Support Vector Machine

Support vector machine for classification. Calls e1071::svm() from package e1071.

Dictionary

This Learner can be instantiated via the dictionary mlr_learners or with the associated sugar function lrn():

mlr_learners$get("classif.svm")
lrn("classif.svm")

Meta Information

• Task type: “classif”

• Predict Types: “response”, “prob”

• Feature Types: “logical”, “integer”, “numeric”

• Required Packages: mlr3, mlr3learners, e1071

Parameters

Id	Type	Default	Levels	Range
cachesize	numeric	40		\((-\infty, \infty)\)
class.weights	untyped			-
coef0	numeric	0		\((-\infty, \infty)\)
cost	numeric	1		\([0, \infty)\)
cross	integer	0		\([0, \infty)\)
decision.values	logical	FALSE	TRUE, FALSE	-
degree	integer	3		\([1, \infty)\)
epsilon	numeric	0.1		\([0, \infty)\)
fitted	logical	TRUE	TRUE, FALSE	-
gamma	numeric	-		\([0, \infty)\)
kernel	character	radial	linear, polynomial, radial, sigmoid	-
nu	numeric	0.5		\((-\infty, \infty)\)
scale	untyped	TRUE		-
shrinking	logical	TRUE	TRUE, FALSE	-
tolerance	numeric	0.001		\([0, \infty)\)
type	character	C-classification	C-classification, nu-classification	-

References

Cortes, Corinna, Vapnik, Vladimir (1995). “Support-vector networks.” Machine Learning, 20(3), 273--297. doi:10.1007/BF00994018 .

See also

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

• Package mlr3extralearners for more learners.

• Dictionary of Learners: mlr_learners

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

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

• Extension packages for additional task types:

  • mlr3proba for probabilistic supervised regression and survival analysis.

  • mlr3cluster for unsupervised clustering.

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

Other Learner: mlr_learners_classif.cv_glmnet, mlr_learners_classif.glmnet, mlr_learners_classif.kknn, mlr_learners_classif.lda, mlr_learners_classif.log_reg, mlr_learners_classif.multinom, mlr_learners_classif.naive_bayes, mlr_learners_classif.nnet, mlr_learners_classif.qda, mlr_learners_classif.ranger, mlr_learners_classif.xgboost, mlr_learners_regr.cv_glmnet, mlr_learners_regr.glmnet, mlr_learners_regr.kknn, mlr_learners_regr.km, mlr_learners_regr.lm, mlr_learners_regr.nnet, mlr_learners_regr.ranger, mlr_learners_regr.svm, mlr_learners_regr.xgboost

Super classes

mlr3::Learner -> mlr3::LearnerClassif -> LearnerClassifSVM

Methods

Public methods

• LearnerClassifSVM$new()

• LearnerClassifSVM$clone()

Inherited methods

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

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Method new()

Creates a new instance of this R6 class.

Usage

LearnerClassifSVM$new()

---

Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerClassifSVM$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

if (requireNamespace("e1071", quietly = TRUE)) {
  learner = mlr3::lrn("classif.svm")
  print(learner)

  # available parameters:
learner$param_set$ids()
}
#> <LearnerClassifSVM:classif.svm>
#> * Model: -
#> * Parameters: list()
#> * Packages: mlr3, mlr3learners, e1071
#> * Predict Types:  [response], prob
#> * Feature Types: logical, integer, numeric
#> * Properties: multiclass, twoclass
#>  [1] "cachesize"       "class.weights"   "coef0"           "cost"           
#>  [5] "cross"           "decision.values" "degree"          "epsilon"        
#>  [9] "fitted"          "gamma"           "kernel"          "nu"             
#> [13] "scale"           "shrinking"       "tolerance"       "type"