68 lines
3.8 KiB
Markdown
68 lines
3.8 KiB
Markdown
XGBoost: eXtreme Gradient Boosting
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==================================
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[](https://travis-ci.org/dmlc/xgboost)
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An optimized general purpose gradient boosting library. The library is parallelized, and also provides an optimized distributed version.
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It implements machine learning algorithm under gradient boosting framework, including generalized linear model and gradient boosted regression tree (GBDT). XGBoost can also be distributed and scale to Terascale data
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Contributors: https://github.com/dmlc/xgboost/graphs/contributors
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Documentations: [Documentation of xgboost](doc/README.md)
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Issues Tracker: [https://github.com/dmlc/xgboost/issues](https://github.com/dmlc/xgboost/issues?q=is%3Aissue+label%3Aquestion)
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Please join [XGBoost User Group](https://groups.google.com/forum/#!forum/xgboost-user/) to ask questions and share your experience on xgboost.
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- Use issue tracker for bug reports, feature requests etc.
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- Use the user group to post your experience, ask questions about general usages.
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Gitter for developers [](https://gitter.im/dmlc/xgboost?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge)
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Distributed Version: [Distributed XGBoost](multi-node)
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Highlights of Usecases: [Highlight Links](doc/README.md#highlight-links)
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XGBoost is part of [Distributed Machine Learning Common](http://dmlc.github.io/) projects
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What's New
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==========
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* XGBoost-0.4 release, see [CHANGES.md](CHANGES.md#xgboost-04)
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* XGBoost wins [WWW2015 Microsoft Malware Classification Challenge (BIG 2015)](http://www.kaggle.com/c/malware-classification/forums/t/13490/say-no-to-overfitting-approaches-sharing)
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- Checkout the winning solution at [Highlight links](doc/README.md#highlight-links)
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* [External Memory Version](doc/external_memory.md)
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Contributing to XGBoost
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=========
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XGBoost has been developed and used by a group of active community. Everyone is more than welcomed to is a great way to make the project better and more accessible to more users.
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* Checkout [Feature Wish List](https://github.com/dmlc/xgboost/labels/Wish-List) to see what can be improved, or open an issue if you want something.
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* Contribute to the [documents and examples](https://github.com/dmlc/xgboost/blob/master/doc/) to share your experience with other users.
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Features
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========
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* Easily accessible in python, R, Julia, CLI
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* Fast speed and memory efficient
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- Can be more than 10 times faster than GBM in sklearn and R
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- Handles sparse matrices, support external memory
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* Accurate prediction, and used extensively by data scientists and kagglers
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- See [highlight links](https://github.com/dmlc/xgboost/blob/master/doc/README.md#highlight-links)
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* Distributed and Portable
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- The distributed version runs on Hadoop (YARN), MPI, SGE etc.
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- Scales to billions of examples and beyond
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Build
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=======
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* Run ```bash build.sh``` (you can also type make)
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- Normally it gives what you want
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- See [Build Instruction](doc/build.md) for more information
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Version
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=======
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* Current version xgboost-0.4, a lot improvment has been made since 0.3
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- Change log in [CHANGES.md](CHANGES.md)
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- This version is compatible with 0.3x versions
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XGBoost in Graphlab Create
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==========================
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* 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
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* 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
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