mlr3learners (version 0.5.3)

mlr_learners_regr.svm: Support Vector Machine

Description

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

, |Id |Type |Default |Levels |Range |, |:---------|:---------|:--------------|:-----------------------------------|:------------------------------------|, |cachesize |numeric |40 | |\((-\infty, \infty)\) |, |coef0 |numeric |0 | |\((-\infty, \infty)\) |, |cost |numeric |1 | |\([0, \infty)\) |, |cross |integer |0 | |\([0, \infty)\) |, |degree |integer |3 | |\([1, \infty)\) |, |epsilon |numeric |- | |\([0, \infty)\) |, |fitted |logical |TRUE |TRUE, FALSE |- |, |gamma |numeric |- | |\([0, \infty)\) |, |kernel |character |radial |linear, polynomial, radial, sigmoid |- |, |nu |numeric |0.5 | |\((-\infty, \infty)\) |, |scale |untyped |TRUE | |- |, |shrinking |logical |TRUE |TRUE, FALSE |- |, |tolerance |numeric |0.001 | |\([0, \infty)\) |, |type |character |eps-regression |eps-regression, nu-regression |- |

Super classes

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

Methods

Public methods

• LearnerRegrSVM$new()

• LearnerRegrSVM$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

LearnerRegrSVM$new()

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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.

References

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

See Also

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

• Package mlr3extralearners for more learners.

• Dictionary of Learners: mlr_learners

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

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

• Extension packages for additional task types:

  • mlr3proba for probabilistic supervised regression and survival analysis.

  • mlr3cluster for unsupervised clustering.

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

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.ranger, mlr_learners_regr.xgboost

Examples

if (requireNamespace("e1071", quietly = TRUE)) {
  learner = mlr3::lrn("regr.svm")
  print(learner)

  # available parameters:
learner$param_set$ids()
}