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.

翻译：将二元概率分类器重新校准至目标先验概率是信用风险管理等领域的一项重要任务。然而，将基于训练数据集学习的分类器重新校准至测试数据集上的目标，通常并非一个明确定义的问题，因为可能存在多种变换原始后验概率以匹配目标的方式。本文从分布偏移的角度分析了重新校准方法。研究发现，与概率分类器曲线下面积（AUC）相关的分布偏移假设对于设计有意义的重新校准方法具有重要作用。本文提出了两种新方法：参数化协变量偏移与后验漂移（CSPD）以及基于ROC的拟矩匹配（QMM），并在示例场景中与其他方法一同进行了测试。测试结果表明，本文讨论的QMM方法在使用凹函数（例如信用风险的风险权重函数）的评估中能够提供适度保守的结果。