Forklarlig Isolation Forest
Forklarlig Isolation Forest kombinerer Isolation Forest-algoritmen til anomalidetektion med post-hoc forklaringsværktøjer — oftest SHAP (SHapley Additive exPlanations) — for ikke blot at markere anomale observationer, men også at afsløre, hvilke features der drev hver anomaliskore. Den bygger bro mellem uovervåget anomalidetektion og de fortolkningskrav, der stilles i regulerede og højrisikodomæner.
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Method map
The neighbourhood of related methods — select a node to explore.
Kilder
- Lundberg, S. M., & Lee, S.-I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, 30, 4765–4774. link ↗
- Liu, F. T., Ting, K. M., & Zhou, Z.-H. (2008). Isolation forest. In Proceedings of the 8th IEEE International Conference on Data Mining (ICDM 2008), pp. 413–422. IEEE. DOI: 10.1109/ICDM.2008.17 ↗
Sådan citerer du denne side
ScholarGate. (2026, June 3). Explainable Isolation Forest (Isolation Forest with SHAP-based Interpretability). ScholarGate. https://scholargate.app/da/machine-learning/explainable-isolation-forest
Which method?
Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
- Autoencoder AnomalidetektionMaskinlæring↔ compare
- Forklarlig Gradient BoostingMaskinlæring↔ compare
- Forklarlig Random ForestMaskinlæring↔ compare
- Isolation ForestMaskinlæring↔ compare
- One-Class SVMMaskinlæring↔ compare
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