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	Regresja logistyczna	Analiza Głównych Składowych	Regularyzacja grzbietowa (Ridge Regression)
Dziedzina	Statystyka w badaniach	Uczenie maszynowe	Uczenie maszynowe
Rodzina	Process / pipeline	Machine learning	Machine learning
Rok powstania	1958	2002	1970
Twórca	David Roxbee Cox	Jolliffe, I.T. (textbook); Pearson & Hotelling (origins)	Hoerl, A.E. & Kennard, R.W.
Typ	Method	Unsupervised dimensionality reduction	L2-regularized linear regression
Źródło pierwotne	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.	Hoerl, A.E. & Kennard, R.W. (1970). Ridge Regression: Biased Estimation for Nonorthogonal Problems. Technometrics, 12(1), 55–67.
Inne nazwy	logit model, binomial logistic regression, LR	Temel Bileşenler Analizi (PCA), PCA, principal components analysis, Karhunen-Loève transform	Ridge Regresyonu, ridge regresyonu, L2-regularized regression, Tikhonov regularization
Pokrewne	3	3	4
Podsumowanie	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.	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.