Awesome XGBoost
===============
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).

Please send a pull request if you find things that belongs to here.

Contents
--------
- [Code Examples](#code-examples)
  - [Features Walkthrough](#features-walkthrough)
  - [Basic Examples by Tasks](#basic-examples-by-tasks)
  - [Benchmarks](#benchmarks)
- [Machine Learning Challenge Winning Solutions](#machine-learning-challenge-winning-solutions)
- [Tutorials](#tutorials)
- [Usecases](#usecases)
- [Tools using XGBoost](#tools-using-xgboost)
- [Awards](#awards)

Code Examples
-------------
### Features Walkthrough

This is a list of short codes introducing different functionalities of xgboost packages.

* Basic walkthrough of packages
  [python](guide-python/basic_walkthrough.py)
  [R](../R-package/demo/basic_walkthrough.R)
  [Julia](https://github.com/antinucleon/XGBoost.jl/blob/master/demo/basic_walkthrough.jl)
* Customize loss function, and evaluation metric
  [python](guide-python/custom_objective.py)
  [R](../R-package/demo/custom_objective.R)
  [Julia](https://github.com/antinucleon/XGBoost.jl/blob/master/demo/custom_objective.jl)
* Boosting from existing prediction
  [python](guide-python/boost_from_prediction.py)
  [R](../R-package/demo/boost_from_prediction.R)
  [Julia](https://github.com/antinucleon/XGBoost.jl/blob/master/demo/boost_from_prediction.jl)
* Predicting using first n trees
  [python](guide-python/predict_first_ntree.py)
  [R](../R-package/demo/predict_first_ntree.R)
  [Julia](https://github.com/antinucleon/XGBoost.jl/blob/master/demo/predict_first_ntree.jl)
* Generalized Linear Model
  [python](guide-python/generalized_linear_model.py)
  [R](../R-package/demo/generalized_linear_model.R)
  [Julia](https://github.com/antinucleon/XGBoost.jl/blob/master/demo/generalized_linear_model.jl)
* Cross validation
  [python](guide-python/cross_validation.py)
  [R](../R-package/demo/cross_validation.R)
  [Julia](https://github.com/antinucleon/XGBoost.jl/blob/master/demo/cross_validation.jl)
* Predicting leaf indices
  [python](guide-python/predict_leaf_indices.py)
  [R](../R-package/demo/predict_leaf_indices.R)

### Basic Examples by Tasks

Most of examples in this section are based on CLI or python version.
However, the parameter settings can be applied to all versions

- [Binary classification](binary_classification)
- [Multiclass classification](multiclass_classification)
- [Regression](regression)
- [Learning to Rank](rank)

### Benchmarks

- [Starter script for Kaggle Higgs Boson](kaggle-higgs)
- [Kaggle Tradeshift winning solution by daxiongshu](https://github.com/daxiongshu/kaggle-tradeshift-winning-solution)


## Machine Learning Challenge Winning Solutions

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.
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/).

## Tutorials

- [XGBoost Official RMarkdown Tutorials](https://xgboost.readthedocs.org/en/latest/R-package/index.html#tutorials)
- [Open Source Tools & Data Science Competitions](http://www.slideshare.net/odsc/owen-zhangopen-sourcetoolsanddscompetitions1) by Owen Zhang - XGBoost parameter tuning tips
* [Feature Importance Analysis with XGBoost in Tax audit](http://fr.slideshare.net/MichaelBENESTY/feature-importance-analysis-with-xgboost-in-tax-audit)
* [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
- [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))
- [Complete Guide to Parameter Tuning in XGBoost](http://www.analyticsvidhya.com/blog/2016/03/complete-guide-parameter-tuning-xgboost-with-codes-python/) by Aarshay Jain


## Usecases
If you have particular usecase of xgboost that you would like to highlight.
Send a PR to add a one sentence description:)

- XGBoost is used in [Kaggle Script](https://www.kaggle.com/scripts) to solve data science challenges.
- [Seldon predictive service powered by XGBoost](http://docs.seldon.io/iris-demo.html)
- XGBoost Distributed is used in [ODPS Cloud Service by Alibaba](https://yq.aliyun.com/articles/6355) (in Chinese)
- XGBoost is incoporated as part of [Graphlab Create](https://dato.com/products/create/) for scalable machine learning.
- [Hanjing Su](https://www.52cs.org) from Tencent data platform team: "We use distributed XGBoost for click through prediction in wechat shopping and lookalikes. The problems involve hundreds millions of users and thousands of features. XGBoost is cleanly designed and can be easily integrated into our production environment, reducing our cost in developments."



## Tools using XGBoost

- [BayesBoost](https://github.com/mpearmain/BayesBoost) - Bayesian Optimization using xgboost and sklearn API
- [gp_xgboost_gridsearch](https://github.com/vatsan/gp_xgboost_gridsearch) - In-database parallel grid-search for XGBoost on [Greenplum](https://github.com/greenplum-db/gpdb) using PL/Python
- [tpot](https://github.com/rhiever/tpot) - A Python tool that automatically creates and optimizes machine learning pipelines using genetic programming.

## 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)
