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$train()
• mlr3::LearnerClassif$predict_newdata_fast()

---

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 = 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 = 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,] -0.2060339 -0.7063694 -1.252557 -1.570395 -0.9097729 -0.01436652 0.461203
#>            V16       V17       V18        V19        V2        V20        V21
#> [1,] 0.6445488 0.6743596 0.3640874 -0.3366012 -0.307473 -0.8474674 -0.4222117
#>             V22        V23        V24       V25       V26        V27       V28
#> [1,] -0.3155334 -0.5743897 -0.1353136 0.5443686 0.3513806 0.02642717 -0.350829
#>             V29         V3       V30      V31        V32        V33      V34
#> [1,] -0.1985192 -0.2667241 0.4953511 1.346782 -0.1682488 0.08042011 0.157795
#>            V35      V36       V37         V38        V39         V4       V40
#> [1,] 0.3872168 1.115335 0.7479369 -0.03157963 -0.1281457 -0.3673548 0.4090931
#>              V41        V42        V43        V44       V45       V46
#> [1,] -0.09252528 -0.8417086 -0.9602945 -0.8537748 -1.132665 -1.012483
#>             V47        V48        V49         V5         V50         V51
#> [1,] -0.6502071 -0.5116804 -0.3654636 -0.1498352 -0.05901754 -0.06197609
#>             V52         V53     V54         V55         V56        V57
#> [1,] -0.0831947 -0.01557825 -0.0374 -0.01522015 -0.03278789 0.01152512
#>              V58         V59          V6         V60          V7         V8
#> [1,] -0.04835799 -0.02035307 -0.03270572 -0.02228545 -0.08921066 -0.1376004
#>             V9      Bias
#> [1,] -0.379114 0.6446468
#> 
#> $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 
#>  0.3478261