mlr_learners_regr.liblinear: L2-Regularized Support Vector Regression Learner

In mlr-org/mlr3extralearners: Extra Learners For mlr3

L2-Regularized Support Vector Regression Learner

Description

L2 regularized support vector regression. Calls LiblineaR::LiblineaR() from LiblineaR.

Details

Type of SVR depends on type argument:

• type = 11 - L2-regularized L2-loss support vector regression (primal)

• type = 12 – L2-regularized L2-loss support vector regression (dual)

• type = 13 – L2-regularized L1-loss support vector regression (dual)

Dictionary

This Learner can be instantiated via lrn():

lrn("regr.liblinear")

Meta Information

• Task type: “regr”

• Predict Types: “response”

• Feature Types: “integer”, “numeric”

• Required Packages: mlr3, mlr3extralearners, LiblineaR

Parameters

Id	Type	Default	Levels	Range
type	integer	11		[11, 13]
cost	numeric	1		[0, \infty)
bias	numeric	1		(-\infty, \infty)
svr_eps	numeric	NULL		[0, \infty)
cross	integer	0		[0, \infty)
verbose	logical	FALSE	TRUE, FALSE	-
findC	logical	FALSE	TRUE, FALSE	-
useInitC	logical	TRUE	TRUE, FALSE	-

Initial parameter values

• svr_eps:

  • Actual default: NULL

  • Initial value: 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".

Super classes

mlr3::Learner -> mlr3::LearnerRegr -> LearnerRegrLiblineaR

Methods

Public methods

• LearnerRegrLiblineaR$new()

• LearnerRegrLiblineaR$clone()

Inherited methods

• mlr3::Learner$base_learner()
• mlr3::Learner$encapsulate()
• mlr3::Learner$format()
• mlr3::Learner$help()
• mlr3::Learner$predict()
• mlr3::Learner$predict_newdata()
• mlr3::Learner$print()
• mlr3::Learner$reset()
• mlr3::Learner$train()

---

Method new()

Creates a new instance of this R6 class.

Usage

LearnerRegrLiblineaR$new()

---

Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerRegrLiblineaR$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Author(s)

be-marc

References

Fan, Rong-En, Chang, Kai-Wei, Hsieh, Cho-Jui, Wang, Xiang-Rui, Lin, Chih-Jen (2008). “LIBLINEAR: A library for large linear classification.” the Journal of machine Learning research, 9, 1871–1874.

See Also

• Dictionary of Learners: mlr3::mlr_learners.

• as.data.table(mlr_learners) for a table of available Learners in the running session (depending on the loaded packages).

• Chapter in the mlr3book: https://mlr3book.mlr-org.com/basics.html#learners

• mlr3learners for a selection of recommended learners.

• mlr3cluster for unsupervised clustering learners.

• mlr3pipelines to combine learners with pre- and postprocessing steps.

• mlr3tuning for tuning of hyperparameters, mlr3tuningspaces for established default tuning spaces.

Examples

# Define the Learner
learner = mlr3::lrn("regr.liblinear")
print(learner)

# Define a Task
task = mlr3::tsk("mtcars")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

mlr-org/mlr3extralearners documentation built on Nov. 11, 2024, 11:11 a.m.