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.

在机器学习模型中，为了避免社会伦理问题影响人们的生活，必须对决策过程进行透明解释。为此，本文提出了一种名为 LionForests 的随机森林解释技术，其提供规则作为解释，并在二元分类任务到多类别分类和回归任务中均适用，并且通过实验表明了其有效性和其与先前技术的不同之处。