数据策划和信心水平对使用机器学习模型的复合预测的影响
Elena Xerxa1,2, Martin Vogt1,2, Jürgen Bajorath1,2,3
1B-IT, Department of Life Science Informatics and Data Science, Rheinische Friedrich-Wilhelms-Universität, Friedrich-Hirzebruch-Allee 5/6, Bonn D-53115, Germany.
Journal of chemical information and modeling
|December 10, 2024
概括
数据策划显著提高了机器学习 (ML) 模型在化学中的性能. 对化学数据进行顺序处理,通过完善数据质量和化学空间分离,逐步提高分类准确性.
科学领域:
- 化学 化学 化学
- 数据科学数据科学数据科学
- 机器学习 机器学习
背景情况:
- 数据策划在数据科学中至关重要,但在化学机器学习中经常被忽视.
- 评估数据策划对分子机器学习 (ML) 模型的影响至关重要.
研究的目的:
- 评估数据策划对分子ML模型性能的影响.
- 开发和评估化合物和活性数据的顺序治理方案.
主要方法:
- 为化学化合物和活性数据开发了一种连续的固化方案.
- 机器学习分类模型是在不断增加的数据置信度水平时生成的.
- 在不同的数据策划级别中评估了模型性能.
主要成果:
- 通过顺序的数据策划,观察到分类性能有系统和渐进的增加.
- 数据处理增强了化学空间中具有不同类别标签的化合物的分离.
- 取消单元,而不是模拟序列,主要推动了化学空间分离的改善.
结论:
- 严格的数据策划直接导致化学应用中ML模型的性能提高.
- 在开发和评估化学ML模型时,应仔细考虑不同的数据处理和置信级别.
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