Porovnat metody

	Elastic Net	Analýza hlavních komponent
Obor	Strojové učení	Strojové učení
Rodina	Machine learning	Machine learning
Rok vzniku	2005	2002
Tvůrce	Zou, H. & Hastie, T.	Jolliffe, I.T. (textbook); Pearson & Hotelling (origins)
Typ	Regularized linear regression (L1 + L2 penalty)	Unsupervised dimensionality reduction
Původní zdroj	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.
Další názvy	Elastic Net Regresyon, elastic net regression, ElasticNet, L1/L2 regularized regression	Temel Bileşenler Analizi (PCA), PCA, principal components analysis, Karhunen-Loève transform
Příbuzné	4	3
Shrnutí	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.