LiblineaR Classification Learner

L2 regularized support vector classification. Calls LiblineaR::LiblineaR() from 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 lrn():

lrn("classif.liblinear")

Meta Information

• Task type: “classif”

• Predict Types: “response”, “prob”

• Feature Types: “numeric”

• Required Packages: mlr3, mlr3extralearners, LiblineaR

Parameters

Id	Type	Default	Levels	Range
type	integer	0		\([0, 7]\)
cost	numeric	1		\([0, \infty)\)
epsilon	numeric	-		\([0, \infty)\)
bias	numeric	1		\((-\infty, \infty)\)
cross	integer	0		\([0, \infty)\)
verbose	logical	FALSE	TRUE, FALSE	-
wi	untyped	NULL		-
findC	logical	FALSE	TRUE, FALSE	-
useInitC	logical	TRUE	TRUE, FALSE	-

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.

Author

be-marc

Super classes

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

Methods

Public methods

• LearnerClassifLiblineaR$new()

• LearnerClassifLiblineaR$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

LearnerClassifLiblineaR$new()

---

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

# Define the Learner
learner = mlr3::lrn("classif.liblinear")
print(learner)
#> <LearnerClassifLiblineaR:classif.liblinear>: Support Vector Machine
#> * Model: -
#> * Parameters: list()
#> * Packages: mlr3, mlr3extralearners, LiblineaR
#> * Predict Types:  [response], prob
#> * Feature Types: numeric
#> * Properties: multiclass, twoclass

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

# 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)
#> $TypeDetail
#> [1] "L2-regularized logistic regression primal (L2R_LR)"
#> 
#> $Type
#> [1] 0
#> 
#> $W
#>              V1        V10       V11       V12        V13        V14       V15
#> [1,] -0.1377876 -0.7047766 -1.098179 -1.019706 -0.6832497 -0.1094154 0.3619942
#>            V16       V17       V18         V19         V2        V20        V21
#> [1,] 0.8110644 0.4207263 0.2028633 -0.09790151 -0.3048851 -0.3349879 -0.2712929
#>            V22        V23        V24       V25       V26       V27        V28
#> [1,] -0.235157 -0.5588025 -0.4516719 0.3168683 0.2255533 0.2497448 -0.2704269
#>             V29         V3        V30       V31         V32       V33       V34
#> [1,] -0.2232352 -0.3532993 -0.1118702 0.7601825 -0.07415511 0.1857099 0.4791852
#>            V35       V36       V37       V38         V39         V4       V40
#> [1,] 0.4749974 0.8634666 0.7556131 0.1194989 -0.07440937 -0.4771586 0.5664937
#>              V41        V42        V43        V44       V45       V46
#> [1,] -0.02738167 -0.8903491 -0.8813797 -0.7724972 -1.117117 -0.942956
#>             V47        V48       V49         V5         V50         V51
#> [1,] -0.5630921 -0.6925133 -0.436498 -0.4197876 -0.01936831 -0.09675127
#>             V52         V53         V54         V55         V56        V57
#> [1,] -0.1033983 -0.04467036 -0.06850629 0.004217908 -0.02307086 0.01515884
#>              V58         V59         V6          V60       V7        V8
#> [1,] -0.05035951 -0.03766793 -0.2411818 -0.001999319 0.130377 -0.351562
#>              V9      Bias
#> [1,] -0.8189618 0.9856689
#> 
#> $Bias
#> [1] 1
#> 
#> $ClassNames
#> [1] R M
#> Levels: M R
#> 
#> $NbClass
#> [1] 2
#> 
#> attr(,"class")
#> [1] "LiblineaR"


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

# Score the predictions
predictions$score()
#> classif.ce 
#>  0.2608696