API reference

Decision trees

neural_trees.SoftDecisionTree([depth, ...])	Soft Decision Tree Classifier (sklearn-compatible).
neural_trees.MultivariateDecisionTree([...])	Multivariate Decision Tree Classifier (sklearn-compatible).
neural_trees.OmnivariateDecisionTree([...])	Omnivariate Decision Tree Classifier (sklearn-compatible).
neural_trees.HardDecisionTree(weights, ...)	A trained Soft Decision Tree with its gates read as hard decisions.

Mixtures of experts

neural_trees.HierarchicalMixtureOfExperts([...])	Hierarchical Mixture of Experts with Dropout Regularization.
neural_trees.HardRoutedExperts(gate_weights, ...)	A trained mixture of experts with its gates read as hard routing decisions.

Constructive and classical models

neural_trees.GALNetwork([initial_hidden, ...])	GAL (Grow and Learn) Constructive Neural Network.
neural_trees.WeightedKNN([k, weight_power, ...])	Distance-Weighted K-Nearest Neighbors Classifier.
neural_trees.NaiveBayesClassifier([...])	Naive Bayes Classifier with selectable likelihood.

Statistical tests

neural_trees.combined_5x2cv_f_test(clf_A, ...)	Alpaydın's Combined 5×2 Cross-Validation F Test.
neural_trees.mcnemar_test(y_true, y_pred_A, ...)	McNemar's Test for comparing two classifiers on the same test set.
neural_trees.paired_t_test(clf_A, clf_B, X, y)	Paired t-test for classifier comparison.