Сравнение методов

	Объяснимый Isolation Forest	Объяснимый случайный лес
Область	Машинное обучение	Машинное обучение
Семейство	Machine learning	Machine learning
Год появления	2008 / 2017	2001–2017
Автор метода	Liu, F. T., Ting, K. M., & Zhou, Z.-H. (Isolation Forest); Lundberg, S. M. & Lee, S.-I. (SHAP explainability layer)	Breiman, L. (RF); Lundberg & Lee (SHAP attribution)
Тип	Anomaly detection with post-hoc explainability	Interpretable ensemble (bagging + post-hoc attribution)
Основополагающий источник	Lundberg, S. M., & Lee, S.-I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, 30, 4765–4774.	Lundberg, S. M., & Lee, S.-I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, 30, 4765–4774.
Другие названия	XIF, Isolation Forest with SHAP, interpretable anomaly detection, explainable anomaly isolation	XRF, interpretable random forest, transparent random forest, random forest with explainability
Связанные	5	4
Сводка	Explainable Isolation Forest combines the Isolation Forest anomaly detection algorithm with post-hoc explainability tools — most commonly SHAP (SHapley Additive exPlanations) — to not only flag anomalous observations but also reveal which features drove each anomaly score. It bridges unsupervised anomaly detection with the interpretability demands of regulated and high-stakes domains.	Explainable Random Forest (XRF) combines the predictive power of Breiman's Random Forest ensemble with systematic post-hoc attribution methods — principally SHAP values and mean-decrease-in-impurity importance — to make model decisions transparent and auditable. It delivers both high accuracy and human-interpretable feature contributions, satisfying demands from regulators, domain experts, and academic reviewers alike.