Bandingkan metode

	HDBSCAN yang Dapat Dijelaskan	Random Forest yang Dapat Dijelaskan
Bidang	Pembelajaran Mesin	Pembelajaran Mesin
Keluarga	Machine learning	Machine learning
Tahun asal	2017–2020	2001–2017
Pencetus	McInnes, L.; Healy, J. (HDBSCAN); Lundberg & Lee (SHAP-based explanation)	Breiman, L. (RF); Lundberg & Lee (SHAP attribution)
Tipe	Explainable clustering	Interpretable ensemble (bagging + post-hoc attribution)
Sumber perintis	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.
Alias	XAI-HDBSCAN, Interpretable HDBSCAN, Explainable Hierarchical DBSCAN, HDBSCAN with XAI	XRF, interpretable random forest, transparent random forest, random forest with explainability
Terkait	6	4
Ringkasan	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 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.