mlr_learners_regr.liblinearl2l1svr: L2-Regularized L1-Loss Support Vector Regression Learner

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

Description

L2-Regularized L1-Loss support vector regression learner. Calls LiblineaR::LiblineaR() (type = 13) from package LiblineaR.

Custom mlr3 defaults

• 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".

• epsilon:

  • Actual default: 0.01

  • Adjusted default: Removed

  • Reason for change: For regr SVR learners paramter svr_eps overwrites param epsilon.

Dictionary

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

mlr_learners$get("regr.liblinearl2l1svr")
lrn("regr.liblinearl2l1svr")

Super classes

mlr3::Learner -> mlr3::LearnerRegr -> LearnerRegrLiblineaRL2L1SVR

Methods

Public methods

• LearnerRegrLiblineaRL2L1SVR$new()

• LearnerRegrLiblineaRL2L1SVR$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

LearnerRegrLiblineaRL2L1SVR$new()

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Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerRegrLiblineaRL2L1SVR$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

See Also

Dictionary of Learners: mlr3::mlr_learners

Examples

learner = mlr3::lrn("regr.liblinearl2l1svr")
print(learner)

# available parameters:
learner$param_set$ids()

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