Gunce E. Walton is a Sr. Product Manager in Advanced Analytics. Prior to working at SAS, Gunce was an assistant professor at Instituto Tecnologico Autonomo de Mexico, Mexico City. She taught graduate and undergraduate level empirical micro-econometrics classes. She has many published articles in the areas of instrumental variable estimation, instrument selection methods, and dynamic panel data model estimation. Gunce has a Ph.D. in Economics from North Carolina State University.
A model can finish running in seconds. Understanding what it is telling you can take much longer. A fit statistic signals how well a model performs. A residual plot reveals whether assumptions hold. A decision tree shows how observations are split into groups. But before any of that can support
SAS' Gunce Walton introduces to you a new scoring capability, how it utilizes Deep Neural Networks (DNNs) and shares use cases with PROC DEEPCAUSAL.
