makeMulticlassWrapper {mlr}

Fuse learner with multiclass method.

Description

Fuses a base learner with a multi-class method. Creates a learner object, which can be used like any other learner object. This way learners which can only handle binary classification will be able to handle multi-class problems, too.

We use a multiclass-to-binary reduction principle, where multiple binary problems are created from the multiclass task. How these binary problems are generated is defined by an error-correcting-output-code (ECOC) code book. This also allows the simple and well-known one-vs-one and one-vs-rest approaches. Decoding is currently done via Hamming decoding, see e.g. here https://jmlr.org/papers/volume11/escalera10a/escalera10a.pdf.

Currently, the approach always operates on the discrete predicted labels of the binary base models (instead of their probabilities) and the created wrapper cannot predict posterior probabilities.

Usage

makeMulticlassWrapper(learner, mcw.method = "onevsrest")

Arguments

learner

(Learner | character(1))
The learner. If you pass a string the learner will be created via makeLearner.

mcw.method

(character(1) | function)
“onevsone” or “onevsrest”. You can also pass a function, with signature ⁠function(task)⁠ and which returns a ECOC codematrix with entries +1,-1,0. Columns define new binary problems, rows correspond to classes (rows must be named). 0 means class is not included in binary problem. Default is “onevsrest”.

Value

Learner.

See Also

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