mlr (version 2.19.1)

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)

Value

Learner.

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.

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()