mlr_learners_classif.liblinearl2l1svc: L2-Regularized L1-Loss Support Vector Classification Learner

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

Description

L2-Regularized L1-Loss support vector classification learner. Calls LiblineaR::LiblineaR() (type = 3) from package LiblineaR.

Custom mlr3 defaults

• epsilon:

  • Actual default: 0.01

  • Adjusted default: 0.1

  • Reason for change: Param depends on param "type" which is handled internally by choosing the mlr3 learner. The default is set to the actual default of the respective "type".

Dictionary

This Learner can be instantiated via the dictionary mlr_learners or with the associated sugar function lrn():

mlr_learners$get("classif.liblinearl2l1svc")
lrn("classif.liblinearl2l1svc")

Super classes

mlr3::Learner -> mlr3::LearnerClassif -> LearnerClassifLiblineaRL2L1SVC

Methods

Public methods

• LearnerClassifLiblineaRL2L1SVC$new()

• LearnerClassifLiblineaRL2L1SVC$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

LearnerClassifLiblineaRL2L1SVC$new()

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Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerClassifLiblineaRL2L1SVC$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

See Also

Dictionary of Learners: mlr3::mlr_learners

Examples

learner = mlr3::lrn("classif.liblinearl2l1svc")
print(learner)

# available parameters:
learner$param_set$ids()

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