mlr: makeSMOTEWrapper – R documentation

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makeSMOTEWrapper

Fuse learner with SMOTE oversampling for imbalancy correction in binary classification.

Description

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.

Usage

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.
`sw.rate`	(`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.
`sw.nn`	(`integer(1)`) Number of nearest neighbors to consider. Default is 5.
`sw.standardize`	(`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`.
`sw.alt.logic`	(`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

Learner.

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()

mlr

Machine Learning in R

v2.19.0

BSD_2_clause + file LICENSE

Authors

Bernd Bischl [aut] (<https://orcid.org/0000-0001-6002-6980>), Michel Lang [aut] (<https://orcid.org/0000-0001-9754-0393>), Lars Kotthoff [aut], Patrick Schratz [aut, cre] (<https://orcid.org/0000-0003-0748-6624>), Julia Schiffner [aut], Jakob Richter [aut], Zachary Jones [aut], Giuseppe Casalicchio [aut] (<https://orcid.org/0000-0001-5324-5966>), Mason Gallo [aut], Jakob Bossek [ctb] (<https://orcid.org/0000-0002-4121-4668>), Erich Studerus [ctb] (<https://orcid.org/0000-0003-4233-0182>), Leonard Judt [ctb], Tobias Kuehn [ctb], Pascal Kerschke [ctb] (<https://orcid.org/0000-0003-2862-1418>), Florian Fendt [ctb], Philipp Probst [ctb] (<https://orcid.org/0000-0001-8402-6790>), Xudong Sun [ctb] (<https://orcid.org/0000-0003-3269-2307>), Janek Thomas [ctb] (<https://orcid.org/0000-0003-4511-6245>), Bruno Vieira [ctb], Laura Beggel [ctb] (<https://orcid.org/0000-0002-8872-8535>), Quay Au [ctb] (<https://orcid.org/0000-0002-5252-8902>), Martin Binder [ctb], Florian Pfisterer [ctb], Stefan Coors [ctb], Steve Bronder [ctb], Alexander Engelhardt [ctb], Christoph Molnar [ctb], Annette Spooner [ctb]

Initial release

makeSMOTEWrapper

Description

Usage

Arguments

Value

See Also

mlr

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