12 September 2018
This two-day workshop will step through the process of building, visualizing, testing and comparing models that are focused on prediction. The goal of the workshop is to provide a thorough workflow in R that can be used with many different regression or classification techniques. Case studies are used to illustrate functionality. Basic familiarity with R is required.
By the end of this workshop, you should be able to easily build predictive/machine learning models in R using a variety of packages and model types.
Participants will receive a copy of Max Kuhn's book, Applied Predictive Modeling.
The workshop is broken into five parts.
Part 1 Introduction
Part 2 Basic Principals: Data Splitting, Models in R, Resampling, Tuning
Part 3 Feature Engineering and Preprocessing: Data treatments
Part 4 Regression Modeling: Measuring Performance, penalized regression, multivariate adaptive regression splines (MARS), ensembles
Part 5 Classification Modeling: Measuring Performance, trees, ensembles, naive Bayes