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机器学习在营养研究中的最佳实践和常见陷
1Department of Twin Research & Genetic Epidemiology, King's College London, London, UK.
The Proceedings of the Nutrition Society
|December 6, 2024
概括
本综述指导营养研究人员对机器学习 (ML) 的最佳实践. 它强调了关键的概念和陷,以确保强大的研究设计和ML应用中的有效结果.
科学领域:
- 营养研究 营养研究
- 计算生物学是一种计算生物学.
- 数据科学是数据科学.
背景情况:
- 机器学习 (ML) 在营养研究中越来越多地用于复杂数据分析.
- 对机器学习的热情有时超过了理解,导致设计不稳固,结果可疑.
- 确保高的研究标准需要研究界对ML概念有明确的理解.
研究的目的:
- 提高对机器学习在营养研究中的理解.
- 概述ML过程中的最佳实践和常见陷.
- 促进采用可靠的ML方法论,以获得可靠的发现.
主要方法:
- 审查机器学习概念和应用在营养中的应用.
- 在ML工作流程中识别关键步骤.
- 在ML实施中分析共同的挑战和潜在的解决方案.
主要成果:
- 强调高质量的数据生成和强大的验证技术.
- 量化结果稳定性和准确解释ML输出的重要性.
- 需要适当的方法描述和报告透明度.
结论:
- 遵守概述的最佳实践将提高ML在营养研究中的可靠性.
- 减少错误发现和加强基于ML的研究的批判性评估是预期的结果.
- 促进机器学习在科学研究中的有效和负责任的使用.
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