The right tool for the job: Guidelines for algorithm selection in predictive modeling

The goal of this talk to lay out a framework for what algorithms work best in which situations, and why. Drawing on results of hundreds of crowd-sourced predictive modeling contests, this talk shows examples of how structure informs a choice in algorithm. As an illustration of these concepts, ZestFinance's work with China's retail giant, JD.com is used to describe how the right algorithms were applied to the right datasets to turn shopping data into credit data -- creating credit scores from scratch.