Rpart Survival Trees Survival Learner

Calls rpart::rpart().

• crank is predicted using rpart::predict.rpart()

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

• Task type: “surv”

• Predict Types: “crank”

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

• Required Packages: mlr3, mlr3proba, rpart, distr6, survival

Parameters

Id	Type	Default	Levels	Range
parms	numeric	1		\((-\infty, \infty)\)
minbucket	integer	-		\([1, \infty)\)
minsplit	integer	20		\([1, \infty)\)
cp	numeric	0.01		\([0, 1]\)
maxcompete	integer	4		\([0, \infty)\)
maxsurrogate	integer	5		\([0, \infty)\)
maxdepth	integer	30		\([1, 30]\)
usesurrogate	integer	2		\([0, 2]\)
surrogatestyle	integer	0		\([0, 1]\)
xval	integer	10		\([0, \infty)\)
cost	untyped	-		-
keep_model	logical	FALSE	TRUE, FALSE	-

Initial parameter values

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

• model has been renamed to keep_model.

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

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()

---

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.