mlr_learners_classif.liblinear.Rd
Calls LiblineaR::LiblineaR from package LiblineaR.
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.
This Learner can be instantiated via the
dictionary mlr_learners or with the associated
sugar function lrn()
:
mlr_learners$get("classif.liblinear") lrn("classif.liblinear")
Packages: LiblineaR
Predict Types: response, prob
Feature Types: numeric
Properties: multiclass, twoclass
be-marc
mlr3::Learner
-> mlr3::LearnerClassif
-> LearnerClassifLiblineaR
new()
Creates a new instance of this R6 class.
LearnerClassifLiblineaR$new()
clone()
The objects of this class are cloneable with this method.
LearnerClassifLiblineaR$clone(deep = FALSE)
deep
Whether to make a deep clone.
# stop example failing with warning if package not installed learner = suppressWarnings(mlr3::lrn("classif.liblinear")) print(learner)#> <LearnerClassifLiblineaR:classif.liblinear> #> * Model: - #> * Parameters: list() #> * Packages: LiblineaR #> * Predict Type: response #> * Feature types: numeric #> * Properties: multiclass, twoclass# available parameters: learner$param_set$ids()#> [1] "type" "cost" "epsilon" "bias" "cross" "verbose" "wi" #> [8] "findC" "useInitC"