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XGBoost Code Examples #Awesome XGBoost
=====================
This folder contains all the code examples using xgboost. Welcome to the wonderland of XGBoost. This page contains a curated list of awesome XGBoost examples, tutorials and blogs. It is inspired by [awesom-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
* Contribution of examples, benchmarks is more than welcome! * Contribution of examples, benchmarks is more than welcome!
* If you like to share how you use xgboost to solve your problem, send a pull request:) * If you like to share how you use xgboost to solve your problem, send a pull request:)
* If you want to contribute to this list and the examples, please open a new pull request.
##List of examples
### Features Walkthrough
Features Walkthrough
--------------------
This is a list of short codes introducing different functionalities of xgboost packages. This is a list of short codes introducing different functionalities of xgboost packages.
* Basic walkthrough of packages * Basic walkthrough of packages
[python](guide-python/basic_walkthrough.py) [python](guide-python/basic_walkthrough.py)
[R](../R-package/demo/basic_walkthrough.R) [R](../R-package/demo/basic_walkthrough.R)
@ -36,23 +42,61 @@ This is a list of short codes introducing different functionalities of xgboost p
[python](guide-python/predict_leaf_indices.py) [python](guide-python/predict_leaf_indices.py)
[R](../R-package/demo/predict_leaf_indices.R) [R](../R-package/demo/predict_leaf_indices.R)
Basic Examples by Tasks ### Basic Examples by Tasks
-----------------------
Most of examples in this section are based on CLI or python version. Most of examples in this section are based on CLI or python version.
However, the parameter settings can be applied to all versions However, the parameter settings can be applied to all versions
* [Binary classification](binary_classification) * [Binary classification](binary_classification)
* [Multiclass classification](multiclass_classification) * [Multiclass classification](multiclass_classification)
* [Regression](regression) * [Regression](regression)
* [Learning to Rank](rank) * [Learning to Rank](rank)
* [Distributed Training](distributed-training) * [Distributed Training](distributed-training)
Benchmarks ### Benchmarks
----------
* [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
--------------------------------------------
* XGBoost helps Vlad Mironov, Alexander Guschin to win the [CERN LHCb experiment Flavour of Physics competition](https://www.kaggle.com/c/flavours-of-physics). Check out the [interview from Kaggle](http://blog.kaggle.com/2015/11/30/flavour-of-physics-technical-write-up-1st-place-go-polar-bears/). "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 helps Mario Filho, Josef Feigl, Lucas, Gilberto to win the [Caterpillar Tube Pricing competition](https://www.kaggle.com/c/caterpillar-tube-pricing). Check out the [interview from Kaggle](http://blog.kaggle.com/2015/09/22/caterpillar-winners-interview-1st-place-gilberto-josef-leustagos-mario/).
* XGBoost helps Halla Yang to win the [Recruit Coupon Purchase Prediction Challenge](https://www.kaggle.com/c/coupon-purchase-prediction). Check out the [interview from Kaggle](http://blog.kaggle.com/2015/10/21/recruit-coupon-purchase-winners-interview-2nd-place-halla-yang/). * XGBoost helps Marios Michailidis, Mathias Müller and HJ van Veen to win (1st place) the [Dato Truely Native? competition](https://www.kaggle.com/c/dato-native). Check out the [interview from Kaggle](http://blog.kaggle.com/2015/12/03/dato-winners-interview-1st-place-mad-professors/).
* XGBoost helps Vlad Mironov, Alexander Guschin to win (1st place) the [CERN LHCb experiment Flavour of Physics competition](https://www.kaggle.com/c/flavours-of-physics). Check out the [interview from Kaggle](http://blog.kaggle.com/2015/11/30/flavour-of-physics-technical-write-up-1st-place-go-polar-bears/).
* XGBoost helps Josef Slavicek to win (3rd place) the [CERN LHCb experiment Flavour of Physics competition](https://www.kaggle.com/c/flavours-of-physics). Check out the [interview from Kaggle](http://blog.kaggle.com/2015/11/23/flavour-of-physics-winners-interview-3rd-place-josef-slavicek/).
* XGBoost helps Mario Filho, Josef Feigl, Lucas, Gilberto to win (1st place) the [Caterpillar Tube Pricing competition](https://www.kaggle.com/c/caterpillar-tube-pricing). Check out the [interview from Kaggle](http://blog.kaggle.com/2015/09/22/caterpillar-winners-interview-1st-place-gilberto-josef-leustagos-mario/).
* XGBoost helps Qingchen Wang to win (1st place) the [Liberty Mutual Property Inspection](https://www.kaggle.com/c/liberty-mutual-group-property-inspection-prediction). Check out the [interview from Kaggle](http://blog.kaggle.com/2015/09/28/liberty-mutual-property-inspection-winners-interview-qingchen-wang/).
* XGBoost helps Chenglong Chen to win (1st place) the [Crowdflower Search Results Relevance](https://www.kaggle.com/c/crowdflower-search-relevance). Check out the [Winning solution](https://www.kaggle.com/c/crowdflower-search-relevance/forums/t/15186/1st-place-winner-solution-chenglong-chen/).
* XGBoost helps Alexandre Barachant (“Cat”) and Rafał Cycoń (“Dog”) to win (1st place) the [Grasp-and-Lift EEG Detection](https://www.kaggle.com/c/grasp-and-lift-eeg-detection). Check out the [interview from Kaggle](http://blog.kaggle.com/2015/10/12/grasp-and-lift-eeg-winners-interview-1st-place-cat-dog/).
* XGBoost helps Halla Yang to win (2nd place) the [Recruit Coupon Purchase Prediction Challenge](https://www.kaggle.com/c/coupon-purchase-prediction). Check out the [interview from Kaggle](http://blog.kaggle.com/2015/10/21/recruit-coupon-purchase-winners-interview-2nd-place-halla-yang/).
* XGBoost helps Owen Zhang to win (1st place) the [Avito Context Ad Clicks competition](https://www.kaggle.com/c/avito-context-ad-clicks). Check out the [interview from Kaggle](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.
## List of Tutorials
* "[Open Source Tools & Data Science Competitions](http://www.slideshare.net/odsc/owen-zhangopen-sourcetoolsanddscompetitions1)" by Owen Zhang - XGBoost parameter tuning tips
* "[Tips for data science competitions](http://www.slideshare.net/OwenZhang2/tips-for-data-science-competitions)" by Owen Zhang - Page 14
* "[XGBoost - eXtreme Gradient Boosting](http://www.slideshare.net/ShangxuanZhang/xgboost)" by Tong He
* "[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))
* "[Kaggle Solution: What’s Cooking ? (Text Mining Competition)](http://www.analyticsvidhya.com/blog/2015/12/kaggle-solution-cooking-text-mining-competition/)" by MANISH SARASWAT
* "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))
* "[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
* "[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))
## List of Tools with XGBoost
* [BayesBoost](https://github.com/mpearmain/BayesBoost) - Bayesian Optimization using xgboost and sklearn API
## List of Services Powered by XGBoost
* [Seldon predictive service powered by XGBoost](http://docs.seldon.io/iris-demo.html)
* [ODPS by Alibaba](https://yq.aliyun.com/articles/6355) (in Chinese)
## List of Awards
* [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)