Regression Models and uses a greed search procedure to explore the space of regression models, finding, through Bayesian inference, the main components describing the data, such as “periodic function”, “linear function”, “constant”, “smooth function”, among others. The discovered model’s components are then translated into English phrases, resulting in a report with text, figures, and tables, detailing what has been inferred about the data, besides model checking and criticism [@ghahramani2015probabilistic]. The final report allows users to interpret the system decisions without having to go through its internal operations, still, qualitative interpretability evaluation was not presented.

As well as the ABCD approach, the technique proposed by Ribeiro et al. [-@ribeiro2016should] can be seen as an *interpretability layer* for machine learning models, in the sense that they do not try to adjust the algorithms in order to make them more interpretable, but rather analyze their outputs to build an explanation around it. The Local Interpretable Model-agnostic Explanations (LIME) approach can do so for any classifier by learning an interpretable model that approximates a prediction locally [@ribeiro2016should]. The explanations consist of a set of artifacts that were relevant for the model’s prediction, be it textual (for example, which words were decisive in a text 