Metadata-Version: 1.0
Name: xgboost
Version: 0.4a25
Summary:  eXtreme Gradient Boosting
==========================

|Build Status| |Documentation Status| |CRAN Status Badge| |PyPI version|
|Gitter chat for developers at https://gitter.im/dmlc/xgboost|

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

XGBoost is part of `Distributed Machine Learning
Common <http://dmlc.github.io/>`__ <img
src=https://avatars2.githubusercontent.com/u/11508361?v=3&s=20> projects

Contents
--------

-  `What's New <#whats-new>`__
-  `Version <#version>`__
-  `Documentation <doc/index.md>`__
-  `Build Instruction <doc/build.md>`__
-  `Features <#features>`__
-  `Distributed XGBoost <multi-node>`__
-  `Usecases <doc/index.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>`__

What's New
----------

-  XGBoost helps Owen Zhang to win the `Avito Context Ad Click
   competition <https://www.kaggle.com/c/avito-context-ad-clicks>`__.
   Check out the `interview from
   Kaggle <http://blog.kaggle.com/2015/08/26/avito-winners-interview-1st-place-owen-zhang/>`__.
-  XGBoost helps Chenglong Chen to win `Kaggle CrowdFlower
   Competition <https://www.kaggle.com/c/crowdflower-search-relevance>`__
   Check out the `winning
   solution <https://github.com/ChenglongChen/Kaggle_CrowdFlower>`__
-  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 <doc/README.md#highlight-links>`__
-  `External Memory Version <doc/external_memory.md>`__

Version
-------

-  Current version xgboost-0.4
-  `Change log <CHANGES.md>`__
-  This version is compatible with 0.3x versions

Features
--------

-  Easily accessible through CLI,
   `python <https://github.com/dmlc/xgboost/blob/master/demo/guide-python/basic_walkthrough.py>`__,
   `R <https://github.com/dmlc/xgboost/blob/master/R-package/demo/basic_walkthrough.R>`__,
   `Julia <https://github.com/antinucleon/XGBoost.jl/blob/master/demo/basic_walkthrough.jl>`__
-  Its fast! Benchmark numbers comparing xgboost, H20, Spark, R -
   `benchm-ml numbers <https://github.com/szilard/benchm-ml>`__
-  Memory efficient - Handles sparse matrices, supports external memory
-  Accurate prediction, and used extensively by data scientists and
   kagglers - `highlight
   links <https://github.com/dmlc/xgboost/blob/master/doc/README.md#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 <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/>`__

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.

License
-------

© Contributors, 2015. Licensed under an
`Apache-2 <https://github.com/dmlc/xgboost/blob/master/LICENSE>`__
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 <http://graphlab.com/products/create/quick-start-guide.html>`__
-  Nice
   `blogpost <http://blog.graphlab.com/using-gradient-boosted-trees-to-predict-bike-sharing-demand>`__
   by Jay Gu about using GLC boosted tree to solve kaggle bike sharing
   challenge:

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Home-page: https://github.com/dmlc/xgboost
Author: Hongliang Liu
Author-email: phunter.lau@gmail.com
License: UNKNOWN
Description: UNKNOWN
Platform: UNKNOWN
