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Update xgboost.Rnw

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Tianqi Chen 2014-09-02 23:33:04 -07:00
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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}.
The package includes efficient linear model solver and tree learning algorithm.
It supports various objective functions, including regression, classification
and ranking. The package is made to be extendible, so that user are also allowed
to define there own objectives easily. It has several features:
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:
\begin{enumerate}
\item{Speed: }{\verb@xgboost@ can automatically do parallel computation on
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}
The function \verb@xgboost@ is a simple function with less parameters, in order
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.
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 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
supports advanced features as custom objective and evaluation functions.
\verb@xgb.train@ only accept a \verb@xgb.DMatrix@ object as its input, while it supports advanced features as custom objective and evaluation functions.
<<Customized loss function>>=
logregobj <- function(preds, dtrain) {
@ -213,3 +209,4 @@ competition.
\bibliography{xgboost}
\end{document}