Compara mètodes

	Elastic Net	Regressió Logística	Anàlisi de Components Principals
Camp	Aprenentatge automàtic	Estadística per a la recerca	Aprenentatge automàtic
Família	Machine learning	Process / pipeline	Machine learning
Any d'origen	2005	1958	2002
Autor original	Zou, H. & Hastie, T.	David Roxbee Cox	Jolliffe, I.T. (textbook); Pearson & Hotelling (origins)
Tipus	Regularized linear regression (L1 + L2 penalty)	Method	Unsupervised dimensionality reduction
Font seminal	Zou, H. & Hastie, T. (2005). Regularization and Variable Selection via the Elastic Net. Journal of the Royal Statistical Society: Series B, 67(2), 301–320.	Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242.	Jolliffe, I.T. (2002). Principal Component Analysis (2nd ed.). Springer.
Àlies	Elastic Net Regresyon, elastic net regression, ElasticNet, L1/L2 regularized regression	logit model, binomial logistic regression, LR	Temel Bileşenler Analizi (PCA), PCA, principal components analysis, Karhunen-Loève transform
Relacionats	4	3	3
Resum	Elastic Net is a regularized linear regression method introduced by Zou and Hastie in 2005 that blends the LASSO (L1) and Ridge (L2) penalties, so it performs variable selection and coefficient shrinkage at the same time. It is designed for predictive and explanatory modelling on data with many, possibly correlated, predictors.	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.	Principal Component Analysis (PCA) is an unsupervised dimensionality-reduction method — given its modern textbook treatment by Ian Jolliffe (2002) — that compresses high-dimensional data into fewer dimensions while preserving the maximum possible variance. It re-expresses correlated variables as a small set of uncorrelated principal components ordered by how much of the data's variation each one captures.