השוואת שיטות

	Explainable Isolation Forest	הסברתיות של חיזוק גרדיאנט (Explainable Gradient Boosting)
תחום	למידת מכונה	למידת מכונה
משפחה	Machine learning	Machine learning
שנת המקור	2008 / 2017	2017–2020
הוגה השיטה	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)
סוג	Anomaly detection with post-hoc explainability	Ensemble + explainability layer
מקור מכונן	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.
כינויים	XIF, Isolation Forest with SHAP, interpretable anomaly detection, explainable anomaly isolation	XGB with SHAP, interpretable gradient boosting, transparent gradient boosting, XAI gradient boosting
קשורות	5	6
תקציר	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.