mlr_learners_surv.rpart: Rpart Survival Trees Survival Learner

In mlr3proba: Probabilistic Supervised Learning for 'mlr3'

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. Parameter model has been renamed to keep_model.

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: mlr3 mlr3proba rpart distr6 survival

Super classes

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

Methods

Public methods

• LearnerSurvRpart$new()

• LearnerSurvRpart$importance()

• LearnerSurvRpart$selected_features()

• LearnerSurvRpart$clone()

Inherited methods

• mlr3::Learner$base_learner()

• mlr3::Learner$format()

• mlr3::Learner$help()

• mlr3::Learner$predict()

• mlr3::Learner$predict_newdata()

• mlr3::Learner$print()

• mlr3::Learner$reset()

• mlr3::Learner$train()

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Method new()

Creates a new instance of this R6 class.

Usage

LearnerSurvRpart$new()

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Method importance()

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

Usage

LearnerSurvRpart$importance()

Returns

Named numeric().

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Method selected_features()

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

Usage

LearnerSurvRpart$selected_features()

Returns

character().

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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.

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

mlr3proba documentation built on April 25, 2022, 5:07 p.m.