% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/selectFeatures.R
\name{selectFeatures}
\alias{selectFeatures}
\title{Feature selection by wrapper approach.}
\usage{
selectFeatures(
  learner,
  task,
  resampling,
  measures,
  bit.names,
  bits.to.features,
  control,
  show.info = getMlrOption("show.info")
)
}
\arguments{
\item{learner}{(\link{Learner} | \code{character(1)})\cr
The learner.
If you pass a string the learner will be created via \link{makeLearner}.}

\item{task}{(\link{Task})\cr
The task.}

\item{resampling}{(\link{ResampleInstance} | \link{ResampleDesc})\cr
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 behavior, look
at \link{FeatSelControl}.}

\item{measures}{(list of \link{Measure} | \link{Measure})\cr
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 \link{getDefaultMeasure}.}

\item{bit.names}{\link{character}\cr
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. Has
to be used together with \code{bits.to.features}.}

\item{bits.to.features}{(\verb{function(x, task)})\cr
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. The vector \code{x} will correspond to
the \code{bit.names} and has to be of the same length.}

\item{control}{[see \link{FeatSelControl})
Control object for search method.
Also selects the optimization algorithm for feature selection.}

\item{show.info}{(\code{logical(1)})\cr
Print verbose output on console?
Default is set via \link{configureMlr}.}
}
\value{
(\link{FeatSelResult}).
}
\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
(\link{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 \code{bit.names} and \code{bits.to.features}. Thus
allowing you to switch on whole groups of features with a single bit.
}
\examples{
\donttest{
rdesc = makeResampleDesc("Holdout")
ctrl = makeFeatSelControlSequential(method = "sfs", maxit = NA)
res = selectFeatures("classif.rpart", iris.task, rdesc, control = ctrl)
analyzeFeatSelResult(res)
}
}
\seealso{
Other featsel: 
\code{\link{FeatSelControl}},
\code{\link{analyzeFeatSelResult}()},
\code{\link{getFeatSelResult}()},
\code{\link{makeFeatSelWrapper}()}
}
\concept{featsel}