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`. 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()`

[Package mlr version 2.19.0 Index]