改进二分类概率模型在新数据上的校准方法，提升信用风险评估准确性。
Recalibrating binary probabilistic classifiers
- 从分布偏移视角分析校准问题，提出新方法应对先验概率变化。
- 所提方法在凹函数评估下表现保守，适合信用风险中的权重函数。
- 基于ROC的近似矩匹配法（QMM）在实测中优于传统方法。
将二分类概率模型从训练集校准到测试集的目标先验概率，是信用风险管理等领域的关键任务。然而，由于存在多种方式调整后验概率以匹配目标先验，该问题通常缺乏明确定义。本文从分布偏移角度分析校准方法，发现与分类器的曲线下面积（AUC）相关的假设有助于设计有意义的校准策略。本文提出两种新方法：参数化协变量偏移加后验漂移（CSPD）和基于ROC的近似矩匹配（QMM），并在一个示例设置中与若干其他方法进行对比测试。结果表明，本文提出的QMM方法在使用凹函数（如信用风险中的风险权重函数）评估时，能提供适当保守的结果。
原文摘要 · Abstract (English)
Recalibration of binary probabilistic classifiers to a target prior probability is an important task in areas like credit risk management. However, recalibration of a classifier learned on a training dataset to a target on a test dataset in general is not a well-defined problem because there might be more than one way to transform the original posterior probabilities such that the target is matched. In this paper, methods for recalibration are analysed from a distribution shift perspective. Distribution shift assumptions linked to the area under the curve (AUC) of a probabilistic classifier are found to be useful for the design of meaningful recalibration methods. Two new methods called parametric covariate shift with posterior drift (CSPD) and ROC-based quasi moment matching (QMM) are proposed and tested together with some other methods in an example setting. The outcomes of the test suggest that the QMM methods discussed in the paper can provide appropriately conservative results in evaluations with concave functions like for instance risk weights functions for credit risk.
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