mlr_learners_classif.liblinearl2l2svc: L2-Regularized L2-Loss Support Vector Classification Learner

In mlr3learners/mlr3learners.liblinear: Learners from LiblineaR package for mlr3

Description

L2-Regularized L2-Loss support vector classification learner. Calls LiblineaR::LiblineaR() (type = 1 or type = 2) from package LiblineaR.

Details

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")

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.

References

mlr3learners.liblinearhsu_2003

See Also

Dictionary of Learners: mlr3::mlr_learners

Examples

learner = mlr3::lrn("classif.liblinearl2l2svc")
print(learner)

# available parameters:
learner$param_set$ids()

mlr3learners/mlr3learners.liblinear documentation built on June 4, 2020, 8:16 p.m.