I'm curious about the assumptions of random forest feature importance.
In this paper, the author says that
"We show that random forest variable importance measures are a sensible means for variable selection in many applications, but are not reliable in situations where potential predictor variables vary in their scale of measurement or their number of categories."
I don't understand what it means by "predictor variables vary in their scale of measurement." Is this referring to the need to standardize data before fitting a random forest model?
Does RF based feature importance require standardization of features?
Does it require fitting a good RF model (ex: pruning), as if I'm making an actual prediction with RF?
What do I do if I have categorical variables and continuous variables at the same time? Is permutation method the only option?