Creates a learner object, which can be used like any other learner object. Internally uses smote before every model fit.

Note that observation weights do not influence the sampling and are simply passed down to the next learner.

makeSMOTEWrapper(
learner,
sw.rate = 1,
sw.nn = 5L,
sw.standardize = TRUE,
sw.alt.logic = FALSE
)

Arguments

learner (Learner | character(1)) The learner. If you pass a string the learner will be created via makeLearner. (numeric(1)) Factor to oversample the smaller class. Must be between 1 and Inf, where 1 means no oversampling and 2 would mean doubling the class size. Default is 1. (integer(1)) Number of nearest neighbors to consider. Default is 5. (logical(1)) Standardize input variables before calculating the nearest neighbors for data sets with numeric input variables only. For mixed variables (numeric and factor) the gower distance is used and variables are standardized anyway. Default is TRUE. (logical(1)) Use an alternative logic for selection of minority class observations. Instead of sampling a minority class element AND one of its nearest neighbors, each minority class element is taken multiple times (depending on rate) for the interpolation and only the corresponding nearest neighbor is sampled. Default is FALSE.

Value

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