পদ্ধতির তুলনা করুন

	বহুচলক রৈখিক নির্ভরণ	রিজ রিগ্রেশন
ক্ষেত্র	পরিসংখ্যান	যন্ত্র শিখন
পরিবার	Regression model	Machine learning
উদ্ভবের বছর	1886	1970
প্রবর্তক	Francis Galton; formalized by Karl Pearson	Hoerl, A.E. & Kennard, R.W.
ধরন	Parametric linear model	L2-regularized linear regression
মৌলিক উৎস	Galton, F. (1886). Regression towards mediocrity in hereditary stature. Journal of the Anthropological Institute of Great Britain and Ireland, 15, 246–263.	Hoerl, A.E. & Kennard, R.W. (1970). Ridge Regression: Biased Estimation for Nonorthogonal Problems. Technometrics, 12(1), 55–67.
অপর নাম	MLR, OLS regression, multiple regression, linear regression with multiple predictors	Ridge Regresyonu, ridge regresyonu, L2-regularized regression, Tikhonov regularization
সম্পর্কিত	8	4
সারসংক্ষেপ	Multiple linear regression (MLR) is a parametric regression model that expresses a continuous outcome as a weighted linear combination of two or more predictor variables plus a random error term. The unknown weights (regression coefficients) are estimated by ordinary least squares (OLS), which minimises the sum of squared residuals. The method traces to Francis Galton's 1886 work on hereditary stature and was placed on firm mathematical footing by Karl Pearson; Draper and Smith's 1966 textbook established it as the standard framework for applied regression.	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.