Interpretability of machine learning models and representations: an introduction.
Adrien Bibal, Benoît Frénay · Repository of the University of Namur · 2016
Interpretability is often a major concern in machine learning. Although many authors agree with this statement, interpretability is often tackled with intuitive arguments, distinct (yet related) terms and heuristic quan- tifications. This short survey aims to clarify the concepts related to interpretability and emphasises the distinction between interpreting models and representations, as well as heuristic-based and user-based approaches.