mlr_learners_classif.svm {mlr3learners}

Support Vector Machine

Description

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

Dictionary

This mlr3::Learner can be instantiated via the dictionary mlr3::mlr_learners or with the associated sugar function mlr3::lrn():

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

Meta Information

• Task type: “classif”

• Predict Types: “response”, “prob”

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

• Required Packages: mlr3, mlr3learners, e1071

Parameters

Id	Type	Default	Levels	Range
cachesize	numeric	40		(-\infty, \infty)
class.weights	untyped	NULL		-
coef0	numeric	0		(-\infty, \infty)
cost	numeric	1		[0, \infty)
cross	integer	0		[0, \infty)
decision.values	logical	FALSE	TRUE, FALSE	-
degree	integer	3		[1, \infty)
epsilon	numeric	0.1		[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	C-classification	C-classification, nu-classification	-

Super classes

mlr3::Learner -> mlr3::LearnerClassif -> LearnerClassifSVM

Methods

Public methods

• LearnerClassifSVM$new()

• LearnerClassifSVM$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()

Method new()

Creates a new instance of this R6 class.

Usage

LearnerClassifSVM$new()

Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerClassifSVM$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. doi:10.1007/BF00994018.

See Also

• Chapter in the mlr3book: https://mlr3book.mlr-org.com/chapters/chapter2/data_and_basic_modeling.html#sec-learners

• Package mlr3extralearners for more learners.

• 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).

• 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.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.svm, mlr_learners_regr.xgboost

Examples

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

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

# 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()
}