MLcps:机器学习对分类问题的累积绩效评分
Akshay Akshay1,2, Masoud Abedi3, Navid Shekarchizadeh3,4
1Functional Urology Research Group, Department for BioMedical Research DBMR, University of Bern, 3008 Bern, Switzerland.
GigaScience
|December 13, 2023
概括
通过新的机器学习累积性能得分 (MLcps) 简化了机器学习 (ML) 模型的评估. 这种统一的指标提供了全面的绩效评估,节省了时间,减少了模型选择中的偏差.
科学领域:
- 计算机科学 计算机科学
- 数据科学数据科学数据科学
- 机器学习 机器学习
背景情况:
- 评估机器学习 (ML) 模型性能需要多个评估指标,以获得全面的理解.
- 用于模型选择的个别指标比较是耗时的,容易产生用户偏见.
研究的目的:
- 引入机器学习累积性能得分 (MLcps) 作为一种新的,统一的分类模型评估指标.
- 提供一个整体的方法来评估ML模型的性能,将各种指标整合到一个单一的得分.
主要方法:
- 开发了一种新的指标MLcps,该指标整合了多个预先计算的评估指标.
- 在四个公开可用的数据集上测试了MLcps,用于分类问题.
主要成果:
- MLcps为全面的模型评估提供了统一的分数,突出了优点和弱点.
- 证明了MLcps在数据集中对模型稳定性和整体性能进行整体评估的能力.
结论:
- MLcps简化了模型评估过程,取代了对个别指标进行比较的需要.
- 研究人员和从业人员可以使用单个MLcps值高效地评估ML模型,节省时间和精力.
- 为了更广泛的可访问性,MLcps作为开源的Python包提供.
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