Confronta i metodi

	Analisi Discriminante Lineare (LDA)	Regressione Logistica
Campo	Apprendimento automatico	Statistica per la ricerca
Famiglia	Latent structure	Process / pipeline
Anno di origine	1936	1958
Ideatore	Fisher, R. A.	David Roxbee Cox
Tipo	Supervised dimensionality reduction and linear classifier	Method
Fonte seminale	Fisher, R. A. (1936). The use of multiple measurements in taxonomic problems. Annals of Eugenics, 7(2), 179–188.	Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242.
Alias	LDA, Fisher's discriminant analysis, Fisher linear discriminant, normal discriminant analysis	logit model, binomial logistic regression, LR
Correlati	4	3
Sintesi	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.	Logistic regression is a statistical method for modeling the probability of a binary outcome (disease present/absent, success/failure) as a function of continuous and categorical predictors. Developed by David Roxbee Cox (1958), it solves the problem of predicting categorical outcomes by applying a logistic transformation to constrain predictions to the [0,1] probability interval, enabling accurate risk stratification, diagnostic prediction, and causal inference in epidemiology, medicine, and social science.