পদ্ধতির তুলনা করুন

	ল্যাসো রিগ্রেশন	রেসপন্স সারফেস মেথডোলজি (RSM)	রিজ রিগ্রেশন
ক্ষেত্র	যন্ত্র শিখন	পরীক্ষামূলক নকশা	যন্ত্র শিখন
পরিবার	Machine learning	Hypothesis test	Machine learning
উদ্ভবের বছর	1996	1951	1970
প্রবর্তক	Tibshirani, R.	George E. P. Box & K. B. Wilson	Hoerl, A.E. & Kennard, R.W.
ধরন	Regularized linear regression (L1 penalty)	Second-order polynomial response surface model	L2-regularized linear regression
মৌলিক উৎস	Tibshirani, R. (1996). Regression Shrinkage and Selection via the Lasso. Journal of the Royal Statistical Society: Series B, 58(1), 267–288.	Box, G. E. P. & Wilson, K. B. (1951). On the experimental attainment of optimum conditions. Journal of the Royal Statistical Society, Series B, 13(1), 1–45.	Hoerl, A.E. & Kennard, R.W. (1970). Ridge Regression: Biased Estimation for Nonorthogonal Problems. Technometrics, 12(1), 55–67.
অপর নাম	LASSO Regresyonu, lasso, L1-regularized regression, L1 regularization	RSM, Central Composite Design, Box-Behnken Design, CCD	Ridge Regresyonu, ridge regresyonu, L2-regularized regression, Tikhonov regularization
সম্পর্কিত	4	7	4
সারসংক্ষেপ	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.	Response Surface Methodology is a collection of statistical and mathematical techniques for building an empirical second-order polynomial model that relates a continuous response variable to two or more controllable input factors, and then locating the factor settings that optimize that response. The approach was introduced by George E. P. Box and K. B. Wilson in their landmark 1951 paper and has since become a cornerstone of process optimization across engineering, chemistry, food science, and pharmaceutics.	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.