Сравнение на методи

	Обясним HDBSCAN	Обясним изолационен лес
Област	Машинно обучение	Машинно обучение
Семейство	Machine learning	Machine learning
Година на възникване	2017–2020	2008 / 2017
Създател	McInnes, L.; Healy, J. (HDBSCAN); Lundberg & Lee (SHAP-based explanation)	Liu, F. T., Ting, K. M., & Zhou, Z.-H. (Isolation Forest); Lundberg, S. M. & Lee, S.-I. (SHAP explainability layer)
Тип	Explainable clustering	Anomaly detection with post-hoc explainability
Основополагащ източник	McInnes, L., Healy, J., & Astels, S. (2017). hdbscan: Hierarchical density based clustering. Journal of Open Source Software, 2(11), 205.	Lundberg, S. M., & Lee, S.-I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, 30, 4765–4774.
Други названия	XAI-HDBSCAN, Interpretable HDBSCAN, Explainable Hierarchical DBSCAN, HDBSCAN with XAI	XIF, Isolation Forest with SHAP, interpretable anomaly detection, explainable anomaly isolation
Свързани	6	5
Резюме	Explainable HDBSCAN combines the hierarchical density-based clustering algorithm HDBSCAN with post-hoc explainability methods — primarily SHAP — to reveal which input features drive cluster membership and separation. It retains HDBSCAN's ability to find clusters of varying shape and density while adding a principled, auditable explanation layer.	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.