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.

Dictionary

This Learner can be instantiated via the dictionary mlr_learners or with the associated sugar function lrn():

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

  • Properties: importance, missings, weights

  • 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 UB, Blackstone EH, Lauer MS, others (2008). “Random survival forests.” The annals of applied statistics, 2(3), 841--860.

Breiman L (2001). “Random Forests.” Machine Learning, 45(1), 5--32. ISSN 1573-0565, doi: 10.1023/A:1010933404324 .

See also

Super classes

mlr3::Learner -> mlr3proba::LearnerSurv -> LearnerSurvRandomForestSRC

Methods

Public methods

Inherited methods

Method new()

Creates a new instance of this R6 class.

Usage

LearnerSurvRandomForestSRC$new()


Method importance()

The importance scores are extracted from the model slot variable.importance.

Usage

LearnerSurvRandomForestSRC$importance()

Returns

Named numeric().


Method selected_features()

Selected features are extracted from the model slot frame$var.

Usage

LearnerSurvRandomForestSRC$selected_features()

Returns

character().


Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerSurvRandomForestSRC$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.