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XGBoost

Table Of Contents

  • Installation Guide
  • Get Started with XGBoost
  • XGBoost Tutorials
  • Frequently Asked Questions
  • XGBoost User Forum
  • GPU support
  • XGBoost Parameters
  • Python package
  • R package
  • JVM package
  • Julia package
  • CLI interface
  • Contribute to XGBoost
  1. Docs
  2. XGBoost Documentation

XGBoost 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.

Contents¶

  • Installation Guide
  • Get Started with XGBoost
  • XGBoost Tutorials
    • Introduction to Boosted Trees
    • Distributed XGBoost with AWS YARN
    • Distributed XGBoost with XGBoost4J-Spark
    • DART booster
    • Monotonic Constraints
    • Feature Interaction Constraints
    • Text Input Format of DMatrix
    • Notes on Parameter Tuning
    • Using XGBoost External Memory Version (beta)
  • Frequently Asked Questions
  • XGBoost User Forum
  • GPU support
  • XGBoost Parameters
  • Python package
    • Python Package Introduction
    • Python API Reference
    • Python examples
  • R package
    • Introduction to XGBoost in R
    • Understanding your dataset with XGBoost
  • JVM package
    • Getting Started with XGBoost4J
    • XGBoost4J-Spark Tutorial
    • Code Examples
    • XGBoost4J Java API
    • XGBoost4J Scala API
    • XGBoost4J-Spark Scala API
    • XGBoost4J-Flink Scala API
  • Julia package
  • CLI interface
  • Contribute to XGBoost
Installation Guide

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