在化学机器学习中描述不确定性
Esther Heid1,2, Charles J McGill1,3, Florence H Vermeire1,4
1Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States.
Journal of chemical information and modeling
|June 20, 2023
概括
了解机器学习的不确定性是可靠人工智能的关键. 这项研究将数据噪声 (aleatoric) 与模型限制 (epistemic) 分开,以改善化学性质预测.
科学领域:
- 计算化学是一种计算化学.
- 机器学习可靠性 机器学习可靠性
- 科学中的人工智能.
背景情况:
- 在机器学习 (ML) 模型中描述不确定性对于可靠性,稳定性,安全性和主动学习至关重要.
- 总的不确定性可以分解为 aleatoric (数据噪声) 和 epistemic (模型缺陷) 的贡献.
- 认识不确定性可以进一步划分为模型偏差和差异.
研究的目的:
- 系统地分析噪声,模型偏差和模型偏差对化学性质预测的影响.
- 在各种化学空间中识别不同的预测错误来源.
- 根据其不确定性背景,制定指导方针来改进表现不佳的ML模型.
主要方法:
- 总不确定性的分解成 aleatoric 和 epistemic 组件 (偏差和变异).
- 对分子性质数据集进行受控实验.
- 对有关数据噪声,数据集大小,模型架构,分子表示,集合大小和数据分割的ML模型性能进行分析.
主要成果:
- 测试组中的噪音可以掩盖模型的真实性能.
- 规模广泛的模型聚合对于预测广泛的属性至关重要.
- 组合有效量化并改善模型方差不确定性.
结论:
- 不同的预测错误来源以不同的方式对ML模型产生重大影响,需要量身定制的解决方案.
- 了解和解决特定的不确定性贡献对于开发可靠的化学性质预测模型至关重要.
- 在各种不确定性环境中提供了模型改进的一般指导方针.
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