# Feature selection and parameter tuning with caret for random forest

I have data with a few thousand features and I want to do recursive feature selection (RFE) to remove uninformative ones. I do this with caret and RFE. However, I started thinking, if I want to get the best regression fit (random forest, for example), when should I perform parameter tuning (mtry for RF)? That is, as I understand caret trains RF repeatedly on different feature subsets with a fixed mtry. I suppose the optimum mtry should be found after the feature selection is finished, but will the mtry value that caret uses influence the selected subset of features? Using caret with low mtry is much faster, of course.

Hope someone can explain this to me.

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 RF has a robust built-in feature selection - no need to use RFE so one can just tune mtry and be done with it. – Yevgeny Sep 5 '12 at 21:42