'Build a Bagging ensemble of estimators from the training
set (X, y).
Parameters
X : {array-like, sparse matrix} of shape = [n_samples, n_features]
The training input samples. Sparse matrices are accepted only if
they are supported by the base estimator.
y : array-like, shape = [n_samples]
The target values (class labels in classification, real numbers in
regression).
sample_weight : array-like, shape = [n_samples] or None
Sample weights. If None, then samples are equally weighted.
Note that this is supported only if the base estimator supports
sample weighting.
Returns
self : object
Returns self.'

def fit(self, X, y, sample_weight=None):

    return self._fit(X, y, self.max_samples, sample_weight=sample_weight)
