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 sparsecsc_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.