  In critical situations involving discrimination, gender inequality, economic
damage, and even the possibility of casualties, machine learning models must be
able to provide clear interpretations for their decisions. Otherwise, their
obscure decision-making processes can lead to socioethical issues as they
interfere with people's lives. In the aforementioned sectors, random forest
algorithms strive, thus their ability to explain themselves is an obvious
requirement. In this paper, we present LionForests, which relies on a
preliminary work of ours. LionForests is a random forest-specific
interpretation technique, which provides rules as explanations. It is
applicable from binary classification tasks to multi-class classification and
regression tasks, and it is supported by a stable theoretical background.
Experimentation, including sensitivity analysis and comparison with
state-of-the-art techniques, is also performed to demonstrate the efficacy of
our contribution. Finally, we highlight a unique property of LionForests,
called conclusiveness, that provides interpretation validity and distinguishes
it from previous techniques.
