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Activity Number: 21
Type: Topic Contributed
Date/Time: Sunday, July 31, 2016 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistical Computing
Abstract #320561
Title: Xgboost: An R Package for Fast and Accurate Gradient Boosting
Author(s): Tong He*
Companies: Simon Fraser University
Keywords: xgboost ; gradient boosting machine ; R package ; John M. Chambers Statistical Software Award

The R package xgboost is a library designed and optimized for boosting trees algorithms. It won the 2016 John M. Chambers Statistical Software Award.

The underlying algorithm of xgboost is an extension of the classic gradient boosting machine algorithm. By employing multi-threads and imposing regularization, xgboost is able to utilize more computational power and get more accurate prediction compared to the traditional version. Moreover, a friendly user interface and comprehensive documentation are provided for user convenience. The package has been downloaded for more than 3,000 times on average from CRAN per-month. It has now been widely applied in both industrial business and academic researches.

Authors who are presenting talks have a * after their name.

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