
predict_proba(X)
Probability estimates, shape (n_samples, n_classes).