##Statistics Seminar##\\ Department of Mathematical Sciences ^ **DATE:**|Thursday, Feb. 17, 2022 | ^ **TIME:**|1:15pm -- 2:15pm | ^ **LOCATION:**|Zoom meeting | ^ **SPEAKER:**|Shaofei Zhao, Binghamton University | ^ **TITLE:**|Unbiased measurement of feature importance in tree-based methods | \\ **Abstract** 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.