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避免常见的机器学习陷
1School of Mathematical and Computer Sciences, Heriot-Watt University, Edinburgh, UK.
Patterns (New York, N.Y.)
|November 21, 2024
概括
常见的机器学习 (ML) 错误破坏了研究的信心. 本教程指导用户避免ML实践,模型构建,评估,比较和报告可靠的学术发现的陷.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 数据科学数据科学数据科学
背景情况:
- 机器学习 (ML) 实践容易出现错误,可能会破坏对ML衍生结果和产品的信任.
- 使用ML的学术研究需要严格的比较和有效的结论,这些领域往往容易犯常见错误.
研究的目的:
- 概述机器学习实践中经常出现的错误.
- 提供有关在ML生命周期中避免这些错误的指导.
- 提高机器学习应用在学术研究中的可靠性和有效性.
主要方法:
- 该教程涵盖了机器学习过程的五个关键阶段.
- 模型前的建筑考虑因素.
- 可靠的模型构建技术.
- 强大的模型评估策略.
- 公平的模型比较方法.
- 有效的结果报告标准.
主要成果:
- 在ML工作流程中识别常见的错误.
- 用于减少模型构建和评估中的错误的策略.
- 确保公平的模型比较的指南.
- 对ML结果的透明和准确报告的最佳实践.
结论:
- 避免常见的错误对于保持对机器学习的信心至关重要.
- 实施概述的实践可以提高学术ML研究的严谨性和有效性.
- 本教程是为研究人员改善他们的ML方法的实用指南.
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