def feature_importances(self):
'''
Return the feature importances.
'''
if self.trained is False:
raise ValueError('the model has not been trained yet')
importances = Parallel(n_jobs=self.n_jobs, backend="threading")(delayed(getattr, check_pickle=False)
(tree, 'feature_importances_') for tree in self.estimators)
return sum(importances) / self.n_estimators
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