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| Random Survival Forest× | Estymator przeżycia Kaplana-Meiera× | |
|---|---|---|
| Dziedzina | Analiza przeżycia | Analiza przeżycia |
| Rodzina | Survival analysis | Survival analysis |
| Rok powstania≠ | 2008 | 1958 |
| Twórca≠ | Ishwaran, H., Kogalur, U.B., Blackstone, E.H. & Lauer, M.S. | Kaplan, E. L. & Meier, P. |
| Typ≠ | Ensemble machine learning survival model | Non-parametric survival estimator |
| Źródło pierwotne≠ | Ishwaran, H., Kogalur, U.B., Blackstone, E.H. & Lauer, M.S. (2008). Random Survival Forests. Annals of Applied Statistics, 2(3), 841–860. DOI ↗ | Kaplan, E. L. & Meier, P. (1958). Nonparametric Estimation from Incomplete Observations. Journal of the American Statistical Association, 53(282), 457–481. DOI ↗ |
| Inne nazwy | RSF, Rastgele Sağkalım Ormanı (RSF), survival random forest | product-limit estimator, km curve, kaplan-meier sağkalım analizi |
| Pokrewne | 2 | 2 |
| Podsumowanie≠ | 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. |
| ScholarGateZbiór danych ↗ |
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