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 http://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.
makeMulticlassWrapper(learner, mcw.method = "onevsrest")(Learner | character(1))
The learner.
If you pass a string the learner will be created via makeLearner.
(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”.
Other wrapper:
makeBaggingWrapper(),
makeClassificationViaRegressionWrapper(),
makeConstantClassWrapper(),
makeCostSensClassifWrapper(),
makeCostSensRegrWrapper(),
makeDownsampleWrapper(),
makeDummyFeaturesWrapper(),
makeExtractFDAFeatsWrapper(),
makeFeatSelWrapper(),
makeFilterWrapper(),
makeImputeWrapper(),
makeMultilabelBinaryRelevanceWrapper(),
makeMultilabelClassifierChainsWrapper(),
makeMultilabelDBRWrapper(),
makeMultilabelNestedStackingWrapper(),
makeMultilabelStackingWrapper(),
makeOverBaggingWrapper(),
makePreprocWrapperCaret(),
makePreprocWrapper(),
makeRemoveConstantFeaturesWrapper(),
makeSMOTEWrapper(),
makeTuneWrapper(),
makeUndersampleWrapper(),
makeWeightedClassesWrapper()