selectFeatures: Feature selection by wrapper approach.
In riebetob/mlr: Machine Learning in R

Description Usage Arguments Value See Also Examples

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.

1 2	selectFeatures(learner, task, resampling, measures, bit.names, bits.to.features, control, show.info = getMlrOption("show.info"))

`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`.

[FeatSelResult].

Other featsel: FeatSelControl, analyzeFeatSelResult, getFeatSelResult, makeFeatSelWrapper

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