What is Ridge Regression?

Ridge Regression is a type of linear regression that applies L2 regularization to the model. It is used to address issues of multicollinearity in regression analysis, where independent variables are highly correlated. By adding a penalty term to the loss function, Ridge Regression helps to shrink the coefficients of the model, which can lead to more stable and reliable predictions, especially in the presence of noise in the data. This technique is particularly useful when dealing with datasets that have more features than observations or when features are highly correlated.