From 582ef2f9d58f865b91e0df1a412beb27157e84ba Mon Sep 17 00:00:00 2001 From: Tianqi Chen Date: Tue, 2 Sep 2014 23:29:48 -0700 Subject: [PATCH 1/3] Update DESCRIPTION --- R-package/DESCRIPTION | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/R-package/DESCRIPTION b/R-package/DESCRIPTION index 40705e317..baf5912f4 100644 --- a/R-package/DESCRIPTION +++ b/R-package/DESCRIPTION @@ -12,7 +12,7 @@ Description: This package is a R wrapper of xgboost, which is short for eXtreme 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 From 642b5bda0a6928588619678f0fa323bfe6011d92 Mon Sep 17 00:00:00 2001 From: Tianqi Chen Date: Tue, 2 Sep 2014 23:30:53 -0700 Subject: [PATCH 2/3] Update DESCRIPTION --- R-package/DESCRIPTION | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/R-package/DESCRIPTION b/R-package/DESCRIPTION index baf5912f4..33258bf5c 100644 --- a/R-package/DESCRIPTION +++ b/R-package/DESCRIPTION @@ -1,14 +1,14 @@ 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 From 85dbaf638bfbb75c023203893cd851920f948cd9 Mon Sep 17 00:00:00 2001 From: Tianqi Chen Date: Tue, 2 Sep 2014 23:33:04 -0700 Subject: [PATCH 3/3] Update xgboost.Rnw --- R-package/vignettes/xgboost.Rnw | 13 +++++-------- 1 file changed, 5 insertions(+), 8 deletions(-) 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} +