Comparar métodos

	Regresión logística regularizada	Análisis Discriminante Lineal (LDA)
Campo	Aprendizaje automático	Aprendizaje automático
Familia	Machine learning	Latent structure
Año de origen	1996–2005	1936
Autor original	Tibshirani, R. (lasso); Hoerl & Kennard (ridge); Zou & Hastie (elastic net)	Fisher, R. A.
Tipo	Penalized classification model	Supervised dimensionality reduction and linear classifier
Fuente seminal	Tibshirani, R. (1996). Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society: Series B, 58(1), 267–288.	Fisher, R. A. (1936). The use of multiple measurements in taxonomic problems. Annals of Eugenics, 7(2), 179–188.
Alias	penalized logistic regression, L1 logistic regression, L2 logistic regression, elastic net logistic regression	LDA, Fisher's discriminant analysis, Fisher linear discriminant, normal discriminant analysis
Relacionados	5	4
Resumen	Regularized logistic regression extends standard logistic regression by adding an L1 (lasso), L2 (ridge), or elastic net penalty to the log-likelihood, shrinking coefficients toward zero and preventing overfitting. It is the default choice for binary or multinomial classification when you want interpretable, sparse, or stable coefficient estimates in high-dimensional or collinear feature spaces.	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.