makeUndersampleWrapper {mlr} | R Documentation |
Fuse learner with simple ove/underrsampling for imbalancy correction in binary classification.
Description
Creates a learner object, which can be used like any other learner object. Internally uses oversample or undersample before every model fit.
Note that observation weights do not influence the sampling and are simply passed down to the next learner.
Usage
makeUndersampleWrapper(learner, usw.rate = 1, usw.cl = NULL)
makeOversampleWrapper(learner, osw.rate = 1, osw.cl = NULL)
Arguments
learner |
(Learner | |
usw.rate |
( |
usw.cl |
( |
osw.rate |
( |
osw.cl |
( |
Value
See Also
Other imbalancy:
makeOverBaggingWrapper()
,
oversample()
,
smote()
Other wrapper:
makeBaggingWrapper()
,
makeClassificationViaRegressionWrapper()
,
makeConstantClassWrapper()
,
makeCostSensClassifWrapper()
,
makeCostSensRegrWrapper()
,
makeDownsampleWrapper()
,
makeDummyFeaturesWrapper()
,
makeExtractFDAFeatsWrapper()
,
makeFeatSelWrapper()
,
makeFilterWrapper()
,
makeImputeWrapper()
,
makeMulticlassWrapper()
,
makeMultilabelBinaryRelevanceWrapper()
,
makeMultilabelClassifierChainsWrapper()
,
makeMultilabelDBRWrapper()
,
makeMultilabelNestedStackingWrapper()
,
makeMultilabelStackingWrapper()
,
makeOverBaggingWrapper()
,
makePreprocWrapper()
,
makePreprocWrapperCaret()
,
makeRemoveConstantFeaturesWrapper()
,
makeSMOTEWrapper()
,
makeTuneWrapper()
,
makeWeightedClassesWrapper()