R: Fuse learner with removal of constant features preprocessing.

makeRemoveConstantFeaturesWrapper {mlr} | R Documentation

## Fuse learner with removal of constant features preprocessing.

### Description

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()`

[Package mlr version 2.19.0 Index]