Polynomial Regression Learner

Polynomial regression. Calls polyreg::polyFit() from polyreg.

Initial parameter values

• deg: We have set this to 2, pretty arbitrarily.

• noisy: We have set this to FALSE, to get no output on the console.

Dictionary

This Learner can be instantiated via lrn():

lrn("regr.polyFit")

Meta Information

• Task type: “regr”

• Predict Types: “response”

• Feature Types: “numeric”, “factor”

• Required Packages: mlr3, mlr3extralearners, polyreg

Parameters

Id	Type	Default	Levels	Range
deg	integer	-		\([0, \infty)\)
maxInteractDeg	integer	-		\([0, \infty)\)
return_xy	logical	FALSE	TRUE, FALSE	-
returnPoly	logical	FALSE	TRUE, FALSE	-
noisy	logical	TRUE	TRUE, FALSE	-

See also

• Dictionary of Learners: mlr3::mlr_learners.

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

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

• mlr3learners for a selection of recommended learners.

• mlr3cluster for unsupervised clustering learners.

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

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

Super classes

mlr3::Learner -> mlr3::LearnerRegr -> LearnerRegrPolyFit

Methods

Public methods

• LearnerRegrPoly$new()

• LearnerRegrPoly$clone()

Inherited methods

• mlr3::Learner$base_learner()
• mlr3::Learner$configure()
• mlr3::Learner$encapsulate()
• mlr3::Learner$format()
• mlr3::Learner$help()
• mlr3::Learner$predict()
• mlr3::Learner$predict_newdata()
• mlr3::Learner$print()
• mlr3::Learner$reset()
• mlr3::Learner$selected_features()
• mlr3::Learner$train()
• mlr3::LearnerRegr$predict_newdata_fast()

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Method new()

Creates a new instance of this R6 class.

Usage

LearnerRegrPoly$new()

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Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerRegrPoly$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.