Comparer des méthodes

	Naive Bayes	Arbre de décision
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	1997	1984
Auteur d'origine	Mitchell, T. M. (textbook treatment)	Breiman, Friedman, Olshen & Stone
Type	Probabilistic classifier (Bayes' theorem with conditional independence)	Recursive partitioning (if-then rules)
Source fondatrice	Mitchell, T. M. (1997). Machine Learning. McGraw-Hill. ISBN: 978-0070428072	Breiman, L., Friedman, J.H., Olshen, R.A. & Stone, C.J. (1984). Classification and Regression Trees. Wadsworth.
Alias	Naive Bayes Sınıflandırıcı, naive bayes classifier, simple Bayes, Gaussian Naive Bayes	Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree
Apparentées	4	5
Résumé	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.	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.