@property
    def feature_importances_(self):
        if self.estimators_ is None or len(self.estimators_) == 0:
            raise NotFittedError("Estimator not fitted, "
                                 "call `fit` before `feature_importances_`.")

        all_importances = Parallel(n_jobs=self.n_jobs,
                                   backend="threading")(
            delayed(getattr)(tree, 'feature_importances_')
            for tree in self.estimators_)

        return sum(all_importances) / len(self.estimators_)
Return the feature importances (the higher, the more important the
dataset/ETHPy150Open scikit-learn/scikit-learn/sklearn/ensemble/forest.py/BaseForest.feature_importances_