Philip Hyunsu Cho 02130af47d
Enable auto-locking of issues closed long ago (#3821)
* Enable auto-locking of issues closed long ago

Issues that were closed more than 90 days ago will be locked automatically so
that no additional comments would be allowed. We will use a bot to do
this: https://probot.github.io/apps/lock/

Background: As a maintainer, I often see people leaving comments to old issue
posts that were closed long ago. Those comments are hard to discover and assist
with, since they get buried under list of other active issues.

With the change, users who want to follow up with an old issue would be asked
to file a new issue.

* Exempt `feature-request` from auto locking

* Disable comment to avoid triggering notification
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eXtreme Gradient Boosting

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Community | Documentation | Resources | Contributors | Release Notes

XGBoost is an optimized distributed gradient boosting library designed to be highly efficient, flexible and portable. It implements machine learning algorithms under the Gradient Boosting framework. XGBoost provides a parallel tree boosting (also known as GBDT, GBM) that solve many data science problems in a fast and accurate way. The same code runs on major distributed environment (Hadoop, SGE, MPI) and can solve problems beyond billions of examples.

License

© Contributors, 2016. Licensed under an Apache-2 license.

Contribute to XGBoost

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. Checkout the Community Page

Reference

  • Tianqi Chen and Carlos Guestrin. XGBoost: A Scalable Tree Boosting System. In 22nd SIGKDD Conference on Knowledge Discovery and Data Mining, 2016
  • XGBoost originates from research project at University of Washington.
Description
Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Dask, Flink and DataFlow
Readme 33 MiB
Languages
C++ 45.5%
Python 20.3%
Cuda 15.2%
R 6.8%
Scala 6.4%
Other 5.6%