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

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Repository: dmlc/xgboost


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README.md

<img src="https://xgboost.ai/images/logo/xgboost-logo-trimmed.png" width=200/> eXtreme Gradient Boosting
===========

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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
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© 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
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- 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
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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).

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