mlr: makePreprocWrapper – R documentation

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makePreprocWrapper

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).

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()

mlr

Machine Learning in R

v2.19.0

BSD_2_clause + file LICENSE

Authors

Bernd Bischl [aut] (<https://orcid.org/0000-0001-6002-6980>), Michel Lang [aut] (<https://orcid.org/0000-0001-9754-0393>), Lars Kotthoff [aut], Patrick Schratz [aut, cre] (<https://orcid.org/0000-0003-0748-6624>), Julia Schiffner [aut], Jakob Richter [aut], Zachary Jones [aut], Giuseppe Casalicchio [aut] (<https://orcid.org/0000-0001-5324-5966>), Mason Gallo [aut], Jakob Bossek [ctb] (<https://orcid.org/0000-0002-4121-4668>), Erich Studerus [ctb] (<https://orcid.org/0000-0003-4233-0182>), Leonard Judt [ctb], Tobias Kuehn [ctb], Pascal Kerschke [ctb] (<https://orcid.org/0000-0003-2862-1418>), Florian Fendt [ctb], Philipp Probst [ctb] (<https://orcid.org/0000-0001-8402-6790>), Xudong Sun [ctb] (<https://orcid.org/0000-0003-3269-2307>), Janek Thomas [ctb] (<https://orcid.org/0000-0003-4511-6245>), Bruno Vieira [ctb], Laura Beggel [ctb] (<https://orcid.org/0000-0002-8872-8535>), Quay Au [ctb] (<https://orcid.org/0000-0002-5252-8902>), Martin Binder [ctb], Florian Pfisterer [ctb], Stefan Coors [ctb], Steve Bronder [ctb], Alexander Engelhardt [ctb], Christoph Molnar [ctb], Annette Spooner [ctb]

Initial release

makePreprocWrapper

Description

Usage

Arguments

Value

See Also

mlr

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