Calls randomForestSRC::rfsrc().

randomForestSRC::predict.rfsrc() returns both cumulative hazard function (chf) and survival function (surv) but uses different estimators to derive these. chf uses a bootstrapped Nelson-Aalen estimator, (Ishwaran, 2008) whereas surv uses a bootstrapped Kaplan-Meier estimator https://kogalur.github.io/randomForestSRC/theory.html. The choice of which estimator to use is given by the extra estimator hyper-parameter, default is nelson.

Format

R6::R6Class() inheriting from LearnerSurv.

Construction

LearnerSurvRandomForestSRC$new()
mlr_learners$get("surv.randomForestSRC")
lrn("surv.randomForestSRC")

Meta Information

  • Type: "surv"

  • Predict Types: crank, distr

  • Feature Types: logical, integer, numeric, factor, ordered

  • Packages: randomForestSRC distr6

References

Ishwaran H. and Kogalur U.B. (2019). Fast Unified Random Forests for Survival, Regression, and Classification (RF-SRC), R package version 2.9.1.

Ishwaran H., Kogalur U.B., Blackstone E.H. and Lauer M.S. (2008). Random survival forests. Ann. Appl. Statist. 2(3), 841--860.

Breiman, L. (2001). Random Forests. Machine Learning 45(1). doi: 10.1023/A:1010933404324 .

See also