RandomForestRegressor
#include <Skigen/Ensemble>
template <typename Scalar = double>
class Skigen::RandomForestRegressor(n_estimators=100, criterion=CriterionReg::SquaredError, max_depth=std::nullopt, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0, max_features_mode=MaxFeaturesMode::OneThird, max_features_value=std::nullopt, max_leaf_nodes=std::nullopt, min_impurity_decrease=0, bootstrap=true, oob_score=false, n_jobs=1, random_state=std::nullopt, verbose=0, warm_start=false, ccp_alpha=std::nullopt, max_samples=std::nullopt)
A random forest regressor.
A meta-estimator that fits a number of decision tree regressors on bootstrap samples of the training set and averages their predictions to improve accuracy and control over-fitting.
Mirrors sklearn.ensemble.RandomForestRegressor.
Attributes:
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n_estimators : int
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bootstrap : bool
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n_jobs : int
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estimators : const std::vector< DecisionTreeRegressor< Scalar > > &
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feature_importances : RowVectorType
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oob_prediction : VectorType
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oob_score : Scalar