Ridge regression minimizes a squared-error loss plus the quadratic penalty . Its positive penalty stabilizes inversion in collinear or high-dimensional designs.
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Ridge regression, also known as Tikhonov regularization, is a technique used in linear regression that introduces a regularization term to prevent overfitting and improve the model's generalization to new data. It is particularly useful when dealing with multicollinearity, where predictor variables are highly correlated.