Regression Gaussian Process Learner

Gaussian process for regression. Calls kernlab::gausspr() from kernlab. Parameters sigma, degree, scale, offset and order are added to make tuning kpar easier. If kpar is provided then these new parameters are ignored. If none are provided then the default "automatic" is used for kpar.

Dictionary

This Learner can be instantiated via the dictionary mlr_learners or with the associated sugar function lrn():

mlr_learners$get("regr.gausspr")
lrn("regr.gausspr")

Meta Information

• Task type: “regr”

• Predict Types: “response”

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

• Required Packages: mlr3, mlr3extralearners, kernlab

Parameters

Id	Type	Default	Levels	Range
scaled	untyped	TRUE		-
kernel	character	rbfdot	rbfdot, polydot, vanilladot, tanhdot, laplacedot, besseldot, anovadot, splinedot	-
sigma	numeric	-		\((-\infty, \infty)\)
degree	numeric	-		\((-\infty, \infty)\)
scale	numeric	-		\((-\infty, \infty)\)
offset	numeric	-		\((-\infty, \infty)\)
order	numeric	-		\((-\infty, \infty)\)
kpar	untyped	"automatic"		-
var	numeric	0.001		\([0.001, \infty)\)
variance.model	logical	FALSE	TRUE, FALSE	-
tol	numeric	0.001		\([0, \infty)\)
fit	logical	TRUE	TRUE, FALSE	-
na.action	untyped	na.omit		-

References

Karatzoglou, Alexandros, Smola, Alex, Hornik, Kurt, Zeileis, Achim (2004). “kernlab-an S4 package for kernel methods in R.” Journal of statistical software, 11(9), 1--20.

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.

Author

RaphaelS1

Super classes

mlr3::Learner -> mlr3::LearnerRegr -> LearnerRegrGausspr

Methods

Public methods

• LearnerRegrGausspr$new()

• LearnerRegrGausspr$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()

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Method new()

Creates a new instance of this R6 class.

Usage

LearnerRegrGausspr$new()

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Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerRegrGausspr$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

learner = mlr3::lrn("regr.gausspr")
print(learner)
#> <LearnerRegrGausspr:regr.gausspr>: Gaussian Process
#> * Model: -
#> * Parameters: list()
#> * Packages: mlr3, mlr3extralearners, kernlab
#> * Predict Types:  [response]
#> * Feature Types: logical, integer, numeric, character, factor, ordered
#> * Properties: -

# available parameters:
learner$param_set$ids()
#>  [1] "scaled"         "kernel"         "sigma"          "degree"        
#>  [5] "scale"          "offset"         "order"          "kpar"          
#>  [9] "var"            "variance.model" "tol"            "fit"           
#> [13] "na.action"