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mlr (version 2.13)

makeDownsampleWrapper: Fuse learner with simple downsampling (subsampling).

Description

Creates a learner object, which can be used like any other learner object. It will only be trained on a subset of the original data to save computational time.

Usage

makeDownsampleWrapper(learner, dw.perc = 1, dw.stratify = FALSE)

Arguments

learner

(Learner | character(1)) The learner. If you pass a string the learner will be created via makeLearner.

dw.perc

(numeric(1)) See downsample. Default is 1.

dw.stratify

(logical(1)) See downsample. Default is FALSE.

Value

Learner.

See Also

Other downsample: downsample

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