השוואת שיטות

	K-Nearest Neighbors מוסבר (Explainable K-Nearest Neighbors)	LIME: הסברים מפורשים מקומיים שאינם תלויים במודל
תחום	למידת מכונה	למידת מכונה
משפחה	Machine learning	Machine learning
שנת המקור	1967 (KNN); 2010s (explainability extensions)	2016
הוגה השיטה	Cover, T. & Hart, P. (KNN); XAI extensions by various authors	Marco Ribeiro, Sameer Singh & Carlos Guestrin
סוג	Instance-based learning with explainability layer	post-hoc local explanation
מקור מכונן	Cover, T. & Hart, P. (1967). Nearest neighbor pattern classification. IEEE Transactions on Information Theory, 13(1), 21–27.	Ribeiro, M. T., Singh, S., & Guestrin, C. (2016). "Why should I trust you?": Explaining the predictions of any classifier. ACM SIGKDD, 1135–1144.
כינויים	XKNN, Interpretable KNN, Explainable KNN, Transparent K-Nearest Neighbors	Local Surrogate Explanations, Model-Agnostic Local Explanations, Locally Faithful Approximations, Yerel Yorumlanabilir Model-Bağımsız Açıklamalar
קשורות	4	2
תקציר	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.	LIME, introduced by Ribeiro, Singh, and Guestrin in 2016, explains the predictions of any black-box classifier or regressor by building a simple, locally faithful surrogate model around a single prediction of interest. Rather than explaining the global model, LIME focuses on why a specific instance was classified the way it was, making complex models such as deep neural networks and ensemble methods interpretable to end-users, domain experts, and auditors.