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Welcome to the wonderland of XGBoost. This page contains a curated list of awesome XGBoost examples, tutorials and blogs. It is inspired by [awesome-MXNet](https://github.com/dmlc/mxnet/blob/master/example/README.md), [awesome-php](https://github.com/ziadoz/awesome-php) and [awesome-machine-learning](https://github.com/josephmisiti/awesome-machine-learning).
This page contains a curated list of examples, tutorials, blogs about XGBoost usecases.
It is inspired by [awesome-MXNet](https://github.com/dmlc/mxnet/blob/master/example/README.md),
[awesome-php](https://github.com/ziadoz/awesome-php) and [awesome-machine-learning](https://github.com/josephmisiti/awesome-machine-learning).
- [Contributing](#contributing)
Please send a pull request if you find things that belongs to here.
- [Examples](#examples)
Contents
--------
- [Code Examples](#code-examples)
- [Features Walkthrough](#features-walkthrough)
- [Features Walkthrough](#features-walkthrough)
- [Basic Examples by Tasks](#basic-examples-by-tasks)
- [Basic Examples by Tasks](#basic-examples-by-tasks)
* [Starter script for Kaggle Higgs Boson](kaggle-higgs)
- [Starter script for Kaggle Higgs Boson](kaggle-higgs)
* [Kaggle Tradeshift winning solution by daxiongshu](https://github.com/daxiongshu/kaggle-tradeshift-winning-solution)
- [Kaggle Tradeshift winning solution by daxiongshu](https://github.com/daxiongshu/kaggle-tradeshift-winning-solution)
## Machine Learning Challenge Winning Solutions
## Machine Learning Challenge Winning Solutions
"Over the last six months, a new algorithm has come up on Kaggle __winning every single competition__ in this category, it is an algorithm called __XGBoost__." -- Anthony Goldbloom, Founder & CEO of Kaggle (from his presentation "What Is Winning on Kaggle?" [youtube link](https://youtu.be/GTs5ZQ6XwUM?t=7m7s))
XGBoost is extensively used by machine learning practitioners to create state of art data science solutions,
this is a list of machine learning winning solutions with XGBoost.
XGBoost has helped on these winning solutions:
Please send pull requests if you find ones that are missing here.
* Marios Michailidis, Mathias Müller and HJ van Veen, 1st place of the [Dato Truely Native? competition](https://www.kaggle.com/c/dato-native). Link to [the Kaggle interview](http://blog.kaggle.com/2015/12/03/dato-winners-interview-1st-place-mad-professors/).
* Vlad Mironov, Alexander Guschin, 1st place of the [CERN LHCb experiment Flavour of Physics competition](https://www.kaggle.com/c/flavours-of-physics). Link to [the Kaggle interview](http://blog.kaggle.com/2015/11/30/flavour-of-physics-technical-write-up-1st-place-go-polar-bears/).
* Josef Slavicek, 3rd place of the [CERN LHCb experiment Flavour of Physics competition](https://www.kaggle.com/c/flavours-of-physics). Link to [the Kaggle interview](http://blog.kaggle.com/2015/11/23/flavour-of-physics-winners-interview-3rd-place-josef-slavicek/).
* Mario Filho, Josef Feigl, Lucas, Gilberto, 1st place of the [Caterpillar Tube Pricing competition](https://www.kaggle.com/c/caterpillar-tube-pricing). Link to [the Kaggle interview](http://blog.kaggle.com/2015/09/22/caterpillar-winners-interview-1st-place-gilberto-josef-leustagos-mario/).
* Qingchen Wang, 1st place of the [Liberty Mutual Property Inspection](https://www.kaggle.com/c/liberty-mutual-group-property-inspection-prediction). Link to [the Kaggle interview] (http://blog.kaggle.com/2015/09/28/liberty-mutual-property-inspection-winners-interview-qingchen-wang/).
* Chenglong Chen, 1st place of the [Crowdflower Search Results Relevance](https://www.kaggle.com/c/crowdflower-search-relevance). [Link to the winning solution](https://www.kaggle.com/c/crowdflower-search-relevance/forums/t/15186/1st-place-winner-solution-chenglong-chen/).
* Alexandre Barachant (“Cat”) and Rafał Cycoń (“Dog”), 1st place of the [Grasp-and-Lift EEG Detection](https://www.kaggle.com/c/grasp-and-lift-eeg-detection). Link to [the Kaggle interview](http://blog.kaggle.com/2015/10/12/grasp-and-lift-eeg-winners-interview-1st-place-cat-dog/).
* Halla Yang, 2nd place of the [Recruit Coupon Purchase Prediction Challenge](https://www.kaggle.com/c/coupon-purchase-prediction). Link to [the Kaggle interview](http://blog.kaggle.com/2015/10/21/recruit-coupon-purchase-winners-interview-2nd-place-halla-yang/).
* Owen Zhang, 1st place of the [Avito Context Ad Clicks competition](https://www.kaggle.com/c/avito-context-ad-clicks). Link to [the Kaggle interview](http://blog.kaggle.com/2015/08/26/avito-winners-interview-1st-place-owen-zhang/).
There are many other great winning solutions and interviews, but this list is [too small](https://en.wikipedia.org/wiki/Fermat%27s_Last_Theorem) to put all of them here. Please send pull requests if important ones appear.
- Marios Michailidis, Mathias Müller and HJ van Veen, 1st place of the [Dato Truely Native? competition](https://www.kaggle.com/c/dato-native). Link to [the Kaggle interview](http://blog.kaggle.com/2015/12/03/dato-winners-interview-1st-place-mad-professors/).
- Vlad Mironov, Alexander Guschin, 1st place of the [CERN LHCb experiment Flavour of Physics competition](https://www.kaggle.com/c/flavours-of-physics). Link to [the Kaggle interview](http://blog.kaggle.com/2015/11/30/flavour-of-physics-technical-write-up-1st-place-go-polar-bears/).
- Josef Slavicek, 3rd place of the [CERN LHCb experiment Flavour of Physics competition](https://www.kaggle.com/c/flavours-of-physics). Link to [the Kaggle interview](http://blog.kaggle.com/2015/11/23/flavour-of-physics-winners-interview-3rd-place-josef-slavicek/).
- Mario Filho, Josef Feigl, Lucas, Gilberto, 1st place of the [Caterpillar Tube Pricing competition](https://www.kaggle.com/c/caterpillar-tube-pricing). Link to [the Kaggle interview](http://blog.kaggle.com/2015/09/22/caterpillar-winners-interview-1st-place-gilberto-josef-leustagos-mario/).
- Qingchen Wang, 1st place of the [Liberty Mutual Property Inspection](https://www.kaggle.com/c/liberty-mutual-group-property-inspection-prediction). Link to [the Kaggle interview] (http://blog.kaggle.com/2015/09/28/liberty-mutual-property-inspection-winners-interview-qingchen-wang/).
- Chenglong Chen, 1st place of the [Crowdflower Search Results Relevance](https://www.kaggle.com/c/crowdflower-search-relevance). [Link to the winning solution](https://www.kaggle.com/c/crowdflower-search-relevance/forums/t/15186/1st-place-winner-solution-chenglong-chen/).
- Alexandre Barachant (“Cat”) and Rafał Cycoń (“Dog”), 1st place of the [Grasp-and-Lift EEG Detection](https://www.kaggle.com/c/grasp-and-lift-eeg-detection). Link to [the Kaggle interview](http://blog.kaggle.com/2015/10/12/grasp-and-lift-eeg-winners-interview-1st-place-cat-dog/).
- Halla Yang, 2nd place of the [Recruit Coupon Purchase Prediction Challenge](https://www.kaggle.com/c/coupon-purchase-prediction). Link to [the Kaggle interview](http://blog.kaggle.com/2015/10/21/recruit-coupon-purchase-winners-interview-2nd-place-halla-yang/).
- Owen Zhang, 1st place of the [Avito Context Ad Clicks competition](https://www.kaggle.com/c/avito-context-ad-clicks). Link to [the Kaggle interview](http://blog.kaggle.com/2015/08/26/avito-winners-interview-1st-place-owen-zhang/).
## Tutorials
## Tutorials
* "[Open Source Tools & Data Science Competitions](http://www.slideshare.net/odsc/owen-zhangopen-sourcetoolsanddscompetitions1)" by Owen Zhang - XGBoost parameter tuning tips
- [XGBoost Official RMarkdown Tutorials](https://xgboost.readthedocs.org/en/latest/R-package/index.html#tutorials)
* "[Tips for data science competitions](http://www.slideshare.net/OwenZhang2/tips-for-data-science-competitions)" by Owen Zhang - Page 14
- [Open Source Tools & Data Science Competitions](http://www.slideshare.net/odsc/owen-zhangopen-sourcetoolsanddscompetitions1) by Owen Zhang - XGBoost parameter tuning tips
*"[XGBoost - eXtreme Gradient Boosting](http://www.slideshare.net/ShangxuanZhang/xgboost)" by Tong He
* [Feature Importance Analysis with XGBoostin Tax audit](http://fr.slideshare.net/MichaelBENESTY/feature-importance-analysis-with-xgboost-in-tax-audit)
*"[How to use XGBoost algorithm in R in easy steps](http://www.analyticsvidhya.com/blog/2016/01/xgboost-algorithm-easy-steps/)" by TAVISH SRIVASTAVA ([Chinese Translation 中文翻译](https://segmentfault.com/a/1190000004421821) by [HarryZhu](https://segmentfault.com/u/harryprince))
* [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/)
- [XGBoost - eXtreme Gradient Boosting](http://www.slideshare.net/ShangxuanZhang/xgboost) by Tong He
* "Better Optimization with Repeated Cross Validation and the XGBoost model - Machine Learning with R)" by Manuel Amunategui ([Youtube Link](https://www.youtube.com/watch?v=Og7CGAfSr_Y)) ([Github Link](https://github.com/amunategui/BetterCrossValidation))
- [How to use XGBoost algorithm in R in easy steps](http://www.analyticsvidhya.com/blog/2016/01/xgboost-algorithm-easy-steps/) by TAVISH SRIVASTAVA ([Chinese Translation 中文翻译](https://segmentfault.com/a/1190000004421821) by [HarryZhu](https://segmentfault.com/u/harryprince))
* "[XGBoost Rossman Parameter Tuning](https://www.kaggle.com/khozzy/rossmann-store-sales/xgboost-parameter-tuning-template/run/90168/notebook)" by [Norbert Kozlowski](https://www.kaggle.com/khozzy)
* "[Featurizing log data before XGBoost](http://www.slideshare.net/DataRobot/featurizing-log-data-before-xgboost)" by Xavier Conort, Owen Zhang etc
- Better Optimization with Repeated Cross Validation and the XGBoost model - Machine Learning with R) by Manuel Amunategui ([Youtube Link](https://www.youtube.com/watch?v=Og7CGAfSr_Y)) ([Github Link](https://github.com/amunategui/BetterCrossValidation))
* "[West Nile Virus Competition Benchmarks & Tutorials](http://blog.kaggle.com/2015/07/21/west-nile-virus-competition-benchmarks-tutorials/)" by [Anna Montoya](http://blog.kaggle.com/author/annamontoya/)
- [XGBoost Rossman Parameter Tuning](https://www.kaggle.com/khozzy/rossmann-store-sales/xgboost-parameter-tuning-template/run/90168/notebook) by [Norbert Kozlowski](https://www.kaggle.com/khozzy)
* "[Ensemble Decision Tree with XGBoost](https://www.kaggle.com/binghsu/predict-west-nile-virus/xgboost-starter-code-python-0-69)" by [Bing Xu](https://www.kaggle.com/binghsu)
- [Featurizing log data before XGBoost](http://www.slideshare.net/DataRobot/featurizing-log-data-before-xgboost) by Xavier Conort, Owen Zhang etc
* "[Notes on eXtreme Gradient Boosting](http://startup.ml/blog/xgboost)" by ARSHAK NAVRUZYAN ([iPython Notebook](https://github.com/startupml/koan/blob/master/eXtreme%20Gradient%20Boosting.ipynb))
- [West Nile Virus Competition Benchmarks & Tutorials](http://blog.kaggle.com/2015/07/21/west-nile-virus-competition-benchmarks-tutorials/) by [Anna Montoya](http://blog.kaggle.com/author/annamontoya/)
- [Ensemble Decision Tree with XGBoost](https://www.kaggle.com/binghsu/predict-west-nile-virus/xgboost-starter-code-python-0-69) by [Bing Xu](https://www.kaggle.com/binghsu)
- [Notes on eXtreme Gradient Boosting](http://startup.ml/blog/xgboost) by ARSHAK NAVRUZYAN ([iPython Notebook](https://github.com/startupml/koan/blob/master/eXtreme%20Gradient%20Boosting.ipynb))
## Tools with XGBoost
* [BayesBoost](https://github.com/mpearmain/BayesBoost) - Bayesian Optimization using xgboost and sklearn API
## Tools using XGBoost
- [BayesBoost](https://github.com/mpearmain/BayesBoost) - Bayesian Optimization using xgboost and sklearn API
## Services Powered by XGBoost
## Services Powered by XGBoost
* [Seldon predictive service powered by XGBoost](http://docs.seldon.io/iris-demo.html)
- [Seldon predictive service powered by XGBoost](http://docs.seldon.io/iris-demo.html)
* [ODPS by Alibaba](https://yq.aliyun.com/articles/6355) (in Chinese)
- [ODPS by Alibaba](https://yq.aliyun.com/articles/6355) (in Chinese)
## Awards
## Awards
- [John Chambers Award](http://stat-computing.org/awards/jmc/winners.html) - 2016 Winner: XGBoost R Package, by Tong He (Simon Fraser University) and Tianqi Chen (University of Washington)
* [John Chambers Award](http://stat-computing.org/awards/jmc/winners.html) - 2016 Winner: XGBoost, by Tong He (Simon Fraser University) and Tianqi Chen (University of Washington)
@@ -41,15 +41,10 @@ are great resources to learn xgboost by real examples. If you think you have som
* [Understanding XGBoost Model on Otto Dataset](../demo/kaggle-otto/understandingXGBoostModel.Rmd) (R package)
* [Understanding XGBoost Model on Otto Dataset](../demo/kaggle-otto/understandingXGBoostModel.Rmd) (R package)
- This tutorial teaches you how to use xgboost to compete kaggle otto challenge.
- This tutorial teaches you how to use xgboost to compete kaggle otto challenge.
Highlight Solutions
Resources
-------------------
---------
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.
See [awesome xgboost page](https://github.com/dmlc/xgboost/tree/master/demo) for links to other resources.
* [Kaggle CrowdFlower winner's solution by Chenglong Chen](https://github.com/ChenglongChen/Kaggle_CrowdFlower)
* [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/)
@@ -26,10 +26,4 @@ if [ ${TASK} == "python_test" ]; then
conda info -a
conda info -a
conda create -n python3 python=3.5
conda create -n python3 python=3.5
conda create -n python2 python=2.7
conda create -n python2 python=2.7
source activate python3
conda install numpy scipy pandas matplotlib nose scikit-learn
python -m pip install graphviz
source activate python2
conda install numpy scipy pandas matplotlib nose scikit-learn
python -m pip install graphviz
fi
fi
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Tianqi Chen