Calls rpart::rpart().

Parameter xval is set to 0 in order to save some computation time.

Dictionary

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

LearnerSurvRpart$new()
mlr_learners$get("surv.rpart")
lrn("surv.rpart")

Meta Information

  • Type: "surv"

  • Predict Types: crank, distr

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

  • Properties: importance, missings, selected_features, weights

  • Packages: rpart distr6 survival

References

Breiman L, Friedman JH, Olshen RA, Stone CJ (1984). Classification And Regression Trees. Routledge. doi: 10.1201/9781315139470 .

See also

Super classes

mlr3::Learner -> mlr3proba::LearnerSurv -> LearnerSurvRpart

Methods

Public methods

Inherited methods

Method new()

Creates a new instance of this R6 class.

Usage

LearnerSurvRpart$new()


Method importance()

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

Usage

LearnerSurvRpart$importance()

Returns

Named numeric().


Method selected_features()

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

Usage

LearnerSurvRpart$selected_features()

Returns

character().


Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerSurvRpart$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.