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Calls kernlab::rvm from package kernlab.

Details

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.

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: “numeric”, “integer”, “logical”, “character”, “factor”, “ordered”

  • Required Packages: mlr3extralearners, kernlab

Parameters

IdTypeDefaultLevelsRange
kernelcharacterrbfdotrbfdot, polydot, vanilladot, tanhdot, laplacedot, besseldot, anovadot, splinedot, stringdot\((-\infty, \infty)\)
sigmanumeric-\((-\infty, \infty)\)
degreenumeric-\((-\infty, \infty)\)
scalenumeric-\((-\infty, \infty)\)
offsetnumeric-\((-\infty, \infty)\)
ordernumeric-\((-\infty, \infty)\)
lengthinteger-\([0, \infty)\)
lambdanumeric-\((-\infty, \infty)\)
normalizedlogical-TRUE, FALSE\((-\infty, \infty)\)
kparlistautomatic\((-\infty, \infty)\)
alphalist5\((-\infty, \infty)\)
varnumeric0.1\([0.001, \infty)\)
var.fixlogicalFALSETRUE, FALSE\((-\infty, \infty)\)
iterationsinteger100\([0, \infty)\)
tolnumeric2.220446e-16\([0, \infty)\)
minmaxdiffnumeric0.001\([0, \infty)\)
verbositylogicalFALSETRUE, FALSE\((-\infty, \infty)\)
fitlogicalTRUETRUE, FALSE\((-\infty, \infty)\)
crossinteger0\([0, \infty)\)
na.actionlistfunction (object, ...) , UseMethod("na.omit")\((-\infty, \infty)\)

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

if (requireNamespace("kernlab", quietly = TRUE)) {
  learner = mlr3::lrn("regr.rvm")
  print(learner)

  # available parameters:
  learner$param_set$ids()
}
#> <LearnerRegrRVM:regr.rvm>
#> * Model: -
#> * Parameters: list()
#> * Packages: mlr3, mlr3extralearners, kernlab
#> * Predict Type: response
#> * Feature types: numeric, integer, logical, character, factor, ordered
#> * Properties: -
#>  [1] "kernel"     "sigma"      "degree"     "scale"      "offset"    
#>  [6] "order"      "length"     "lambda"     "normalized" "kpar"      
#> [11] "alpha"      "var"        "var.fix"    "iterations" "tol"       
#> [16] "minmaxdiff" "verbosity"  "fit"        "cross"      "na.action"