imblearn.ensemble.BalancedRandomForestClassifier.fit

BalancedRandomForestClassifier.fit(X, y, sample_weight=None)[source]

Build a forest of trees from the training set (X, y).

Parameters
X{array-like, sparse matrix} of shape (n_samples, n_features)

The training 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 csc_matrix.

yarray-like of shape (n_samples,) or (n_samples, n_outputs)

The target values (class labels in classification, real numbers in regression).

sample_weightarray-like of shape (n_samples,)

Sample weights. If None, then samples are equally weighted. Splits that would create child nodes with net zero or negative weight are ignored while searching for a split in each node. In the case of classification, splits are also ignored if they would result in any single class carrying a negative weight in either child node.

Returns
selfobject

The fitted instance.