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El Potaeto 2015-04-15 18:49:52 +02:00
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@ -5,25 +5,27 @@ It implements machine learning algorithm under gradient boosting framework, incl
Contributors: https://github.com/dmlc/xgboost/graphs/contributors
Turorial and Documentation: https://github.com/dmlc/xgboost/wiki
Issues Tracker: [https://github.com/dmlc/xgboost/issues](https://github.com/dmlc/xgboost/issues?q=is%3Aissue+label%3Aquestion)
Issues Tracker: [https://github.com/dmlc/xgboost/issues](https://github.com/dmlc/xgboost/issues?q=is%3Aissue+label%3Aquestion) for bugreport and other issues
Please join [XGBoost User Group](https://groups.google.com/forum/#!forum/xgboost-user/) to ask usage questions and share your experience on xgboost.
Please join [XGBoost User Group](https://groups.google.com/forum/#!forum/xgboost-user/) to ask questions and share your experience on xgboost.
Examples Code: [Learning to use xgboost by examples](demo)
Video tutorial: [Better Optimization with Repeated Cross Validation and the XGBoost model - Machine Learning with R](https://www.youtube.com/watch?v=Og7CGAfSr_Y)
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)
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
==========
* XGBoost now support HDFS and S3