XGBoost Documentation
=====================



* [Using XGBoost in Python](python/python_intro.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)
* [Build Instruction](build.md)
* [Notes on the Code](../src)
* List of all parameters and their usage: [Parameters](parameter.md)
  - [Notes on Parameter Tunning](param_tuning.md)
* Learning about the model: [Introduction to Boosted Trees](http://homes.cs.washington.edu/~tqchen/pdf/BoostedTree.pdf)


How to Get Started
------------------
* 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

Example Highlight Links
-----------------------
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.
* [Kaggle CrowdFlower winner's solution by Chenglong Chen](https://github.com/ChenglongChen/Kaggle_CrowdFlower)
* [Kaggle Malware Prediction winner's solution](https://github.com/xiaozhouwang/kaggle_Microsoft_Malware)
* [Kaggle Tradeshift winning solution by daxiongshu](https://github.com/daxiongshu/kaggle-tradeshift-winning-solution)
* [Feature Importance Analysis with XGBoost in Tax audit](http://fr.slideshare.net/MichaelBENESTY/feature-importance-analysis-with-xgboost-in-tax-audit)
* Video tutorial: [Better Optimization with Repeated Cross Validation and the XGBoost model](https://www.youtube.com/watch?v=Og7CGAfSr_Y)
* [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/)

API Reference
-------------
  * [Python API Reference](python/python_api.rst)

Contribution
------------
Contribution of documents and use-cases are welcomed!
* This package use Google C++ style
* Check tool of codestyle
  - clone https://github.com/dmlc/dmlc-core into root directory
  - type ```make lint``` and fix possible errors.