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XGBoost: Difference between revisions
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'''[https://xgboost.readthedocs.io/en/latest/ XGBoost]''' ''is an optimized distributed gradient boosting library designed to be highly efficient, flexible and portable''. It is a popular package used for a wide variety of machine learning and datascience tasks, serving the role of a convenient, domain-agnostic black box classifier. XGBoost provides GPU accelerated learning for some problems, and Compute Canada provides a GPU enabled build. | '''[https://xgboost.readthedocs.io/en/latest/ XGBoost]''' ''is an optimized distributed gradient boosting library designed to be highly efficient, flexible and portable''. It is a popular package used for a wide variety of machine learning and datascience tasks, serving the role of a convenient, domain-agnostic black box classifier. XGBoost provides GPU accelerated learning for some problems, and Compute Canada provides a GPU enabled build. | ||
== Python Module == | == Python Module Installation == | ||
A very common way to use XGBoost is though its python interface, provided as the <code>xgboost</code> python module. Compute Canada provides an optimized, multi-GPU enabled build as a [[Python]] wheel. The reader is recommended to familiarize oneself with the basics of creating a python environment before starting and XGBoost project. | A very common way to use XGBoost is though its python interface, provided as the <code>xgboost</code> python module. Compute Canada provides an optimized, multi-GPU enabled build as a [[Python]] wheel. The reader is recommended to familiarize oneself with the basics of creating a python environment before starting and XGBoost project. | ||