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Bayesian version of the support vector machine. Parameters sigma, degree, scale, offset, order, length, lambda, and normalized 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. Calls kernlab::rvm() from package kernlab.

Dictionary

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

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

Meta Information

  • Task type: “regr”

  • Predict Types: “response”

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

  • Required Packages: mlr3, mlr3extralearners, kernlab

Parameters

IdTypeDefaultLevelsRange
kernelcharacterrbfdotrbfdot, polydot, vanilladot, tanhdot, laplacedot, besseldot, anovadot, splinedot, stringdot-
sigmanumeric-\((-\infty, \infty)\)
degreenumeric-\((-\infty, \infty)\)
scalenumeric-\((-\infty, \infty)\)
offsetnumeric-\((-\infty, \infty)\)
ordernumeric-\((-\infty, \infty)\)
lengthinteger-\([0, \infty)\)
lambdanumeric-\((-\infty, \infty)\)
normalizedlogical-TRUE, FALSE-
kparuntypedautomatic-
alphauntyped5-
varnumeric0.1\([0.001, \infty)\)
var.fixlogicalFALSETRUE, FALSE-
iterationsinteger100\([0, \infty)\)
tolnumeric2.220446e-16\([0, \infty)\)
minmaxdiffnumeric0.001\([0, \infty)\)
verbositylogicalFALSETRUE, FALSE-
fitlogicalTRUETRUE, FALSE-
na.actionuntypedna.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

Author

RaphaelS1

Super classes

mlr3::Learner -> mlr3::LearnerRegr -> LearnerRegrRVM

Methods

Inherited methods


Method new()

Creates a new instance of this R6 class.

Usage


Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerRegrRVM$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

learner = mlr3::lrn("regr.rvm")
print(learner)
#> <LearnerRegrRVM:regr.rvm>: Relevance Vector Machine
#> * Model: -
#> * Parameters: list()
#> * Packages: mlr3, mlr3extralearners, kernlab
#> * Predict Type: response
#> * Feature types: numeric, integer, logical, character, factor, ordered
#> * Properties: -

# available parameters:
learner$param_set$ids()
#>  [1] "kernel"     "sigma"      "degree"     "scale"      "offset"    
#>  [6] "order"      "length"     "lambda"     "normalized" "kpar"      
#> [11] "alpha"      "var"        "var.fix"    "iterations" "tol"       
#> [16] "minmaxdiff" "verbosity"  "fit"        "na.action"