在可解释模型选择中捕捉非线性效应的SplitWise回归
Marcell T Kurbucz1, Nikolaos Tzivanakis2, Nilufer Sari Aslam2
1Institute for Global Prosperity, The Bartlett, University College London, 9-11 Endsleigh Gardens, London, WC1H 0EH, UK. m.kurbucz@ucl.ac.uk.
Scientific reports
|November 27, 2025
概括
SplitWise是一个新的回归框架,它平衡了模型的可解释性与非线性关系. 它适应性地转换预测器,提高准确性,同时保持透明,线性模型.
科学领域:
- 统计 统计 统计 统计
- 机器学习 机器学习
- 数据科学数据科学数据科学
背景情况:
- 回归建模经常努力平衡捕捉非线性关系与保持模型可解释性.
- 经典的线性模型提供透明度,但不能有效地模拟复杂的非线性模式.
- 现有的可解释的非线性方法可能缺乏灵活性或引入复杂性.
研究的目的:
- 介绍SplitWise,这是一种新的逐步回归框架,旨在弥合线性可解释性和非线性灵活性之间的差距.
- 开发一种方法,适应性地将数值预测器转换为二进制特征,只有在有利于模型合适的情况下.
- 提供一种回归方法,在捕捉基于值的非线性效应的同时,仍然可以解释和验证.
主要方法:
- SplitWise采用浅层决策树来识别将数值预测器转换为二进制特征的最佳值.
- 模型合适性是使用Akaike信息标准 (AIC) 或贝叶斯信息标准 (BIC) 来评估的,以指导预测器转换.
- 该框架将这些二进制特征集成到全球线性方程中,保持整体模型透明度.
主要成果:
- 在合成数据上,与线性基线相比,SplitWise将中位数根平均平方误差 (RMSE) 降低了7-14%.
- 变量选择精度得到了改进,马修斯相关系数 (MCC) 达到0.79比LASSO的0.51.
- 在真实数据集 (葡萄酒质量,体脂) 上,SplitWise 实现了与其他方法相比较少选择预测因素的可比或改进的 RMSE.
结论:
- SplitWise提供了一个可解释但灵活的回归建模方法,有效地捕获基于值的非线性.
- 与传统的线性模型和一些先进的方法相比,该框架提高了预测准确性和变量选择.
- 对于寻求具有增强预测能力的透明模型的研究人员和从业人员来说,SplitWise是一个有价值的工具.
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