השוואת שיטות

	Explainable Isolation Forest	K-Nearest Neighbors מוסבר (Explainable K-Nearest Neighbors)
תחום	למידת מכונה	למידת מכונה
משפחה	Machine learning	Machine learning
שנת המקור	2008 / 2017	1967 (KNN); 2010s (explainability extensions)
הוגה השיטה	Liu, F. T., Ting, K. M., & Zhou, Z.-H. (Isolation Forest); Lundberg, S. M. & Lee, S.-I. (SHAP explainability layer)	Cover, T. & Hart, P. (KNN); XAI extensions by various authors
סוג	Anomaly detection with post-hoc explainability	Instance-based learning with 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.	Cover, T. & Hart, P. (1967). Nearest neighbor pattern classification. IEEE Transactions on Information Theory, 13(1), 21–27.
כינויים	XIF, Isolation Forest with SHAP, interpretable anomaly detection, explainable anomaly isolation	XKNN, Interpretable KNN, Explainable KNN, Transparent K-Nearest Neighbors
קשורות	5	4
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 K-Nearest Neighbors (XKNN) augments the classic KNN classifier or regressor with structured post-hoc or built-in explanation mechanisms, exposing which retrieved neighbors, which features, and which distance contributions drive each individual prediction — making the model's reasoning transparent and auditable for human decision-makers.