ISE Special Seminar: Russell Barton

Details

“Response Grid Plots for Model-Agnostic Machine Learning Insight”
Russell R. Barton, Penn State

Abstract
Machine learning (ML) models are used to provide predictive characterization of response functions based on observed or simulated data. Insight on the nature of the approximation to the underlying response function is essential for the model output to be trusted and used by decision makers. When a response function is well-behaved, methods that identify marginal effects and interactions for important input variables can provide adequate insight on its nature. Alternatively, interpretable ML models can provide an ensemble of simple models to decompose a complex response into interpretable elements. This talk introduces a model-generated but model-independent visual display of functional information that provides direct insight, not interpreted through model form, model coefficients, or numerical characterizations such as those produced by sensitivity analysis. Further, the method can be applied directly to system response data if the data are collected from a factorial (grid) design. The value of response grid plots (RGPs) is shown through several examples.

Brief Biography for Russell Barton

Russell Barton is Distinguished Professor Emeritus of Supply Chain and Information Systems and Distinguished Professor Emeritus of Industrial Engineering at Penn State. He holds a B.S. in electrical engineering from Princeton University, and M.S. and Ph.D. degrees in operations research from Cornell University. Dr. Barton’s research focuses on the interface between applied statistics, simulation, and product design and manufacturing. He is the author of two books: Graphical Methods for the Design of Experiments and Predictive Analytics for Business Using R. He served as Associate Editor for the INFORMS Journal on Computing, IIE Transactions, Operations Research, Naval Research Logistics, Management Science, and the Institute of Mathematics and Its Applications IMA Journal of Management Mathematics. He served as Chair of the INFORMS Subdivisions Council, and on the INFORMS Board of Directors as Vice President of INFORMS Sections and Societies. He is a Fellow of IISE and INFORMS, and a Senior Life Member of IEEE. His is a Certified Analytics Professional – Expert (CAP-X®) and has served on the Analytics Certification Board.