A Game-Theoretic Framework for Interpretable Preference and Feature Learning

We are living in an era that we can call machine learning revolution. Started as a pure academic and research-oriented domain, we have seen widespread commercial adoption across diverse domains, such as retail, healthcare, finance, and many more. However, the usage of machine learning poses its own set of challenges when it comes to explain what is going on under the hood. The reason being models interpretability is very important for the business is to explain each and every decision being taken by the model. In order to take a step forward in this direction, we propose a principled algorithm inspired by both preference learning and game theory for classification. Particularly, the learning problem is posed as a two player zero-sum game which we show having theoretical guarantees about its convergence. Interestingly, feature selection can be straightforwardly plugged into such algorithm. As a consequence, the hypotheses space consists on a set of preference prototypes along with (possibly non-linear) features making the resulting models easy to interpret.