Сравнение на методи

	Линеен дискриминантен анализ (LDA)	Наивен Бейс
Област	Машинно обучение	Машинно обучение
Семейство	Latent structure	Machine learning
Година на възникване	1936	1997
Създател	Fisher, R. A.	Mitchell, T. M. (textbook treatment)
Тип	Supervised dimensionality reduction and linear classifier	Probabilistic classifier (Bayes' theorem with conditional independence)
Основополагащ източник	Fisher, R. A. (1936). The use of multiple measurements in taxonomic problems. Annals of Eugenics, 7(2), 179–188.	Mitchell, T. M. (1997). Machine Learning. McGraw-Hill. ISBN: 978-0070428072
Други названия	LDA, Fisher's discriminant analysis, Fisher linear discriminant, normal discriminant analysis	Naive Bayes Sınıflandırıcı, naive bayes classifier, simple Bayes, Gaussian Naive Bayes
Свързани	4	4
Резюме	Linear Discriminant Analysis is a supervised method for dimensionality reduction and classification, introduced by Ronald A. Fisher in 1936, that finds linear combinations of features which maximally separate predefined classes while preserving as much class-discriminatory information as possible. It simultaneously serves as a feature-projection technique and a probabilistic classifier, making it one of the foundational methods in pattern recognition and statistical learning.	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.