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| DMLC/XGBoost | ||
| ================================== | ||
| XGBoost | ||
| ======= | ||
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| [](https://travis-ci.org/dmlc/xgboost) [](https://gitter.im/dmlc/xgboost?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge) | ||
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| An optimized general purpose gradient boosting library. The library is parallelized, and also provides an optimized distributed version. | ||
| It implements machine learning algorithms under the [Gradient Boosting](https://en.wikipedia.org/wiki/Gradient_boosting) framework, including [Generalized Linear Model](https://en.wikipedia.org/wiki/Generalized_linear_model) (GLM) and [Gradient Boosted Decision Trees](https://en.wikipedia.org/wiki/Gradient_boosting#Gradient_tree_boosting) (GBDT). XGBoost can also be [distributed](#features) and scale to Terascale data | ||
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| Check out our [Committers and Contributors](CONTRIBUTORS.md) who help make xgboost better. | ||
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| Documentation: [Documentation of dmlc/xgboost](doc/README.md) | ||
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| Issue Tracker: [https://github.com/dmlc/xgboost/issues](https://github.com/dmlc/xgboost/issues?q=is%3Aissue+label%3Aquestion) | ||
| An optimized general purpose gradient boosting library. The library is parallelized, and also provides an optimized distributed version. | ||
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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. | ||
| - Use issue tracker for bug reports, feature requests etc. | ||
| - Use the user group to post your experience, ask questions about general usages. | ||
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| Distributed Version: [Distributed XGBoost](multi-node) | ||
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| Highlights of Usecases: [Highlight Links](doc/README.md#highlight-links) | ||
| It implements machine learning algorithms under the [Gradient Boosting](https://en.wikipedia.org/wiki/Gradient_boosting) framework, including [Generalized Linear Model](https://en.wikipedia.org/wiki/Generalized_linear_model) (GLM) and [Gradient Boosted Decision Trees](https://en.wikipedia.org/wiki/Gradient_boosting#Gradient_tree_boosting) (GBDT). XGBoost can also be [distributed](#features) and scale to Terascale data | ||
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| XGBoost is part of [Distributed Machine Learning Common](http://dmlc.github.io/) projects | ||
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| Contents | ||
| -------- | ||
| * [What's New](#whats-new) | ||
| * [Version](#version) | ||
| * [Documentation](doc/README.md) | ||
| * [Build Instruction](doc/build.md) | ||
| * [Features](#features) | ||
| * [Distributed XGBoost](multi-node) | ||
| * [Usecases](doc/README.md#highlight-links) | ||
| * [Bug Reporting](#bug-reporting) | ||
| * [Contributing to XGBoost](#contributing-to-xgboost) | ||
| * [Committers and Contributors](CONTRIBUTORS.md) | ||
| * [License](#license) | ||
| * [XGBoost in Graphlab Create](#xgboost-in-graphlab-create) | ||
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| What's New | ||
| ========== | ||
| ---------- | ||
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| * XGBoost helps Chenglong Chen to win [Kaggle CrowdFlower Competition](https://www.kaggle.com/c/crowdflower-search-relevance) | ||
| - Check out the winning solution at [Highlight links](doc/README.md#highlight-links) | ||
| * XGBoost-0.4 release, see [CHANGES.md](CHANGES.md#xgboost-04) | ||
| * XGBoost helps three champion teams to win [WWW2015 Microsoft Malware Classification Challenge (BIG 2015)](http://www.kaggle.com/c/malware-classification/forums/t/13490/say-no-to-overfitting-approaches-sharing) | ||
| - Check out the winning solution at [Highlight links](doc/README.md#highlight-links) | ||
| * [External Memory Version](doc/external_memory.md) | ||
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| Contributing to XGBoost | ||
| ========= | ||
| XGBoost has been developed and used by a group of active community members. Everyone is more than welcome to contribute. It is a way to make the project better and more accessible to more users. | ||
| * Check out [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. | ||
| * Contribute to the [documents and examples](https://github.com/dmlc/xgboost/blob/master/doc/) to share your experience with other users. | ||
| * Please add your name to [CONTRIBUTORS.md](CONTRIBUTORS.md) after your patch has been merged. | ||
| Version | ||
| ------- | ||
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| * Current version xgboost-0.4, a lot improvment has been made since 0.3 | ||
| - Change log in [CHANGES.md](CHANGES.md) | ||
| - This version is compatible with 0.3x versions | ||
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| Features | ||
| ======== | ||
| * Easily accessible in python, R, Julia, CLI | ||
| * Fast speed and memory efficient | ||
| - Can be more than 10 times faster than GBM in sklearn and R | ||
| -------- | ||
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| * Easily accessible through python, R, Julia, CLI | ||
| * Fast and memory efficient | ||
| - Can be more than 10 times faster than GBM in sklearn and R. [benchm-ml numbers](https://github.com/szilard/benchm-ml) | ||
| - Handles sparse matrices, support external memory | ||
| * Accurate prediction, and used extensively by data scientists and kagglers | ||
| - See [highlight links](https://github.com/dmlc/xgboost/blob/master/doc/README.md#highlight-links) | ||
| * Distributed and Portable | ||
| - The distributed version runs on Hadoop (YARN), MPI, SGE etc. | ||
| - Scales to billions of examples and beyond | ||
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| Bug Reporting | ||
| ------------- | ||
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| Build | ||
| ======= | ||
| * Run ```bash build.sh``` (you can also type make) | ||
| - Normally it gives what you want | ||
| - See [Build Instruction](doc/build.md) for more information | ||
| * For reporting bugs please use the [xgboost/issues](https://github.com/dmlc/xgboost/issues) page. | ||
| * For generic questions or to share your experience using xgboost please use the [XGBoost User Group](https://groups.google.com/forum/#!forum/xgboost-user/) | ||
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| Version | ||
| ======= | ||
| * Current version xgboost-0.4, a lot improvment has been made since 0.3 | ||
| - Change log in [CHANGES.md](CHANGES.md) | ||
| - This version is compatible with 0.3x versions | ||
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| Contributing to XGBoost | ||
| ----------------------- | ||
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| XGBoost has been developed and used by a group of active community members. Everyone is more than welcome to contribute. It is a way to make the project better and more accessible to more users. | ||
| * Check out [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. | ||
| * Contribute to the [documents and examples](https://github.com/dmlc/xgboost/blob/master/doc/) to share your experience with other users. | ||
| * Please add your name to [CONTRIBUTORS.md](CONTRIBUTORS.md) after your patch has been merged. | ||
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| License | ||
| ======= | ||
| ------- | ||
| © Contributors, 2015. Licensed under an [Apache-2](https://github.com/dmlc/xgboost/blob/master/LICENSE) license. | ||
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| 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 do 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 about using GLC boosted tree to solve kaggle bike sharing challenge: http://blog.graphlab.com/using-gradient-boosted-trees-to-predict-bike-sharing-demand |