Most Kaggle competitions are won using one of two techniques. The first is deep learning. The second is XGBoost.
This talk is being given by the maintainer of the XGBoost R package.
XGBoost: A package for fast and accurate gradient boosting
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. It has now been widely applied in both industrial business and academic researches, as the R package has been downloaded for more than 7,000 times on average from CRAN per-month and the number is growing rapidly.
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 introduce the details of the model, as well as several highlights that we think users would love to know.
About Tong He:
Tong He (http://www.sfu.ca/~hetongh/) is a Data Scientist, and the maintainer of the XGBoost R package.
• 6:00PM Doors are open, feel free to mingle
• 6:30 Presentation start
• ~7:45 Off to a nearby restaurant for food, drinks, and breakout discussions
By transit there a number of high frequency buses (check Google Maps or the Translink site for your particular case) that will get you there. For the drivers, there is a fair bit of street parking (free and pay) in the area, especially after 6.