mlr3 (version 0.13.1)

mlr_learners_regr.rpart: Regression Tree Learner

Description

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("regr.rpart")
lrn("regr.rpart")

Meta Information

• Task type: “regr”

• Predict Types: “response”

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

• Required Packages: mlr3, rpart

Parameters

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

Super classes

mlr3::Learner -> mlr3::LearnerRegr -> LearnerRegrRpart

Methods

Public methods

• LearnerRegrRpart$new()

• LearnerRegrRpart$importance()

• LearnerRegrRpart$selected_features()

• LearnerRegrRpart$clone()

• 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

LearnerRegrRpart$new()

Method importance()

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

Usage

LearnerRegrRpart$importance()

Returns

Named numeric().

Method selected_features()

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

Usage

LearnerRegrRpart$selected_features()

Returns

character().

Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerRegrRpart$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. 10.1201/9781315139470.

See Also

• Chapter in the mlr3book: https://mlr3book.mlr-org.com/basics.html#learners

• Package mlr3learners for a solid collection of essential learners.

• Package mlr3extralearners for more learners.

• Dictionary of Learners: mlr_learners

• as.data.table(mlr_learners) for a table of available Learners in the running session (depending on the loaded packages).

• mlr3pipelines to combine learners with pre- and postprocessing steps.

• Package mlr3viz for some generic visualizations.

• Extension packages for additional task types:

  • mlr3proba for probabilistic supervised regression and survival analysis.

  • mlr3cluster for unsupervised clustering.

• mlr3tuning for tuning of hyperparameters, mlr3tuningspaces for established default tuning spaces.

Other Learner: LearnerClassif, LearnerRegr, Learner, mlr_learners_classif.debug, mlr_learners_classif.featureless, mlr_learners_classif.rpart, mlr_learners_regr.debug, mlr_learners_regr.featureless, mlr_learners