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https://github.com/zhztheplayer/xgboost

Last synced: 5 days ago
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Host: GitHub
URL: https://github.com/zhztheplayer/xgboost
Owner: zhztheplayer
License: apache-2.0
Created: 2020-03-19T08:38:52.000Z (over 4 years ago)
Default Branch: hongze-dev
Last Pushed: 2022-04-12T21:57:16.000Z (over 2 years ago)
Last Synced: 2023-03-01T21:32:30.318Z (over 1 year ago)
Language: C++
Size: 13 MB
Stars: 1
Watchers: 1
Forks: 0
Open Issues: 2
Metadata Files:
- Readme: README.md
- License: LICENSE
- Citation: CITATION

Awesome Lists containing this project

README

          eXtreme Gradient Boosting

===========

[![Build Status](https://xgboost-ci.net/job/xgboost/job/master/badge/icon?style=plastic)](https://xgboost-ci.net/blue/organizations/jenkins/xgboost/activity)

[![Build Status](https://img.shields.io/travis/dmlc/xgboost.svg?label=build&logo=travis&branch=master)](https://travis-ci.org/dmlc/xgboost)

[![Build Status](https://ci.appveyor.com/api/projects/status/5ypa8vaed6kpmli8?svg=true)](https://ci.appveyor.com/project/tqchen/xgboost)

[![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/)

[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, MPI, Dask) and can solve problems beyond billions of examples.

License

-------

© Contributors, 2019. 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)]



## Other sponsors

The sponsors in this list are donating cloud hours in lieu of cash donation.