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Merge branch 'master' of github.com:tqchen/xgboost

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antinucleon 2014-09-03 00:38:06 -06:00
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R-package/DESCRIPTION
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Package: xgboost Package: xgboost
Type: Package Type: Package
Title: eXtreme Gradient Boosting Title: eXtreme Gradient Boosting
Version: 0.3-0 Version: 0.3-1
Date: 2014-08-23 Date: 2014-08-23
Author: Tianqi Chen <tianqi.tchen@gmail.com>, Tong He <hetong007@gmail.com> Author: Tianqi Chen <tianqi.tchen@gmail.com>, Tong He <hetong007@gmail.com>
Maintainer: Tong He <hetong007@gmail.com> Maintainer: Tong He <hetong007@gmail.com>
Description: This package is a R wrapper of xgboost, which is short for eXtreme Description: This package is a R wrapper of xgboost, which is short for eXtreme
Gradient Boosting. It is an efficient and scalable implementation of Gradient Boosting. It is an efficient and scalable implementation of
gradient boosting framework. The package includes efficient linear model gradient boosting framework. The package includes efficient linear model
solver and tree learning algorithm. The package can automatically do solver and tree learning algorithms. The package can automatically do
parallel computation with OpenMP, and it can be more than 10 times faster parallel computation with OpenMP, and it can be more than 10 times faster
than existing gradient boosting packages such as gbm. It supports various than existing gradient boosting packages such as gbm. It supports various
objective functions, including regression, classification and ranking. The objective functions, including regression, classification and ranking. The
package is made to be extensible, so that user are also allowed to define package is made to be extensible, so that users are also allowed to define
their own objectives easily. their own objectives easily.
License: Apache License (== 2.0) | file LICENSE License: Apache License (== 2.0) | file LICENSE
URL: https://github.com/tqchen/xgboost URL: https://github.com/tqchen/xgboost

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R-package/vignettes/xgboost.Rnw
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@ -52,8 +52,7 @@ This is an introductory document of using the \verb@xgboost@ package in R.
and scalable implementation of gradient boosting framework by \citep{friedman2001greedy}. and scalable implementation of gradient boosting framework by \citep{friedman2001greedy}.
The package includes efficient linear model solver and tree learning algorithm. The package includes efficient linear model solver and tree learning algorithm.
It supports various objective functions, including regression, classification It supports various objective functions, including regression, classification
and ranking. The package is made to be extendible, so that user are also allowed and ranking. The package is made to be extendible, so that users are also allowed to define their own objectives easily. It has several features:
to define there own objectives easily. It has several features:
\begin{enumerate} \begin{enumerate}
\item{Speed: }{\verb@xgboost@ can automatically do parallel computation on \item{Speed: }{\verb@xgboost@ can automatically do parallel computation on
Windows and Linux, with openmp. It is generally over 10 times faster than Windows and Linux, with openmp. It is generally over 10 times faster than
@ -137,13 +136,10 @@ diris = xgb.DMatrix('iris.xgb.DMatrix')
\section{Advanced Examples} \section{Advanced Examples}
The function \verb@xgboost@ is a simple function with less parameters, in order The function \verb@xgboost@ is a simple function with less parameter, in order
to be R-friendly. The core training function is wrapped in \verb@xgb.train@. It to be R-friendly. The core training function is wrapped in \verb@xgb.train@. It is more flexible than \verb@xgboost@, but it requires users to read the document a bit more carefully.
is more flexible than \verb@xgboost@, but it requires users to read the document
a bit more carefully.
\verb@xgb.train@ only accept a \verb@xgb.DMatrix@ object as its input, while it \verb@xgb.train@ only accept a \verb@xgb.DMatrix@ object as its input, while it supports advanced features as custom objective and evaluation functions.
supports advanced features as custom objective and evaluation functions.
<<Customized loss function>>= <<Customized loss function>>=
logregobj <- function(preds, dtrain) { logregobj <- function(preds, dtrain) {
@ -213,3 +209,4 @@ competition.
\bibliography{xgboost} \bibliography{xgboost}
\end{document} \end{document}