השוואת שיטות

	רגרסיה פולינומית	רגרסיית לאסו
תחום	סטטיסטיקה	למידת מכונה
משפחה	Regression model	Machine learning
שנת המקור	2012	1996
הוגה השיטה	Montgomery, Peck & Vining (textbook treatment); classical least squares	Tibshirani, R.
סוג	Linear regression in transformed predictors	Regularized linear regression (L1 penalty)
מקור מכונן	Montgomery, D. C., Peck, E. A. & Vining, G. G. (2012). Introduction to Linear Regression Analysis. Wiley. ISBN: 978-0470542811	Tibshirani, R. (1996). Regression Shrinkage and Selection via the Lasso. Journal of the Royal Statistical Society: Series B, 58(1), 267–288.
כינויים	polynomial least squares, curvilinear regression, Polinom Regresyonu	LASSO Regresyonu, lasso, L1-regularized regression, L1 regularization
קשורות	4	4
תקציר	Polynomial regression is a regression method that models non-linear relationships by including squared and higher-degree terms of an explanatory variable, and it is a core tool of response surface analysis. As developed in Montgomery, Peck and Vining's Introduction to Linear Regression Analysis (2012), it remains linear in its parameters even though the fitted curve bends.	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.