Ridge Regression¶
Hoerl, A. E., & Kennard, R. W. (1970). Ridge Regression: Biased Estimation for Nonorthogonal Problems. Technometrics, 12(1), 55-67.
Cited by¶
1 citation across 1 artifact.
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Primes¶
- Regularization
- The under-determined solution space arises when fitting a linear model with many correlated predictors: ordinary least squares minimizes the residual sum of squares \(\|y - X\beta\|^2\), but when \(X\) has near-collinear columns the problem is ill-conditioned and many coefficient vectors \(\beta\) fit the data nearly equally well, including wild high-variance solutions that chase noise.
This sourceIntroduces ridge regression: an L2 penalty on the coefficient vector to stabilize estimates under near-collinearity.
- The under-determined solution space arises when fitting a linear model with many correlated predictors: ordinary least squares minimizes the residual sum of squares \(\|y - X\beta\|^2\), but when \(X\) has near-collinear columns the problem is ill-conditioned and many coefficient vectors \(\beta\) fit the data nearly equally well, including wild high-variance solutions that chase noise.
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Registry ID ref:69519c9a3d5a · see in the full table