% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/TuneWrapper.R
\name{makeTuneWrapper}
\alias{makeTuneWrapper}
\title{Fuse learner with tuning.}
\usage{
makeTuneWrapper(learner, resampling, measures, par.set, control,
  show.info = getMlrOption("show.info"))
}
\arguments{
\item{learner}{([Learner] | `character(1)`)\cr
The learner.
If you pass (`character(1)`) string the learner will be created via [makeLearner].}

\item{resampling}{(\link{ResampleInstance} | \link{ResampleDesc})\cr
Resampling strategy to evaluate points in hyperparameter space. 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{TuneControl}.}

\item{measures}{(list of [Measure] | [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 [getDefaultMeasure].}

\item{par.set}{(\link[ParamHelpers:ParamSet]{ParamHelpers::ParamSet})\cr
Collection of parameters and their constraints for optimization.
Dependent parameters with a \code{requires} field must use \code{quote} and not
\code{expression} to define it.}

\item{control}{(\link{TuneControl})\cr
Control object for search method. Also selects the optimization algorithm for tuning.}

\item{show.info}{(`logical(1)`)\cr
Print verbose output on console?
Default is set via [configureMlr].}
}
\value{
[Learner].
}
\description{
Fuses a base learner with a search strategy to select its hyperparameters.
Creates a learner object, which can be used like any other learner object,
but which internally uses \link{tuneParams}.
If the train function is called on it,
the search strategy and resampling are invoked
to select an optimal set of hyperparameter values. Finally, a model is fitted on the
complete training data with these optimal hyperparameters and returned.
See \link{tuneParams} for more details.

After training, the optimal hyperparameters (and other related information) can be retrieved with
\link{getTuneResult}.
}
\examples{
\donttest{
task = makeClassifTask(data = iris, target = "Species")
lrn = makeLearner("classif.rpart")
# stupid mini grid
ps = makeParamSet(
  makeDiscreteParam("cp", values = c(0.05, 0.1)),
  makeDiscreteParam("minsplit", values = c(10, 20))
)
ctrl = makeTuneControlGrid()
inner = makeResampleDesc("Holdout")
outer = makeResampleDesc("CV", iters = 2)
lrn = makeTuneWrapper(lrn, resampling = inner, par.set = ps, control = ctrl)
mod = train(lrn, task)
print(getTuneResult(mod))
# nested resampling for evaluation
# we also extract tuned hyper pars in each iteration
r = resample(lrn, task, outer, extract = getTuneResult)
print(r$extract)
getNestedTuneResultsOptPathDf(r)
getNestedTuneResultsX(r)
}
}
\seealso{
Other tune: \code{\link{TuneControl}},
  \code{\link{getNestedTuneResultsOptPathDf}},
  \code{\link{getNestedTuneResultsX}},
  \code{\link{getTuneResult}},
  \code{\link{makeModelMultiplexerParamSet}},
  \code{\link{makeModelMultiplexer}},
  \code{\link{makeTuneControlCMAES}},
  \code{\link{makeTuneControlDesign}},
  \code{\link{makeTuneControlGenSA}},
  \code{\link{makeTuneControlGrid}},
  \code{\link{makeTuneControlIrace}},
  \code{\link{makeTuneControlMBO}},
  \code{\link{makeTuneControlRandom}},
  \code{\link{tuneParams}}, \code{\link{tuneThreshold}}

Other wrapper: \code{\link{makeBaggingWrapper}},
  \code{\link{makeClassificationViaRegressionWrapper}},
  \code{\link{makeConstantClassWrapper}},
  \code{\link{makeCostSensClassifWrapper}},
  \code{\link{makeCostSensRegrWrapper}},
  \code{\link{makeDownsampleWrapper}},
  \code{\link{makeDummyFeaturesWrapper}},
  \code{\link{makeExtractFDAFeatsWrapper}},
  \code{\link{makeFeatSelWrapper}},
  \code{\link{makeFilterWrapper}},
  \code{\link{makeImputeWrapper}},
  \code{\link{makeMulticlassWrapper}},
  \code{\link{makeMultilabelBinaryRelevanceWrapper}},
  \code{\link{makeMultilabelClassifierChainsWrapper}},
  \code{\link{makeMultilabelDBRWrapper}},
  \code{\link{makeMultilabelNestedStackingWrapper}},
  \code{\link{makeMultilabelStackingWrapper}},
  \code{\link{makeOverBaggingWrapper}},
  \code{\link{makePreprocWrapperCaret}},
  \code{\link{makePreprocWrapper}},
  \code{\link{makeRemoveConstantFeaturesWrapper}},
  \code{\link{makeSMOTEWrapper}},
  \code{\link{makeUndersampleWrapper}},
  \code{\link{makeWeightedClassesWrapper}}
}