Ridge Regression, also known as Tikhonov regularization, is a technique used to analyze multiple regression data that suffer from multicollinearity. When multicollinearity occurs, least squares estimates are unbiased, but their variances are large, which may lead to overfitting. Ridge regression addresses this issue by adding a degree of bias to the regression estimates, which in turn reduces the standard errors.
