পদ্ধতির তুলনা করুন

	রিজ রিগ্রেশন	লজিস্টিক রিগ্রেশন	প্রধান উপাদান বিশ্লেষণ
ক্ষেত্র	যন্ত্র শিখন	গবেষণা পরিসংখ্যান	যন্ত্র শিখন
পরিবার	Machine learning	Process / pipeline	Machine learning
উদ্ভবের বছর	1970	1958	2002
প্রবর্তক	Hoerl, A.E. & Kennard, R.W.	David Roxbee Cox	Jolliffe, I.T. (textbook); Pearson & Hotelling (origins)
ধরন	L2-regularized linear regression	Method	Unsupervised dimensionality reduction
মৌলিক উৎস	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.
অপর নাম	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
সম্পর্কিত	4	3	3
সারসংক্ষেপ	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.