Skip to contents

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

IdTypeDefaultRangeLevels
cachesizenumeric40\((-\infty, \infty)\)-
coef0numeric0\((-\infty, \infty)\)-
costnumeric1\([0, \infty)\)-
crossinteger0\([0, \infty)\)-
degreeinteger3\([1, \infty)\)-
epsilonnumeric-\([0, \infty)\)-
fittedlogicalTRUE-TRUE, FALSE
gammanumeric-\([0, \infty)\)-
kernelcharacterradial-linear, polynomial, radial, sigmoid
nunumeric0.5\((-\infty, \infty)\)-
scalelistTRUE--
shrinkinglogicalTRUE-TRUE, FALSE
tolerancenumeric0.001\([0, \infty)\)-
typecharactereps-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

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

Inherited methods


Method new()

Creates a new instance of this R6 class.

Usage


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"