In this paper, we consider a perturbation-based metric of predictive
faithfulness of feature rankings (or attributions) that we call PGI squared.
When applied to decision tree-based regression models, the metric can be
computed accurately and efficiently for arbitrary independent feature
perturbation distributions. In particular, the computation does not involve
Monte Carlo sampling that has been typically used for computing similar metrics
and which is inherently prone to inaccuracies. Moreover, we propose a method of
ranking features by their importance for the tree model's predictions based on
PGI squared. Our experiments indicate that in some respects, the method may
identify the globally important features better than the state-of-the-art SHAP
explainer

本文提出了一种基于扰动的预测忠实度度量方法，称为 PGI 平方，在决策树回归模型中可以准确高效地计算任意独立特征扰动分布的度量。同时，根据 PGI 平方，我们提出了一种根据特征重要性对树模型预测进行排名的方法，实验结果表明，在某些方面，该方法可能会更好地识别全局重要特征，超越了现有的 SHAP 解释器。