论文标题
从公正的MDI特征至关重要到可解释的树木
From unbiased MDI Feature Importance to Explainable AI for Trees
论文作者
论文摘要
我们试图对（i）提高基于树模型的解释性和（ii）Debias的各种尝试的各种尝试统一观点，即在随机森林中默认变量符号度量，Gini的重要性。特别是，我们证明了基于面包外的偏置校正方法及其与树木的局部解释的联系之间的共同线程。此外，我们指出了由于在新开发的可解释的树木算法AI中包含进口数据而引起的偏见。
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.
