随机森林模型的前期变量选择
Jasper Velthoen1, Juan-Juan Cai2, Geurt Jongbloed1
1Department of Applied Mathematics, Delft University of Technology, Delft, The Netherlands.
Journal of applied statistics
|September 18, 2023
概括
本研究引入了一种使用前变量选择和连续排列概率得分 (CRPS) 的新可解释的预测方法. 该方法在高维数据的变量选择中显著减少了假阳性.
科学领域:
- 统计 统计 统计 统计
- 机器学习 机器学习
- 环境科学 环境科学
背景情况:
- 随机森林对于高维数据有效,但缺乏可解释性.
- 可解释的预测模型对于理解复杂的关系至关重要.
研究的目的:
- 为可解释的预测建模开发一个前变量选择方法.
- 为了最大限度地减少持续排名概率得分 (CRPS) 以获得最佳的变量选择.
- 提供一个统计严格的方法来选择相关的共变量.
主要方法:
- 一个逐步推进的选择程序,尽量减少CRPS.
- 一个基于CRPS风险差异估计的停止标准.
- 在人口意义上的最佳性的数学证明.
- 模拟研究将性能与现有方法进行比较.
主要成果:
- 与现有技术相比,拟议的方法实现了较低的错误阳性率.
- 在温度预测的统计后处理中证明有效.
- 在保持预测能力的同时选择了大约10%的共同变量.
结论:
- 开发的方法为高维预测提供了一个可解释的替代方案.
- 它为统计建模中的变量选择提供了一个强大的方法.
- 该方法适用于现实世界的预测问题,提高模型的透明度.
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