Vertaile menetelmiä

	Lineaarinen erotteluanalyysi (LDA	Faktorianalyysi	K-lähimmät naapurit
Tieteenala	Tilastotiede	Tutkimuksen tilastomenetelmät	Koneoppiminen
Menetelmäperhe	Hypothesis test	Process / pipeline	Machine learning
Syntyvuosi	1936	1931	1967
Kehittäjä	Ronald A. Fisher	Louis Leon Thurstone	Cover, T.M. & Hart, P.E.
Tyyppi	Parametric linear classifier / dimensionality reduction	Method	Instance-based (non-parametric) learning
Alkuperäislähde	Fisher, R.A. (1936). The Use of Multiple Measurements in Taxonomic Problems. Annals of Eugenics, 7(2), 179–188.	Thurstone, L. L. (1947). Multiple Factor Analysis. University of Chicago Press.	Cover, T.M. & Hart, P.E. (1967). Nearest Neighbor Pattern Classification. IEEE Transactions on Information Theory, 13(1), 21–27.
Rinnakkaisnimet	LDA, Fisher's LDA, Fisher's linear discriminant, discriminant function analysis	EFA, CFA, latent variable modeling	KNN, K-En Yakın Komşu (KNN), nearest neighbor classifier, instance-based learning
Liittyvät	7	3	5
Tiivistelmä	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.	Factor analysis is a statistical technique for identifying latent (unobserved) dimensions underlying observed variables, developed by Louis Leon Thurstone in the 1930s and formalized by Jöreskog (1969). Exploratory factor analysis (EFA) discovers unknown factor structure from data; confirmatory factor analysis (CFA) tests hypothesized relationships between observed and latent variables. Essential in psychometrics (test development), organizational research (measuring constructs like leadership style), and biomedicine (identifying disease subtypes), factor analysis reduces dimensionality while revealing conceptual organization in multivariate data.	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.