Ridge regression is a method that helps deal with the problem of multicollinearity, which occurs when predictors are highly correlated. It achieves this by adding a penalty term to the least squares objective function. The size of this penalty term is determined by a parameter called lambda or alpha. As lambda increases, the coefficients are shrunk towards zero. This is useful because it prevents overfitting and reduces the sensitivity of the model to the predictors.

Sure! In ridge regression, a penalty term is added to the traditional least squares objective function. This penalty term is scaled by a parameter called lambda or alpha, which controls the amount of shrinkage applied to the coefficients. As lambda increases, the coefficients are pushed towards zero, reducing their overall magnitude. Ridge regression is useful when dealing with multicollinearity, where predictors are highly correlated, as it helps to address the issue of instability in coefficient estimates.

Ridge regression is a regularization method that helps address the issue of multicollinearity in regression models. It does this by adding a penalty term to the traditional least squares objective function. The size of the penalty term is controlled by a parameter known as lambda or alpha. Increasing the value of lambda leads to greater shrinkage of the coefficients towards zero. This can be beneficial when dealing with highly correlated predictors, as ridge regression helps to stabilize the coefficient estimates and reduces the impact of collinearity on the model's performance.