Porównaj metody

	Regresja Lasso	Regresja metodą najmniejszych kwadratów (OLS)	Metodologia Powierzchni Odpowiedzi (RSM)
Dziedzina	Uczenie maszynowe	Ekonometria	Planowanie eksperymentów
Rodzina	Machine learning	Regression model	Hypothesis test
Rok powstania	1996	2019	1951
Twórca	Tibshirani, R.	Wooldridge (textbook treatment); classical least squares	George E. P. Box & K. B. Wilson
Typ	Regularized linear regression (L1 penalty)	Linear regression	Second-order polynomial response surface model
Źródło pierwotne	Tibshirani, R. (1996). Regression Shrinkage and Selection via the Lasso. Journal of the Royal Statistical Society: Series B, 58(1), 267–288.	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860	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.
Inne nazwy	LASSO Regresyonu, lasso, L1-regularized regression, L1 regularization	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu	RSM, Central Composite Design, Box-Behnken Design, CCD
Pokrewne	4	5	7
Podsumowanie	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.	Ordinary Least Squares is the classical linear regression method that explains a continuous outcome as a linear combination of predictors. It estimates the coefficients by minimising the sum of squared residuals, and under the Gauss-Markov assumptions these estimates are the best linear unbiased estimator (BLUE).	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.