Optimal Decision Lists using SAT

TL;DR

This paper introduces a novel approach to constructing optimal, perfectly accurate decision lists using SAT solvers, enhancing explainability and minimality in machine learning models.

Contribution

It presents the first method for creating optimal 'perfect' decision lists with SAT, and a new approach for balancing size and accuracy in sparse decision lists.

Findings

Optimal perfect decision lists are achievable with SAT technology.

Optimal decision lists are smaller and more accurate than decision sets.

Decision lists generate more concise explanations than decision sets.

Abstract

Decision lists are one of the most easily explainable machine learning models. Given the renewed emphasis on explainable machine learning decisions, this machine learning model is increasingly attractive, combining small size and clear explainability. In this paper, we show for the first time how to construct optimal "perfect" decision lists which are perfectly accurate on the training data, and minimal in size, making use of modern SAT solving technology. We also give a new method for determining optimal sparse decision lists, which trade off size and accuracy. We contrast the size and test accuracy of optimal decisions lists versus optimal decision sets, as well as other state-of-the-art methods for determining optimal decision lists. We also examine the size of average explanations generated by decision sets and decision lists.