Support Vector Machine

A learner for a regression support vector machine implemented in e1071::svm().

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")

References

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

See also

Dictionary of Learners: mlr3::mlr_learners

Super classes

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

Methods

Public methods

• LearnerRegrSVM$new()

• LearnerRegrSVM$clone()

Inherited methods

• 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()

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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")) {
  learner = mlr3::lrn("regr.svm")
  print(learner)

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