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update links dmlc

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El Potaeto
2015-03-22 16:41:05 +01:00
parent 70045c41f9
commit 7d0ac3a3dd
10 changed files with 15 additions and 15 deletions
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6
R-package/vignettes/discoverYourData.Rmd
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@@ -17,7 +17,7 @@ Introduction
The purpose of this Vignette is to show you how to use **Xgboost** to discover and understand your own dataset better.
This Vignette is not about predicting anything (see [Xgboost presentation](https://github.com/tqchen/xgboost/blob/master/R-package/vignettes/xgboostPresentation.Rmd)). We will explain how to use **Xgboost** to highlight the *link* between the *features* of your data and the *outcome*.
This Vignette is not about predicting anything (see [Xgboost presentation](https://github.com/dmlc/xgboost/blob/master/R-package/vignettes/xgboostPresentation.Rmd)). We will explain how to use **Xgboost** to highlight the *link* between the *features* of your data and the *outcome*.
Pacakge loading:
@@ -34,7 +34,7 @@ Preparation of the dataset
==========================
Numeric VS categorical variables
----------------------------------
--------------------------------
**Xgboost** manages only `numeric` vectors.
@@ -163,7 +163,7 @@ output_vector = df[,Improved] == "Marked"
Build the model
===============
The code below is very usual. For more information, you can look at the documentation of `xgboost` function (or at the vignette [Xgboost presentation](https://github.com/tqchen/xgboost/blob/master/R-package/vignettes/xgboostPresentation.Rmd)).
The code below is very usual. For more information, you can look at the documentation of `xgboost` function (or at the vignette [Xgboost presentation](https://github.com/dmlc/xgboost/blob/master/R-package/vignettes/xgboostPresentation.Rmd)).
```{r}
bst <- xgboost(data = sparse_matrix, label = output_vector, max.depth = 4,

4
R-package/vignettes/xgboostPresentation.Rmd
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@@ -27,7 +27,7 @@ It is an efficient and scalable implementation of gradient boosting framework by
It supports various objective functions, including *regression*, *classification* and *ranking*. The package is made to be extendible, so that users are also allowed to define their own objective functions easily.
It has been [used](https://github.com/tqchen/xgboost) to win several [Kaggle](http://www.kaggle.com) competitions.
It has been [used](https://github.com/dmlc/xgboost) to win several [Kaggle](http://www.kaggle.com) competitions.
It has several features:
@@ -49,7 +49,7 @@ Github version
For up-to-date version (highly recommended), install from *Github*:
```{r installGithub, eval=FALSE}
devtools::install_github('tqchen/xgboost', subdir='R-package')
devtools::install_github('dmlc/xgboost', subdir='R-package')
```
> *Windows* user will need to install [RTools](http://cran.r-project.org/bin/windows/Rtools/) first.