从点到概率梯度提升,用于索赔频率和严重性预测
Dominik Chevalier1, Marie-Pier Côté1
1École d'actuariat, Université Laval, 2425, rue de l'Agriculture, Québec, Qc G1V 0A6 Canada.
European actuarial journal
|November 3, 2025
概括
渐变增强决策树算法提供了卓越的精算预测. 这项研究统一和比较了11个算法,发现LightGBM和XGBoostLSS在效率方面表现出色,而CatBoost和EGBM显示出强大的预测性能.
科学领域:
- 精算科学 精算科学
- 机器学习 机器学习
- 数据科学数据科学数据科学
背景情况:
- 渐变增强决策树算法在精算应用中越来越受欢迎,因为它们的预测性能优于传统的通用线性模型.
- 已经开发了大量对初始梯度增强机器算法的改进.
研究的目的:
- 为决策树算法提供统一的符号和对比现有的点和概率梯度提升.
- 在精算数据集上对这些算法进行全面的数值比较.
主要方法:
- 这是一项对11个梯度增强算法的比较数值研究:GBM,XGBoost,DART,LightGBM,CatBoost,EGBM,PGBM,XGBoostLSS,循环GBM和NGBoost.
- 评估了五个公共数据集的索赔频率和严重程度,考虑了变量大小和高卡丁级分类变量.
- 分析计算效率,预测性能和模型充分性,包括处理频率模型中的不同风险暴露.
主要成果:
- 轻GBM和XGBoostLSS展示了卓越的计算效率.
- CatBoost 显示出更好的预测性能,特别是在高卡丁度的分类变量中.
- 可解释的 EGBM 实现了与黑盒模型相比具有竞争力的预测性能.
结论:
- 在梯度增强算法中,模型充分性和预测准确性之间没有权衡;两者可以同时实现.
- 算法选择对计算效率和预测性能产生影响,特别是在精算建模中的分类变量处理方面.
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