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Tianqi Chen 2014-08-30 09:31:14 -07:00
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R-package/inst/doc/xgboost.Rnw
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@ -24,11 +24,12 @@ foo <- packageDescription("xgboost")
This is an introductory document of using the \verb@xgboost@ package in R. This is an introductory document of using the \verb@xgboost@ package in R.
\verb@xgboost@ is short for eXtreme Gradient Boosting (Tree). It is an efficient \verb@xgboost@ is short for eXtreme Gradient Boosting package. It is an efficient
and scalable implementation of \cite{gbm}. It supports regression and and scalable implementation of gradient boosting framework by \cite{gbm}.
classification analysis on different types of input datasets. The package includes efficient linear model solver and tree learning algorithm.
It supports various objective functions, including regression, classification
It has several features: 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:
\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
@ -41,12 +42,11 @@ It has several features:
\item{xgb.DMatrix: }{\verb@xgboost@'s own class. Recommended.} \item{xgb.DMatrix: }{\verb@xgboost@'s own class. Recommended.}
\end{itemize} \end{itemize}
\item{Sparsity: }{\verb@xgboost@ accepts sparse input for both tree booster \item{Sparsity: }{\verb@xgboost@ accepts sparse input for both tree booster
and linear booster.} and linear booster, and is optimized for sparse input.}
\item{Customization: }{\verb@xgboost@ supports customized objective function \item{Customization: }{\verb@xgboost@ supports customized objective function
and evaluation function} and evaluation function}
\item{Performance: }{\verb@xgboost@ has better performance on several different \item{Performance: }{\verb@xgboost@ has better performance on several different
datasets. Its rising popularity and fame in different Kaggle competitions datasets.}
is the evidence.}
\end{enumerate} \end{enumerate}
\section{Example with iris} \section{Example with iris}
@ -91,7 +91,8 @@ booster[1]:
\end{verbatim} \end{verbatim}
It is important to know \verb@xgboost@'s own data type: \verb@xgb.DMatrix@. It is important to know \verb@xgboost@'s own data type: \verb@xgb.DMatrix@.
It speeds up \verb@xgboost@. It speeds up \verb@xgboost@, and is needed for advanced features such as
training from initial prediction value, weighted training instance.
We can use \verb@xgb.DMatrix@ to construct an \verb@xgb.DMatrix@ object: We can use \verb@xgb.DMatrix@ to construct an \verb@xgb.DMatrix@ object:
<<xgb.DMatrix>>= <<xgb.DMatrix>>=
@ -117,7 +118,7 @@ is more flexible than \verb@xgboost@, but it requires users to read the document
a bit more carefully. 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 some additional 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) {