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	Regularyzacja grzbietowa (Ridge Regression)	Elastic Net	Analiza Głównych Składowych
Dziedzina	Uczenie maszynowe	Uczenie maszynowe	Uczenie maszynowe
Rodzina	Machine learning	Machine learning	Machine learning
Rok powstania	1970	2005	2002
Twórca	Hoerl, A.E. & Kennard, R.W.	Zou, H. & Hastie, T.	Jolliffe, I.T. (textbook); Pearson & Hotelling (origins)
Typ	L2-regularized linear regression	Regularized linear regression (L1 + L2 penalty)	Unsupervised dimensionality reduction
Źródło pierwotne	Hoerl, A.E. & Kennard, R.W. (1970). Ridge Regression: Biased Estimation for Nonorthogonal Problems. Technometrics, 12(1), 55–67.	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.
Inne nazwy	Ridge Regresyonu, ridge regresyonu, L2-regularized regression, Tikhonov regularization	Elastic Net Regresyon, elastic net regression, ElasticNet, L1/L2 regularized regression	Temel Bileşenler Analizi (PCA), PCA, principal components analysis, Karhunen-Loève transform
Pokrewne	4	4	3
Podsumowanie	Ridge Regression is an L2-regularized linear regression method, introduced by Arthur Hoerl and Robert Kennard in 1970, that reduces multicollinearity by adding a penalty on the size of the coefficients. It shrinks coefficients toward zero without setting any of them exactly to zero, producing more stable estimates when predictors are highly correlated.	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.