论文标题
重要性的重要性
The Importance of Variable Importance
论文作者
论文摘要
可变重要性定义为对每个回归器对模型拟合的贡献的度量。使用r^2作为线性模型中的拟合标准，将带有沙普利值（LMG）和比例值（PMVD）作为可变重要性度量。使用随机森林作为示例，定义了集合模型的类似措施。比较LMG和PMVD的性质。提出了可变的重要性来评估回归者的实际效果或“ oomph”。讨论了在建模，干预措施和因果分析中的重要性。
Variable importance is defined as a measure of each regressor's contribution to model fit. Using R^2 as the fit criterion in linear models leads to the Shapley value (LMG) and proportionate value (PMVD) as variable importance measures. Similar measures are defined for ensemble models, using random forests as the example. The properties of the LMG and PMVD are compared. Variable importance is proposed to assess regressors' practical effects or "oomph." The uses of variable importance in modelling, interventions and causal analysis are discussed.
