XGBoost
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About
XGBoost is an optimized, distributed gradient boosting library lauded for its efficiency, flexibility, and portability. It implements powerful machine learning algorithms under the Gradient Boosting framework, offering parallel tree boosting (GBDT, GBM) to solve diverse data science problems with speed and accuracy. The library is highly versatile, supporting regression, classification, ranking, and user-defined objectives across multiple platforms including Windows, Linux, OS X, and major cloud environments. It boasts extensive language support, including C++, Python, R, Java, Scala, and Julia. Proven through numerous data science and machine learning competitions, XGBoost is widely adopted in production by various companies. Its distributed architecture allows training on multiple machines and integration with cloud dataflow systems like AWS, GCE, Azure, Yarn, Flink, and Spark, capable of handling problems involving billions of examples. An optimized backend system ensures superior performance even with limited resources.
Platform
Task
Features
• battle-tested in many data science and machine learning challenges
• parallel tree boosting (gbdt, gbm) for data science problems
• optimized backend system for best performance
• distributed training on multiple machines and cloud platforms
• supports c++, python, r, java, scala, and julia languages
• runs on windows, linux, os x, and various cloud platforms
• supports regression, classification, ranking, and user-defined objectives
• scalable and flexible gradient boosting
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