Optimal Decision Lists using SAT
This work addresses the need for explainable AI models by providing a method to generate compact and accurate decision lists, which is incremental as it builds on existing SAT technology for optimization.
The paper tackles the problem of constructing optimal decision lists for explainable machine learning, achieving perfect accuracy on training data with minimal size using SAT solving, and introduces a method for optimal sparse decision lists that trade off size and accuracy.
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.