Comparer des méthodes

	Forêt aléatoire de survie	Estimateur de risque cumulé de Nelson-Aalen
Domaine	Analyse de survie	Analyse de survie
Famille	Survival analysis	Survival analysis
Année d'origine	2008	1972
Auteur d'origine	Ishwaran, H., Kogalur, U.B., Blackstone, E.H. & Lauer, M.S.	Wayne Nelson & Odd Aalen
Type	Ensemble machine learning survival model	Non-parametric cumulative hazard estimator
Source fondatrice	Ishwaran, H., Kogalur, U.B., Blackstone, E.H. & Lauer, M.S. (2008). Random Survival Forests. Annals of Applied Statistics, 2(3), 841–860.	Nelson, W. (1972). Theory and applications of hazard plotting for censored failure data. Technometrics, 14(4), 945–966.
Alias	RSF, Rastgele Sağkalım Ormanı (RSF), survival random forest	Nelson-Aalen cumulative hazard, Aalen estimator, empirical cumulative hazard, Nelson-Aalen kümülatif hazard tahmincisi
Apparentées	2	5
Résumé	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.	The Nelson-Aalen estimator is a non-parametric estimator of the cumulative hazard function from right-censored time-to-event data. Developed by Wayne Nelson for reliability hazard plotting in 1972 and placed on a rigorous counting-process foundation by Odd Aalen in 1978, it accumulates the ratio of observed events to the number at risk at each event time, providing the natural hazard-scale companion to the Kaplan-Meier survival curve.