Jämför metoder

	Förklaringsbar isolationsskogar	Förklarbar gradient-boosting
Ämnesområde	Maskininlärning	Maskininlärning
Familj	Machine learning	Machine learning
Ursprungsår	2008 / 2017	2017–2020
Upphovsperson	Liu, F. T., Ting, K. M., & Zhou, Z.-H. (Isolation Forest); Lundberg, S. M. & Lee, S.-I. (SHAP explainability layer)	Lundberg, S. M. & Lee, S.-I. (TreeSHAP for tree ensembles)
Typ	Anomaly detection with post-hoc explainability	Ensemble + explainability layer
Ursprungskälla	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., Erion, G., Chen, H., DeGrave, A., Prutkin, J. M., Nair, B., Katz, R., Himmelfarb, J., Bansal, N., & Lee, S.-I. (2020). From local explanations to global understanding with explainable AI for trees. Nature Machine Intelligence, 2, 56–67.
Alias	XIF, Isolation Forest with SHAP, interpretable anomaly detection, explainable anomaly isolation	XGB with SHAP, interpretable gradient boosting, transparent gradient boosting, XAI gradient boosting
Närliggande	5	6
Sammanfattning	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 Gradient Boosting combines the predictive power of gradient boosting ensembles with structured interpretability tools — principally SHAP (SHapley Additive exPlanations) — to produce models that are both highly accurate and transparently auditable. Practitioners obtain global feature rankings and individual-level explanations alongside standard performance metrics.