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")

Meta Information

• Task type: “classif”

• Predict Types: “response”, “prob”

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

• Required Packages: mlr3extralearners, kernlab

Parameters

Id	Type	Default	Levels	Range
scaled	logical	TRUE	TRUE, FALSE	\((-\infty, \infty)\)
type	character	C-svc	C-svc, nu-svc, C-bsvc, spoc-svc, kbb-svc	\((-\infty, \infty)\)
kernel	character	rbfdot	rbfdot, polydot, vanilladot, laplacedot, besseldot, anovadot	\((-\infty, \infty)\)
C	numeric	1		\((-\infty, \infty)\)
nu	numeric	0.2		\([0, \infty)\)
cache	integer	40		\([1, \infty)\)
tol	numeric	0.001		\([0, \infty)\)
shrinking	logical	TRUE	TRUE, FALSE	\((-\infty, \infty)\)
sigma	numeric	-		\([0, \infty)\)
degree	integer	-		\([1, \infty)\)
scale	numeric	-		\([0, \infty)\)
order	integer	-		\((-\infty, \infty)\)
offset	numeric	-		\((-\infty, \infty)\)

See also

• Dictionary of Learners: mlr3::mlr_learners.

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

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

• mlr3learners for a selection of recommended learners.

• mlr3cluster for unsupervised clustering learners.

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

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

Author

mboecker

Super classes

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

Methods

Public methods

• LearnerClassifKSVM$new()

• LearnerClassifKSVM$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

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

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

  # available parameters:
  learner$param_set$ids()
}
#> <LearnerClassifKSVM:classif.ksvm>
#> * Model: -
#> * Parameters: list()
#> * Packages: mlr3, mlr3extralearners, kernlab
#> * Predict Type: response
#> * Feature types: logical, integer, numeric, character, factor, ordered
#> * Properties: multiclass, twoclass, weights
#>  [1] "scaled"    "type"      "kernel"    "C"         "nu"        "cache"    
#>  [7] "tol"       "shrinking" "sigma"     "degree"    "scale"     "order"    
#> [13] "offset"