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| Metodologia delle Superfici di Risposta (RSM)× | Regressione Ridge× | |
|---|---|---|
| Campo≠ | Disegno sperimentale | Apprendimento automatico |
| Famiglia≠ | Hypothesis test | Machine learning |
| Anno di origine≠ | 1951 | 1970 |
| Ideatore≠ | George E. P. Box & K. B. Wilson | Hoerl, A.E. & Kennard, R.W. |
| Tipo≠ | Second-order polynomial response surface model | L2-regularized linear regression |
| Fonte seminale≠ | 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. link ↗ | Hoerl, A.E. & Kennard, R.W. (1970). Ridge Regression: Biased Estimation for Nonorthogonal Problems. Technometrics, 12(1), 55–67. DOI ↗ |
| Alias≠ | RSM, Central Composite Design, Box-Behnken Design, CCD | Ridge Regresyonu, ridge regresyonu, L2-regularized regression, Tikhonov regularization |
| Correlati≠ | 7 | 4 |
| Sintesi≠ | 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. |
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