59 lines
4.2 KiB
Markdown
59 lines
4.2 KiB
Markdown
<img src="https://xgboost.ai/images/logo/xgboost-logo-trimmed.png" width=200/> eXtreme Gradient Boosting
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===========
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[](https://buildkite.com/xgboost/xgboost-ci)
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[](https://github.com/dmlc/xgboost/actions)
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[](https://xgboost.readthedocs.org)
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[](./LICENSE)
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[](https://cran.r-project.org/web/packages/xgboost)
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[](https://pypi.python.org/pypi/xgboost/)
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[](https://anaconda.org/conda-forge/py-xgboost)
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[](https://optuna.org)
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[](https://twitter.com/XGBoostProject)
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[](https://api.securityscorecards.dev/projects/github.com/dmlc/xgboost)
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[](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)
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[Community](https://xgboost.ai/community) |
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[Documentation](https://xgboost.readthedocs.org) |
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[Resources](demo/README.md) |
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[Contributors](CONTRIBUTORS.md) |
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[Release Notes](https://xgboost.readthedocs.io/en/latest/changes/index.html)
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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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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 (Kubernetes, Hadoop, SGE, Dask, Spark, PySpark) and can solve problems beyond billions of examples.
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License
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-------
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© Contributors, 2021. 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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---------
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- 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
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- XGBoost originates from research project at University of Washington.
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Sponsors
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--------
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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).
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## Open Source Collective sponsors
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[](#backers) [](#sponsors)
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### Sponsors
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[[Become a sponsor](https://opencollective.com/xgboost#sponsor)]
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<a href="https://www.nvidia.com/en-us/" target="_blank"><img src="https://raw.githubusercontent.com/xgboost-ai/xgboost-ai.github.io/master/images/sponsors/nvidia.jpg" alt="NVIDIA" width="72" height="72"></a>
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<a href="https://www.intel.com/" target="_blank"><img src="https://images.opencollective.com/intel-corporation/2fa85c1/logo/256.png" width="72" height="72"></a>
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<a href="https://www.comet.com/site/?utm_source=xgboost&utm_medium=github&utm_content=readme" target="_blank"><img src="https://cdn.comet.ml/img/notebook_logo.png" height="72"></a>
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### Backers
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[[Become a backer](https://opencollective.com/xgboost#backer)]
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<a href="https://opencollective.com/xgboost#backers" target="_blank"><img src="https://opencollective.com/xgboost/backers.svg?width=890"></a>
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