Fuses a base learner with an extractFDAFeatures method. Creates a learner object, which can be used like any other learner object. Internally uses extractFDAFeatures before training the learner and reextractFDAFeatures before predicting.
makeExtractFDAFeatsWrapper(learner, feat.methods = list())Learner.
(Learner | character(1))
The learner.
If you pass a string the learner will be created via makeLearner.
(named list)
List of functional features along with the desired methods for each functional feature.
“all” applies the extractFDAFeatures method to each
functional feature.
Names of feat.methods must match column names of functional features.
Available feature extraction methods are available under family fda_featextractor.
Specifying a functional feature multiple times with different extraction methods allows
for the extraction of different features from the same functional.
Default is list() which does nothing.
Other fda:
extractFDAFeatures(),
makeExtractFDAFeatMethod()
Other wrapper:
makeBaggingWrapper(),
makeClassificationViaRegressionWrapper(),
makeConstantClassWrapper(),
makeCostSensClassifWrapper(),
makeCostSensRegrWrapper(),
makeDownsampleWrapper(),
makeDummyFeaturesWrapper(),
makeFeatSelWrapper(),
makeFilterWrapper(),
makeImputeWrapper(),
makeMulticlassWrapper(),
makeMultilabelBinaryRelevanceWrapper(),
makeMultilabelClassifierChainsWrapper(),
makeMultilabelDBRWrapper(),
makeMultilabelNestedStackingWrapper(),
makeMultilabelStackingWrapper(),
makeOverBaggingWrapper(),
makePreprocWrapperCaret(),
makePreprocWrapper(),
makeRemoveConstantFeaturesWrapper(),
makeSMOTEWrapper(),
makeTuneWrapper(),
makeUndersampleWrapper(),
makeWeightedClassesWrapper()