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.targetis a string and denotes the target variable indata.argsis a list of further arguments and parameters to influence the preprocessing. Must return alist(data, control), wheredatais the preprocessed data andcontrolstores all information necessary to do the preprocessing before predictions.- predict
(
function(data, target, args, control))
Function to preprocess the data before prediction.targetis a string and denotes the target variable indata.argsare the args that were passed totrain.controlis the object you returned intrain. Must return the processed data.- par.set
(ParamHelpers::ParamSet)
Parameter set of ParamHelpers::LearnerParam objects to describe the parameters inargs. Default is empty set.- par.vals
(list)
Named list of default values for params inargsrespectivelypar.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()
