Explaining classifiers with measures of statistical association
提出一类新的概率敏感性度量，量化分类中协变量与目标之间的关联程度，并证明其零独立性性质；引入估计量并证明渐近一致性，用自助法量化不确定性，通过表格、图像和文本数据展示其作为解释工具的效果。
A new class of probabilistic sensitivity measures that quantifies the degree of association between covariates and generic targets used in classification is proposed, and it is shown that such class possesses the zero-independence property. Corresponding estimators are introduced, asymptotic consistency is proven and bootstrap is used to quantify uncertainty in the estimates. The use of the new dependence measures as explanations in a statistical machine learning context is illustrated. The resulting approach, called Xi-method, is demonstrated through applications involving different data formats: tabular, visual and textual.