The paper introduces a tree-based varying coefficient model (VCM) where the varying coefficients are modelled using the cyclic gradient boosting machine (CGBM) from Delong et al. (2023). Modelling the coefficient functions using a CGBM allows for dimension-wise early stopping and feature importance scores. The dimension-wise early stopping not only reduces the risk of dimension-specific overfitting, but also reveals differences in model complexity across dimensions. The use of feature importance scores allows for simple feature selection and easy model interpretation. The model is evaluated on the same simulated and real data examples as those used in Richman and W\"uthrich (2023), and the results show that it produces results in terms of out of sample loss that are comparable to those of their neural network-based VCM called LocalGLMnet.

翻译：本文介绍一种基于树的变系数模型（VCM），其中变系数采用Delong等人（2023）提出的循环梯度提升机（CGBM）进行建模。使用CGBM建模系数函数可实现按维度提前停止和特征重要性评分。按维度提前停止不仅降低了特定维度过拟合的风险，还揭示了不同维度间模型复杂度的差异。特征重要性评分的使用使特征选择和模型解释变得简单。通过Richman与Wüthrich（2023）中相同的模拟数据和真实数据案例进行评估，结果表明：在样本外损失方面，本文模型与基于神经网络的变系数模型LocalGLMnet性能相当。