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
)(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.
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()