xgboost-cpu

3.0.0last stable release 3 weeks ago
Complexity Score
High
Open Issues
N/A
Dependent Projects
1
Weekly Downloadsglobal
9,847

License

  • Apache-2.0
    • Yesattribution
    • Permissivelinking
    • Permissivedistribution
    • Permissivemodification
    • Yespatent grant
    • Yesprivate use
    • Permissivesublicensing
    • Notrademark grant

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Readme

eXtreme Gradient Boosting

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 (Kubernetes, Hadoop, SGE, Dask, Spark, PySpark) and can solve problems beyond billions of examples.

License

© Contributors, 2021. 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.

Sponsors

Become a sponsor and get a logo here. See details at Sponsoring the XGBoost Project. The funds are used to defray the cost of continuous integration and testing infrastructure (https://xgboost-ci.net).

Open Source Collective sponsors

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Dependencies

No runtime dependency information found for this package.

CVE IssuesActive
0
Scorecards Score
5.60
Test Coverage
No Data
Follows Semver
Yes
Github Stars
26,694
Dependenciestotal
13
DependenciesOutdated
0
DependenciesDeprecated
0
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