Random Survival Forest

Random Survival Forests (RSFs) are ensemble machine learning methods designed to predict time-to-event data, a crucial task in fields like healthcare and engineering where data often includes censoring (incomplete observations). Current research focuses on improving RSF performance, particularly through optimization techniques like dynamic programming for optimal tree construction and novel ensemble strategies that outperform standard RSFs. These advancements aim to enhance both the predictive accuracy and interpretability of survival models, leading to more reliable risk assessments and improved decision-making in various applications.