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Support vector machine for regression. Calls e1071::svm() from package e1071.

Dictionary

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

mlr_learners$get("regr.svm")
lrn("regr.svm")

Meta Information

  • Task type: “regr”

  • Predict Types: “response”

  • Feature Types: “logical”, “integer”, “numeric”

  • Required Packages: mlr3, mlr3learners, e1071

Parameters

IdTypeDefaultLevelsRange
cachesizenumeric40\((-\infty, \infty)\)
coef0numeric0\((-\infty, \infty)\)
costnumeric1\([0, \infty)\)
crossinteger0\([0, \infty)\)
degreeinteger3\([1, \infty)\)
epsilonnumeric0.1\([0, \infty)\)
fittedlogicalTRUETRUE, FALSE-
gammanumeric-\([0, \infty)\)
kernelcharacterradiallinear, polynomial, radial, sigmoid-
nunumeric0.5\((-\infty, \infty)\)
scaleuntypedTRUE-
shrinkinglogicalTRUETRUE, FALSE-
tolerancenumeric0.001\([0, \infty)\)
typecharactereps-regressioneps-regression, nu-regression-

References

Cortes, Corinna, Vapnik, Vladimir (1995). “Support-vector networks.” Machine Learning, 20(3), 273--297. doi:10.1007/BF00994018 .

See also

Other Learner: mlr_learners_classif.cv_glmnet, mlr_learners_classif.glmnet, mlr_learners_classif.kknn, mlr_learners_classif.lda, mlr_learners_classif.log_reg, mlr_learners_classif.multinom, mlr_learners_classif.naive_bayes, mlr_learners_classif.nnet, mlr_learners_classif.qda, mlr_learners_classif.ranger, mlr_learners_classif.svm, mlr_learners_classif.xgboost, mlr_learners_regr.cv_glmnet, mlr_learners_regr.glmnet, mlr_learners_regr.kknn, mlr_learners_regr.km, mlr_learners_regr.lm, mlr_learners_regr.nnet, mlr_learners_regr.ranger, mlr_learners_regr.xgboost

Super classes

mlr3::Learner -> mlr3::LearnerRegr -> LearnerRegrSVM

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

LearnerRegrSVM$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

if (requireNamespace("e1071", quietly = TRUE)) {
# Define the Learner and set parameter values
learner = lrn("regr.svm")
print(learner)

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

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

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

# print the model
print(learner$model)

# importance method
if("importance" %in% learner$properties) print(learner$importance)

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

# Score the predictions
predictions$score()
}
#> <LearnerRegrSVM:regr.svm>: Support Vector Machine
#> * Model: -
#> * Parameters: list()
#> * Packages: mlr3, mlr3learners, e1071
#> * Predict Types:  [response]
#> * Feature Types: logical, integer, numeric
#> * Properties: -
#> 
#> Call:
#> svm.default(x = data, y = task$truth())
#> 
#> 
#> Parameters:
#>    SVM-Type:  eps-regression 
#>  SVM-Kernel:  radial 
#>        cost:  1 
#>       gamma:  0.1 
#>     epsilon:  0.1 
#> 
#> 
#> Number of Support Vectors:  20
#> 
#> regr.mse 
#> 11.51758