改革:基于机器学习的科学共识的建议
Sayash Kapoor1,2, Emily M Cantrell3,4, Kenny Peng5
1Department of Computer Science, Princeton University, Princeton, NJ 08544, USA.
Science advances
|May 1, 2024
概括
机器学习 (ML) 的有效性和可重现性的失败在科学中很常见. REFORMS检查清单为基于ML的科学进行和报告提供了明确的指导方针,以提高严谨性和可信度.
科学领域:
- 机器学习在科学研究中的跨学科应用.
背景情况:
- 机器学习 (ML) 方法在科学中的广泛采用受到有效性,可重现性和可概括性的频繁失败的阻碍.
- 这些失败阻碍了科学进步,促进了对错误发现的共识,并损害了基于机器学习的研究的可信度.
- 在各种科学学科中观察到ML应用失败的一致模式.
研究的目的:
- 为科学研究中机器学习的严格应用和透明报告提供可操作的建议.
- 解决各种领域基于ML的科学研究中遇到的常见陷.
主要方法:
- 基于广泛的文献审查,制定了REFORMS检查清单 (基于机器学习的科学建议).
- 达成共识的过程涉及计算机科学,数据科学,数学,社会科学和生物医学科学领域的19名研究人员.
- 检查表包括32个问题和附带的指导方针.
主要成果:
- REFORMS检查清单提供了一种结构化的方法,以提高基于机器学习的科学研究质量.
- 这些指南旨在提高ML应用的有效性,可重复性和通用性.
- 检查清单是通过严格的,跨学科的共识过程来制定的.
结论:
- REFORMS检查清单是研究人员设计和执行研究的重要资源.
- 它有助于同行评审者评估基于ML的研究的方法论健全性.
- 期刊可以利用REFORMS在科学出版物中强制执行更高的透明度和可重复性标准.
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