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：多目标回归在大量应用中很有用。虽然随机森林模型在这些任务中表现良好，但它们往往很难解释。可解释性在机器学习中至关重要，特别是当它可以直接影响人类福祉时。虽然存在适用于多目标回归的模型不可知技术，但没有针对随机森林模型量身定制的特定技术。为解决这个问题，我们提出了一种技术，针对随机森林模型多目标回归中由实例生成的规则解释，受到最近的随机森林可解释性模型特定技术的影响。通过广泛的实验评估了所提出的技术，并证明相对于最先进的技术，具有竞争性的解释。