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README.md
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@ -15,19 +15,16 @@ Distributed Version: [Distributed XGBoost](multi-node)
Notes on the Code: [Code Guide](src)
Turorial and Documentation: https://github.com/dmlc/xgboost/wiki
Video tutorial: [Better Optimization with Repeated Cross Validation and the XGBoost model - Machine Learning with R](https://www.youtube.com/watch?v=Og7CGAfSr_Y)
Documentation: https://github.com/dmlc/xgboost/doc
Learning about the model: [Introduction to Boosted Trees](http://homes.cs.washington.edu/~tqchen/pdf/BoostedTree.pdf)
* This slide is made by Tianqi Chen to introduce gradient boosting in a statistical view.
* It present boosted tree learning as formal functional space optimization of defined objective.
* The model presented is used by xgboost for boosted trees
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)
What's New
==========
* [External Memory Version](doc/external_memory.md)
* XGBoost wins [WWW2015 Microsoft Malware Classification Challenge (BIG 2015)](http://www.kaggle.com/c/malware-classification/forums/t/13490/say-no-to-overfitting-approaches-sharing)
* XGBoost now support HDFS and S3
* [Distributed XGBoost now runs on YARN](https://github.com/dmlc/wormhole/tree/master/learn/xgboost)!

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XGBoost Documentation
====
This is an ongoing effort to move the [wiki document](https://github.com/dmlc/xgboost/wiki) to here.
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
====
@ -13,7 +13,9 @@ Highlights 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.
* 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
====
Contribution of document usecases are welcomed!
Contribution of document and usecases are welcomed!

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Using XGBoost External Memory Version
Using XGBoost External Memory Version(beta)
====
There is no big difference between using external memory version and in-memory version.
The only difference is the filename format.