调整机器学习模型超参数的方法比较:与预测高需求高成本医疗保健用户的应用
Christopher Meaney1, Xuesong Wang2, Jun Guan2
1Department of Family and Community Medicine, University of Toronto, Toronto, Canada. christopher.meaney@utoronto.ca.
BMC medical research methodology
|May 15, 2025
概括
超参数优化方法改善了极端梯度增强模型,用于预测高需要,高成本的患者. 所有测试的方法都产生了类似的性能增长,这表明在各种调方法中具有稳定性.
科学领域:
- 机器学习 机器学习
- 医疗信息学 医疗信息学
- 预测建模预测建模
背景情况:
- 监督机器学习对于临床预测模型至关重要.
- 有效的超参数优化 (HPO) 对模型性能至关重要.
研究的目的:
- 比较九种超参数优化 (HPO) 方法.
- 调整极端梯度增强 (EGB) 模型,以预测高需要,高成本 (HNHC) 医疗保健用户.
主要方法:
- 使用九种HPO方法 (随机抽样,贝叶斯优化变体,进化策略) 训练了EGB模型.
- 在验证和测试数据集上评估了每种HPO方法的100个EGB模型.
- 使用歧视 (AUC) 和校准指标评估概括性能.
主要成果:
- 默认的EGB模型显示了中度的歧视 (AUC=0.82),但校准不佳.
- 所有HPO方法都提高了分辨率 (AUC=0.84) 和改进了校准.
- 对于HNHC用户来说,HPO始终确定了关键特征.
结论:
- 所有测试的HPO算法都比基线模型提供了可比的改进.
- 这一发现很可能是由于数据集的大小,特征少,信号与噪声比强.
- 结果可以概括为具有相似特征的数据集.
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