Multi-target regression is useful in a plethora of applications. Although random forest models perform well in these tasks, they are often difficult to interpret. Interpretability is crucial in machine learning, especially when it can directly impact human well-being. Although model-agnostic techniques exist for multi-target regression, specific techniques tailored to random forest models are not available. To address this issue, we propose a technique that provides rule-based interpretations for instances made by a random forest model for multi-target regression, influenced by a recent model-specific technique for random forest interpretability. The proposed technique was evaluated through extensive experiments and shown to offer competitive interpretations compared to state-of-the-art techniques.

翻译：多目标回归在众多应用中具有重要价值。尽管随机森林模型在这些任务中表现良好，但其结果往往难以解释。在机器学习中，可解释性至关重要，特别是当它可能直接影响人类福祉时。虽然有适用于多目标回归的模型无关解释技术，但尚无专为随机森林模型定制的技术。为解决这一问题，我们受近期一种针对随机森林可解释性的模型特定技术启发，提出了一种为随机森林模型在多目标回归中的实例提供基于规则解释的方法。通过大量实验评估，该技术能够提供与现有最优方法相竞争的可解释性结果。