Ridge Regression: Combat Multicollinearity for Better Models

Ridge Regression: Combat Multicollinearity for Better Models

Key points Ridge regression is a statistical technique used to address the issue of multicollinearity in regression analysis. It adds a penalty term to the regression equation, which helps stabilize the model and reduce the impact of multicollinearity on coefficient estimates. Multicollinearity refers to strong correlations between independent variables in a regression model. It can make it difficult to interpret the effects of individual variables and leads to unstable and unreliable coefficient estimates. Ridge regression provides a solution to this problem. Ridge regression differs from ordinary least squares regression by minimizing the sum of squared residuals along with a penalty term. This trade-off between model fit and coefficient magnitude helps create a more stable and reliable model, especially in multicollinearity. The ridge parameter (λ) choice is crucial in ridge regression. It controls the amount of regularization applied to the coefficient estimates. The optimal value…