\HeaderA{best}{Show best hyperparameter settings}{best}
\aliasA{best,tuningresult-method}{best}{best,tuningresult.Rdash.method}
\keyword{multivariate}{best}
\begin{Description}\relax
In this package hyperparameter tuning is performed by
an inner cross-validation step for each \code{learningset}.
A grid of values is tried and evaluated in terms of the
misclassification rate, the results are saved in an object
of class \code{\LinkA{tuningresult}{tuningresult}}. This method displays
(separately for each \code{learningset}) the hyperparameter/
hyperparameter combination that showed the best results. Note
that this must not be unique; in this case, only one combination
is displayed.
\end{Description}
\begin{Usage}
\begin{verbatim}
best(object, ...)
\end{verbatim}
\end{Usage}
\begin{Arguments}
\begin{ldescription}
\item[\code{object}] An object of class \code{\LinkA{tuningresult}{tuningresult}}.
\item[\code{...}] Currently unused argument.
\end{ldescription}
\end{Arguments}
\begin{Value}
A list with elements equal to the number of different learningsets.
Each element contains the best hyperparameter combination and
the corresponding misclassification rate.
\end{Value}
\begin{Author}\relax
Martin Slawski \email{martin.slawski@campus.lmu.de}

Anne-Laure Boulesteix \url{http://www.slcmsr.net/boulesteix}
\end{Author}
\begin{SeeAlso}\relax
\code{tune}
\end{SeeAlso}

