\section{M\+SE Class Reference}
\label{classmlpack_1_1cv_1_1MSE}\index{M\+SE@{M\+SE}}


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.  


\subsection*{Static Public Member Functions}
\begin{DoxyCompactItemize}
\item 
{\footnotesize template$<$typename M\+L\+Algorithm , typename Data\+Type , typename Responses\+Type $>$ }\\static double \textbf{ Evaluate} (M\+L\+Algorithm \&model, const Data\+Type \&data, const Responses\+Type \&responses)
\begin{DoxyCompactList}\small\item\em Run prediction and calculate the mean squared error. \end{DoxyCompactList}\end{DoxyCompactItemize}
\subsection*{Static Public Attributes}
\begin{DoxyCompactItemize}
\item 
static const bool \textbf{ Needs\+Minimization} = true
\begin{DoxyCompactList}\small\item\em Information for hyper-\/parameter tuning code. \end{DoxyCompactList}\end{DoxyCompactItemize}


\subsection{Detailed Description}
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. 

Definition at line 25 of file mse.\+hpp.



\subsection{Member Function Documentation}
\mbox{\label{classmlpack_1_1cv_1_1MSE_a8df5d5ca0ff562cab8a12f07e4c7f06d}} 
\index{mlpack\+::cv\+::\+M\+SE@{mlpack\+::cv\+::\+M\+SE}!Evaluate@{Evaluate}}
\index{Evaluate@{Evaluate}!mlpack\+::cv\+::\+M\+SE@{mlpack\+::cv\+::\+M\+SE}}
\subsubsection{Evaluate()}
{\footnotesize\ttfamily static double Evaluate (\begin{DoxyParamCaption}\item[{M\+L\+Algorithm \&}]{model,  }\item[{const Data\+Type \&}]{data,  }\item[{const Responses\+Type \&}]{responses }\end{DoxyParamCaption})\hspace{0.3cm}{\ttfamily [static]}}



Run prediction and calculate the mean squared error. 


\begin{DoxyParams}{Parameters}
{\em model} & A regression model. \\
\hline
{\em data} & Column-\/major data containing test items. \\
\hline
{\em responses} & Ground truth (correct) target values for the test items, should be either a row vector or a column-\/major matrix. \\
\hline
\end{DoxyParams}


\subsection{Member Data Documentation}
\mbox{\label{classmlpack_1_1cv_1_1MSE_a59117419810548f86c24651ffa3500d5}} 
\index{mlpack\+::cv\+::\+M\+SE@{mlpack\+::cv\+::\+M\+SE}!Needs\+Minimization@{Needs\+Minimization}}
\index{Needs\+Minimization@{Needs\+Minimization}!mlpack\+::cv\+::\+M\+SE@{mlpack\+::cv\+::\+M\+SE}}
\subsubsection{Needs\+Minimization}
{\footnotesize\ttfamily const bool Needs\+Minimization = true\hspace{0.3cm}{\ttfamily [static]}}



Information for hyper-\/parameter tuning code. 

It indicates that we want to minimize the measurement. 

Definition at line 45 of file mse.\+hpp.



The documentation for this class was generated from the following file\+:\begin{DoxyCompactItemize}
\item 
/var/www/mlpack.\+ratml.\+org/mlpack.\+org/\+\_\+src/mlpack-\/3.\+3.\+1/src/mlpack/core/cv/metrics/\textbf{ mse.\+hpp}\end{DoxyCompactItemize}
