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.
makePreprocWrapper(learner, train, predict, par.set = makeParamSet(),
par.vals = list())(Learner | character(1))
The learner.
If you pass a string the learner will be created via makeLearner.
(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.
(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.
(ParamHelpers::ParamSet)
Parameter set of ParamHelpers::LearnerParam objects to describe the
parameters in args.
Default is empty set.
(list)
Named list of default values for params in args respectively par.set.
Default is empty list.
(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