R: Fuse learner with feature selection.

makeFeatSelWrapper {mlr} | R Documentation

## Fuse learner with feature selection.

### Description

Fuses a base learner with a search strategy to select variables. Creates a learner object, which can be used like any other learner object, but which internally uses [selectFeatures]. If the train function is called on it, the search strategy and resampling are invoked to select an optimal set of variables. Finally, a model is fitted on the complete training data with these variables and returned. See [selectFeatures] for more details.

After training, the optimal features (and other related information) can be retrieved with [getFeatSelResult].

### Usage

    makeFeatSelWrapper(learner, resampling, measures, bit.names,
      bits.to.features, control, show.info = getMlrOption("show.info"))

### Arguments

`learner` | (Learner | `character(1)`) The learner. If you pass a string the learner will be created via makeLearner.
`resampling` | ([ResampleInstance] | [ResampleDesc]) Resampling strategy for feature selection. If you pass a description, it is instantiated once at the beginning by default, so all points are evaluated on the same training/test sets. If you want to change that behaviour, look at [FeatSelControl].
`measures` | (list of Measure | Measure) Performance measures to evaluate. The first measure, aggregated by the first aggregation function is optimized, others are simply evaluated. Default is the default measure for the task, see here getDefaultMeasure.
`bit.names` | [character] Names of bits encoding the solutions. Also defines the total number of bits in the encoding. Per default these are the feature names of the task.
`bits.to.features` | [function(x, task)] Function which transforms an integer-0-1 vector into a character vector of selected features. Per default a value of 1 in the ith bit selects the ith feature to be in the candidate solution.
`control` | [see [FeatSelControl]) Control object for search method. Also selects the optimization algorithm for feature selection.
`show.info` | (`logical(1)`) Print verbose output on console? Default is set via configureMlr.

### Value

Learner.

### See Also

Other featsel: `FeatSelControl`, `analyzeFeatSelResult`, `getFeatSelResult`, `selectFeatures`

Other wrapper: `makeBaggingWrapper`, `makeClassificationViaRegressionWrapper`, `makeConstantClassWrapper`, `makeCostSensClassifWrapper`, `makeCostSensRegrWrapper`, `makeDownsampleWrapper`, `makeDummyFeaturesWrapper`, `makeExtractFDAFeatsWrapper`, `makeFilterWrapper`, `makeImputeWrapper`, `makeMulticlassWrapper`, `makeMultilabelBinaryRelevanceWrapper`, `makeMultilabelClassifierChainsWrapper`, `makeMultilabelDBRWrapper`, `makeMultilabelNestedStackingWrapper`, `makeMultilabelStackingWrapper`, `makeOverBaggingWrapper`, `makePreprocWrapperCaret`, `makePreprocWrapper`, `makeRemoveConstantFeaturesWrapper`, `makeSMOTEWrapper`, `makeTuneWrapper`, `makeUndersampleWrapper`, `makeWeightedClassesWrapper`

### Examples

    # nested resampling with feature selection (with a pretty stupid algorithm for selection)
    outer = makeResampleDesc("CV", iters = 2L)
    inner = makeResampleDesc("Holdout")
    ctrl = makeFeatSelControlRandom(maxit = 1)
    lrn = makeFeatSelWrapper("classif.ksvm", resampling = inner, control = ctrl)
    # we also extract the selected features for all iteration here
    r = resample(lrn, iris.task, outer, extract = getFeatSelResult)

[Package mlr version 2.13 Index]