adaboost和决策树的特征是重要性的不同吗？
我有一个多类分类问题，并且根据杂质减少提取了重要性。我比较了一个决策树和adaboost分类器，并对决策树排在最重要的情况下，而根据Adaboost的重要性，它的重要性非常低。 那是正常的行为吗？ 谢谢
I have a multiclass classification problem and I extracted features importances based on impurity decrease. I compared a decision tree and AdaBoost classifiers and I ovserved that there is a feature that was ranked on top with the decision tree while it has a very lower importance according to AdaBoost.
Is that a normal behavior?
Thanks
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是的，这是正常行为。这些功能重要性计算模型的所有输入特征的分数。但是，每个模型都有（略有不同的）技术。例如：线性回归将研究线性关系。如果功能与您的目标有完美的线性关系，那么它将具有很高的特征。具有非线性关系的功能可能无法提高准确性，从而导致特征重要性得分较低。
有一些与特征重要性度量差异有关的研究。一个例子是：
https://link.springer.com/article.com/article.com/Article/10.1007/s42452-2- 021-04148-9
Yes it is normal behavior. The features importance calculates a score for all the input features of a model. However, each model has a (slightly) different technique. For example: a linear regression will look at linear relationships. If a feature has a perfect linear relationship with your target, then it will have a high feature importance. Features with a non-linear relationship may not improve the accuracy resulting in a lower feature importance score.
There is some research related to the difference in feature importance measures. An example is:
https://link.springer.com/article/10.1007/s42452-021-04148-9