

@property
def feature_importances_(self):
    'Return the feature importances\n           (the higher, the more important the feature).\n        Returns:\n            array, shape = [n_features]\n        '
    if ((self.estimators_ is None) or (len(self.estimators_) == 0)):
        raise ValueError('Estimator not fitted, call `fit` before `feature_importances_`.')
    return sum(((tree.feature_importances_ * self) for (tree, weight) in zip(self.estimators_, self.estimator_weights_)))

dataset/ETHPy150Open anaderi/lhcb_trigger_ml/hep_ml/uboost.py uBoostBDT.feature_importances_/VarMisuse@55/137 `weight`->`self`