A framework for analytical approaches to combine interpretable models

Abstract

Analytic approaches to combine interpretable models, although presented in different contexts, can be generalized to highlight the components that can be specialized. We propose a framework that structures the combination process, formalizes the problems that can be solved in alternative ways and evaluates the combined models based on their predictive ability to replace the base ones, without loss of interpretability. The framework is illustrated with a case study using data from the University of Porto, Portugal, where experiments were carried out. The results show that grouping base models by scientific areas, ordering by the number of variables and intersecting their underlying rules creates conditions for the combined models to outperform them.