Machine learningMachine learning
可解释隔离森林
可解释隔离森林将隔离森林异常检测算法与事后可解释性工具（最常见的是SHAP（SHapley Additive exPlanations））相结合，不仅可以标记异常观测值，还可以揭示哪些特征驱动了每个异常分数。它将无监督异常检测与受监管和高风险领域的可解释性需求联系起来。
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Method map
The neighbourhood of related methods — select a node to explore.
来源
- 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 ↗
如何引用本页
ScholarGate. (2026, June 3). Explainable Isolation Forest (Isolation Forest with SHAP-based Interpretability). ScholarGate. https://scholargate.app/zh/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.
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