arXiv Machine Learning September 10, 2026 By Tiffany M. Tang, Elizaveta Levina, Ji Zhu

Interpretable Network-assisted Random Forest+

Read the original on arXiv Machine Learning →

The paper introduces Interpretable Network-assisted Random Forest+ (RF+), a family of flexible models that combine the predictive power of random forests with network information. It offers intrinsic interpretability by providing global and local feature importance measures, as well as sample influence metrics, allowing researchers to assess both feature effects and the contribution of network neighbors. The authors claim that RF+ achieves competitive prediction accuracy while remaining transparent, making it suitable for high-impact problems where understanding model decisions is crucial.

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