LiblineaR Classification Learner

Calls LiblineaR::LiblineaR from package LiblineaR.

Details

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.

Dictionary

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")

Traits

• Packages: LiblineaR

• Predict Types: response, prob

• Feature Types: numeric

• Properties: multiclass, twoclass

See also

Dictionary of Learners: mlr3::mlr_learners

Author

be-marc

Super classes

mlr3::Learner -> mlr3::LearnerClassif -> LearnerClassifLiblineaR

Methods

Public methods

• LearnerClassifLiblineaR$new()

• LearnerClassifLiblineaR$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

LearnerClassifLiblineaR$new()

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Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerClassifLiblineaR$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.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"