Trustable symbolic regression models: using ensembles, interval arithmetic and pareto fronts to develop robust and trust-aware models

Trust is a major issue with deploying empirical models in the real world since changes in the underlying system or use of the model in new regions of parameter space can produce (potentially dangerous) incorrect predictions. The trepidation involved with model usage can be mitigated by assembling ensembles of diverse models and using their consensus as a trust metric, since these models will be constrained to agree in the data region used for model development and also constrained to disagree outside that region. The problem is to define an appropriate model complexity (since the ensemble should consist of models of similar complexity), as well as to identify diverse models from the candidate model set.

In this chapter we discuss strategies for the development and selection of robust models and model ensembles and demonstrate those strategies against industrial data sets. An important benefit of this approach is that all available data may be used in the model development rather than a partition into training, test and validation subsets. The result is constituent models are more accurate without risk of over-fitting, the ensemble predictions are more accurate and the ensemble predictions have a meaningful trust metric.