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Started In
August 2014

XGBoost.jl

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eXtreme Gradient Boosting in Julia.

Abstract

This package is a Julia interface of 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 for more information.

Installation

] add XGBoost

This package uses xgboost_jll to package the xgboost binaries (will be installed automatically).

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