# PCA before random forest classification

Does it make sense to do PCA before carrying out a Random Forest Classification?

I'm dealing with high dimensional text data, and I want to do feature reduction to help avoid the curse of dimensionality, but don't Random Forests already to some sort of dimension reduction?

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The RF algorithm does not really suffer from high number of predictors since it only take a random subset of them (so called mtry parameter) to build each tree. There is also a recursive feature elimination technique built on top of the RF algorithm (see the varSelRF R package and references therein). It is, however, certainly possible to add an initial data reduction scheme, although it should be part of the cross-validation process. So the question is: do you want to input a linear combination of your features to RF? –  chl Jan 10 '13 at 21:03