This two-day course will provide an overview of using R for supervised learning. The session will step through the process of building, visualizing, testing, and comparing models that are focused on prediction. The goal of the course is to provide a thorough workflow in R that can be used with many different regression or classification techniques. Case studies on real data will be used to illustrate the functionality and several different predictive models are illustrated.
The course focuses on both high-level approaches to modeling (e.g., the caret package) and newer modeling packages in the tidyverse: recipes, rsample, yardstick, and tidyposterior. Basic familiarity with R and the tidyverse is required.