Metodebevisregistrering

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.

Sources recorded, not reviewed

Kilderegistrering

Citater kopieret ordret fra metodens kilderegistrering. Ingen påstandsniveauverifikation er udledt heraf.

Taksonomisk metoderegistrering · 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

Åbn fuld metode

Kuraterede påstande

Påstande gemt i bevis-loggen, hver med sin egen vurdering.

Ingen kuraterede påstande endnu

Denne visning opfinder ikke en påstandsvurdering, når loggen ingen har.

Relaterede metoder

Genereret fra metodegrafen og vist som maskinelt foreslåede relationer — ingen bevispåstand er udledt.

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