论文标题
评估和汇总基于功能的模型解释
Evaluating and Aggregating Feature-based Model Explanations
论文作者
论文摘要
基于功能的模型说明表示每个输入功能对给定数据点的模型输出有多大贡献。随着拟议的解释功能的数量的增长，我们缺乏定量评估标准，可以帮助从业者知道何时使用哪种解释功能。本文提出了基于特征的解释的定量评估标准：低灵敏度，忠诚度高和复杂性低。我们设计了一个用于汇总解释功能的框架。我们开发了一种以较低的复杂性来学习汇总解释函数的过程，然后得出一个新的总莎普利价值解释函数，以最大程度地降低灵敏度。
A feature-based model explanation denotes how much each input feature contributes to a model's output for a given data point. As the number of proposed explanation functions grows, we lack quantitative evaluation criteria to help practitioners know when to use which explanation function. This paper proposes quantitative evaluation criteria for feature-based explanations: low sensitivity, high faithfulness, and low complexity. We devise a framework for aggregating explanation functions. We develop a procedure for learning an aggregate explanation function with lower complexity and then derive a new aggregate Shapley value explanation function that minimizes sensitivity.
