Salīdzināt metodes

	Nejaušais izdzīvošanas mežs	Kaplana-Meiera izdzīvošanas novērtētājs
Nozare	Dzīvildze	Dzīvildze
Saime	Survival analysis	Survival analysis
Izcelsmes gads	2008	1958
Autors	Ishwaran, H., Kogalur, U.B., Blackstone, E.H. & Lauer, M.S.	Kaplan, E. L. & Meier, P.
Tips	Ensemble machine learning survival model	Non-parametric survival estimator
Pirmavots	Ishwaran, H., Kogalur, U.B., Blackstone, E.H. & Lauer, M.S. (2008). Random Survival Forests. Annals of Applied Statistics, 2(3), 841–860.	Kaplan, E. L. & Meier, P. (1958). Nonparametric Estimation from Incomplete Observations. Journal of the American Statistical Association, 53(282), 457–481.
Citi nosaukumi	RSF, Rastgele Sağkalım Ormanı (RSF), survival random forest	product-limit estimator, km curve, kaplan-meier sağkalım analizi
Saistītās	2	2
Kopsavilkums	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 Kaplan-Meier estimator, introduced by Kaplan and Meier in 1958, is a non-parametric method that estimates the survival curve — the probability of remaining event-free over time — from right-censored time-to-event data. The log-rank test is the companion procedure used to compare survival curves between groups.