Features importance in Random forest classifier

Question

I'm using the random forest classifier (RandomForestClassifier) from scikit-learn on a dataset of two classes (0 and 1). RandomForestClassifier provides directly the importances of the features through the feature_importances_ attribute.

Is features importance in random forest classification depends on classes(0 or 1) of the samples? If I changes the class of data samples (1 and 0), does it effect the features important or they will remain same or it show the features importance of specific class (say 0 or 1)?

I just want to know that the feature importance that comes in RF Classifier are of class 0 or Class 1? Want to check the expressed gene in treatment sample, So the class of the treatment sample should be always 1 and class of the control sample always 0. Is this correct

Answer 1

No, that's not how feature importance works. The feature importance does not understand the classes as treatments. Feature importance essentially measures how well each feature can be used to construct a split that divides the data into the classes. The feature importance does not describe one class individually.

You can verify this by fitting a random forest, and saving the feature importance, and then comparing them to the feature importance of a model with the reversed class labels. Neglecting random variation, the importance measures will be similar.

I'd recommend reading a high-quality reference on random forest, such as Leo Breiman's papers or the treatment in Hastie et al.'s Elements of Statistical Learning.