[DOCS] Update link to readme (#3437)
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Thanks for participating in the XGBoost community! We use https://discuss.xgboost.ai for any general usage questions and discussions. The issue tracker is used for actionable items such as feature proposals discussion, roadmaps, and bug tracking. You are always welcomed to post on the forum first :)
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Issues that are inactive for a period of time may get closed. We adopt this policy so that we won't lose track of actionable issues that may fall at the bottom of the pile. Feel free to reopen a new one if you feel there is an additional problem that needs attention when an old one gets closed.
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For bug reports, to help the developer act on the issues, please include a description of your environment, preferably a minimum script to reproduce the problem.
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For feature proposals, list clear, small actionable items so we can track the progress of the change.
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For bugs or installation issues, please provide the following information.
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The more information you provide, the more easily we will be able to offer
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help and advice.
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## Environment info
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Operating System:
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Compiler:
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Package used (python/R/jvm/C++):
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`xgboost` version used:
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If installing from source, please provide
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1. The commit hash (`git rev-parse HEAD`)
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2. Logs will be helpful (If logs are large, please upload as attachment).
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If you are using jvm package, please
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1. add [jvm-packages] in the title to make it quickly be identified
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2. the gcc version and distribution
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If you are using python package, please provide
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1. The python version and distribution
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2. The command to install `xgboost` if you are not installing from source
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If you are using R package, please provide
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1. The R `sessionInfo()`
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2. The command to install `xgboost` if you are not installing from source
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## Steps to reproduce
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1.
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3.
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## What have you tried?
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35
README.md
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README.md
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[Documentation](https://xgboost.readthedocs.org) |
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[Documentation](https://xgboost.readthedocs.org) |
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[Resources](demo/README.md) |
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[Resources](demo/README.md) |
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[Installation](https://xgboost.readthedocs.org/en/latest/build.html) |
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[Contributors](CONTRIBUTORS.md) |
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[Release Notes](NEWS.md) |
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[Community](https://xgboost.ai/community) |
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[RoadMap](https://github.com/dmlc/xgboost/issues/873)
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[Release Notes](NEWS.md)
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XGBoost is an optimized distributed gradient boosting library designed to be highly ***efficient***, ***flexible*** and ***portable***.
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XGBoost is an optimized distributed gradient boosting library designed to be highly ***efficient***, ***flexible*** and ***portable***.
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It implements machine learning algorithms under the [Gradient Boosting](https://en.wikipedia.org/wiki/Gradient_boosting) framework.
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It implements machine learning algorithms under the [Gradient Boosting](https://en.wikipedia.org/wiki/Gradient_boosting) framework.
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XGBoost provides a parallel tree boosting (also known as GBDT, GBM) that solve many data science problems in a fast and accurate way.
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XGBoost provides a parallel tree boosting (also known as GBDT, GBM) that solve many data science problems in a fast and accurate way.
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The same code runs on major distributed environment (Hadoop, SGE, MPI) and can solve problems beyond billions of examples.
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The same code runs on major distributed environment (Hadoop, SGE, MPI) and can solve problems beyond billions of examples.
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What's New
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----------
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* [XGBoost GPU support with fast histogram algorithm](https://github.com/dmlc/xgboost/tree/master/plugin/updater_gpu)
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* [XGBoost4J: Portable Distributed XGboost in Spark, Flink and Dataflow](http://dmlc.ml/2016/03/14/xgboost4j-portable-distributed-xgboost-in-spark-flink-and-dataflow.html), see [JVM-Package](https://github.com/dmlc/xgboost/tree/master/jvm-packages)
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* [Story and Lessons Behind the Evolution of XGBoost](http://homes.cs.washington.edu/~tqchen/2016/03/10/story-and-lessons-behind-the-evolution-of-xgboost.html)
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* [Tutorial: Distributed XGBoost on AWS with YARN](https://xgboost.readthedocs.io/en/latest/tutorials/aws_yarn.html)
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* [XGBoost brick](NEWS.md) Release
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Ask a Question
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--------------
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* For reporting bugs please use the [xgboost/issues](https://github.com/dmlc/xgboost/issues) page.
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* 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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Help to Make XGBoost Better
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---------------------------
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XGBoost has been developed and used by a group of active community members. Your help is very valuable to make the package better for everyone.
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- Check out [call for contributions](https://github.com/dmlc/xgboost/issues?q=is%3Aissue+label%3Acall-for-contribution+is%3Aopen) and [Roadmap](https://github.com/dmlc/xgboost/issues/873) 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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- Add your stories and experience to [Awesome XGBoost](demo/README.md).
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- Please add your name to [CONTRIBUTORS.md](CONTRIBUTORS.md) and after your patch has been merged.
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- Please also update [NEWS.md](NEWS.md) on changes and improvements in API and docs.
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License
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License
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-------
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-------
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© Contributors, 2016. Licensed under an [Apache-2](https://github.com/dmlc/xgboost/blob/master/LICENSE) license.
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© Contributors, 2016. Licensed under an [Apache-2](https://github.com/dmlc/xgboost/blob/master/LICENSE) license.
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Contribute to XGBoost
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---------------------
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XGBoost has been developed and used by a group of active community members. Your help is very valuable to make the package better for everyone.
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Checkout the [Community Page](https://xgboost.ai/community)
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Reference
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Reference
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- Tianqi Chen and Carlos Guestrin. [XGBoost: A Scalable Tree Boosting System](http://arxiv.org/abs/1603.02754). In 22nd SIGKDD Conference on Knowledge Discovery and Data Mining, 2016
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- Tianqi Chen and Carlos Guestrin. [XGBoost: A Scalable Tree Boosting System](http://arxiv.org/abs/1603.02754). In 22nd SIGKDD Conference on Knowledge Discovery and Data Mining, 2016
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- XGBoost originates from research project at University of Washington, see also the [Project Page at UW](http://dmlc.cs.washington.edu/xgboost.html).
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- XGBoost originates from research project at University of Washington.
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