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I have built random forest classifier and regressor models on same data, target and independent variables. For classifier, I am giving a classification parameter that divides the data into more or less half good and half bad. When I then compare the important features given by the models, I see that they are different in both weights and chronology. Can someone help me with understanding this? Which set of features makes most sense to use? Am I doing something wrong?

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