Salīdzināt metodes

	Kvadrātiskā diskriminantanalīze (QDA)	Lineārā diskriminanta analīze (LDA)	Naive Bayes
Nozare	Mašīnmācīšanās	Mašīnmācīšanās	Mašīnmācīšanās
Saime	Latent structure	Latent structure	Machine learning
Izcelsmes gads	1939	1936	1997
Autors	Classical Gaussian discriminant analysis (Fisher / Welch lineage)	Fisher, R. A.	Mitchell, T. M. (textbook treatment)
Tips	Generative Gaussian classifier	Supervised dimensionality reduction and linear classifier	Probabilistic classifier (Bayes' theorem with conditional independence)
Pirmavots	Hastie, T., Tibshirani, R., & Friedman, J. (2009). The Elements of Statistical Learning (2nd ed.). Springer. ISBN: 978-0-387-84857-0	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
Citi nosaukumi	QDA, quadratic classifier, kuadratik diskriminant analizi	LDA, Fisher's discriminant analysis, Fisher linear discriminant, normal discriminant analysis	Naive Bayes Sınıflandırıcı, naive bayes classifier, simple Bayes, Gaussian Naive Bayes
Saistītās	2	4	4
Kopsavilkums	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.	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.