R: Fuse learner with an extractFDAFeatures method.

makeExtractFDAFeatsWrapper {mlr} | R Documentation

## Fuse learner with an extractFDAFeatures method.

### Description

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.

### Usage

    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`. 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`

[Package mlr version 2.15.0 Index]