我们是调整数据还是调整噪声? 分析药物,材料和分子发现中常用的数据集的预测能力
Daniel Crusius1, Flaviu Cipcigan2, Philip C Biggin1
1Department of Biochemistry, University of Oxford, South Parks Road, Oxford OX1 3QU, UK. philip.biggin@bioch.ox.ac.uk.
Faraday discussions
|September 23, 2024
概括
化学科学中的机器学习模型可能会过度调整噪音数据. 这项研究量化了数据集的局限性,揭示了四种模型超越了极限,可能适应噪声. 提供了工具,以设定对模型性能的现实期望.
科学领域:
- 计算化学和材料科学计算化学和材料科学
- 机器学习在科学发现中的应用.
背景情况:
- 数据驱动的定量结构-属性关系对于材料和分子发现至关重要.
- 深度学习和算法的进步推动了复杂的机器学习 (ML) 模型的开发.
- 实验数据的局限性,包括小数据集大小和实验错误,在ML模型评估中经常被忽视.
研究的目的:
- 量化评估实验数据限制对化学科学中的ML模型性能的影响.
- 为在药物发现,分子发现和材料发现中常用的ML数据集建立现实的性能界限.
- 为了识别可能因数据集限制而过度匹配的ML模型.
主要方法:
- 对用于回归和分类任务的9个常用ML数据集的分析.
- 通过引入基于实验错误的噪声来推导最大和现实的性能极限.
- 推导的性能边界与文献中领先的ML模型报告的性能进行比较.
主要成果:
- 在9个考虑的数据集和ML模型中,有4个达到或超过了数据集性能限制.
- 这些模型可能过度适应数据,可能适应实验噪音而不是真正的潜在关系.
- 系统地检查数据范围,实验误差大小和数据点数量如何影响性能界限.
结论:
- 化学科学中的许多ML模型可能会过度匹配杂的实验数据,导致过高估计的性能.
- 该研究提供了工具 (Python 软件包 NoiseEstimator 和 Web 应用程序) 来计算现实的性能界限.
- 这些资源将帮助研究人员为ML模型的性能设定现实的期望,并指导未来的模型开发.
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