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
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].CostSensClassifModel,
CostSensClassifWrapper,
makeCostSensClassifWrapper;
CostSensRegrModel,
CostSensRegrWrapper,
makeCostSensRegrWrapper;
makeBaggingWrapper;
makeDownsampleWrapper;
makeFeatSelWrapper;
makeFilterWrapper;
makeImputeWrapper;
makeOverBaggingWrapper;
makeOversampleWrapper,
makeUndersampleWrapper;
makePreprocWrapperCaret;
makePreprocWrapper;
makeSMOTEWrapper;
makeTuneWrapper;
makeWeightedClassesWrapper