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/chapters/chapter2/data_and_basic_modeling.html#sec-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$train()
• mlr3::LearnerClassif$predict_newdata_fast()

---

LearnerClassifLiblineaR$new()

Creates a new instance of this R6 class.

Usage

LearnerClassifLiblineaR$new()

---

LearnerClassifLiblineaR$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 = 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', predict_raw = 'FALSE'

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

# Create train and test set
ids = 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,] -0.1883448 -1.036459 -1.33723 -1.237435 -0.6319726 -0.1471302 0.3255567
#>            V16      V17      V18       V19         V2        V20        V21
#> [1,] 0.8112799 1.070759 0.306881 -0.530663 -0.2292612 -0.9552142 -0.5782552
#>             V22        V23        V24       V25       V26        V27        V28
#> [1,] -0.2877327 -0.2700365 -0.1056708 0.3586397 0.6318139 -0.0130515 -0.3945027
#>             V29         V3       V30       V31         V32        V33       V34
#> [1,] -0.4119368 -0.1200355 0.1001423 0.5719916 -0.05107934 0.05134617 0.5300132
#>            V35      V36       V37        V38        V39         V4      V40
#> [1,] 0.4744862 1.309703 0.6603508 -0.1985702 -0.2974338 -0.2646901 0.378796
#>            V41        V42        V43        V44       V45        V46        V47
#> [1,] 0.1818089 -0.5310827 -0.8188741 -0.8333911 -1.211569 -0.9153935 -0.8151963
#>             V48        V49         V5         V50        V51         V52
#> [1,] -0.6406258 -0.3992325 -0.1788242 -0.05374701 -0.0494516 -0.08742109
#>              V53         V54         V55         V56         V57         V58
#> [1,] -0.02970635 -0.03192747 0.008838402 -0.01112986 0.008171016 -0.02745695
#>              V59          V6         V60         V7         V8         V9
#> [1,] -0.01522702 -0.06140789 0.003692468 0.06319721 -0.1425655 -0.9021503
#>           Bias
#> [1,] 0.9756815
#> 
#> $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 
#>  0.3333333