Vignette text
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@ -16,19 +16,15 @@ Introduction
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The purpose of this Vignette is to show you how to use **Xgboost** to discover and better understand your own dataset.
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The purpose of this Vignette is to show you how to use **Xgboost** to discover and better understand your own dataset.
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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.
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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*.
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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*.
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For the purpose of this tutorial we will first load the required packages.
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For the purpose of this tutorial we will first load the required packages.
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--> ADD PART REGARDING INSTALLATION FROM GITHUB
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```{r libLoading, results='hold', message=F, warning=F}
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```{r libLoading, results='hold', message=F, warning=F}
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require(xgboost)
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require(xgboost)
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require(Matrix)
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require(Matrix)
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require(data.table)
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require(data.table)
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if (!require(vcd)) install.packages('vcd')
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if (!require('vcd')) install.packages('vcd')
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```
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```
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> **VCD** package is used for one of its embedded dataset only (and not for its own functions).
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> **VCD** package is used for one of its embedded dataset only (and not for its own functions).
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@ -41,16 +41,27 @@ The purpose of this Vignette is to show you how to use **Xgboost** to make predi
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Installation
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Installation
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============
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============
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For the purpose of this tutorial we will first load the required packages.
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For up-to-date version(which is *highly* recommended), please install from Github:
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--> ADD PART REGARDING INSTALLATION FROM GITHUB
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```{r installGithub, eval=FALSE}
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devtools::install_github('tqchen/xgboost',subdir='R-package')
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```
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> *Windows* user will need to install [RTools](http://cran.r-project.org/bin/windows/Rtools/) first.
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For stable version on CRAN, please run
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```{r installCran, eval=FALSE}
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install.packages('xgboost')
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```
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For the purpose of this tutorial we will load the required package.
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```{r libLoading, results='hold', message=F, warning=F}
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```{r libLoading, results='hold', message=F, warning=F}
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require(xgboost)
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require(xgboost)
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require(methods)
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```
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```
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In this example, we are aiming to predict whether a mushroom can be eated.
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In this example, we are aiming to predict whether a mushroom can be eated (yeah, as always, example data are super interesting :-).
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Learning
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Learning
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========
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========
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