A LearnerClassif for a classification tree implemented in rpart::rpart() in package 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():

mlr_learners$get("classif.rpart")
lrn("classif.rpart")

Meta Information

  • Task type: “classif”

  • Predict Types: “response”, “prob”

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

  • Required Packages: rpart

Parameters

IdTypeDefaultRangeLevels
minsplitinteger20\([1, \infty)\)-
minbucketinteger-\([1, \infty)\)-
cpnumeric0.01\([0, 1]\)-
maxcompeteinteger4\([0, \infty)\)-
maxsurrogateinteger5\([0, \infty)\)-
maxdepthinteger30\([1, 30]\)-
usesurrogateinteger2\([0, 2]\)-
surrogatestyleinteger0\([0, 1]\)-
xvalinteger10\([0, \infty)\)-
keep_modellogicalFALSE\((-\infty, \infty)\)TRUE, FALSE

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 -> mlr3::LearnerClassif -> LearnerClassifRpart

Methods

Public methods

Inherited methods

Method new()

Creates a new instance of this R6 class.

Usage

LearnerClassifRpart$new()


Method importance()

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

Usage

LearnerClassifRpart$importance()

Returns

Named numeric().


Method selected_features()

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

Usage

LearnerClassifRpart$selected_features()

Returns

character().


Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerClassifRpart$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.