SklearnBasedRandomForestTabPFN ¶
RandomForestTabPFNBase ¶
Base Class for common functionalities.
RandomForestTabPFNClassifier ¶
Bases: RandomForestTabPFNBase, RandomForestClassifier
RandomForestTabPFNClassifier.
fit ¶
Fits RandomForestTabPFN :param X: Feature training data :param y: Label training data :param sample_weight: Weights of each sample :return: None.
predict ¶
Predict class for X.
The predicted class of an input sample is a vote by the trees in the forest, weighted by their probability estimates. That is, the predicted class is the one with highest mean probability estimate across the trees.
Parameters¶
X : {array-like, sparse matrix} of shape (n_samples, n_features)
The input samples. Internally, its dtype will be converted to
dtype=np.float32. If a sparse matrix is provided, it will be
converted into a sparse csr_matrix.
Returns:¶
y : ndarray of shape (n_samples,) or (n_samples, n_outputs) The predicted classes.
predict_proba ¶
Predict class probabilities for X.
The predicted class probabilities of an input sample are computed as the mean predicted class probabilities of the trees in the forest. The class probability of a single tree is the fraction of samples of the same class in a leaf.
Parameters¶
X : {array-like, sparse matrix} of shape (n_samples, n_features)
The input samples. Internally, its dtype will be converted to
dtype=np.float32. If a sparse matrix is provided, it will be
converted into a sparse csr_matrix.
Returns:¶
p : ndarray of shape (n_samples, n_classes), or a list of such arrays
The class probabilities of the input samples. The order of the
classes corresponds to that in the attribute :term:classes_.
set_categorical_features ¶
Sets categorical features :param categorical_features: Categorical features :return: None.
RandomForestTabPFNRegressor ¶
Bases: RandomForestTabPFNBase, RandomForestRegressor
RandomForestTabPFNClassifier.
fit ¶
Fits RandomForestTabPFN :param X: Feature training data :param y: Label training data :param sample_weight: Weights of each sample :return: None.
predict ¶
Predict regression target for X.
The predicted regression target of an input sample is computed as the mean predicted regression targets of the trees in the forest.
Parameters¶
X : {array-like, sparse matrix} of shape (n_samples, n_features)
The input samples. Internally, its dtype will be converted to
dtype=np.float32. If a sparse matrix is provided, it will be
converted into a sparse csr_matrix.
Returns:¶
y : ndarray of shape (n_samples,) or (n_samples, n_outputs) The predicted values.
set_categorical_features ¶
Sets categorical features :param categorical_features: Categorical features :return: None.