Type of SVC depends on
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
epsilon value depends on the
type parameter, see LiblineaR::LiblineaR.
Predict Types: response, prob
Feature Types: numeric
Properties: multiclass, twoclass
Creates a new instance of this R6 class.
The objects of this class are cloneable with this method.
LearnerClassifLiblineaR$clone(deep = FALSE)
Whether to make a deep clone.
# stop example failing with warning if package not installed learner = suppressWarnings(mlr3::lrn("classif.liblinear")) print(learner)#> <LearnerClassifLiblineaR:classif.liblinear> #> * Model: - #> * Parameters: list() #> * Packages: LiblineaR #> * Predict Type: response #> * Feature types: numeric #> * Properties: multiclass, twoclass# available parameters: learner$param_set$ids()#>  "type" "cost" "epsilon" "bias" "cross" "verbose" "wi" #>  "findC" "useInitC"