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	Wyjaśnialny K-Najbliższych Sąsiadów	Naiwny Klasyfikator Bayesowski	Random Forest
Dziedzina	Uczenie maszynowe	Uczenie maszynowe	Uczenie maszynowe
Rodzina	Machine learning	Machine learning	Machine learning
Rok powstania	1967 (KNN); 2010s (explainability extensions)	1997	2001
Twórca	Cover, T. & Hart, P. (KNN); XAI extensions by various authors	Mitchell, T. M. (textbook treatment)	Breiman, L.
Typ	Instance-based learning with explainability layer	Probabilistic classifier (Bayes' theorem with conditional independence)	Ensemble (bagging of decision trees)
Źródło pierwotne	Cover, T. & Hart, P. (1967). Nearest neighbor pattern classification. IEEE Transactions on Information Theory, 13(1), 21–27.	Mitchell, T. M. (1997). Machine Learning. McGraw-Hill. ISBN: 978-0070428072	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.
Inne nazwy	XKNN, Interpretable KNN, Explainable KNN, Transparent K-Nearest Neighbors	Naive Bayes Sınıflandırıcı, naive bayes classifier, simple Bayes, Gaussian Naive Bayes	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble
Pokrewne	4	4	4
Podsumowanie	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.	Naive Bayes is a fast probabilistic classifier that applies Bayes' theorem while assuming that the features are conditionally independent given the class — a method given its standard machine-learning treatment in Tom Mitchell's 1997 textbook Machine Learning. Despite this simplifying ('naive') assumption, it is quick to train and often surprisingly accurate.	Random Forest is an ensemble learning method, introduced by Leo Breiman in 2001, that grows many decision trees on bootstrap samples of the data and combines their votes to produce strong classification and regression. By pooling many slightly different trees, it produces more accurate and more stable predictions than any single tree.