将偏依赖图和置换特征重要性与数据生成过程联系起来
Relating the Partial Dependence Plot and Permutation Feature Importance to the Data Generating Process
AI总结：
本文将PD与PFI形式化为数据生成过程中真实待估量的统计估计量，分析偏差、模型方差和蒙特卡洛误差，并提出基于重拟合的learner-PD、learner-PFI及校正推断方法。
摘要：
科学家和从业者越来越依赖机器学习来对数据建模并得出结论。与统计建模方法相比，机器学习对线性等数据结构作出的显式假设更少。然而，其模型参数通常难以与数据生成过程联系起来。为了解所建模的关系，偏依赖（PD）图和置换特征重要性（PFI）常被用作解释方法。但是，PD和PFI缺乏将其与数据生成过程联系起来的理论。我们将PD和PFI形式化为根源于数据生成过程的真实待估量的统计估计量。我们表明，由于统计偏差、模型方差和蒙特卡洛近似误差，PD和PFI估计会偏离这一真实值。为在PD和PFI估计中考虑模型方差，我们提出基于模型重新拟合的learner-PD和learner-PFI，并提出经校正的方差和置信区间估计量。
英文摘要：
Scientists and practitioners increasingly rely on machine learning to model data and draw conclusions. Compared to statistical modeling approaches, machine learning makes fewer explicit assumptions about data structures, such as linearity. However, their model parameters usually cannot be easily related to the data generating process. To learn about the modeled relationships, partial dependence (PD) plots and permutation feature importance (PFI) are often used as interpretation methods. However, PD and PFI lack a theory that relates them to the data generating process. We formalize PD and PFI as statistical estimators of ground truth estimands rooted in the data generating process. We show that PD and PFI estimates deviate from this ground truth due to statistical biases, model variance and Monte Carlo approximation errors. To account for model variance in PD and PFI estimation, we propose the learner-PD and the learner-PFI based on model refits, and propose corrected variance and confidence interval estimators.
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。
