Level: Advanced

Job Function: Analyst / Scientist / Engineer

**Chris Gotwalt, JMP Director of Statistical Research and Development, SAS**

In this tutorial we will give a general overview of how to apply machine learning methods using the tools in JMP Pro. The essential idea is that the basic tools of machine learning actually make modeling easier than earlier approaches to modeling data. We will begin by introducing the basic concepts of how models are chosen in a machine learning framework, then show how to set that up with the Make Validation Column utility. Then we will demonstrate ADI, a new approach to missing data imputation that has been added to JMP Pro 14. After that we will illustrate the most important platforms for machine learning in JMP Pro: Neural, Partition and Generalized Regression. At the end we will use Model Comparison to make a final decision about what model to use and use the Formula Depot to create scoring code to use the model outside of JMP Pro. Throughout we will illustrate the unique interactivity in JMP and its close association between data and graphs, which makes the modeling decision process intuitive and transparent, while also also letting one "see inside the black box" of machine learning models in a way that no other product can.