accuracy. They define decision sets as “a model class that can both accurately predict class labels and interpretably describe its decision boundaries” [@lakkaraju2016interpretable], and claim they are more interpretable than decision lists because the if-then rules, organized in a non-hierarchical structure, apply independently and can be considered in any order. On the other hand, the rules in decision lists are in the if-then-else format, and each rule depends on all the rules above it, being necessary to interpret the whole hierarchical structure to understand why a given rule is applied. This difference is exemplified in Figure \[fig:set\_list\]. Decision sets are concerned only with model transparency, and the authors measure the model interpretability both quantitatively (see the Evaluation Methods and Initiatives Section) and through a qualitative evaluation, which measured to what extent human subjects could interpret the rules, that is, determine which decision each rule was leading to [@lakkaraju2016interpretable].

Aiming at making statistical modeling accessible to non-experts, Lloyd et al. [-@lloyd2014automatic] introduces the Automatic Statistician, a system that analyses data sets and automatically discovers the statistical model describing the data, generating a report with figures and natural language text. The proposed approach, called Automatic Bayesian Covariance Discovery (ABCD), focus on Nonparametric 