论文标题
评分模型及其实际使用的二阶准确度指标
Second-order accuracy metrics for scoring models and their practical use
论文作者
论文摘要
本文提出了用于评分或评分模型的新的二阶准确度指标，这些指标显示了模型的目标偏好，最好诊断好物体或更好地诊断不良的对象，以通过被称为GINI INDEX的一阶度量确定的常数公认的预测能力来诊断不良的对象。有两个指标，它们既有整体表示，又有数值。指标的数值表示为两种类型，第一种是基于二进制事件来评估模型的，第二个是基于模型给出的默认概率。比较计算指标的结果使您可以验证评分或评分模型的校准设置，并揭示其失真。本文提供了计算几个评级机构评级的二阶准确度指标的示例，以及基于Van der Burg的ROC曲线的众所周知的校准方法。
The paper proposes new second-order accuracy metrics for scoring or rating models, which show the target preference of the model, it is better to diagnose good objects or better to diagnose bad ones for a constant generally accepted predictive power determined by the first order metric that is known as the Gini index. There are two metrics, they have both an integral representation and a numerical one. The numerical representation of metrics is of two types, the first of which is based on binary events to evaluate the model, the second on the default probability given by the model. Comparison of the results of calculating the metrics allows you to validate the calibration settings of the scoring or rating model and reveals its distortions. The article provides examples of calculating second-order accuracy metrics for ratings of several rating agencies, as well as for the well known approach to calibration based on van der Burg's ROC curves.
