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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
Web
Task
model training

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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