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Ridge Regression

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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Ridge Regression (L2-Regularized Linear Regression)

Taksonomisk metoderegister · ml-model / machine-learning

Hoerl, A.E. & Kennard, R.W. (1970). Ridge Regression: Biased Estimation for Nonorthogonal Problems. Technometrics, 12(1), 55–67. · DOI 10.1080/00401706.1970.10488634

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