RandomForestClassifier

#include <Skigen/Ensemble>

template <typename Scalar = double>
class Skigen::RandomForestClassifier(n_estimators=100, criterion=CriterionClf::Gini, max_depth=std::nullopt, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0, max_features_mode=MaxFeaturesMode::Sqrt, 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 classifier.

A meta-estimator that fits a number of decision tree classifiers on various sub-samples of the dataset and uses averaging to improve the predictive accuracy and control over-fitting. Each tree is trained on a bootstrap sample of the training data and considers only a random subset of features at each split.

Mirrors sklearn.ensemble.RandomForestClassifier.

---

---

Attributes:

• n_estimators : int

• bootstrap : bool

• n_jobs : int

• estimators : const std::vector< DecisionTreeClassifier< Scalar > > &

• classes : const Eigen::VectorXi

• n_classes : int

• feature_importances : RowVectorType

• oob_decision_function : MatrixType

• oob_score : Scalar

---

Methods

SKIGEN_PARAMS()

---

predict(X)

---

predict_proba(X)

Average of per-tree class probabilities.

---