XGBoost Documentation
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This is an ongoing effort to move the [wiki document](https://github.com/dmlc/xgboost/wiki) to here. You can already find all the most useful parts here.

List of Documentations
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* [Parameters](parameter.md)
* [Using XGBoost in Python](python.md)
* [External Memory Version](external_memory.md)

Highlights 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.
* Blogpost by phunther: [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/) 
* [Kaggle Tradeshift winning solution by daxiongshu](https://github.com/daxiongshu/kaggle-tradeshift-winning-solution) 
* Video tutorial: [Better Optimization with Repeated Cross Validation and the XGBoost model - Machine Learning with R](https://www.youtube.com/watch?v=Og7CGAfSr_Y)
* Presention of a real use case of XGBoost to prepare tax audit in France: [Feature Importance Analysis with XGBoost in Tax audit](http://fr.slideshare.net/MichaelBENESTY/feature-importance-analysis-with-xgboost-in-tax-audit)

Contribution
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Contribution of document and usecases are welcomed!