Abstract: In this 90 minute tutorial, we'll get a brief demonstration of how modeling is conducted in the tidyverse. Using an example data set, we'll walk though the process of pre-processing and feature engineering of data, model fitting and tuning, and performance estimation.
The workshop assumes that you have used simple R modeling functions (like `lm()`) and basic tidyverse functions. You don't need to be an expert. The materials will be on GitHub and we'll have RStudio servers that you can use if you can't (or don't want to) install anything locally.
Bio: Max was a nonclinical statistician for 12 years in the pharmaceutical industry and for 6 years in the medical diagnostic industry. His degrees are in Biostatistics (Ph.D.) and Mathematics (B.S.). He has released several R packages for predictive modeling and machine learning, including caret, C50, and Cubist. He is the author of the Springer book Applied Predictive Modeling (with Kjell Johnson), which won the American Statistical Association’s Ziegel Award for best book in 2014.