XGBoost
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About
XGBoost is an optimized distributed gradient boosting library that implements machine learning algorithms under the gradient boosting framework. It is flexible and can support various tasks like regression, classification, and ranking. It is portable, running on multiple platforms such as Windows, Linux, and OS X and supports various cloud platforms. The library is well-adapted for performance, providing an optimized backend system designed for efficiency. It supports multiple languages (C++, Python, R, Java, Scala, Julia) and is effective in distributed training across different machines including cloud services like AWS, GCE, and Azure. XGBoost has been proven to be battle-tested, having won many data science competitions and successfully deployed in production environments.
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Task
Features
• well-optimized for performance with limited resources.
• supports distributed training on multiple machines and cloud services.
• battle-tested performance, effective in data science competitions.
• supports multiple programming languages such as c++, python, r, java, scala, julia.
• portable across windows, linux, and os x, as well as cloud platforms.
• flexible for various types of tasks including regression, classification, and ranking.
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