Machine Learning: Between Accuracy and Interpretability

Abstract

Predictive accuracy is the usual measure of success of Machine Learning (ML) applications. However, experience from many ML applications in difficult, domains indicates the importance of interpretability of induced descriptions. Often in such domains, predictive accuracy is hardly of interest to the user. Instead, the users’ interest now lies in the interpretion of the induced descriptions and not, in their use for prediction. In such cases, ML is essentially used as a tool for exploring the domain, to generate new, potentially useful ideas about the domain, and thus improve the user’s understanding of the domain. The important questions are how to make domain-specific background knowledge usable by the learning system, and how to interpret the results in the light of this background expertise. These questions are discussed and illustrated by relevant example applications of ML, including: medical diagnosis, ecological modelling, and interpreting discrete event simulations. The observations in these applications show that predictive accuracy, the usual measure of success in ML, should be accompanied by a. criterion of interpretability of induced descriptions. The formalisation of interpretability is however a completely new challenge for ML.