Fuse learner with simple ove/underrsampling for imbalancy correction in binary classification.
Source:R/OverUndersampleWrapper.R
makeUndersampleWrapper.RdCreates 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 |
character(1))
The learner. If you pass a string the learner will be created via makeLearner.- usw.rate
(
numeric(1))
Factor to downsample a class. Must be between 0 and 1, where 1 means no downsampling, 0.5 implies reduction to 50 percent and 0 would imply reduction to 0 observations. Default is 1.- usw.cl
(
character(1))
Class that should be undersampled. Default isNULL, which means the larger one.- osw.rate
(
numeric(1))
Factor to oversample a class. Must be between 1 andInf, where 1 means no oversampling and 2 would mean doubling the class size. Default is 1.- osw.cl
(
character(1))
Class that should be oversampled. Default isNULL, which means the smaller one.
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(),
makePreprocWrapperCaret(),
makePreprocWrapper(),
makeRemoveConstantFeaturesWrapper(),
makeSMOTEWrapper(),
makeTuneWrapper(),
makeWeightedClassesWrapper()