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Data Mining Workshop with Chris Gotwalt Day 1

 

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