From unbiased MDI Feature Importance to Explainable AI for Trees
Berlin School of Economics and Law（柏林经济与法律学院）
英文摘要：
We attempt to give a unifying view of the various recent attempts to (i) improve the interpretability of tree-based models and (ii) debias the the default variable-importance measure in random Forests, Gini importance. In particular, we demonstrate a common thread among the out-of-bag based bias correction methods and their connection to local explanation for trees. In addition, we point out a bias caused by the inclusion of inbag data in the newly developed explainable AI for trees algorithms.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
