Fuse learner with an extractFDAFeatures method.

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())

Arguments

learner

(Learner | character(1)) The learner. If you pass a string the learner will be created via makeLearner.

feat.methods

(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.

Value

Learner.

See also

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()