XGBoost
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About
XGBoost is an optimized, distributed gradient boosting library known for its efficiency, flexibility, and portability. It implements machine learning algorithms under the Gradient Boosting framework, offering parallel tree boosting to quickly and accurately solve diverse data science problems. The library supports regression, classification, ranking, and custom objectives, running on Windows, Linux, OS X, and various cloud platforms. It provides multilingual support for C++, Python, R, Java, Scala, and Julia. Battle-tested through many data science challenges and widely used in production, XGBoost also supports distributed training on multiple machines and cloud environments (AWS, GCE, Azure, Yarn), integrating with Flink and Spark. Its optimized backend ensures high performance, handling problems with billions of examples efficiently.
Platform
Task
Features
• performance: optimized backend for the best performance with limited resources.
• distributed on cloud: supports distributed training on aws, gce, azure, and yarn clusters.
• battle-tested: wins many data science and machine learning challenges. used in production by multiple companies.
• multiple languages: supports c++, python, r, java, scala, julia.
• portable: runs on windows, linux and os x, as well as various cloud platforms
• flexible: supports regression, classification, ranking and user defined objectives.
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