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.

中文翻译：

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基于树的方法中特征重要性的无偏测量

我们提出了一种修改，以纠正随机森林和其他基于树的方法中的拆分改进变量重要性度量。这些方法已被证明偏向于增加具有更多潜在分裂的特征的重要性。我们表明，通过适当地结合对样本外数据进行测量的拆分改进，可以纠正这种偏差，从而产生更好的摘要和筛选工具。