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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: about 2 months ago
JSON representation

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 (over 10 years ago)
Default Branch: master
Last Pushed: 2024-03-25T18:47:15.000Z (3 months ago)
Last Synced: 2024-03-25T20:06:14.566Z (3 months ago)
Topics: distributed-systems, gbdt, gbm, gbrt, machine-learning, xgboost
Language: C++
Homepage: https://xgboost.readthedocs.io/en/stable/
Size: 26.5 MB
Stars: 25,446
Watchers: 909
Forks: 8,653
Open Issues: 442
Metadata Files:
- Readme: README.md
- Funding: .github/FUNDING.yml
- License: LICENSE
- Citation: CITATION
- Security: SECURITY.md

Lists

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awesome-python-resources - GitHub - 5% open · ⏱️ 25.08.2022): (机器学习)
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awesome-python - xgboost - A scalable, portable, and distributed gradient boosting library. (Machine Learning)
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awesome-machine-learning - XGBoost - Python bindings for eXtreme Gradient Boosting (Tree) Library. (Python / General-Purpose Machine Learning)
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awesome-python - xgboost - A scalable, portable, and distributed gradient boosting library. (Machine Learning)
awesome-machine-learnings - XGBoost - Python bindings for eXtreme Gradient Boosting (Tree) Library. (Python / General-Purpose Machine Learning)
awesome-python - xgboost - A scalable, portable, and distributed gradient boosting library. (Machine Learning)
awesome-python - xgboost - A scalable, portable, and distributed gradient boosting library. (Machine Learning)
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awesome-machine-learning - XGBoost - Python bindings for eXtreme Gradient Boosting (Tree) Library (Python / General-Purpose Machine Learning)
Python-Awesome - xgboost - A scalable, portable, and distributed gradient boosting library. (Machine Learning)
awesome-gradient-boosting-papers - [Code
awesome-machine-learning - XGBoost - Python bindings for eXtreme Gradient Boosting (Tree) Library. (Python / General-Purpose Machine Learning)
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awesome-python - xgboost - A scalable, portable, and distributed gradient boosting library. (Machine Learning)
awesome-python-cn - xgboost
awesome-machine-learning - XGBoost - Python bindings for eXtreme Gradient Boosting (Tree) Library. (Python / General-Purpose Machine Learning)
awesome-machine-learning - XGBoost - Python bindings for eXtreme Gradient Boosting (Tree) Library. (Python / General-Purpose Machine Learning)
awesome-machine-learning - XGBoost - Python bindings for eXtreme Gradient Boosting (Tree) Library. (Python / General-Purpose Machine Learning)
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StarryDivineSky - dmlc/xgboost
awesome-python - 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 ` 📝 2 days ago ` (Machine Learning [🔝](#readme))
awesome-machine-learning - XGBoost - Python bindings for eXtreme Gradient Boosting (Tree) Library. (Python / General-Purpose Machine Learning)

README

          eXtreme Gradient Boosting

===========

[![Build Status](https://badge.buildkite.com/aca47f40a32735c00a8550540c5eeff6a4c1d246a580cae9b0.svg?branch=master)](https://buildkite.com/xgboost/xgboost-ci)

[![XGBoost-CI](https://github.com/dmlc/xgboost/workflows/XGBoost-CI/badge.svg?branch=master)](https://github.com/dmlc/xgboost/actions)

[![Documentation Status](https://readthedocs.org/projects/xgboost/badge/?version=latest)](https://xgboost.readthedocs.org)

[![GitHub license](http://dmlc.github.io/img/apache2.svg)](./LICENSE)

[![CRAN Status Badge](http://www.r-pkg.org/badges/version/xgboost)](http://cran.r-project.org/web/packages/xgboost)

[![PyPI version](https://badge.fury.io/py/xgboost.svg)](https://pypi.python.org/pypi/xgboost/)

[![Conda version](https://img.shields.io/conda/vn/conda-forge/py-xgboost.svg)](https://anaconda.org/conda-forge/py-xgboost)

[![Optuna](https://img.shields.io/badge/Optuna-integrated-blue)](https://optuna.org)

[![Twitter](https://img.shields.io/badge/@XGBoostProject--_.svg?style=social&logo=twitter)](https://twitter.com/XGBoostProject)

[![OpenSSF Scorecard](https://api.securityscorecards.dev/projects/github.com/dmlc/xgboost/badge)](https://api.securityscorecards.dev/projects/github.com/dmlc/xgboost)

[Community](https://xgboost.ai/community) |

[Documentation](https://xgboost.readthedocs.org) |

[Resources](demo/README.md) |

[Contributors](CONTRIBUTORS.md) |

[Release Notes](NEWS.md)

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](http://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 on Open Collective](https://opencollective.com/xgboost/backers/badge.svg)](#backers) [![Sponsors on Open Collective](https://opencollective.com/xgboost/sponsors/badge.svg)](#sponsors)

### Sponsors

[[Become a sponsor](https://opencollective.com/xgboost#sponsor)]





### Backers

[[Become a backer](https://opencollective.com/xgboost#backer)]