Symbolic Regression Is Not Enough: It Takes a Village to Raise a Model

Abstract

From a real-world perspective, good enough has been achieved in the core representations and evolutionary strategies of genetic programming assuming state-of-the-art algorithms and implementations are being used. What is needed for industrial symbolic regression are tools to (a) explore and refine the data, (b) explore the developed model space and extract insight and guidance from the available sample of the infinite possibilities of model forms and (c) identify appropriate models for deployment as predictors, emulators, etc. This chapter focuses on the approaches used in DataModeler to address the modeling life cycle. A special focus in this chapter is the identification of driving variables and metavariables. Exploiting the diversity of search paths followed during independent evolutions and, then, looking at the distributions of variables and metavariable usage also provides an opportunity to gather key insights. The goal in this framework, however, is not to replace the modeler but, rather, to augment the inclusion of context and collection of insight by removing mechanistic requirements and facilitating the ability to think. We believe that the net result is higher quality and more robust models.