Method evidence record

Random Survival Forest

Random Survival Forest (RSF), introduced by Ishwaran, Kogalur, Blackstone, and Lauer in 2008, is an ensemble machine learning method that adapts the Random Forest algorithm to time-to-event (survival) data. Trees are grown using log-rank splitting to handle censored observations naturally, and the ensemble aggregates cumulative hazard functions across hundreds of trees to produce predictions and variable importance rankings.

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Taxonomic method record · survival / survival

Ishwaran, H., Kogalur, U.B., Blackstone, E.H. & Lauer, M.S. (2008). Random Survival Forests. Annals of Applied Statistics, 2(3), 841–860. · DOI 10.1214/08-AOAS169

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Same method familyKaplan-Meiermachine-suggested · Relational suggestion, not evidence.Same method familyNelson-Aalen Estimatormachine-suggested · Relational suggestion, not evidence.