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L2 regularized support vector regression. Calls LiblineaR::LiblineaR() from 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")

Meta Information

  • Task type: “regr”

  • Predict Types: “response”

  • Feature Types: “integer”, “numeric”

  • Required Packages: mlr3, mlr3extralearners, LiblineaR

Parameters

IdTypeDefaultLevelsRange
typeinteger11\([11, 13]\)
costnumeric1\([0, \infty)\)
biasnumeric1\((-\infty, \infty)\)
svr_epsnumericNULL\([0, \infty)\)
crossinteger0\([0, \infty)\)
verboselogicalFALSETRUE, FALSE-
findClogicalFALSETRUE, FALSE-
useInitClogicalTRUETRUE, FALSE-

Parameter Changes

  • 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".

References

Fan, Rong-En, Chang, Kai-Wei, Hsieh, Cho-Jui, Wang, Xiang-Rui, Lin, Chih-Jen (2008). “LIBLINEAR: A library for large linear classification.” the Journal of machine Learning research, 9, 1871--1874.

See also

Author

be-marc

Super classes

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

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

LearnerRegrLiblineaR$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

learner = mlr3::lrn("regr.liblinear")
print(learner)
#> <LearnerRegrLiblineaR:regr.liblinear>: Support Vector Machine
#> * Model: -
#> * Parameters: svr_eps=0.001
#> * Packages: mlr3, mlr3extralearners, 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"