mlr_learners_regr.rpart: Regression Tree Learner

In mlr3: Machine Learning in R - Next Generation

Regression Tree Learner

Description

A LearnerRegr for a regression tree implemented in rpart::rpart() in package rpart.

Initial parameter values

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

Custom mlr3 parameters

• 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	Levels	Range
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()

Inherited methods

• mlr3::Learner$base_learner()
• mlr3::Learner$encapsulate()
• 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

LearnerRegrRpart$new()

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Method importance()

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

Usage

LearnerRegrRpart$importance()

Returns

Named numeric().

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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. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1201/9781315139470")}.

See Also

• Chapter in the mlr3book: https://mlr3book.mlr-org.com/chapters/chapter2/data_and_basic_modeling.html#sec-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: Learner, LearnerClassif, LearnerRegr, mlr_learners, mlr_learners_classif.debug, mlr_learners_classif.featureless, mlr_learners_classif.rpart, mlr_learners_regr.debug, mlr_learners_regr.featureless

mlr3 documentation built on Oct. 18, 2024, 5:11 p.m.