R: Fuse learner with preprocessing.

makePreprocWrapperCaret {mlr} | R Documentation

## Fuse learner with preprocessing.

### Description

Fuses a learner with preprocessing methods provided by caret::preProcess. Before training the preprocessing will be performed and the preprocessing model will be stored. Before prediction the preprocessing model will transform the test data according to the trained model.

After being wrapped the learner will support missing values although this will only be the case if `ppc.knnImpute`, `ppc.bagImpute` or `ppc.medianImpute` is set to `TRUE`.

### Usage

    makePreprocWrapperCaret(learner, ...)

### Arguments

`learner` | (Learner | `character(1)`) The learner. If you pass a string the learner will be created via makeLearner.
`...` | (any) See caret::preProcess for parameters not listed above. If you use them you might want to define them in the `add.par.set` so that they can be tuned.

### Value

Learner.

### See Also

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

[Package mlr version 2.17.1 Index]