Rpart Survival Trees Survival Learner

Description

Calls rpart::rpart().

• crank is predicted using rpart::predict.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

Super classes

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

Methods

Public methods

• LearnerSurvRpart$new()

• LearnerSurvRpart$importance()

• LearnerSurvRpart$selected_features()

• LearnerSurvRpart$clone()

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

References

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

See Also

Other survival learners: mlr_learners_surv.coxph, mlr_learners_surv.kaplan