Abstract Varying coefficient models are a flexible extension of generic parametric whose coefficients functions set effect-modifying covariates instead fitted constants. They capable achieving higher model complexity while preserving the structure underlying models, hence generating interpretable predictions. In this paper we study use gradient boosted decision trees as those coefficient-decidi...

data parallelism for a decision tree, 489–492 data set structure, 479–480 decision tree algorithm, 480–481 decision tree classification, 477–480 processes, 480–488 structure, 478–479 result parallelism for the decision tree, 492–495