Interpretable Survival Gradient Boosting Models with Bagged Trees Base Learners

Abstract

In this paper we present a novel survival analysis modeling approach based on gradient boosting using bagged trees as base learners. The resulting models consist of additive components of single variable models and their pairwise interactions, which makes them visually interpretable. We show that our method produces competitive results often having the predictive power higher than full-complexity models. This is achieved while maintaining full interpretability of the model, which makes our method useful in medical applications.