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