Support Vector Machine

Support vector machine for regression. 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("regr.svm")
lrn("regr.svm")

Meta Information

• Task type: “regr”

• Predict Types: “response”

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

• Required Packages: mlr3, mlr3learners, e1071

Parameters

Id	Type	Default	Range	Levels
cachesize	numeric	40	\((-\infty, \infty)\)	-
coef0	numeric	0	\((-\infty, \infty)\)	-
cost	numeric	1	\([0, \infty)\)	-
cross	integer	0	\([0, \infty)\)	-
degree	integer	3	\([1, \infty)\)	-
epsilon	numeric	-	\([0, \infty)\)	-
fitted	logical	TRUE	-	TRUE, FALSE
gamma	numeric	-	\([0, \infty)\)	-
kernel	character	radial	-	linear, polynomial, radial, sigmoid
nu	numeric	0.5	\((-\infty, \infty)\)	-
scale	list	TRUE	-	-
shrinking	logical	TRUE	-	TRUE, FALSE
tolerance	numeric	0.001	\([0, \infty)\)	-
type	character	eps-regression	-	eps-regression, nu-regression

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.svm, 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.ranger, mlr_learners_regr.xgboost, mlr_learners_surv.cv_glmnet, mlr_learners_surv.glmnet, mlr_learners_surv.ranger, mlr_learners_surv.xgboost

Super classes

mlr3::Learner -> mlr3::LearnerRegr -> LearnerRegrSVM

Methods

Public methods

• LearnerRegrSVM$new()

• LearnerRegrSVM$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

LearnerRegrSVM$new()

---

Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerRegrSVM$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

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

  # available parameters:
learner$param_set$ids()
}
#> <LearnerRegrSVM:regr.svm>
#> * Model: -
#> * Parameters: list()
#> * Packages: mlr3, mlr3learners, e1071
#> * Predict Type: response
#> * Feature types: logical, integer, numeric
#> * Properties: -
#>  [1] "cachesize" "coef0"     "cost"      "cross"     "degree"    "epsilon"  
#>  [7] "fitted"    "gamma"     "kernel"    "nu"        "scale"     "shrinking"
#> [13] "tolerance" "type"