一切都有它的价格:成本敏感机器学习的基础及其在心理学中的应用
Philipp Sterner1, David Goretzko1, Florian Pargent1
1Department Psychology, LMU Munich.
Psychological methods
|August 10, 2023
概括
成本敏感的机器学习 (CSL) 方法减少了心理学中的错误分类成本. 将CSL应用于心理数据,如药物消费,与标准机器学习算法相比,显著降低了成本.
科学领域:
- 心理学 心理学 心理学
- 计算机科学 计算机科学
- 机器学习 机器学习
背景情况:
- 机器学习 (ML) 在心理学中越来越多地用于二进制分类任务.
- 标准的ML算法通常假定错误分类成本相等,这在心理学研究中往往是不现实的.
- 具有成本敏感性的机器学习 (CSL) 通过结合差异错误分类成本提供了一个解决方案.
研究的目的:
- 介绍CSL方法的数学基础.
- 介绍一个常见的CSL技术的分类法.
- 为了证明CSL在心理数据上的实际应用和好处.
主要方法:
- 关于CSL原则的数学表述.
- 将常见的CSL方法分类为一个分类学.
- 在UCI药物消费数据集 (N=1885) 上应用和对比CSL算法.
- 在R中使用mlr3框架进行实现.
主要成果:
- 与标准ML算法相比,所有评估的CSL方法都显示了平均错误分类成本的显著降低.
- 在处理与心理应用相关的不平等错误分类成本方面,CSL的方法被证明是有效的.
结论:
- 与标准ML相比,CSL方法在减少心理研究中的错误分类成本方面具有显著的优势.
- 鼓励研究人员对其特定应用进行CSL方法的基准比较.
- 提供开源R代码,以促进在mlr3框架内采用CSL.
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