SklearnBasedDecisionTreeTabPFN ¶

DecisionTreeTabPFNBase ¶

Bases: BaseDecisionTree

Class that implements a DT-TabPFN model based on sklearn package

apply_tree ¶

apply_tree(X)

Apply tree for different kinds of tree types. TODO: This function could also be overwritten in each type of tree

(N_samples, N_nodes, N_estimators) :param bootstrap_X: :return:

predict_ ¶

predict_(X, y=None, check_input=True)

Predicts X :param X: Data that should be evaluated :param y: True labels of holdout data used for adaptive tree. - If not None: Prunes nodes based on the performance of the holdout data y - If None: Predicts the data based on the previous hold out performances :param check_input: :return: Probabilities of each class

set_categorical_features ¶

set_categorical_features(categorical_features)

Sets categorical features :param categorical_features: Categorical features :return: None

DecisionTreeTabPFNClassifier ¶

Bases: ClassifierMixin, DecisionTreeTabPFNBase

Class that implements a DT-TabPFN model based on sklearn package

apply_tree ¶

apply_tree(X)

Apply tree for different kinds of tree types. TODO: This function could also be overwritten in each type of tree

(N_samples, N_nodes, N_estimators) :param bootstrap_X: :return:

predict ¶

predict(X, check_input=True)

Predicts X_test :param X: Data that should be evaluated :param check_input: :return: Labels of the predictions

predict_ ¶

predict_(X, y=None, check_input=True)

Predicts X :param X: Data that should be evaluated :param y: True labels of holdout data used for adaptive tree. - If not None: Prunes nodes based on the performance of the holdout data y - If None: Predicts the data based on the previous hold out performances :param check_input: :return: Probabilities of each class

predict_proba ¶

predict_proba(X, check_input=True)

Predicts X_test :param X: Data that should be evaluated :param check_input: :return: Probabilities of each class

set_categorical_features ¶

set_categorical_features(categorical_features)

Sets categorical features :param categorical_features: Categorical features :return: None

DecisionTreeTabPFNRegressor ¶

Bases: RegressorMixin, DecisionTreeTabPFNBase

Class that implements a DT-TabPFN model based on sklearn package

apply_tree ¶

apply_tree(X)

Apply tree for different kinds of tree types. TODO: This function could also be overwritten in each type of tree

(N_samples, N_nodes, N_estimators) :param bootstrap_X: :return:

predict ¶

predict(X, check_input=True)

Predicts X_test :param X: Data that should be evaluated :param check_input: :return: Labels of the predictions

predict_ ¶

predict_(X, y=None, check_input=True)

Predicts X :param X: Data that should be evaluated :param y: True labels of holdout data used for adaptive tree. - If not None: Prunes nodes based on the performance of the holdout data y - If None: Predicts the data based on the previous hold out performances :param check_input: :return: Probabilities of each class

predict_full ¶

predict_full(X)

Predicts X :param X: Data that should be evaluated :param check_input: :return: Labels of the predictions

set_categorical_features ¶

set_categorical_features(categorical_features)

Sets categorical features :param categorical_features: Categorical features :return: None