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  • Ridge Regression is a regression method used to address multicollinearity issues in linear regression models, where the independent variables are highly correlated with each other. This technique adds a penalty to the size of the regression coefficients, thereby reducing model complexity and preventing overfitting. This penalty is determined by the lambda (╬╗) parameter, which controls the amount of penalty imposed. The larger the value of ╬╗, the smaller the regression coefficients, resulting in a simpler and more stable model. Ridge Regression is particularly effective when we work with data that has many variables and high multicollinearity.