Enhancing visual interpretability and explainability in functional survival trees and forests

Functional survival models are key tools for analyzing time-to-event data with complex predictors, such as functional or high-dimensional inputs. Despite their predictive strength, these models often lack interpretability, which limits their value in practical decision-making and risk analysis. This study investigates two key survival models: the functional survival tree (FST) and the functional random survival forests (FRSFs). It introduces novel methods and tools to enhance the interpretability of FST models and improve the explainability of FRSF ensembles. Applications to simulated data and to a real intensive care dataset illustrate that the proposed approaches provide post-hoc graphical and functional summaries that facilitate the interpretation of FSTs and the explanation of forest-level predictions.