XGBoost¶

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

Logo
Website	https://xgboost.ai/
Repository	https://github.com/dmlc/xgboost
Byline	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, Flink and DataFlow.
License	Apache 2.0
Project age	8 years 10 months
Backers	AWS (Sponsored by), Distributed (Deep) Machine Learning Community @ University of Washington (Creator and maintainer), Intel (Sponsored by), NVIDIA (Sponsored by)
Lastest News (2022-10-31)	Release 1.7.0 stable We are excited to announce the feature packed XGBoost 1.7 release. Some major improvements include: initial support … more
Size score (1 to 10, higher is better)	6.25
Trend score (1 to 10, higher is better)	5.25

Education Resources¶

URL	Resource Type	Description
https://xgboost.readthedocs.io/en/latest/index.html	Documentation	Official project documentation.

Git Commit Statistics¶

Statistics computed using Git data through November 30, 2022.

Statistic	Lifetime	Last 12 Months
Commits	70,545	4,678
Lines committed	13,831,096	830,238
Unique committers	589	61
Core committers	10	9

../_images/dmlc_xgboost-monthly-commits.png

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