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El Potaeto 2015-02-12 13:59:45 +01:00
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8
R-package/vignettes/discoverYourData.Rmd
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@ -16,19 +16,15 @@ Introduction
The purpose of this Vignette is to show you how to use **Xgboost** to discover and better understand your own dataset.
You may know **Xgboost** as a state of the art tool to build some kind of Machine learning models. It has been [used](https://github.com/tqchen/xgboost) to win several [Kaggle](http://www.kaggle.com) competition.
During these competition, the purpose is to make prediction. This Vignette is not about showing you how to predict anything (see [Xgboost presentation](www.somewhere.org)). The purpose of this document is to explain how to use **Xgboost** to understand the *link* between the *features* of your data and an *outcome*.
This Vignette is not about showing you how to predict anything (see [Xgboost presentation](www.somewhere.org)). The purpose of this document is to explain how to use **Xgboost** to understand the *link* between the *features* of your data and an *outcome*.
For the purpose of this tutorial we will first load the required packages.
--> ADD PART REGARDING INSTALLATION FROM GITHUB
```{r libLoading, results='hold', message=F, warning=F}
require(xgboost)
require(Matrix)
require(data.table)
if (!require(vcd)) install.packages('vcd')
if (!require('vcd')) install.packages('vcd')
```
> **VCD** package is used for one of its embedded dataset only (and not for its own functions).

19
R-package/vignettes/xgboostPresentation.Rmd
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@ -41,16 +41,27 @@ The purpose of this Vignette is to show you how to use **Xgboost** to make predi
Installation
============
For the purpose of this tutorial we will first load the required packages.
For up-to-date version(which is *highly* recommended), please install from Github:
--> ADD PART REGARDING INSTALLATION FROM GITHUB
```{r installGithub, eval=FALSE}
devtools::install_github('tqchen/xgboost',subdir='R-package')
```
> *Windows* user will need to install [RTools](http://cran.r-project.org/bin/windows/Rtools/) first.
For stable version on CRAN, please run
```{r installCran, eval=FALSE}
install.packages('xgboost')
```
For the purpose of this tutorial we will load the required package.
```{r libLoading, results='hold', message=F, warning=F}
require(xgboost)
require(methods)
```
In this example, we are aiming to predict whether a mushroom can be eated.
In this example, we are aiming to predict whether a mushroom can be eated (yeah, as always, example data are super interesting :-).
Learning
========