  We propose a modification that corrects for split-improvement variable
importance measures in Random Forests and other tree-based methods. These
methods have been shown to be biased towards increasing the importance of
features with more potential splits. We show that by appropriately
incorporating split-improvement as measured on out of sample data, this bias
can be corrected yielding better summaries and screening tools.
