Compara mètodes

	Elastic Net	Anàlisi de Components Principals
Camp	Aprenentatge automàtic	Aprenentatge automàtic
Família	Machine learning	Machine learning
Any d'origen	2005	2002
Autor original	Zou, H. & Hastie, T.	Jolliffe, I.T. (textbook); Pearson & Hotelling (origins)
Tipus	Regularized linear regression (L1 + L2 penalty)	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.	Jolliffe, I.T. (2002). Principal Component Analysis (2nd ed.). Springer.
Àlies	Elastic Net Regresyon, elastic net regression, ElasticNet, L1/L2 regularized regression	Temel Bileşenler Analizi (PCA), PCA, principal components analysis, Karhunen-Loève transform
Relacionats	4	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.	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.