  Interpretability is becoming increasingly important for predictive model
analysis. Unfortunately, as remarked by many authors, there is still no
consensus regarding this notion. The goal of this paper is to propose the
definition of a score that allows to quickly compare interpretable algorithms.
This definition consists of three terms, each one being quantitatively measured
with a simple formula: predictivity, stability and simplicity. While
predictivity has been extensively studied to measure the accuracy of predictive
algorithms, stability is based on the Dice-Sorensen index for comparing two
rule sets generated by an algorithm using two independent samples. The
simplicity is based on the sum of the lengths of the rules derived from the
predictive model. The proposed score is a weighted sum of the three terms
mentioned above. We use this score to compare the interpretability of a set of
rule-based algorithms and tree-based algorithms for the regression case and for
the classification case.
