\section{mlpack\+:\+:regression Namespace Reference}
\label{namespacemlpack_1_1regression}\index{mlpack\+::regression@{mlpack\+::regression}}


Regression methods.  


\subsection*{Classes}
\begin{DoxyCompactItemize}
\item 
class \textbf{ Bayesian\+Linear\+Regression}
\begin{DoxyCompactList}\small\item\em A Bayesian approach to the maximum likelihood estimation of the parameters $ \omega $ of the linear regression model. \end{DoxyCompactList}\item 
class \textbf{ L\+A\+RS}
\begin{DoxyCompactList}\small\item\em An implementation of \doxyref{L\+A\+RS}{p.}{classmlpack_1_1regression_1_1LARS}, a stage-\/wise homotopy-\/based algorithm for l1-\/regularized linear regression (L\+A\+S\+SO) and l1+l2 regularized linear regression (Elastic Net). \end{DoxyCompactList}\item 
class \textbf{ Linear\+Regression}
\begin{DoxyCompactList}\small\item\em A simple linear regression algorithm using ordinary least squares. \end{DoxyCompactList}\item 
class \textbf{ Logistic\+Regression}
\begin{DoxyCompactList}\small\item\em The \doxyref{Logistic\+Regression}{p.}{classmlpack_1_1regression_1_1LogisticRegression} class implements an L2-\/regularized logistic regression model, and supports training with multiple optimizers and classification. \end{DoxyCompactList}\item 
class \textbf{ Logistic\+Regression\+Function}
\begin{DoxyCompactList}\small\item\em The log-\/likelihood function for the logistic regression objective function. \end{DoxyCompactList}\item 
class \textbf{ Softmax\+Regression}
\begin{DoxyCompactList}\small\item\em Softmax Regression is a classifier which can be used for classification when the data available can take two or more class values. \end{DoxyCompactList}\item 
class \textbf{ Softmax\+Regression\+Function}
\end{DoxyCompactItemize}


\subsection{Detailed Description}
Regression methods.