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），其中变系数采用Delong等人（2023）提出的循环梯度提升机（CGBM）进行建模。利用CGBM对系数函数进行建模可实现逐维早停和特征重要性评分。逐维早停不仅降低了维度特定过拟合的风险，还揭示了各维度间模型复杂度的差异。特征重要性评分的应用简化了特征选择过程，并提升了模型的可解释性。该模型在与Richman和Würthrich（2023）相同的模拟与真实数据实例上进行评估，结果表明其在样本外损失方面的表现与基于神经网络的变系数模型（LocalGLMnet）相当。