'Predict using the linear model
Parameters
X : {array-like, sparse matrix}, shape (n_samples, n_features)
Returns
array, shape (n_samples,)
Predicted target values per element in X.'

def _decision_function(self, X):

    check_is_fitted(self, ['t_', 'coef_', 'intercept_'], all_or_any=all)
    X = check_array(X, accept_sparse='csr')
    scores = (safe_sparse_dot(X, self.coef_.T, dense_output=True) + self.intercept_)
    return scores.ravel()
