Classification Kernlab Support Vector Machine

Support vector machine for classification. Calls kernlab::ksvm() from kernlab.

Dictionary

This Learner can be instantiated via lrn():

lrn("classif.ksvm")

Meta Information

• Task type: “classif”

• Predict Types: “response”, “prob”

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

• Required Packages: mlr3, mlr3extralearners, kernlab

Parameters

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

References

Karatzoglou, Alexandros, Smola, Alex, Hornik, Kurt, Zeileis, Achim (2004). “kernlab-an S4 package for kernel methods in R.” Journal of statistical software, 11(9), 1–20.

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$base_learner()
• mlr3::Learner$configure()
• mlr3::Learner$encapsulate()
• mlr3::Learner$format()
• mlr3::Learner$help()
• mlr3::Learner$predict()
• mlr3::Learner$predict_newdata()
• mlr3::Learner$print()
• mlr3::Learner$reset()
• mlr3::Learner$selected_features()
• 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

# Define the Learner
learner = mlr3::lrn("classif.ksvm")
print(learner)
#> 
#> ── <LearnerClassifKSVM> (classif.ksvm): Support Vector Machine ─────────────────
#> • Model: -
#> • Parameters: list()
#> • Packages: mlr3, mlr3extralearners, and kernlab
#> • Predict Types: [response] and prob
#> • Feature Types: logical, integer, numeric, character, factor, and ordered
#> • Encapsulation: none (fallback: -)
#> • Properties: multiclass, twoclass, and weights
#> • Other settings: use_weights = 'use'

# Define a Task
task = mlr3::tsk("sonar")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)
#> Support Vector Machine object of class "ksvm" 
#> 
#> SV type: C-svc  (classification) 
#>  parameter : cost C = 1 
#> 
#> Gaussian Radial Basis kernel function. 
#>  Hyperparameter : sigma =  0.0110313583916186 
#> 
#> Number of Support Vectors : 114 
#> 
#> Objective Function Value : -64.3903 
#> Training error : 0.043165 


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()
#> classif.ce 
#>  0.1304348