# random forest feature selection

For what I know RF can be used as a model for feature importance or feature selection. Also, that RF can be used as a prediction and classification model. The question that I have is if its possible for a classification task, to use first RF as a feature selection method. So that with the most important features selected, to apply again a RF, but as a classifier. What would be the drawbacks to make this methodology? Is it a correct one? I have been searching some theory, and it seems it can be done, but I do not know if it is advisable to use this model. Any help or explanation about this issue would be great.

Thanks

• I don't see any problem with that except you always have to make a subjective choice about the cutoff for 'inclusion' if you want to select features from the variable importance measures. Why don't you use a LASSO first to select the variables and then apply random forest if you want? – Blain Waan Jan 26 '20 at 1:45

• If you use the bootstrap to compute confidence intervals for the importance measures you'll be shocked how little information they provide unless $N >> p$. – Frank Harrell Jan 28 '20 at 14:33