mlr: makeClassificationViaRegressionWrapper – R documentation

Become an expert in R — Interactive courses, Cheat Sheets, certificates and more!

makeClassificationViaRegressionWrapper

Classification via regression wrapper.

Description

Builds regression models that predict for the positive class whether a particular example belongs to it (1) or not (-1).

Probabilities are generated by transforming the predictions with a softmax.

Inspired by WEKA's ClassificationViaRegression (http://weka.sourceforge.net/doc.dev/weka/classifiers/meta/ClassificationViaRegression.html).

Usage

makeClassificationViaRegressionWrapper(learner, predict.type = "response")

Arguments

`learner`	(Learner \| `character(1)`) The learner. If you pass a string the learner will be created via makeLearner.
`predict.type`	(`character(1)`) “response” (= labels) or “prob” (= probabilities and labels by selecting the one with maximal probability).

Value

Learner.

Other wrapper: makeBaggingWrapper(), makeConstantClassWrapper(), makeCostSensClassifWrapper(), makeCostSensRegrWrapper(), makeDownsampleWrapper(), makeDummyFeaturesWrapper(), makeExtractFDAFeatsWrapper(), makeFeatSelWrapper(), makeFilterWrapper(), makeImputeWrapper(), makeMulticlassWrapper(), makeMultilabelBinaryRelevanceWrapper(), makeMultilabelClassifierChainsWrapper(), makeMultilabelDBRWrapper(), makeMultilabelNestedStackingWrapper(), makeMultilabelStackingWrapper(), makeOverBaggingWrapper(), makePreprocWrapperCaret(), makePreprocWrapper(), makeRemoveConstantFeaturesWrapper(), makeSMOTEWrapper(), makeTuneWrapper(), makeUndersampleWrapper(), makeWeightedClassesWrapper()

Examples

lrn = makeLearner("regr.rpart")
lrn = makeClassificationViaRegressionWrapper(lrn)
mod = train(lrn, sonar.task, subset = 1:140)
predictions = predict(mod, newdata = getTaskData(sonar.task)[141:208, 1:60])

mlr

Machine Learning in R

v2.19.0

BSD_2_clause + file LICENSE

Authors

Bernd Bischl [aut] (<https://orcid.org/0000-0001-6002-6980>), Michel Lang [aut] (<https://orcid.org/0000-0001-9754-0393>), Lars Kotthoff [aut], Patrick Schratz [aut, cre] (<https://orcid.org/0000-0003-0748-6624>), Julia Schiffner [aut], Jakob Richter [aut], Zachary Jones [aut], Giuseppe Casalicchio [aut] (<https://orcid.org/0000-0001-5324-5966>), Mason Gallo [aut], Jakob Bossek [ctb] (<https://orcid.org/0000-0002-4121-4668>), Erich Studerus [ctb] (<https://orcid.org/0000-0003-4233-0182>), Leonard Judt [ctb], Tobias Kuehn [ctb], Pascal Kerschke [ctb] (<https://orcid.org/0000-0003-2862-1418>), Florian Fendt [ctb], Philipp Probst [ctb] (<https://orcid.org/0000-0001-8402-6790>), Xudong Sun [ctb] (<https://orcid.org/0000-0003-3269-2307>), Janek Thomas [ctb] (<https://orcid.org/0000-0003-4511-6245>), Bruno Vieira [ctb], Laura Beggel [ctb] (<https://orcid.org/0000-0002-8872-8535>), Quay Au [ctb] (<https://orcid.org/0000-0002-5252-8902>), Martin Binder [ctb], Florian Pfisterer [ctb], Stefan Coors [ctb], Steve Bronder [ctb], Alexander Engelhardt [ctb], Christoph Molnar [ctb], Annette Spooner [ctb]

Initial release