makeRemoveConstantFeaturesWrapper: Fuse learner with removal of constant features preprocessing.

In guillermozbta/mir: Machine Learning in R

Description

Fuses a base learner with the preprocessing implemented in removeConstantFeatures.

Usage

makeRemoveConstantFeaturesWrapper(learner, perc = 0,
  dont.rm = character(0L), na.ignore = FALSE,
  tol = .Machine$double.eps^0.5)

Arguments

learner	[Learner | character(1)] The learner. If you pass a string the learner will be created via makeLearner.
perc	[numeric(1)] The percentage of a feature values in [0, 1) that must differ from the mode value. Default is 0, which means only constant features with exactly one observed level are removed.
dont.rm	[character] Names of the columns which must not be deleted. Default is no columns.
na.ignore	[logical(1)] Should NAs be ignored in the percentage calculation? (Or should they be treated as a single, extra level in the percentage calculation?) Note that if the feature has only missing values, it is always removed. Default is FALSE.
tol	[numeric(1)] Numerical tolerance to treat two numbers as equal. Variables stored as double will get rounded accordingly before computing the mode. Default is sqrt(.Maschine$double.eps).

Value

[Learner].

See Also

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

guillermozbta/mir documentation built on May 11, 2019, 6:27 p.m.