This paper introduces a novel methodology for constructing multiclass ROC curves using the multidimensional Gini index. The proposed methodology leverages the established relationship between the Gini coefficient and the ROC Curve and extends it to multiclass settings through the multidimensional Gini index. The framework is validated by means of two comprehensive case studies in health care and finance. The paper provides a theoretically grounded solution to multiclass performance evaluation, particularly valuable for imbalanced datasets, for which a prudential assessment should take precedence over class frequency considerations.
翻译:本文提出了一种利用多维基尼指数构建多类别ROC曲线的新方法。该方法基于基尼系数与ROC曲线之间的既定关系,通过多维基尼指数将其扩展至多类别场景。该框架通过医疗保健和金融领域的两个综合案例研究得到验证。本文为多类别性能评估提供了理论依据充分的解决方案,尤其适用于不平衡数据集——对于此类数据,审慎评估应优先于类别频率的考量。