不平衡的类分布和绩效评估指标:对确定医疗保健系统模型绩效的预测准确性的系统性审查
Michael Owusu-Adjei1, James Ben Hayfron-Acquah1, Twum Frimpong1
1Department of Computer Science, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana.
PLOS digital health
|November 30, 2023
概括
这项研究质疑不平衡数据集的预测准确度得分的可靠性. 它探讨了平衡准确度得分作为评估现实应用中的预测模型的潜在更好的指标.
科学领域:
- 机器学习 机器学习
- 数据科学数据科学数据科学
- 医疗保健分析 医疗保健分析
背景情况:
- 预测算法性能评估对于选择最佳模型至关重要.
- 预测准确度得分被广泛使用,往往忽视了数据集类不平衡.
- 数据集中的阶级不平等可以显著扭曲绩效指标.
研究的目的:
- 调查阶级不平等对预测准确度评分的影响.
- 在不平衡数据集中,将预测准确度得分与平衡准确度得分进行比较.
- 审查现有系统中不平衡数据的评估指标的当前知识.
主要方法:
- 对现有文献进行系统审查.
- 分析使用不平衡类分布数据集的研究.
- 评估预测准确度得分和平衡准确度得分的使用.
主要成果:
- 预测准确度得分可能会误导不平衡的数据集.
- 均衡准确度得分在不平等的类分布中提供了更强大的评估.
- 目前的研究往往会默认地预测准确度得分,尽管有阶级不平衡.
结论:
- 对于不平衡的数据集,重新考虑依赖预测准确度得分是必要的.
- 为了更可靠的模型性能评估,应考虑平衡精度得分.
- 需要进一步的研究,以建立评估不平衡数据模型的最佳实践.
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