https://github.com/dmlc/XGBoost.jl

XGBoost Julia Package
https://github.com/dmlc/XGBoost.jl

Last synced: 3 months ago
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XGBoost Julia Package

• Host: GitHub
• URL: https://github.com/dmlc/XGBoost.jl
• Owner: dmlc
• License: other
• Created: 2014-08-14T19:10:42.000Z (about 10 years ago)
• Default Branch: master
• Last Pushed: 2023-11-22T19:06:16.000Z (12 months ago)
• Last Synced: 2024-05-12T17:33:03.277Z (6 months ago)
• Language: Julia
• Homepage:
• Size: 835 KB
• Stars: 283
• Watchers: 27
• Forks: 111
• Open Issues: 24
• Metadata Files:
  • Readme: README.md
  • License: LICENSE.md

Awesome Lists containing this project

• awesome-julia-datasciences - XGBoost - eXtreme Gradient Boosting Package in Julia. (APL / General-Purpose Machine Learning)

README

        
# XGBoost.jl

[![Build Status](https://github.com/dmlc/XGBoost.jl/workflows/CI/badge.svg)](https://github.com/dmlc/XGBoost.jl/actions)

[![Latest Version](https://juliahub.com/docs/XGBoost/version.svg)](https://juliahub.com/ui/Packages/XGBoost/rSeEh/)

[![Pkg Eval](https://juliahub.com/docs/XGBoost/pkgeval.svg)](https://juliahub.com/ui/Packages/XGBoost/rSeEh/)

[![Dependents](https://juliahub.com/docs/XGBoost/deps.svg)](https://juliahub.com/ui/Packages/XGBoost/rSeEh/?t=2)

[![docs](https://img.shields.io/badge/docs-latest-blue?style=flat&logo=julia)](https://dmlc.github.io/XGBoost.jl/)

eXtreme Gradient Boosting in Julia.

## Abstract

This package is a Julia interface of [XGBoost](https://github.com/dmlc/xgboost). 

It is an efficient and scalable implementation of distributed gradient boosting

framework. The package includes efficient linear model solver and tree learning algorithms. The

library is parallelized using OpenMP, and it can be more than 10 times faster than some existing

gradient boosting packages. It supports various objective functions, including regression,

classification and ranking. The package is also made to be extensible, so that users are also

allowed to define their own objectives easily.

See the [documentation](https://dmlc.github.io/XGBoost.jl/) for more information.

## Installation

```julia

] add XGBoost

```

This package uses [`xgboost_jll`](https://github.com/JuliaBinaryWrappers/XGBoost_jll.jl) to package

the `xgboost` binaries (will be installed automatically).

## Preview

![](/assets/xgboost_demo_1.jpg)