Porovnat metody

	Ridge regrese	Regrese Lasso	Analýza hlavních komponent
Obor	Strojové učení	Strojové učení	Strojové učení
Rodina	Machine learning	Machine learning	Machine learning
Rok vzniku	1970	1996	2002
Tvůrce	Hoerl, A.E. & Kennard, R.W.	Tibshirani, R.	Jolliffe, I.T. (textbook); Pearson & Hotelling (origins)
Typ	L2-regularized linear regression	Regularized linear regression (L1 penalty)	Unsupervised dimensionality reduction
Původní zdroj	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.	Jolliffe, I.T. (2002). Principal Component Analysis (2nd ed.). Springer.
Další názvy	Ridge Regresyonu, ridge regresyonu, L2-regularized regression, Tikhonov regularization	LASSO Regresyonu, lasso, L1-regularized regression, L1 regularization	Temel Bileşenler Analizi (PCA), PCA, principal components analysis, Karhunen-Loève transform
Příbuzné	4	4	3
Shrnutí	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.	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.