Conclusive local interpretation rules for random forests

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 their decisions. Otherwise, obscure decision-making processes can lead socioethical issues as they interfere with people’s lives. Random forest algorithms excel in aforementioned sectors, where ability explain themselves is an obvious requirement. this paper, we present LionForests, which relies on a preliminary work ours. LionForests random forest-specific interpretation technique that provides rules explanations. It applies binary classification tasks up multi-class regression tasks, while stable theoretical background supports it. A time scalability analysis suggests much faster than our also applicable large datasets. Experimentation, including comparison state-of-the-art techniques, demonstrate efficacy contribution. outperformed other techniques terms precision, variance, response time, but fell short rule length coverage. Finally, highlight conclusiveness, unique property validity distinguishes it from previous techniques.