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https://github.com/dmlc/xgboost
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
https://github.com/dmlc/xgboost
distributed-systems gbdt gbm gbrt machine-learning xgboost
Last synced: 3 days ago
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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
- Host: GitHub
- URL: https://github.com/dmlc/xgboost
- Owner: dmlc
- License: apache-2.0
- Created: 2014-02-06T17:28:03.000Z (almost 11 years ago)
- Default Branch: master
- Last Pushed: 2024-12-04T07:52:09.000Z (8 days ago)
- Last Synced: 2024-12-04T08:25:07.614Z (8 days ago)
- Topics: distributed-systems, gbdt, gbm, gbrt, machine-learning, xgboost
- Language: C++
- Homepage: https://xgboost.readthedocs.io/en/stable/
- Size: 31.1 MB
- Stars: 26,366
- Watchers: 908
- Forks: 8,736
- Open Issues: 461
-
Metadata Files:
- Readme: README.md
- Changelog: NEWS.md
- Funding: .github/FUNDING.yml
- License: LICENSE
- Citation: CITATION
- Security: SECURITY.md
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README
eXtreme Gradient Boosting
===========
[](https://buildkite.com/xgboost/xgboost-ci)
[](https://github.com/dmlc/xgboost/actions)
[](https://xgboost.readthedocs.org)
[](./LICENSE)
[](https://cran.r-project.org/web/packages/xgboost)
[](https://pypi.python.org/pypi/xgboost/)
[](https://anaconda.org/conda-forge/py-xgboost)
[](https://optuna.org)
[](https://twitter.com/XGBoostProject)
[](https://api.securityscorecards.dev/projects/github.com/dmlc/xgboost)
[](https://colab.research.google.com/github/comet-ml/comet-examples/blob/master/integrations/model-training/xgboost/notebooks/how_to_use_comet_with_xgboost_tutorial.ipynb)
[Community](https://xgboost.ai/community) |
[Documentation](https://xgboost.readthedocs.org) |
[Resources](demo/README.md) |
[Contributors](CONTRIBUTORS.md) |
[Release Notes](https://xgboost.readthedocs.io/en/latest/changes/index.html)
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](https://en.wikipedia.org/wiki/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 (Kubernetes, Hadoop, SGE, Dask, Spark, PySpark) and can solve problems beyond billions of examples.
License
-------
© Contributors, 2021. Licensed under an [Apache-2](https://github.com/dmlc/xgboost/blob/master/LICENSE) 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](https://xgboost.ai/community).
Reference
---------
- Tianqi Chen and Carlos Guestrin. [XGBoost: A Scalable Tree Boosting System](https://arxiv.org/abs/1603.02754). In 22nd SIGKDD Conference on Knowledge Discovery and Data Mining, 2016
- XGBoost originates from research project at University of Washington.
Sponsors
--------
Become a sponsor and get a logo here. See details at [Sponsoring the XGBoost Project](https://xgboost.ai/sponsors). The funds are used to defray the cost of continuous integration and testing infrastructure (https://xgboost-ci.net).
## Open Source Collective sponsors
[](#backers) [](#sponsors)
### Sponsors
[[Become a sponsor](https://opencollective.com/xgboost#sponsor)]
### Backers
[[Become a backer](https://opencollective.com/xgboost#backer)]
