arXiv:2512.11081v2 Announce Type: replace-cross Abstract: Feature and Interaction Importance (FII) methods are essential in supervised learning for assessing the relevance of input variables and their interactions in complex prediction models. In many domains, such as personalized medicine, local interpretations for individual predictions are often required, rather than global scores summarizing overall feature importance. Random Forests (RFs) are widely used in these settings, and existing interpretability methods typically exploit tree structures and split statistics to provide model-specifi
This paper addresses an ongoing need in AI for more transparent and explainable models, particularly as AI systems become more complex and are deployed in sensitive domains like personalized medicine.
Improved interpretability methods for Random Forests enhance trust and accountability in machine learning applications, which is crucial for their adoption in regulated and high-stakes environments.
The ability to provably recover locally important signed features and interactions offers a more robust and granular understanding of individual model predictions, moving beyond general feature importance scores.
- · AI ethicists
- · Healthcare AI providers
- · Regulatory bodies
- · Machine learning researchers
- · Black-box AI models
- · Developers relying solely on global interpretability
Increased adoption of Random Forests and similar ensemble methods in interpretability-critical applications.
Development of new AI compliance frameworks that incorporate metrics derived from local interpretability methods.
Enhanced public and professional trust in AI systems due to their increased explainability, potentially accelerating AI integration into societal infrastructure.
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Read at arXiv cs.LG