Exploring large language models capabilities to explain decision trees

Decision trees are widely adopted in Machine Learning tasks due to their operation simplicity and interpretability aspects. However, following the decision process path taken by trees can be difficult in a complex scenario or in a case where a user has no familiarity with them. Prior research showed that converting outcomes to natural language is an accessible way to facilitate understanding for non-expert users in several tasks. More recently, there has been a growing effort to use Large Language Models (LLMs) as a tool for providing natural language texts. In this paper, we examine the proficiency of LLMs to explain decision tree predictions in simple terms through the generation of natural language explanations. By exploring different textual representations and prompt engineering strategies, we identify capabilities that strengthen LLMs as a competent explainer as well as highlight potential challenges and limitations, opening further research possibilities on natural language explanations for decision trees.