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Decision brief
xgboost: Scalable, portable gradient boosting library in C++ under Apache-2 license
Good fit when
- Highly efficient for large datasets over billions of examples due to optimizations for speed and memory use.
- Flexible with multi-language support (Python, R, Java) enhancing cross-platform applicability.
Avoid when
- Avoid if ease-of-use and quick model training are more important than fine-tuning or extreme scalability.
- Not suitable when the dataset fits comfortably in memory on a single node, where other simpler tools may exceed.
Observed Jul 16, 2026 · Source: enrich:decision_facts
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Maintenance and security
Full trust report- Maintenance
- Very active (0d since push)
- As of 3w
- Provenance
- Not a fork · Organization account
- As of 3w
- Security (OSV)
- No lockfile
- As of 1mo
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Install
git clone https://github.com/dmlc/xgboostSimilar tools
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Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
Overview
A highly efficient, flexible, and portable gradient boosting library supporting multiple languages including Python, R, Java, Scala, C++, running on various distributed environments.
Capability facts
- Languages
- c++
Source: github.language · Aug 3, 2026
Categories
Tags
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).
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For agents
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