Σύγκριση μεθόδων

	Παλινδρόμηση Ridge	Elastic Net	Ανάλυση Κύριων Συνιστωσών
Πεδίο	Μηχανική Μάθηση	Μηχανική Μάθηση	Μηχανική Μάθηση
Οικογένεια	Machine learning	Machine learning	Machine learning
Έτος προέλευσης	1970	2005	2002
Δημιουργός	Hoerl, A.E. & Kennard, R.W.	Zou, H. & Hastie, T.	Jolliffe, I.T. (textbook); Pearson & Hotelling (origins)
Τύπος	L2-regularized linear regression	Regularized linear regression (L1 + L2 penalty)	Unsupervised dimensionality reduction
Θεμελιώδης πηγή	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.
Εναλλακτικές ονομασίες	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
Συναφείς	4	4	3
Σύνοψη	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.