  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.
