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XGBoost Documentation — xgboost 3.1.0 documentation

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 (Hadoop, SGE, MPI) and can solve problems beyond billions of examples.

• Installation Guide
• Building From Source
• Get Started with XGBoost
• XGBoost Tutorials
  • Introduction to Boosted Trees
  • Introduction to Model IO
  • Slicing Models
  • Learning to Rank
  • DART booster
  • Monotonic Constraints
  • Feature Interaction Constraints
  • Survival Analysis with Accelerated Failure Time
  • Categorical Data
  • Multiple Outputs
  • Random Forests(TM) in XGBoost
  • Distributed XGBoost on Kubernetes
  • Distributed XGBoost with XGBoost4J-Spark
  • Distributed XGBoost with XGBoost4J-Spark-GPU
  • Distributed XGBoost with Dask
  • Distributed XGBoost with PySpark
  • Distributed XGBoost with Ray
  • Using XGBoost External Memory Version
  • C API Tutorial
  • Text Input Format of DMatrix
  • Notes on Parameter Tuning
  • Custom Objective and Evaluation Metric
  • Advanced Usage of Custom Objectives
  • Intercept
  • Privacy Preserving Inference with Concrete ML
• Frequently Asked Questions
• GPU Support
• XGBoost Parameters
• Prediction
• Tree Methods
• Python Package
  • Python Package Introduction
  • Using the Scikit-Learn Estimator Interface
  • Python API Reference
  • Supported Python data structures
  • Callback Functions
  • XGBoost Python Feature Walkthrough
  • XGBoost Dask Feature Walkthrough
  • Survival Analysis Walkthrough
  • Using XGBoost with RAPIDS Memory Manager (RMM) plugin
• R Package
  • XGBoost for R introduction
  • XGBoost from JSON
  • Migrating code from previous XGBoost versions
  • Handling of indexable elements
  • Developer guide: parameters from core library
• JVM Package
  • Getting Started with XGBoost4J
  • XGBoost4J-Spark Tutorial
  • XGBoost4J-Spark-GPU Tutorial
  • Code Examples
  • API docs
  • How to migrate to XGBoost-Spark jvm 3.x
• Ruby Package
• Swift Package
• Julia Package
• C Package
• C++ Interface
• CLI Interface
• Contribute to XGBoost
  • Community Guideline
  • Donations
  • Coding Guideline
  • Consistency for Language Bindings
  • Notes on packaging XGBoost’s Python package
  • Adding and running tests
  • Docs and Examples
  • XGBoost Internal Feature Map
  • Git Workflow Howtos
  • XGBoost Release Policy
  • Automated testing in XGBoost project
• Release Notes
  • 3.1.0 (2025 Sep 22)
  • 3.0.3 Patch Release (Jul 30 2025)
  • 3.0.2 Patch Release (May 25 2025)
  • 3.0.1 Patch Release (May 13 2025)
  • 3.0.0 (2025 Feb 27)
  • 2.1.4 Patch Release (2025 Feb 6)
  • 2.1.3 Patch Release (2024 Nov 26)
  • 2.1.2 Patch Release (2024 Oct 23)
  • 2.1.1 Patch Release (2024 Jul 31)
  • 2.1.0 (2024 Jun 20)

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