Σύγκριση μεθόδων

	Παλινδρόμηση Ridge	Παλινδρόμηση Lasso	Λογιστική Παλινδρόμηση
Πεδίο	Μηχανική Μάθηση	Μηχανική Μάθηση	Ερευνητική Στατιστική
Οικογένεια	Machine learning	Machine learning	Process / pipeline
Έτος προέλευσης	1970	1996	1958
Δημιουργός	Hoerl, A.E. & Kennard, R.W.	Tibshirani, R.	David Roxbee Cox
Τύπος	L2-regularized linear regression	Regularized linear regression (L1 penalty)	Method
Θεμελιώδης πηγή	Hoerl, A.E. & Kennard, R.W. (1970). Ridge Regression: Biased Estimation for Nonorthogonal Problems. Technometrics, 12(1), 55–67.	Tibshirani, R. (1996). Regression Shrinkage and Selection via the Lasso. Journal of the Royal Statistical Society: Series B, 58(1), 267–288.	Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242.
Εναλλακτικές ονομασίες	Ridge Regresyonu, ridge regresyonu, L2-regularized regression, Tikhonov regularization	LASSO Regresyonu, lasso, L1-regularized regression, L1 regularization	logit model, binomial logistic regression, LR
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.	Lasso regression, introduced by Robert Tibshirani in 1996, is a linear regression method that adds an L1 penalty to the loss so that it shrinks coefficients and performs variable selection at the same time, producing a sparse model. By driving some coefficients exactly to zero it keeps only the predictors that matter.	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.