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Least squares support vector machine for classification. Calls kernlab::lssvm() from kernlab. Parameters sigma, degree, scale, offset, order, length, lambda, and normalized are added to make tuning kpar easier. If kpar is provided then these new parameters are ignored. If none are provided then the default "automatic" is used for kpar.

Dictionary

This Learner can be instantiated via lrn():

lrn("classif.lssvm")

Meta Information

  • Task type: “classif”

  • Predict Types: “response”

  • Feature Types: “integer”, “numeric”

  • Required Packages: mlr3, mlr3extralearners, kernlab

Parameters

IdTypeDefaultLevelsRange
scaleduntypedTRUE-
kernelcharacterrbfdotrbfdot, polydot, vanilladot, tanhdot, laplacedot, besseldot, anovadot, splinedot, stringdot-
sigmanumeric-\((-\infty, \infty)\)
degreenumeric-\((-\infty, \infty)\)
scalenumeric-\((-\infty, \infty)\)
offsetnumeric-\((-\infty, \infty)\)
ordernumeric-\((-\infty, \infty)\)
lengthinteger-\([0, \infty)\)
lambdanumeric-\((-\infty, \infty)\)
normalizedlogical-TRUE, FALSE-
kparuntyped"automatic"-
taunumeric0.01\((-\infty, \infty)\)
reducedlogicalTRUETRUE, FALSE-
rankinteger-\((-\infty, \infty)\)
deltainteger40\((-\infty, \infty)\)
tolnumeric1e-04\((-\infty, \infty)\)
fitlogicalTRUETRUE, FALSE-
na.actionuntypedna.omit-
couplercharacterminpairminpair, 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

Author

RaphaelS1

Super classes

mlr3::Learner -> mlr3::LearnerClassif -> LearnerClassifLSSVM

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

LearnerClassifLSSVM$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

# Define the Learner
learner = mlr3::lrn("classif.lssvm")
print(learner)
#> <LearnerClassifLSSVM:classif.lssvm>: Support Vector Machine
#> * Model: -
#> * Parameters: list()
#> * Packages: mlr3, mlr3extralearners, kernlab
#> * Predict Types:  [response]
#> * Feature Types: integer, numeric
#> * Properties: multiclass, twoclass

# 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)
#> Using automatic sigma estimation (sigest) for RBF or laplace kernel 

print(learner$model)
#> Least Squares Support Vector Machine object of class "lssvm" 
#> 
#> problem type : classification 
#>  parameter : tau = 0.01 
#> 
#> Gaussian Radial Basis kernel function. 
#>  Hyperparameter : sigma =  0.0104611527670791 
#> 
#> Number of data points used for training : 42 
#> 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 
#> 0.08695652