R: Feature selection by wrapper approach.

selectFeatures {mlr} | R Documentation

## Feature selection by wrapper approach.

### Description

Optimizes the features for a classification or regression problem by choosing a variable selection wrapper approach. Allows for different optimization methods, such as forward search or a genetic algorithm. You can select such an algorithm (and its settings) by passing a corresponding control object. For a complete list of implemented algorithms look at the subclasses of ([FeatSelControl]).

All algorithms operate on a 0-1-bit encoding of candidate solutions. Per default a single bit corresponds to a single feature, but you are able to change this by using the arguments 'bit.names' and 'bits.to.features'. Thus allowing you to switch on whole groups of features with a single bit.

### Usage

    selectFeatures(learner, task, 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.
`task` | (Task) The task.
`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

([FeatSelResult]).

### See Also

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

### Examples

    rdesc = makeResampleDesc("Holdout")
    ctrl = makeFeatSelControlSequential(method = "sfs", maxit = NA)
    res = selectFeatures("classif.rpart", iris.task, rdesc, control = ctrl)
    analyzeFeatSelResult(res)

[Package mlr version 2.12.1 Index]