Global variable importance measures are commonly used to interpret machine learning model results. Local variable importance techniques assess how variables contribute to individual observations rather than the entire dataset. Current methods typically fail to accurately reflect locally dependent relationships between variables and instead focus on marginal importance values. 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 captures locally dependent relationships, contains improvements 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 and properly reduces bias in regions where variables do not affect the response.

翻译：全局变量重要性度量常用于解释机器学习模型结果。局部变量重要性技术则评估变量如何影响单个观测而非整个数据集。现有方法通常无法准确反映变量间的局部依赖关系，而侧重于边际重要性值。此外，这些方法本身未适配多类别分类问题。我们提出一种新的模型无关方法CLIQUE，用于计算局部变量重要性。该方法能够捕捉局部依赖关系，在基于置换的方法基础上有所改进，并可直接应用于多类别分类问题。模拟和实际案例表明，CLIQUE能够强调局部依赖信息，并在变量不影响响应的区域有效减少偏差。