XGBoost: Difference between revisions

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Revision as of 19:30, 11 January 2019

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

A very common way to use XGBoost is though its python interface, provided as the xgboost 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.