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XGBoost

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About

XGBoost is an optimized, distributed gradient boosting library renowned for its efficiency, flexibility, and portability. It implements machine learning algorithms within the Gradient Boosting framework, offering parallel tree boosting (GBDT, GBM) to solve complex data science problems quickly and accurately. XGBoost supports regression, classification, and ranking, and is adaptable to user-defined objectives. It runs seamlessly across Windows, Linux, OS X, and various cloud platforms including AWS, GCE, Azure, and Yarn clusters, with support for distributed training. It integrates with systems like Flink and Spark. The library is battle-tested, having won numerous data science and machine learning challenges, and is used in production by multiple companies. It features a highly optimized backend for superior performance with limited resources and can handle problems with billions of examples, supporting multiple languages like C++, Python, R, Java, Scala, and Julia.

Platform

Task

Features

• scalable and flexible gradient boosting

• solves problems beyond billions of examples

• well-optimized backend for high performance with limited resources

• distributed training on multiple machines and cloud environments

• battle-tested in data science challenges and production environments

• multiple language support (c++, python, r, java, scala, julia)

• portable across windows, linux, os x, and various cloud platforms

• supports regression, classification, ranking and user-defined objectives

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