校准机器学习方法用于概率估计:一个全面的比较
Francisco M Ojeda1,2, Max L Jansen3, Alexandre Thiéry3
1Department of Cardiology, University Heart and Vascular Center Hamburg, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
Statistics in medicine
|October 18, 2023
概括
统计预测模型需要对新种群进行校准. 基于回归的方法,特别是对变换概率的逻辑和β校准,为准确的概率估计提供了最佳性能.
科学领域:
- 统计 统计 统计 统计
- 机器学习 机器学习
- 生物统计学 生物统计学
背景情况:
- 统计预测模型越来越多地用于研究.
- 将模型转移到新的群体中,由于结构上的差异,存在挑战.
- 校准技术使模型适应目标人群,但有许多方法存在.
研究的目的:
- 系统地评估两类概率估计的流行的校准方法.
- 基于经验性质,可概括性和软件可用性的校准方法进行比较.
主要方法:
- 对校准技术的文献审查.
- 综合模拟研究,比较各种校准方法.
- 现实数据分析,提供了可用于实际应用的代码.
主要成果:
- 后勤校准和β校准在模拟中表现出卓越的性能.
- 对逻辑转换概率估计的校准通常优于非转换方法.
- 建议基于回归的校准,至少有一个斜率和一个截止点,以更新概率估计.
结论:
- 基于回归的校准,特别是使用估计斜率的转换概率,对于更新验证研究中的概率估计是有效的.
- 在重新估计整个模型与校准之间的选择取决于结构差异和验证数据样本大小.
- 这项研究为研究人员的现实应用提供了实用代码.
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