Classification Kernlab Support Vector Machine

Calls kernlab::ksvm from package kernlab.

Dictionary

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

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

Traits

• Packages: kernlab

• Predict Types: response, prob

• Feature Types: logical, integer, numeric, character, factor, ordered

• Properties: multiclass, twoclass, weights

See also

Dictionary of Learners: mlr3::mlr_learners

Author

mboecker

Super classes

mlr3::Learner -> mlr3::LearnerClassif -> LearnerClassifKSVM

Methods

Public methods

• LearnerClassifKSVM$new()

• LearnerClassifKSVM$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()

---

Method new()

Creates a new instance of this R6 class.

Usage

LearnerClassifKSVM$new()

---

Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerClassifKSVM$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

# stop example failing with warning if package not installed
learner = suppressWarnings(mlr3::lrn("classif.ksvm"))
print(learner)
#> <LearnerClassifKSVM:classif.ksvm>
#> * Model: -
#> * Parameters: list()
#> * Packages: kernlab
#> * Predict Type: response
#> * Feature types: logical, integer, numeric, character, factor, ordered
#> * Properties: multiclass, twoclass, weights

# available parameters:
learner$param_set$ids()
#>  [1] "scaled"    "type"      "kernel"    "C"         "nu"        "cache"    
#>  [7] "tol"       "shrinking" "sigma"     "degree"    "scale"     "order"    
#> [13] "offset"