Regression Kernlab Support Vector Machine

Support Vector Regression. Calls kernlab::ksvm() from kernlab.

Dictionary

This Learner can be instantiated via lrn():

lrn("regr.ksvm")

Meta Information

• Task type: “regr”

• Predict Types: “response”

• 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	eps-svr	eps-svr, nu-svr, eps-bsvr	-
kernel	character	rbfdot	rbfdot, polydot, vanilladot, laplacedot, besseldot, anovadot	-
C	numeric	1		\((-\infty, \infty)\)
nu	numeric	0.2		\([0, \infty)\)
epsilon	numeric	0.1		\((-\infty, \infty)\)
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	-		\((-\infty, \infty)\)
na.action	untyped	na.omit		-
fit	logical	TRUE	TRUE, FALSE	-

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::LearnerRegr -> LearnerRegrKSVM

Methods

Public methods

• LearnerRegrKSVM$new()

• LearnerRegrKSVM$clone()

Inherited methods

• mlr3::Learner$base_learner()
• mlr3::Learner$encapsulate()
• 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

LearnerRegrKSVM$new()

---

Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerRegrKSVM$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

# Define the Learner
learner = mlr3::lrn("regr.ksvm")
print(learner)
#> <LearnerRegrKSVM:regr.ksvm>: Support Vector Machine
#> * Model: -
#> * Parameters: list()
#> * Packages: mlr3, mlr3extralearners, kernlab
#> * Predict Types:  [response]
#> * Feature Types: logical, integer, numeric, character, factor, ordered
#> * Properties: weights

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

# 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: eps-svr  (regression) 
#>  parameter : epsilon = 0.1  cost C = 1 
#> 
#> Gaussian Radial Basis kernel function. 
#>  Hyperparameter : sigma =  0.0904064155682546 
#> 
#> Number of Support Vectors : 19 
#> 
#> Objective Function Value : -5.5883 
#> Training error : 0.169374 


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

# Score the predictions
predictions$score()
#> regr.mse 
#> 15.80787