Comparer des méthodes

	Analyse discriminante quadratique (QDA)	Naive Bayes
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Latent structure	Machine learning
Année d'origine	1939	1997
Auteur d'origine	Classical Gaussian discriminant analysis (Fisher / Welch lineage)	Mitchell, T. M. (textbook treatment)
Type	Generative Gaussian classifier	Probabilistic classifier (Bayes' theorem with conditional independence)
Source fondatrice	Hastie, T., Tibshirani, R., & Friedman, J. (2009). The Elements of Statistical Learning (2nd ed.). Springer. ISBN: 978-0-387-84857-0	Mitchell, T. M. (1997). Machine Learning. McGraw-Hill. ISBN: 978-0070428072
Alias	QDA, quadratic classifier, kuadratik diskriminant analizi	Naive Bayes Sınıflandırıcı, naive bayes classifier, simple Bayes, Gaussian Naive Bayes
Apparentées	2	4
Résumé	Quadratic discriminant analysis is a generative classifier that models each class with its own multivariate Gaussian distribution, allowing each class a separate covariance matrix. Unlike linear discriminant analysis, which assumes a shared covariance and yields linear boundaries, QDA's per-class covariances produce curved (quadratic) decision boundaries, letting it capture differences in the spread and orientation of the classes.	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.