  We introduce a method for computing immediately human interpretable yet
accurate classifiers from tabular data. The classifiers obtained are short
Boolean formulas, computed via first discretizing the original data and then
using feature selection coupled with a very fast algorithm for producing the
best possible Boolean classifier for the setting. We demonstrate the approach
via 13 experiments, obtaining results with accuracies comparable to ones
obtained via random forests, XGBoost, and existing results for the same
datasets in the literature. In most cases, the accuracy of our method is in
fact similar to that of the reference methods, even though the main objective
of our study is the immediate interpretability of our classifiers. We also
prove a new result on the probability that the classifier we obtain from
real-life data corresponds to the ideally best classifier with respect to the
background distribution the data comes from.
