mlr_learners_classif.liblinearl2l2svc.Rd
Calls LiblineaR::LiblineaR from package LiblineaR.
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).
This Learner can be instantiated via the
dictionary mlr_learners or with the associated
sugar function lrn()
:
mlr_learners$get("classif.liblinearl2l2svc") lrn("classif.liblinearl2l2svc")
Packages: LiblineaR
Predict Types: response
Feature Types: numeric
Properties: multiclass, twoclass
be-marc
mlr3::Learner
-> mlr3::LearnerClassif
-> LearnerClassifLiblineaRL2L2SVC
new()
Creates a new instance of this R6 class.
LearnerClassifLiblineaRL2L2SVC$new()
clone()
The objects of this class are cloneable with this method.
LearnerClassifLiblineaRL2L2SVC$clone(deep = FALSE)
deep
Whether to make a deep clone.
# stop example failing with warning if package not installed learner = suppressWarnings(mlr3::lrn("classif.liblinearl2l2svc")) print(learner)#> <LearnerClassifLiblineaRL2L2SVC:classif.liblinearl2l2svc> #> * Model: - #> * Parameters: list() #> * Packages: LiblineaR #> * Predict Type: response #> * Feature types: numeric #> * Properties: multiclass, twoclass# available parameters: learner$param_set$ids()#> [1] "cost" "epsilon" "bias" "type" "cross" "verbose" "wi" #> [8] "findC" "useInitC"