L2-Regularized L2-Loss Support Vector Classification Learner

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():

mlr_learners$get("classif.liblinearl2l2svc")
lrn("classif.liblinearl2l2svc")

Traits

• Packages: LiblineaR

• Predict Types: response

• Feature Types: numeric

• Properties: multiclass, twoclass

See also

Dictionary of Learners: mlr3::mlr_learners

Author

be-marc

Super classes

mlr3::Learner -> mlr3::LearnerClassif -> LearnerClassifLiblineaRL2L2SVC

Methods

Public methods

• LearnerClassifLiblineaRL2L2SVC$new()

• LearnerClassifLiblineaRL2L2SVC$clone()

Inherited methods

• mlr3::Learner$format()

• mlr3::Learner$help()

• mlr3::Learner$predict()

• mlr3::Learner$predict_newdata()

• mlr3::Learner$print()

• mlr3::Learner$reset()

• mlr3::Learner$train()

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Method new()

Creates a new instance of this R6 class.

Usage

LearnerClassifLiblineaRL2L2SVC$new()

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Method clone()

The objects of this class are cloneable with this method.

Usage

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"