Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2507.19455 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866909705418309632 |
|---|---|
| author | Sousa, Lisa Barros de Andrade e Miller, Gregor Gleut, Ronan Le Thalmeier, Dominik Pelin, Helena Piraud, Marie |
| author_facet | Sousa, Lisa Barros de Andrade e Miller, Gregor Gleut, Ronan Le Thalmeier, Dominik Pelin, Helena Piraud, Marie |
| contents | As machine learning models are increasingly deployed in sensitive application areas, the demand for interpretable and trustworthy decision-making has increased. Random Forests (RF), despite their widespread use and strong performance on tabular data, remain difficult to interpret due to their ensemble nature. We present Forest-Guided Clustering (FGC), a model-specific explainability method that reveals both local and global structure in RFs by grouping instances according to shared decision paths. FGC produces human-interpretable clusters aligned with the model's internal logic and computes cluster-specific and global feature importance scores to derive decision rules underlying RF predictions. FGC accurately recovered latent subclass structure on a benchmark dataset and outperformed classical clustering and post-hoc explanation methods. Applied to an AML transcriptomic dataset, FGC uncovered biologically coherent subpopulations, disentangled disease-relevant signals from confounders, and recovered known and novel gene expression patterns. FGC bridges the gap between performance and interpretability by providing structure-aware insights that go beyond feature-level attribution. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_19455 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Forest-Guided Clustering -- Shedding Light into the Random Forest Black Box Sousa, Lisa Barros de Andrade e Miller, Gregor Gleut, Ronan Le Thalmeier, Dominik Pelin, Helena Piraud, Marie Machine Learning As machine learning models are increasingly deployed in sensitive application areas, the demand for interpretable and trustworthy decision-making has increased. Random Forests (RF), despite their widespread use and strong performance on tabular data, remain difficult to interpret due to their ensemble nature. We present Forest-Guided Clustering (FGC), a model-specific explainability method that reveals both local and global structure in RFs by grouping instances according to shared decision paths. FGC produces human-interpretable clusters aligned with the model's internal logic and computes cluster-specific and global feature importance scores to derive decision rules underlying RF predictions. FGC accurately recovered latent subclass structure on a benchmark dataset and outperformed classical clustering and post-hoc explanation methods. Applied to an AML transcriptomic dataset, FGC uncovered biologically coherent subpopulations, disentangled disease-relevant signals from confounders, and recovered known and novel gene expression patterns. FGC bridges the gap between performance and interpretability by providing structure-aware insights that go beyond feature-level attribution. |
| title | Forest-Guided Clustering -- Shedding Light into the Random Forest Black Box |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2507.19455 |