Comparar métodos

	Análisis Discriminante Lineal (LDA	Vecinos más cercanos (K-NN)	Análisis Multivariante de la Varianza (MANOVA)
Campo	Estadística	Aprendizaje automático	Estadística
Familia	Hypothesis test	Machine learning	Hypothesis test
Año de origen	1936	1967	1932
Autor original	Ronald A. Fisher	Cover, T.M. & Hart, P.E.	Samuel Stanley Wilks (Wilks' Lambda, 1932); Roy, Hotelling, Pillai (mid-20th c.)
Tipo	Parametric linear classifier / dimensionality reduction	Instance-based (non-parametric) learning	Parametric multivariate mean comparison
Fuente seminal	Fisher, R.A. (1936). The Use of Multiple Measurements in Taxonomic Problems. Annals of Eugenics, 7(2), 179–188.	Cover, T.M. & Hart, P.E. (1967). Nearest Neighbor Pattern Classification. IEEE Transactions on Information Theory, 13(1), 21–27.	Tabachnick, B.G. & Fidell, L.S. (2013). Using Multivariate Statistics (6th ed.). Pearson. ISBN: 978-0205849574
Alias	LDA, Fisher's LDA, Fisher's linear discriminant, discriminant function analysis	KNN, K-En Yakın Komşu (KNN), nearest neighbor classifier, instance-based learning	Multivariate ANOVA, Çok Değişkenli ANOVA (MANOVA)
Relacionados	7	5	5
Resumen	Linear Discriminant Analysis (LDA) is a parametric supervised classification method that finds the linear combination of continuous predictors that best separates two or more predefined groups. Introduced by Ronald A. Fisher in his landmark 1936 paper on taxonomic measurements, it simultaneously serves as a classifier and a dimensionality-reduction tool, and can be understood as the classification-oriented counterpart of MANOVA.	K-Nearest Neighbors (KNN), formalized by Cover and Hart in 1967, is a non-parametric, instance-based method that classifies or predicts a new observation by looking at the k closest examples in the training data. For classification it takes a majority vote among those neighbors; for regression it averages their values.	MANOVA is a parametric hypothesis test that simultaneously compares group means across multiple continuous dependent variables, controlling the inflation of Type I error that would result from running separate ANOVAs. Key multivariate test statistics — Wilks' Lambda, Pillai's Trace, Hotelling-Lawley Trace, and Roy's Greatest Root — were developed between the 1930s and 1950s, with Wilks' Lambda formalised by Samuel Stanley Wilks in 1932.