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	Rigidna regresija	Regresija Laso	Logistička regresija	Analiza glavnih komponenti
Oblast	Mašinsko učenje	Mašinsko učenje	Istraživačka statistika	Mašinsko učenje
Porodica	Machine learning	Machine learning	Process / pipeline	Machine learning
Godina nastanka	1970	1996	1958	2002
Tvorac	Hoerl, A.E. & Kennard, R.W.	Tibshirani, R.	David Roxbee Cox	Jolliffe, I.T. (textbook); Pearson & Hotelling (origins)
Tip	L2-regularized linear regression	Regularized linear regression (L1 penalty)	Method	Unsupervised dimensionality reduction
Temeljni izvor	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.	Jolliffe, I.T. (2002). Principal Component Analysis (2nd ed.). Springer.
Drugi nazivi	Ridge Regresyonu, ridge regresyonu, L2-regularized regression, Tikhonov regularization	LASSO Regresyonu, lasso, L1-regularized regression, L1 regularization	logit model, binomial logistic regression, LR	Temel Bileşenler Analizi (PCA), PCA, principal components analysis, Karhunen-Loève transform
Srodne	4	4	3	3
Sažetak	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.	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.