Comparer des méthodes

	K-Plus-Proches-Voisins Explicable	Arbre de décision	Naive Bayes
Domaine	Apprentissage automatique	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning	Machine learning
Année d'origine	1967 (KNN); 2010s (explainability extensions)	1984	1997
Auteur d'origine	Cover, T. & Hart, P. (KNN); XAI extensions by various authors	Breiman, Friedman, Olshen & Stone	Mitchell, T. M. (textbook treatment)
Type	Instance-based learning with explainability layer	Recursive partitioning (if-then rules)	Probabilistic classifier (Bayes' theorem with conditional independence)
Source fondatrice	Cover, T. & Hart, P. (1967). Nearest neighbor pattern classification. IEEE Transactions on Information Theory, 13(1), 21–27.	Breiman, L., Friedman, J.H., Olshen, R.A. & Stone, C.J. (1984). Classification and Regression Trees. Wadsworth.	Mitchell, T. M. (1997). Machine Learning. McGraw-Hill. ISBN: 978-0070428072
Alias	XKNN, Interpretable KNN, Explainable KNN, Transparent K-Nearest Neighbors	Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree	Naive Bayes Sınıflandırıcı, naive bayes classifier, simple Bayes, Gaussian Naive Bayes
Apparentées	4	5	4
Résumé	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.	A Decision Tree is an interpretable classification and regression method, formalised by Breiman, Friedman, Olshen and Stone in their 1984 CART framework, that partitions the data with hierarchical if-then rules. Each split sends observations down one branch or another until a prediction is read off the leaf.	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.