# If random forests gives me a bad cross-validation score, should I trust it for feature selection?

I get an R^2 value of about 0.22 when I 10-fold cross-validate with my entire dataset.

My main use for random forests is to analyze feature interactions. But should I trust the feature importances given, despite it's low R^2 value?

• I'd recommend using a better measure of future prediction error rate, like mean squared prediction error when doing 10-fold CV. $R^2$ is not a good measure. Dec 1 '14 at 0:04