关于在假设-演框架下利用机器学习在体育科学中
Jordan Rodu1, Alexandra F DeJong Lempke2, Natalie Kupperman3
1Department of Statistics, University of Virginia, Charlottesville, VA, USA. jsr6q@virginia.edu.
Sports medicine - open
|November 14, 2024
概括
监督机器学习 (ML) 可以增强体育科学研究,但不应该取代统计方法. 仔细整合ML,特别是可解释和可解释的方法,对于避免陷和加强在假设-演框架内的探索性调查至关重要.
科学领域:
- 体育科学 运动科学
- 机器学习 机器学习
- 统计方法 统计方法
背景情况:
- 监督机器学习 (ML) 提供了强大的预测算法,但往往缺乏透明度.
- 可解释的ML和可解释的ML已经出现,以解决ML的"黑子"性质.
- 假设-演框架是科学研究的核心,它依赖于对数据进行假设测试.
研究的目的:
- 检查ML算法和统计方法之间的根本差异.
- 提出监督的ML如何可以增强,而不是取代体育科学中的统计方法.
- 为 ML 谨慎融入科学工作流程提供指导.
主要方法:
- 对监督ML和统计方法进行比较分析.
- 在假设-演框架内检查可解释和可解释的ML.
- 案例研究证明了监督ML在探索性分析中的整合.
主要成果:
- 监督的ML算法和统计模型在动机和方法上有着根本的不同,尽管它们解决了类似的问题 (y = f (x) + ε).
- 虽然透明的ML方法提高了理解,但它们并不等同于统计方法.
- 监督的ML可以在体育科学中进行探索性分析,但需要谨慎的应用.
结论:
- 监督机器学习应该增强,而不是取代体育科学研究中的统计方法.
- 整合ML需要谨慎利用其优势 (例如,复杂的模式适配),同时避免陷 (例如,误导性建议).
- 正确应用,监督的ML可以增强体育科学中的假设-演框架,但滥用镜像统计p值黑客.
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