@property
    def feature_importances_(self):
        self._check_initialized()

        total_sum = np.zeros((self.n_features, ), dtype=np.float64)
        for stage in self.estimators_:
            stage_sum = sum(tree.feature_importances_
                            for tree in stage) / len(stage)
            total_sum += stage_sum

        importances = total_sum / len(self.estimators_)
        return importances
Return the feature importances (the higher, the more important the
dataset/ETHPy150Open scikit-learn/scikit-learn/sklearn/ensemble/gradient_boosting.py/BaseGradientBoosting.feature_importances_