What it does
XGBoost is a machine-learning library for "gradient boosting", a method that combines many small decision trees into one strong predictor. It's commonly used for data like spreadsheets and databases.
Its makers say it is designed to be efficient and flexible, and that the same code can run on a single machine or on large systems such as Spark and Kubernetes. It works from Python, R, Java, Scala, C and more.
It started as a research project at the University of Washington.
Who it's for
Data scientists and analysts who build prediction models from structured data.
Can I use it without coding?
Developers only. It's a programming library used by writing code. New to this? Start here.
Can I run it?
- Cost
- FreeNothing to pay or sign up for.
- API key
- Not needed
Try it
Using Claude Code, Cursor or another AI coding assistant? Paste the prompt and it will walk you through the setup.New to this?
Before you install
- Only download from the official GitHub page. Copies with similar names exist for many popular projects.
- License (Apache-2.0): Permissive: free to use, change and share, including for business.
- More on staying safe
Where people are talking about it
- XGBoost: A Scalable Tree Boosting SystemResearch paper · Mar 2016