# When does random forest feature importance fail?

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?

1. Does RF based feature importance require standardization of features?

2. Does it require fitting a good RF model (ex: pruning), as if I'm making an actual prediction with RF?

3. What do I do if I have categorical variables and continuous variables at the same time? Is permutation method the only option?

• I am not 100% sure about this, but tree based models don't require scaling as such as that's just another value to split for them instead of the unnormalized one – Aditya Nov 14 '19 at 4:26
• The reasons for this and a solution (implemented at least in R package ranger) are well explained in this paper: ncbi.nlm.nih.gov/pmc/articles/PMC6198850 – crazysantaclaus Nov 14 '19 at 8:12