为了信用评分的目的,对分类模型进行多标准评估的框架
Paweł Ziemba1, Jarosław Becker2, Aneta Becker3
1Institute of Management, University of Szczecin, Szczecin, Poland.
概括
本研究引入了使用多标准决策 (MCDM) 评估信用评分分类模型的新框架. 使用较少变量的物流回归模型实现了最佳性能,与专家评估保持一致.
科学领域:
- 计算统计的计算统计.
- 机器学习在金融中的应用.
- 决策科学科学 决策科学
背景情况:
- 选择最佳的信用评分分类模型是一个巨大的挑战,因为许多参数组合.
- 现有的评估方法往往缺乏跨不同数据集和时间段的一致性检查.
研究的目的:
- 开发和验证一个新的框架,用于专门用于信用评分的分类模型的多标准评估.
- 将培训/验证套件和不同时间段的一致性指标纳入模型评估中.
主要方法:
- 使用多标准决策 (MCDM) 方法,PROSA (可持续性分析的PROMETHEE),用于评估分类模型.
- 在培训和验证集以及不同时间段的数据中评估模型的一致性.
- 对比了两个聚合场景:时间段,子标准,标准 (TSC) 和子标准,标准,时间段 (SCT).
主要成果:
- 基于PROSA的框架成功评估了分类模型,提供了对结果一致性的见解.
- 具有有限数量的预测变量的物流回归模型在性能方面始终排名最高.
- 无论是TSC和SCT聚合场景,对于分类模型评估,都产生了非常相似的结果.
结论:
- 开发的MCDM框架为信用评分模型的多标准评估提供了一个强大的方法.
- 这些发现表明,更简单的物流回归模型对于信用评分具有高度有效性和可靠性.
- 该框架的结果与专家团队的评估强烈一致,证实了其实际适用性.
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