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Tuesday, June 28 • 5:39pm - 5:57pm
xgboost: An R package for Fast and Accurate Gradient Boosting

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XGBoost is a multi-language library designed and optimized for boosting trees algorithms. 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 4,000 times on average from CRAN per-month, and the number is growing rapidly. It has now been widely applied in both industrial business and academic researches. The R package has won the 2016 John M. Chambers Statistical Software Award. From the very beginning of the work, our goal is to make a package which brings convenience and joy to the users. In this talk, I will briefly introduce the usage of xgboost, as well as several highlights that we think users would love to know.

Speakers
avatar for Tong  He

Tong He

Simon Fraser University


Tuesday June 28, 2016 5:39pm - 5:57pm
SIEPR 130 366 Galvez St, Stanford, CA 94305

Attendees (119)