arXiv:2506.08928v2 Announce Type: replace Abstract: Tree-based ensembles such as random forests remain the go-to for tabular data over deep learning models due to their prediction performance and computational efficiency. These advantages have led to their widespread deployment in high-stakes domains, where interpretability is essential for ensuring trustworthy predictions. This has motivated the development of popular local feature importance methods such as LIME and TreeSHAP. However, these approaches rely on approximations that ignore the model's internal structure and instead depend on pot
The paper addresses a current need for improved interpretability in widely deployed tree-based AI models, crucial as these models expand into high-stakes applications.
Improved local feature importances enhance transparency and trustworthiness of AI predictions, which is vital for regulatory compliance and broader adoption in critical sectors.
The proposed 'Local MDI+' offers a more accurate and model-aware approach to understanding individual predictions from tree-based models, moving beyond the approximations of previous methods.
- · AI developers
- · Regulatory bodies
- · Industries using tree-based models (e.g., finance, healthcare)
- · Less transparent AI models
- · Methods relying solely on approximate interpretability
Increased trust and adoption of tree-based models in sensitive applications due to better interpretability.
Potential for new regulatory standards or guidelines that incorporate more robust local interpretability methods.
Accelerated development of even more sophisticated and provably interpretable AI systems across different model types, not just trees.
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Read at arXiv cs.LG