Calls LiblineaR::LiblineaR from package LiblineaR.

## Details

Calls LiblineaR::LiblineaR() with type = 1 or type = 2.

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

The default for epsilon is set to match type = 2. If you change to type = 1 remember to eventually adjust the value for epsilon (default = 0.1).

## 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.

LearnerClassifLiblineaRL2L2SVC$clone(deep = FALSE) #### Arguments deep Whether to make a deep clone. ## Examples # stop example failing with warning if package not installed learner = suppressWarnings(mlr3::lrn("classif.liblinearl2l2svc")) print(learner) #> <LearnerClassifLiblineaRL2L2SVC:classif.liblinearl2l2svc> #> * Model: - #> * Parameters: list() #> * Packages: LiblineaR #> * Predict Type: response #> * Feature types: numeric #> * Properties: multiclass, twoclass # available parameters: learner$param_set\$ids()
#> [1] "cost"     "epsilon"  "bias"     "type"     "cross"    "verbose"  "wi"
#> [8] "findC"    "useInitC"