Level: Intermediate
Laura Lancaster, JMP Principal Research Statistician Developer, SAS
Jeremy Ash, JMP Analytics Software Tester, SAS
Chris Gotwalt, JMP Director of Statistical Research and Development, SAS
Uncontrolled model extrapolation leads to two serious kinds of errors: (1) the model may be completely invalid far from the data, and (2) the combinations of variable values may not be physically realizable. Using the Profiler to optimize models that are fit to observational data can lead to extrapolated solutions that are of no practical use without any warning. JMP Pro 16 introduces extrapolation control into many predictive modeling platforms and the Profiler platform itself. This new feature in the Prediction Profiler alerts the user to possible extrapolation or completely avoids drawing extrapolated points where the model may not be valid. Additionally, the user can perform optimization over a constrained region that avoids extrapolation. In this presentation we discuss the motivation and usefulness of extrapolation control, demonstrate how it can be easily used in JMP, and describe details of our methods.
