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：本文介绍了一种基于树的变系数模型（VCM），其中变系数通过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逐维度早停和特征重要性评分。逐维度早停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。特征重要性评分的运用便于进行简单的特征选择与模型解释。该模型在与Richman和Wüthrich（2023）相同的模拟和真实数据实例上进行了评估，结果表明其在样本外损失方面取得了与基于神经网络的变系数模型LocalGLMnet相当的效果。