标题： 将全局AUC分解为聚类级贡献以进行局部模型诊断 显示英文标题

标题： Decomposing Global AUC into Cluster-Level Contributions for Localized Model Diagnostics

摘要： ROC曲线下的面积（AUC）是二元分类器常用的性能指标。 然而，作为全局排名统计量，AUC在整个数据集上汇总了模型的行为，掩盖了特定子种群中的局部弱点。 在信用审批和欺诈检测等高风险应用中，这些弱点可能导致财务风险或操作失败。 在本文中，我们引入了全局AUC的正式分解，分为集群内和集群间成分。 这使得从业者能够评估数据集群内部和跨集群的分类器性能，从而实现细粒度的诊断和子群分析。 我们还将AUC与Brier分数和对数损失等加性性能指标进行比较，这些指标支持可分解性和直接归因。 我们的框架通过提供额外的见解来增强模型开发和验证实践，以检测模型弱点用于模型风险管理。 显示英文摘要

摘要： The Area Under the ROC Curve (AUC) is a widely used performance metric for binary classifiers. However, as a global ranking statistic, the AUC aggregates model behavior over the entire dataset, masking localized weaknesses in specific subpopulations. In high-stakes applications such as credit approval and fraud detection, these weaknesses can lead to financial risk or operational failures. In this paper, we introduce a formal decomposition of global AUC into intra- and inter-cluster components. This allows practitioners to evaluate classifier performance within and across clusters of data, enabling granular diagnostics and subgroup analysis. We also compare the AUC with additive performance metrics such as the Brier score and log loss, which support decomposability and direct attribution. Our framework enhances model development and validation practice by providing additional insights to detect model weakness for model risk management.