Please send a pull request if you find things that belongs to here.
- Code Examples
- Machine Learning Challenge Winning Solutions
- Tools using XGBoost
- Windows Binaries
This is a list of short codes introducing different functionalities of xgboost packages.
- Basic walkthrough of packages
- Customize loss function, and evaluation metric
- Boosting from existing prediction
- Predicting using first n trees
- Generalized Linear Model
- Cross validation
- Predicting leaf indices
Most of examples in this section are based on CLI or python version.
However, the parameter settings can be applied to all versions
- Starter script for Kaggle Higgs Boson
- Kaggle Tradeshift winning solution by daxiongshu
- Benchmarking the most commonly used open source tools for binary classification
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.
- Maksims Volkovs, Guangwei Yu and Tomi Poutanen, 1st place of the 2017 ACM RecSys challenge. Link to paper.
- Vlad Sandulescu, Mihai Chiru, 1st place of the KDD Cup 2016 competition. Link to the arxiv paper.
- Marios Michailidis, Mathias Müller and HJ van Veen, 1st place of the Dato Truely Native? competition. Link to the Kaggle interview.
- Vlad Mironov, Alexander Guschin, 1st place of the CERN LHCb experiment Flavour of Physics competition. Link to the Kaggle interview.
- Josef Slavicek, 3rd place of the CERN LHCb experiment Flavour of Physics competition. Link to the Kaggle interview.
- Mario Filho, Josef Feigl, Lucas, Gilberto, 1st place of the Caterpillar Tube Pricing competition. Link to the Kaggle interview.
- Qingchen Wang, 1st place of the Liberty Mutual Property Inspection. Link to the Kaggle interview.
- Chenglong Chen, 1st place of the Crowdflower Search Results Relevance. Link to the winning solution.
- Alexandre Barachant (“Cat”) and Rafał Cycoń (“Dog”), 1st place of the Grasp-and-Lift EEG Detection. Link to the Kaggle interview.
- Halla Yang, 2nd place of the Recruit Coupon Purchase Prediction Challenge. Link to the Kaggle interview.
- Owen Zhang, 1st place of the Avito Context Ad Clicks competition. Link to the Kaggle interview.
- Keiichi Kuroyanagi, 2nd place of the Airbnb New User Bookings. Link to the Kaggle interview.
- Marios Michailidis, Mathias Müller and Ning Situ, 1st place Homesite Quote Conversion. Link to the Kaggle interview.
- XGBoost: A Scalable Tree Boosting System (video+slides) by Tianqi Chen at the Los Angeles Data Science meetup
- Machine Learning with XGBoost on Qubole Spark Cluster
- XGBoost Official RMarkdown Tutorials
- An Introduction to XGBoost R Package by Tong He
- Open Source Tools & Data Science Competitions by Owen Zhang - XGBoost parameter tuning tips
- Feature Importance Analysis with XGBoost in Tax audit
- Winning solution of Kaggle Higgs competition: what a single model can do
XGBoost - eXtreme Gradient Boosting by Tong He
Kaggle Solution: What’s Cooking ? (Text Mining Competition) by MANISH SARASWAT
Featurizing log data before XGBoost by Xavier Conort, Owen Zhang etc
Complete Guide to Parameter Tuning in XGBoost by Aarshay Jain
Practical XGBoost in Python online course by Parrot Prediction
Spark and XGBoost using Scala by Elena Cuoco
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 to solve data science challenges.
XGBoost Distributed is used in ODPS Cloud Service by Alibaba (in Chinese)
XGBoost is incoporated as part of Graphlab Create for scalable machine learning.
Hanjing Su 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.”
CNevd from autohome.com ad platform team: “Distributed XGBoost is used for click through rate prediction in our display advertising, XGBoost is highly efficient and flexible and can be easily used on our distributed platform, our ctr made a great improvement with hundred millions samples and millions features due to this awesome XGBoost”
- BayesBoost - Bayesian Optimization using xgboost and sklearn API
- gp_xgboost_gridsearch - In-database parallel grid-search for XGBoost on Greenplum using PL/Python
- tpot - A Python tool that automatically creates and optimizes machine learning pipelines using genetic programming.
- John Chambers Award - 2016 Winner: XGBoost R Package, by Tong He (Simon Fraser University) and Tianqi Chen (University of Washington)
Unofficial windows binaries and instructions on how to use them are hosted on Guido Tapia’s blog