Ridge regression, also known as Tikhonov regularization, is a technique used to analyze multiple regression data that suffer from multicollinearity. When independent variables are highly correlated, the estimates of the coefficients can become unstable and exhibit high variance. Ridge regression introduces a penalty term to the loss function to shrink the coefficients, thereby improving the model's performance and stability.
