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Análise Discriminante Linear (LDA)×Regressão Logística×
ÁreaAprendizado de máquinaEstatística para pesquisa
FamíliaLatent structureProcess / pipeline
Ano de origem19361958
Autor originalFisher, R. A.David Roxbee Cox
TipoSupervised dimensionality reduction and linear classifierMethod
Fonte seminalFisher, R. A. (1936). The use of multiple measurements in taxonomic problems. Annals of Eugenics, 7(2), 179–188. DOI ↗Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242. DOI ↗
Outros nomesLDA, Fisher's discriminant analysis, Fisher linear discriminant, normal discriminant analysislogit model, binomial logistic regression, LR
Relacionados43
ResumoLinear 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.
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ScholarGateComparar métodos: Linear Discriminant Analysis · Logistic Regression. Recuperado em 2026-06-18 de https://scholargate.app/pt/compare