Comparar métodos

	Análisis Discriminante Lineal (LDA)	Análisis Discriminante Cuadrático (QDA)
Campo	Aprendizaje automático	Aprendizaje automático
Familia	Latent structure	Latent structure
Año de origen	1936	1939
Autor original	Fisher, R. A.	Classical Gaussian discriminant analysis (Fisher / Welch lineage)
Tipo	Supervised dimensionality reduction and linear classifier	Generative Gaussian classifier
Fuente seminal	Fisher, R. A. (1936). The use of multiple measurements in taxonomic problems. Annals of Eugenics, 7(2), 179–188.	Hastie, T., Tibshirani, R., & Friedman, J. (2009). The Elements of Statistical Learning (2nd ed.). Springer. ISBN: 978-0-387-84857-0
Alias	LDA, Fisher's discriminant analysis, Fisher linear discriminant, normal discriminant analysis	QDA, quadratic classifier, kuadratik diskriminant analizi
Relacionados	4	2
Resumen	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.	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.