'Fit Ridge regression model
Parameters
X : {array-like, sparse matrix}, shape = [n_samples, n_features]
Training data
y : array-like, shape = [n_samples] or [n_samples, n_targets]
Target values
sample_weight : float or numpy array of shape [n_samples]
Individual weights for each sample
Returns
self : returns an instance of self.'

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

    return super(Ridge, self).fit(X, y, sample_weight=sample_weight)
