Fuse learner with removal of constant features preprocessing.

Fuses a base learner with the preprocessing implemented in removeConstantFeatures.

Usage

makeRemoveConstantFeaturesWrapper(
  learner,
  perc = 0,
  dont.rm = character(0L),
  na.ignore = FALSE,
  wrap.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.

wrap.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()