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XGBoost: eXtreme Gradient Boosting library.Contributors:

Project description

<img src= width=135/> eXtreme Gradient Boosting
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[![Documentation Status](](
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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]( framework, including [Generalized Linear Model]( (GLM) and [Gradient Boosted Decision Trees]( (GBDT). XGBoost can also be [distributed](#features) and scale to Terascale data

XGBoost is part of [Distributed Machine Learning Common]( <img src=> projects

* [What's New](#whats-new)
* [Version](#version)
* [Documentation](doc/
* [Build Instruction](doc/
* [Features](#features)
* [Distributed XGBoost](multi-node)
* [Usecases](doc/
* [Bug Reporting](#bug-reporting)
* [Contributing to XGBoost](#contributing-to-xgboost)
* [Committers and Contributors](
* [License](#license)
* [XGBoost in Graphlab Create](#xgboost-in-graphlab-create)

What's New

* XGBoost helps Chenglong Chen to win [Kaggle CrowdFlower Competition](
Check out the [winning solution](doc/
* XGBoost-0.4 release, see [](
* XGBoost helps three champion teams to win [WWW2015 Microsoft Malware Classification Challenge (BIG 2015)](
Check out the [winning solution](doc/
* [External Memory Version](doc/


* Current version xgboost-0.4
- [Change log](
- This version is compatible with 0.3x versions

* Easily accessible through CLI, [python](,
* Its fast! Benchmark numbers comparing xgboost, H20, Spark, R - [benchm-ml numbers](
* Memory efficient - Handles sparse matrices, supports external memory
* Accurate prediction, and used extensively by data scientists and kagglers - [highlight links](
* Distributed version runs on Hadoop (YARN), MPI, SGE etc., scales to billions of examples.

Bug Reporting

* For reporting bugs please use the [xgboost/issues]( page.
* For generic questions or to share your experience using xgboost please use the [XGBoost User Group](!forum/xgboost-user/)

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]( to see what can be improved, or open an issue if you want something.
* Contribute to the [documents and examples]( to share your experience with other users.
* Please add your name to []( after your patch has been merged.

© Contributors, 2015. Licensed under an [Apache-2]( license.

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](
* Nice [blogpost]( by Jay Gu about using GLC boosted tree to solve kaggle bike sharing challenge:

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