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El Potaeto 2015-02-12 10:19:42 +01:00
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R-package/vignettes/xgboostPresentation.Rmd
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@ -14,9 +14,29 @@ vignette: >
Introduction
============
The purpose of this Vignette is to show you how to use **Xgboost** to make prediction from a model based on your own dataset.
This is an introductory document of using the \verb@xgboost@ package in **R**.
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) competitions.
**Xgboost** is short for e**X**treme **G**radient **B**oosting package.
It is an efficient 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 users are also allowed to define their own objectives easily.
It has been [used](https://github.com/tqchen/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 `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`: it's 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 function and evaluation function ;
* Performance: it has better performance on several different datasets.
The purpose of this Vignette is to show you how to use **Xgboost** to make prediction from a model based on your own dataset.
Installation
============