\section{mlpack\+:\+:cv Namespace Reference}
\label{namespacemlpack_1_1cv}\index{mlpack\+::cv@{mlpack\+::cv}}
\subsection*{Classes}
\begin{DoxyCompactItemize}
\item 
class \textbf{ Accuracy}
\begin{DoxyCompactList}\small\item\em The \doxyref{Accuracy}{p.}{classmlpack_1_1cv_1_1Accuracy} is a metric of performance for classification algorithms that is equal to a proportion of correctly labeled test items among all ones for given test items. \end{DoxyCompactList}\item 
class \textbf{ C\+V\+Base}
\begin{DoxyCompactList}\small\item\em An auxiliary class for cross-\/validation. \end{DoxyCompactList}\item 
class \textbf{ F1}
\begin{DoxyCompactList}\small\item\em \doxyref{F1}{p.}{classmlpack_1_1cv_1_1F1} is a metric of performance for classification algorithms that for binary classification is equal to $ 2 * precision * recall / (precision + recall) $. \end{DoxyCompactList}\item 
class \textbf{ K\+Fold\+CV}
\begin{DoxyCompactList}\small\item\em The class \doxyref{K\+Fold\+CV}{p.}{classmlpack_1_1cv_1_1KFoldCV} implements k-\/fold cross-\/validation for regression and classification algorithms. \end{DoxyCompactList}\item 
class \textbf{ Meta\+Info\+Extractor}
\begin{DoxyCompactList}\small\item\em \doxyref{Meta\+Info\+Extractor}{p.}{classmlpack_1_1cv_1_1MetaInfoExtractor} is a tool for extracting meta information about a given machine learning algorithm. \end{DoxyCompactList}\item 
class \textbf{ M\+SE}
\begin{DoxyCompactList}\small\item\em The Mean\+Squared\+Error is a metric of performance for regression algorithms that is equal to the mean squared error between predicted values and ground truth (correct) values for given test items. \end{DoxyCompactList}\item 
struct \textbf{ Not\+Found\+Method\+Form}
\item 
class \textbf{ Precision}
\begin{DoxyCompactList}\small\item\em \doxyref{Precision}{p.}{classmlpack_1_1cv_1_1Precision} is a metric of performance for classification algorithms that for binary classification is equal to $ tp / (tp + fp) $, where $ tp $ and $ fp $ are the numbers of true positives and false positives respectively. \end{DoxyCompactList}\item 
class \textbf{ Recall}
\begin{DoxyCompactList}\small\item\em \doxyref{Recall}{p.}{classmlpack_1_1cv_1_1Recall} is a metric of performance for classification algorithms that for binary classification is equal to $ tp / (tp + fn) $, where $ tp $ and $ fn $ are the numbers of true positives and false negatives respectively. \end{DoxyCompactList}\item 
struct \textbf{ Select\+Method\+Form}
\begin{DoxyCompactList}\small\item\em A type function that selects a right method form. \end{DoxyCompactList}\item 
struct \textbf{ Select\+Method\+Form$<$ M\+L\+Algorithm $>$}
\item 
struct \textbf{ Select\+Method\+Form$<$ M\+L\+Algorithm, Has\+Method\+Form, H\+M\+Fs... $>$}
\item 
class \textbf{ Simple\+CV}
\begin{DoxyCompactList}\small\item\em \doxyref{Simple\+CV}{p.}{classmlpack_1_1cv_1_1SimpleCV} splits data into two sets -\/ training and validation sets -\/ and then runs training on the training set and evaluates performance on the validation set. \end{DoxyCompactList}\item 
struct \textbf{ Train\+Form}
\begin{DoxyCompactList}\small\item\em A wrapper struct for holding a Train form. \end{DoxyCompactList}\item 
struct \textbf{ Train\+Form$<$ M\+T, P\+T, void, false, false $>$}
\item 
struct \textbf{ Train\+Form$<$ M\+T, P\+T, void, false, true $>$}
\item 
struct \textbf{ Train\+Form$<$ M\+T, P\+T, void, true, false $>$}
\item 
struct \textbf{ Train\+Form$<$ M\+T, P\+T, void, true, true $>$}
\item 
struct \textbf{ Train\+Form$<$ M\+T, P\+T, W\+T, false, false $>$}
\item 
struct \textbf{ Train\+Form$<$ M\+T, P\+T, W\+T, false, true $>$}
\item 
struct \textbf{ Train\+Form$<$ M\+T, P\+T, W\+T, true, false $>$}
\item 
struct \textbf{ Train\+Form$<$ M\+T, P\+T, W\+T, true, true $>$}
\item 
struct \textbf{ Train\+Form\+Base4}
\item 
struct \textbf{ Train\+Form\+Base5}
\item 
struct \textbf{ Train\+Form\+Base6}
\item 
struct \textbf{ Train\+Form\+Base7}
\end{DoxyCompactItemize}
\subsection*{Enumerations}
\begin{DoxyCompactItemize}
\item 
enum \textbf{ Average\+Strategy} \{ \newline
\textbf{ Binary}, 
\newline
\textbf{ Micro}, 
\newline
\textbf{ Macro}
 \}\begin{DoxyCompactList}\small\item\em This enum declares possible strategies for averaging that can be used in some metrics like precision, recall, and F1. \end{DoxyCompactList}
\end{DoxyCompactItemize}
\subsection*{Functions}
\begin{DoxyCompactItemize}
\item 
{\footnotesize template$<$typename Data\+Type $>$ }\\void \textbf{ Assert\+Sizes} (const Data\+Type \&data, const arma\+::\+Row$<$ size\+\_\+t $>$ \&labels, const std\+::string \&caller\+Description)
\begin{DoxyCompactList}\small\item\em Assert there is the same number of the given data points and labels. \end{DoxyCompactList}\end{DoxyCompactItemize}


\subsection{Enumeration Type Documentation}
\mbox{\label{namespacemlpack_1_1cv_aff3913a61cbadcd7389264288e51ab06}} 
\index{mlpack\+::cv@{mlpack\+::cv}!Average\+Strategy@{Average\+Strategy}}
\index{Average\+Strategy@{Average\+Strategy}!mlpack\+::cv@{mlpack\+::cv}}
\subsubsection{Average\+Strategy}
{\footnotesize\ttfamily enum \textbf{ Average\+Strategy}}



This enum declares possible strategies for averaging that can be used in some metrics like precision, recall, and \doxyref{F1}{p.}{classmlpack_1_1cv_1_1F1}. 

The \char`\"{}\+Binary\char`\"{} strategy means binary classification is going to be used, and there is no need to average. \begin{DoxyEnumFields}{Enumerator}
\raisebox{\heightof{T}}[0pt][0pt]{\index{Binary@{Binary}!mlpack\+::cv@{mlpack\+::cv}}\index{mlpack\+::cv@{mlpack\+::cv}!Binary@{Binary}}}\mbox{\label{namespacemlpack_1_1cv_aff3913a61cbadcd7389264288e51ab06ae27b0860dfa490c46dd387b06d21a04b}} 
Binary&\\
\hline

\raisebox{\heightof{T}}[0pt][0pt]{\index{Micro@{Micro}!mlpack\+::cv@{mlpack\+::cv}}\index{mlpack\+::cv@{mlpack\+::cv}!Micro@{Micro}}}\mbox{\label{namespacemlpack_1_1cv_aff3913a61cbadcd7389264288e51ab06ae919d9c95f0fd4a08f3c94e13a3f8a9f}} 
Micro&\\
\hline

\raisebox{\heightof{T}}[0pt][0pt]{\index{Macro@{Macro}!mlpack\+::cv@{mlpack\+::cv}}\index{mlpack\+::cv@{mlpack\+::cv}!Macro@{Macro}}}\mbox{\label{namespacemlpack_1_1cv_aff3913a61cbadcd7389264288e51ab06a74c916f3663a0877600e8ebec7f7fe5e}} 
Macro&\\
\hline

\end{DoxyEnumFields}


Definition at line 25 of file average\+\_\+strategy.\+hpp.



\subsection{Function Documentation}
\mbox{\label{namespacemlpack_1_1cv_ac1a7192e5a165bfd24cc84bee93a6099}} 
\index{mlpack\+::cv@{mlpack\+::cv}!Assert\+Sizes@{Assert\+Sizes}}
\index{Assert\+Sizes@{Assert\+Sizes}!mlpack\+::cv@{mlpack\+::cv}}
\subsubsection{Assert\+Sizes()}
{\footnotesize\ttfamily void mlpack\+::cv\+::\+Assert\+Sizes (\begin{DoxyParamCaption}\item[{const Data\+Type \&}]{data,  }\item[{const arma\+::\+Row$<$ size\+\_\+t $>$ \&}]{labels,  }\item[{const std\+::string \&}]{caller\+Description }\end{DoxyParamCaption})}



Assert there is the same number of the given data points and labels. 


\begin{DoxyParams}{Parameters}
{\em data} & Column-\/major data. \\
\hline
{\em labels} & Labels. \\
\hline
{\em caller\+Description} & A description of the caller that can be used for error generation. \\
\hline
\end{DoxyParams}


Definition at line 29 of file facilities.\+hpp.

