方法对比

	Lasso 回归	响应面方法 (RSM)
领域	机器学习	实验设计
方法族	Machine learning	Hypothesis test
起源年份	1996	1951
提出者	Tibshirani, R.	George E. P. Box & K. B. Wilson
类型	Regularized linear regression (L1 penalty)	Second-order polynomial response surface model
开创性文献	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.
别名	LASSO Regresyonu, lasso, L1-regularized regression, L1 regularization	RSM, Central Composite Design, Box-Behnken Design, CCD
相关	4	7
摘要	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.