Chris Gotwalt walks through an introduction to machine learning in JMP. Topics include;
- Introduction to modeling ( What does an underfit and overfit model look like, ROC curves, Why we use holdback validation)
- Machine Learning (How much data is needed, Different techniques)
- Validation column set-up (When to stratify, group, or use DoE candidate set)
- Handling missing data (To impute or not impute that is the question? Attached is more information about the ADI approach to imputation)
For a good overview of Machine Learning in JMP try Module 7 of the free JMP STIPs class
