Graphical User Interface to Automate the Process of Model Tuning
As part of Innovation Management at Evonik, we are interested in developing predictive models for some of our quality performance parameters. JMP Pro’s predictive modeling platforms provides a direct path to just that for their practical, real-world problems. However, given the multiple different platforms available: from Neural Nets to Support Vector Machines, and the latest in the XGBoost platform, it can be daunting not only to find which model performs the best predictively, but also in tuning a model. To address this and to simplify the process of tuning models for each platform, I developed a graphical user interface that helps to automate the process of model tuning. The code is written entirely in JSL and utilizes different modeling platforms as well as the DOE platform within JMP. The code spans everything from simple calls and references to more complicated tasks like clicking an “OK” button, extracting data from reports, and passing data from one window to another. The code could not have been possible without the generous help from the JMP Community pages.
