I'm currently working on a paper where I'm training a bunch of random forest classifiers on a feature set with fairly high degree of multicollinearity. In the end I'm aiming to provide an overview, ranking all my features employed by how "important" they are for the overall classification result. Initially I was planning on
feature_importances_ attribute of the
sklearn random forest implementation.
As described in this question, however, I figured I can't use this method to rank the features due to the high degree of multicollinearity. My question is, if there is any other way of ranking features for classification or regression that is insensitive to multicollinearity?