R: Fuse learner with preprocessing.

makePreprocWrapper {mlr} | R Documentation

## Fuse learner with preprocessing.

### Description

Fuses a base learner with a preprocessing method. Creates a learner object, which can be used like any other learner object, but which internally preprocesses the data as requested. If the train or predict function is called on data / a task, the preprocessing is always performed automatically.

### Usage

    makePreprocWrapper(learner, train, predict, par.set = makeParamSet(),
      par.vals = list())

### Arguments

`learner` | (Learner | `character(1)`) The learner. If you pass a string the learner will be created via makeLearner.
`train` | (`function(data, target, args)`) Function to preprocess the data before training. `target` is a string and denotes the target variable in `data`. `args` is a list of further arguments and parameters to influence the preprocessing. Must return a `list(data, control)`, where `data` is the preprocessed data and `control` stores all information necessary to do the preprocessing before predictions.
`predict` | (`function(data, target, args, control)`) Function to preprocess the data before prediction. `target` is a string and denotes the target variable in `data`. `args` are the args that were passed to `train`. `control` is the object you returned in `train`. Must return the processed data.
`par.set` | (ParamHelpers::ParamSet) Parameter set of ParamHelpers::LearnerParam objects to describe the parameters in `args`. Default is empty set.
`par.vals` | (list) Named list of default values for params in `args` respectively `par.set`. Default is empty list.

### 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`, `makeRemoveConstantFeaturesWrapper`, `makeSMOTEWrapper`, `makeTuneWrapper`, `makeUndersampleWrapper`, `makeWeightedClassesWrapper`

[Package mlr version 2.16.0 Index]