马修斯相关系数 (MCC),流行率值和福尔克斯-马洛斯指数之间的统计比较
Davide Chicco1, Giuseppe Jurman2
1University of Toronto, Canada.
Journal of biomedical informatics
|June 23, 2023
概括
马修斯相关系数 (MCC) 在评估二进制分类时通常是优越的. 这项研究表明,当数据元素具有同等权重时,MCC比流行率值 (PT) 和Fowlkes-Mallows指数更具信息性.
科学领域:
- 统计 统计 统计 统计
- 机器学习 机器学习
- 数据科学数据科学数据科学
背景情况:
- 评估二进制分类在科学研究中至关重要,但对于混矩阵的单一总结统计数据的共识仍然难以达成.
- 之前的工作突出了马修斯相关系数 (MCC) 与其他各种指标 (如精度,F1得分和科恩的kappa) 相比的优势.
- 对于强大的指标的需求在各种科学学科中是显而易见的.
研究的目的:
- 为了比较马修斯相关系数 (MCC) 与流行率值 (PT) 和福尔克斯-马洛斯指数.
- 调查这三个统计指标之间的相互关系.
- 确定MCC为二进制分类评估提供优质信息的条件.
主要方法:
- 对二进制分类的统计指标进行比较分析.
- 研究马修斯相关系数 (MCC),流行率值 (PT) 和福尔克斯-马洛斯指数之间的相互关系.
- 使用科学研究中的相关用例进行评估.
主要成果:
- 马修斯相关系数 (MCC) 与患病率值 (PT) 和福尔克斯-马洛斯指数进行了比较.
- 分析了这三个指标之间的相互关系.
- 结果表明,当正数和负数数据元素具有同等重要性时,MCC可以比PT和Fowlkes-Mallows指数更具信息性.
结论:
- 马修斯相关系数 (MCC) 在评估二进制分类方面具有显著的优势,特别是在考虑类平衡时.
- 与流行率值 (PT) 和Fowlkes-Mallows指数相比,MCC在具有相同数据元素重要性的特定条件下提供了更全面的信息.
- 这些发现支持MCC作为科学研究中二进制分类任务的首选指标的更广泛采用.
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