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$base_learner()
• mlr3::Learner$configure()
• mlr3::Learner$encapsulate()
• mlr3::Learner$format()
• mlr3::Learner$help()
• mlr3::Learner$predict()
• mlr3::Learner$predict_newdata()
• mlr3::Learner$print()
• mlr3::Learner$reset()
• mlr3::Learner$selected_features()
• 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, and LiblineaR
#> • Predict Types: [response] and prob
#> • Feature Types: numeric
#> • Encapsulation: none (fallback: -)
#> • Properties: multiclass and twoclass
#> • Other settings: use_weights = 'error'

# 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,] -0.1521274 -1.01473 -1.400273 -1.296184 -0.7718394 0.1672105 0.6627299
#>            V16       V17        V18        V19         V2        V20        V21
#> [1,] 0.8109408 0.3676042 0.06702335 -0.2731867 -0.2134672 -0.7439989 -0.8654165
#>            V22        V23        V24       V25       V26        V27        V28
#> [1,] -0.513537 -0.2036223 0.06287565 0.5724382 0.7665796 0.05252608 -0.4456103
#>            V29         V3        V30       V31        V32        V33       V34
#> [1,] 0.1027285 -0.1852969 -0.1256383 0.5550419 -0.3101017 -0.2522665 0.5033172
#>           V35      V36       V37        V38         V39         V4       V40
#> [1,] 0.949814 1.316681 0.9576894 0.09509058 -0.09846712 -0.3778988 0.4790584
#>             V41        V42        V43        V44      V45        V46        V47
#> [1,] -0.1854395 -0.3429808 -0.6748284 -0.9005077 -1.19329 -0.8222509 -0.5823661
#>             V48        V49         V5         V50         V51         V52
#> [1,] -0.5709776 -0.3521956 -0.2581671 0.002193209 -0.06235944 -0.07393378
#>              V53         V54         V55         V56         V57         V58
#> [1,] -0.01227961 -0.03644803 0.009975284 -0.03286646 -0.02451926 -0.04273373
#>             V59        V6         V60         V7         V8        V9      Bias
#> [1,] -0.0406709 -0.266374 -0.00315536 -0.1128721 -0.3728892 -1.103574 0.6684888
#> 
#> $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 
#>  0.3188406