Interpretable classifiers for tabular data via discretization and
  feature selection
Reijo Jaakkola Tomi Janhunen Antti Kuusisto Masood Feyzbakhsh Rankooh Miikka Vilander
We introduce a method for computing immediately human interpretable yet
accurate classifiers from tabular data. The classifiers obtained are short
DNF-formulas, computed via first discretizing the original data to Boolean form
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 14 experiments, obtaining results with accuracies mainly
similar to ones obtained via random forests, XGBoost, and existing results for
the same datasets in the literature. In several cases, our approach in fact
outperforms the reference results in relation to accuracy, 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.