Calls LiblineaR::LiblineaR from package LiblineaR.

Details

Type of SVR depends on type argument:

  • type = 11 - L2-regularized L2-loss support vector regression (primal)

  • type = 12 – L2-regularized L2-loss support vector regression (dual)

  • type = 13 – L2-regularized L1-loss support vector regression (dual)

Dictionary

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

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

Traits

  • Packages: LiblineaR

  • Predict Types: response

  • Feature Types: integer, numeric

  • Properties:

Custom mlr3 defaults

  • svr_eps:

    • Actual default: NULL

    • Adjusted default: 0.001

    • Reason for change: svr_eps is type dependent and the "type" is handled by the mlr3learner. The default value is set to th default of the respective "type".

See also

Author

be-marc

Super classes

mlr3::Learner -> mlr3::LearnerRegr -> LearnerRegrLiblineaR

Methods

Public methods

Inherited methods

Method new()

Creates a new instance of this R6 class.

Usage

LearnerRegrLiblineaR$new()


Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerRegrLiblineaR$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

# stop example failing with warning if package not installed learner = suppressWarnings(mlr3::lrn("regr.liblinear")) print(learner)
#> <LearnerRegrLiblineaR:regr.liblinear> #> * Model: - #> * Parameters: svr_eps=0.001 #> * Packages: LiblineaR #> * Predict Type: response #> * Feature types: integer, numeric #> * Properties: -
# available parameters: learner$param_set$ids()
#> [1] "type" "cost" "bias" "svr_eps" "cross" "verbose" "findC" #> [8] "useInitC"