Glossary

Ridge Regression

Ridge regression, also known as Tikhonov regularization, is a statistical method that is commonly used to analyze data with multicollinearity. This method is a variation of linear regression, but it includes an additional penalty term to prevent overfitting.

In traditional linear regression, the objective is to minimize the sum of the squared residuals between the predicted and actual values. However, when there are highly correlated predictor variables, the regression coefficients can become unstable and highly sensitive to small changes in the data. This is known as multicollinearity, and it can lead to unreliable predictions and overfitting of the data.

Ridge regression addresses this issue by adding a penalty term to the regression equation that shrinks the coefficients towards zero. The amount of shrinkage is controlled by a tuning parameter, which can be determined through cross-validation. By adding this penalty term, ridge regression is able to handle multicollinearity and produce more stable and reliable predictions.

Overall, ridge regression is a powerful statistical method that can be used in a variety of applications, including finance, engineering, and biology. Its ability to handle multicollinearity makes it a valuable tool for data analysts and researchers looking to make accurate predictions from their data.

A wide array of use-cases

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