List of Documentations
====
* [Using XGBoost in Python](python.md)
* [Using XGBoost in R](../R-package/vignettes/xgboostPresentation.Rmd)
* [Learning to use xgboost by example](../demo)
* [External Memory Version](external_memory.md)
* [Text input format](input_format.md)
* [Notes on the Code](../src)
* List of all parameters and their usage: [Parameters](parameter.md)
* Learning about the model: [Introduction to Boosted Trees](http://homes.cs.washington.edu/~tqchen/pdf/BoostedTree.pdf)

How to get started
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* Try to read the [binary classification example](../demo/binary_classification) for getting started example
* Find the guide specific language guide above for the language you like to use
* [Learning to use xgboost by example](../demo) contains lots of useful examples

Highlight Links
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This section is about blogposts, presentation and videos discussing how to use xgboost to solve your interesting problem. If you think something belongs to here, send a pull request.
* [Winning solution of Kaggle Higgs competition: what a single model can do](http://no2147483647.wordpress.com/2014/09/17/winning-solution-of-kaggle-higgs-competition-what-a-single-model-can-do/) 
* Video tutorial: [Better Optimization with Repeated Cross Validation and the XGBoost model](https://www.youtube.com/watch?v=Og7CGAfSr_Y)
* [Feature Importance Analysis with XGBoost in Tax audit](http://fr.slideshare.net/MichaelBENESTY/feature-importance-analysis-with-xgboost-in-tax-audit)
* [Kaggle Tradeshift winning solution by daxiongshu](https://github.com/daxiongshu/kaggle-tradeshift-winning-solution)

Contribution
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Contribution of documents and use-cases are welcomed!