XGBoost: eXtreme Gradient Boosting

An optimized general purpose gradient boosting library. The library is parallelized, and also provides an optimized distributed version. It implements machine learning algorithm under gradient boosting framework, including generalized linear model and gradient boosted regression tree (GBDT). XGBoost can also also distributed and scale to Terascale data

Contributors: https://github.com/dmlc/xgboost/graphs/contributors

Documentations: Documentation of xgboost

Issues Tracker: https://github.com/dmlc/xgboost/issues

Please join XGBoost User Group to ask questions and share your experience on xgboost.

• Use issue tracker for bug reports, feature requests etc.
• Use the user group to post your experience, ask questions about general usages.

Gitter for developers

Distributed Version: Distributed XGBoost

Highlights of Usecases: Highlight Links

What's New

• XGBoost wins WWW2015 Microsoft Malware Classification Challenge (BIG 2015)
  • Checkout the winning solution at Highlight links
• External Memory Version
• XGBoost now support HDFS and S3
• Distributed XGBoost now runs on YARN
• xgboost user group for tracking changes, sharing your experience on xgboost
• New features in the lastest changes :)
  • Distributed version that scale xgboost to even larger problems with cluster
  • Feature importance visualization in R module, thanks to Michael Benesty
  • Predict leaf index, see demo/guide-python/predict_leaf_indices.py
• XGBoost wins Tradeshift Text Classification
• XGBoost wins HEP meets ML Award in Higgs Boson Challenge

Features

• Sparse feature format:
  • Sparse feature format allows easy handling of missing values, and improve computation efficiency.
• Push the limit on single machine:
  • Efficient implementation that optimizes memory and computation.
• Speed: XGBoost is very fast
  • IN demo/higgs/speedtest.py, kaggle higgs data it is faster(on our machine 20 times faster using 4 threads) than sklearn.ensemble.GradientBoostingClassifier
• Layout of gradient boosting algorithm to support user defined objective
• Distributed and portable
  • The distributed version of xgboost is highly portable and can be used in different platforms
  • It inheritates all the optimizations made in single machine mode, maximumly utilize the resources using both multi-threading and distributed computing.

Build

• Run bash build.sh (you can also type make)
  • Normally it gives what you want
  • See Build Instruction for more information

Version

• This version xgboost-0.3, the code has been refactored from 0.2x to be cleaner and more flexibility
• This version of xgboost is not compatible with 0.2x, due to huge amount of changes in code structure
  • This means the model and buffer file of previous version can not be loaded in xgboost-3.0
• For legacy 0.2x code, refer to Here
• Change log in CHANGES.md

XGBoost in Graphlab Create

• XGBoost is adopted as part of boosted tree toolkit in Graphlab Create (GLC). Graphlab Create is a powerful python toolkit that allows you to data manipulation, graph processing, hyper-parameter search, and visualization of TeraBytes scale data in one framework. Try the Graphlab Create in http://graphlab.com/products/create/quick-start-guide.html
• Nice blogpost by Jay Gu using GLC boosted tree to solve kaggle bike sharing challenge: http://blog.graphlab.com/using-gradient-boosted-trees-to-predict-bike-sharing-demand