Survival Forest for Left-Truncated Right-Censored Data
Vincent Laurent, Olivier Vo Van
⚠ This is a preprint. It may change before it is accepted for publication.


The estimation of the lifetime of an industrial equipment or a patient is often based on censored data, because the event of interest is observed only for a subsample of observations. The use of the Random Forest algorithm applied to industrial data is relevant because the algorithm presents robust performances in many applications. Coupled with survival approaches, it can produce time trajectories for each subset of the feature space and thus differentiate observed objects with respect to their lifetimes. Our work aims to generalize the existing tree-based approach CART applied to left-truncated right-censored data to obtain a Random Forest algorithm. We provide a simple API to use such algorithm as well as tools to validate a temporal score against censored data.