def feature_importances_(self):
        """Get feature importances.

        Note
        ----
        Feature importance in sklearn interface used to normalize to 1,
        it's deprecated after 2.0.4 and is the same as Booster.feature_importance() now.
        ``importance_type`` attribute is passed to the function
        to configure the type of importance values to be extracted.
        """
        if self._n_features is None:
            raise LGBMNotFittedError('No feature_importances found. Need to call fit beforehand.')
        return self.booster_.feature_importance(importance_type=self.importance_type)