Bandingkan kaedah

	Regresi Rabung	Regresi Logistik	Analisis Komponen Utama
Bidang	Pembelajaran Mesin	Statistik Penyelidikan	Pembelajaran Mesin
Keluarga	Machine learning	Process / pipeline	Machine learning
Tahun asal	1970	1958	2002
Pengasas	Hoerl, A.E. & Kennard, R.W.	David Roxbee Cox	Jolliffe, I.T. (textbook); Pearson & Hotelling (origins)
Jenis	L2-regularized linear regression	Method	Unsupervised dimensionality reduction
Sumber perintis	Hoerl, A.E. & Kennard, R.W. (1970). Ridge Regression: Biased Estimation for Nonorthogonal Problems. Technometrics, 12(1), 55–67.	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.
Alias	Ridge Regresyonu, ridge regresyonu, L2-regularized regression, Tikhonov regularization	logit model, binomial logistic regression, LR	Temel Bileşenler Analizi (PCA), PCA, principal components analysis, Karhunen-Loève transform
Berkaitan	4	3	3
Ringkasan	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.	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.