提出新方法CLIQUE，更精准刻画局部变量重要性。
Conditional Local Importance by Quantile Expectations
- 基于分位数期望构建模型无关的局部重要性度量
- 在多分类任务中稳定表现，能识别变量不变区域
- 适合需要精细解释的复杂模型分析场景
全局变量重要性常用于解释机器学习模型结果，而局部变量重要性技术则评估变量对单个观测的贡献。现有主流方法如LIME和SHAP虽能提供预测空间中的特征贡献度量，但在模型损失空间的局部结构刻画上仍有不足，且不直接适用于多分类问题。本文提出一种新的模型无关方法CLIQUE，可突出局部依赖关系，相比扰动法更具稳定性，并可直接应用于多分类任务。模拟与真实数据案例显示，CLIQUE能强调局部依赖信息，捕捉相关性无法反映的交互行为，并在响应对变量变化无感的区域赋予零重要性。
原文摘要 · Abstract (English)
Global variable importance measures are commonly used to interpret the results of machine learning models. Local variable importance techniques assess how variables contribute to individual observations. Current, popular methods, including LIME and SHAP, provide useful measures of feature contribution in the prediction space, while leaving opportunities for improved characterization of local structure in the model loss space. Additionally, they are not natively adapted for multi-class classification problems. We propose a new model-agnostic method for calculating local variable importance, CLIQUE, that highlights locally dependent relationships, provides improved stability over permutation-based methods, and can be directly applied to multi-class classification problems. Simulated and real-world examples show that CLIQUE emphasizes locally dependent information, captures interaction behavior beyond what can be evaluated by correlations, and assigns zero importance in regions where the response is invariant to changes in variables.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。