diff --git a/R-package/DESCRIPTION b/R-package/DESCRIPTION index 40705e317..33258bf5c 100644 --- a/R-package/DESCRIPTION +++ b/R-package/DESCRIPTION @@ -1,18 +1,18 @@ Package: xgboost Type: Package Title: eXtreme Gradient Boosting -Version: 0.3-0 +Version: 0.3-1 Date: 2014-08-23 Author: Tianqi Chen , Tong He Maintainer: Tong He 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 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 than existing gradient boosting packages such as gbm. It supports various 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. License: Apache License (== 2.0) | file LICENSE URL: https://github.com/tqchen/xgboost diff --git a/R-package/vignettes/xgboost.Rnw b/R-package/vignettes/xgboost.Rnw index 19254abaf..9ecceca17 100644 --- a/R-package/vignettes/xgboost.Rnw +++ b/R-package/vignettes/xgboost.Rnw @@ -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. <>= logregobj <- function(preds, dtrain) { @@ -213,3 +209,4 @@ competition. \bibliography{xgboost} \end{document} +