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在预测模型中选择适当的验证策略的重要性. 第2部分: (避免) 过的配方- 一个教程教程
Eneko Lopez1, Giulia Gorla2, Jaione Etxebarria-Elezgarai3
1CIC nanoGUNE BRTA, Tolosa Hiribidea 76, San Sebastián, 20018, Spain; Department of Physics, University of the Basque Country (UPV/EHU), San Sebastián, 20018, Spain.
Analytica chimica acta
|January 9, 2026
概括
预测建模中的过度拟合通常是由不良的验证和数据问题引起的,而不仅仅是复杂性. 这项研究确定了常见的陷,并为可信,可泛化的模型提供了指导方针.
科学领域:
- 机器学习 机器学习
- 预测建模预测建模
- 数据科学数据科学数据科学
背景情况:
- 过度装配是预测建模中的一个重大挑战,导致了糟糕的概括.
- 它经常被错误地归因于模型复杂性,掩盖了其他关键问题.
研究的目的:
- 为了识别超越模型复杂性的过拟合的被忽视的原因.
- 为可靠的验证和可靠的预测模型提供实用指南.
主要方法:
- 分析导致过度装配的常见做法.
- 检查数据泄露和偏见的模型选择.
- 审查出版压力导致过度优化.
主要成果:
- 不充分的验证策略是过度装配的主要驱动因素.
- 数据预处理的缺陷和偏见的选择会增加明显的准确性.
- 出版激励措施可以鼓励以结果为导向的过度优化.
结论:
- 解决验证,预处理和选择偏差对于可靠的模型至关重要.
- 研究人员需要实用的指导方针,以确保模型的可靠性和通用性.
- 这项工作为可重复和强大的预测建模提供了蓝图.
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