接收器操作特征曲线准确评估不平衡的数据集
Eve Richardson1, Raphael Trevizani1,2, Jason A Greenbaum1
1Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, La Jolla, CA, USA.
Patterns (New York, N.Y.)
|July 15, 2024
概括
接收器操作特征 (ROC) 曲线对于评估生物学中不平衡的数据集来说是强大的,这与人们普遍认为的相反. 精度回忆 (PR) 曲线对类不平衡非常敏感,使ROC分析更可靠的"针在禾"问题.
科学领域:
- 机器学习是机器学习.
- 生物信息学是一种生物信息学.
- 计算生物学是一种计算生物学.
背景情况:
- 阶级不平衡在生物数据中很常见,这对预测建模构成了挑战.
- 接收器操作特征 (ROC) 曲线和精度召回 (PR) 曲线是评估二进制分类器的常见指标.
- 关于ROC和PR曲线对于不平衡数据集的适用性存在误解.
研究的目的:
- 为了澄清在阶级失衡下ROC和PR曲线的行为.
- 为了确定ROC分析与PR分析在不平衡的生物数据中的稳定性.
- 调查类失衡与分类器性能指标之间的关系.
主要方法:
- 进行了模拟,以模拟类不平衡场景.
- 分析了一个现实世界的生物学案例研究.
- 使用ROC和PR曲线及其相关曲线下面的区域 (AUC) 来评估性能.
主要成果:
- ROC曲线对类不平衡具有强度,保持可靠的绩效评估.
- PR曲线对类不平衡非常敏感,可能会误导分类器的表现.
- 将分类器性能从PR-AUC中的类不平衡中分离出来是一个挑战.
结论:
- 在生物数据中存在显著的类不平衡的情况下,ROC分析是评估二元分类器的合适和稳健的方法.
- PR曲线分析可能会误导不平衡的数据,需要仔细解释.
- 了解阶级不平衡对评估指标的影响对于准确的生物数据分析至关重要.
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