# Local Interpretability of Random Forests for Multi-Target Regression

arXiv: https://arxiv.org/abs/2303.16506 · HTML: https://mlsanity.com/paper/2303.16506/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 16 references that are in the map.
- First posted on arXiv 29 Mar 2023.

## Builds on (most cited first, 5 of 5)

- [A Survey Of Methods For Explaining Black Box Models](https://mlsanity.com/paper/1802.01933/) (2018)
- [Local Rule-Based Explanations of Black Box Decision Systems](https://mlsanity.com/paper/1805.10820/) (2018)
- [Multi-Target Prediction: A Unifying View on Problems and Methods](https://mlsanity.com/paper/1809.02352/) (2018)
- [Conclusive Local Interpretation Rules for Random Forests](https://mlsanity.com/paper/2104.06040/) (2021)
- [Local Multi-Label Explanations for Random Forest](https://mlsanity.com/paper/2207.01994/) (2022)

## Abstract (arXiv)

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.