mlr_learners_regr.liblinear.Rd
Calls LiblineaR::LiblineaR from package LiblineaR.
Type of SVR depends on type
argument:
type = 11
- L2-regularized L2-loss support vector regression (primal)
type = 12
– L2-regularized L2-loss support vector regression (dual)
type = 13
– L2-regularized L1-loss support vector regression (dual)
This Learner can be instantiated via the
dictionary mlr_learners or with the associated
sugar function lrn()
:
mlr_learners$get("regr.liblinear") lrn("regr.liblinear")
Packages: LiblineaR
Predict Types: response
Feature Types: integer, numeric
Properties:
svr_eps
:
Actual default: NULL
Adjusted default: 0.001
Reason for change: svr_eps
is type dependent and the "type" is handled
by the mlr3learner. The default value is set to th default of the respective
"type".
be-marc
mlr3::Learner
-> mlr3::LearnerRegr
-> LearnerRegrLiblineaR
new()
Creates a new instance of this R6 class.
LearnerRegrLiblineaR$new()
clone()
The objects of this class are cloneable with this method.
LearnerRegrLiblineaR$clone(deep = FALSE)
deep
Whether to make a deep clone.
# stop example failing with warning if package not installed learner = suppressWarnings(mlr3::lrn("regr.liblinear")) print(learner)#> <LearnerRegrLiblineaR:regr.liblinear> #> * Model: - #> * Parameters: svr_eps=0.001 #> * Packages: LiblineaR #> * Predict Type: response #> * Feature types: integer, numeric #> * Properties: -# available parameters: learner$param_set$ids()#> [1] "type" "cost" "bias" "svr_eps" "cross" "verbose" "findC" #> [8] "useInitC"