L2 regularized support vector classification. Calls LiblineaR::LiblineaR() from LiblineaR.

## Details

Type of SVC depends on type argument:

• 0 – L2-regularized logistic regression (primal)

• 1 - L2-regularized L2-loss support vector classification (dual)

• 3 - L2-regularized L1-loss support vector classification (dual)

• 2 – L2-regularized L2-loss support vector classification (primal)

• 4 – Support vector classification by Crammer and Singer

• 5 - L1-regularized L2-loss support vector classification

• 6 - L1-regularized logistic regression

• 7 - L2-regularized logistic regression (dual)

If number of records > number of features, type = 2 is faster than type = 1 (Hsu et al. 2003).

Note that probabilistic predictions are only available for types 0, 6, and 7. The default epsilon value depends on the type parameter, see LiblineaR::LiblineaR.

## Dictionary

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

### Method clone()

The objects of this class are cloneable with this method.

LearnerClassifLiblineaR$clone(deep = FALSE) #### Arguments deep Whether to make a deep clone. ## Examples learner = mlr3::lrn("classif.liblinear") print(learner) #> <LearnerClassifLiblineaR:classif.liblinear>: Support Vector Machine #> * Model: - #> * Parameters: list() #> * Packages: mlr3, mlr3extralearners, LiblineaR #> * Predict Types: [response], prob #> * Feature Types: numeric #> * Properties: multiclass, twoclass # available parameters: learner$param_set\$ids()
#> [1] "type"     "cost"     "epsilon"  "bias"     "cross"    "verbose"  "wi"
#> [8] "findC"    "useInitC"