當涉及到決策樹時，特征重要性不是一個黑匣子。來自DecisionTreeRegressor的文檔：The importance of a feature is computed as the (normalized) total

reduction of the criterion brought by that feature. It is also known

as the Gini importance.

對于一個森林來說，它只是平均分布在你的森林中不同的樹上。查看source code：def feature_importances_(self):

"""Return the feature importances (the higher, the more important the

feature).

Returns

-

feature_importances_ : array, shape = [n_features]

"""

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_)

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