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XGBoost R Tutorial
==================

## Introduction


**Xgboost** is short for e**X**treme **G**radient **Boost**ing package.

The purpose of this Vignette is to show you how to use **Xgboost** to build a model and make predictions.

It is an efficient and scalable implementation of gradient boosting framework by @friedman2000additive and @friedman2001greedy. Two solvers are included:

- *linear* model ;
- *tree learning* algorithm.

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/dmlc/xgboost) to win several [Kaggle](http://www.kaggle.com) competitions.

It has several features:

* Speed: it can automatically do parallel computation on *Windows* and *Linux*, with *OpenMP*. It is generally over 10 times faster than the classical `gbm`.
* Input Type: it takes several types of input data:
    * *Dense* Matrix: *R*'s *dense* matrix, i.e. `matrix` ;
    * *Sparse* Matrix: *R*'s *sparse* matrix, i.e. `Matrix::dgCMatrix` ;
    * Data File: local data files ;
    * `xgb.DMatrix`: its own class (recommended).
* Sparsity: it accepts *sparse* input for both *tree booster*  and *linear booster*, and is optimized for *sparse* input ;
* Customization: it supports customized objective functions and evaluation functions.

## Installation


### Github version


For weekly updated version (highly recommended), install from *Github*:


```r
install.packages("drat", repos="https://cran.rstudio.com")
drat:::addRepo("dmlc")
install.packages("xgboost", repos="http://dmlc.ml/drat/", type = "source")
```

> *Windows* user will need to install [Rtools](http://cran.r-project.org/bin/windows/Rtools/) first.

### CRAN version


The version 0.4-2 is on CRAN, and you can install it by:


```r
install.packages("xgboost")
```

Formerly available versions can be obtained from the CRAN [archive](http://cran.r-project.org/src/contrib/Archive/xgboost)

## Learning


For the purpose of this tutorial we will load **XGBoost** package.


```r
require(xgboost)
```

### Dataset presentation