Calls ranger::ranger().

  • distr is predicted using ranger::predict.ranger()

  • crank is predicted as the expectation of the survival distribution, distr

Dictionary

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

LearnerSurvRanger$new()
mlr_learners$get("surv.ranger")
lrn("surv.ranger")

Meta Information

  • Type: "surv"

  • Predict Types: distr, crank

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

  • Properties: importance, oob_error, weights

  • Packages: ranger distr6

References

Wright MN, Ziegler A (2017). “ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R.” Journal of Statistical Software, 77(1), 1--17. doi: 10.18637/jss.v077.i01 .

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 -> LearnerSurvRanger

Methods

Public methods

Inherited methods

Method new()

Creates a new instance of this R6 class.

Usage

LearnerSurvRanger$new()


Method importance()

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

Usage

LearnerSurvRanger$importance()

Returns

Named numeric().


Method oob_error()

The out-of-bag error is extracted from the model slot prediction.error.

Usage

LearnerSurvRanger$oob_error()

Returns

numeric(1).


Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerSurvRanger$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.