What is a primary function of ridge regression?

Ridge regression primarily serves the purpose of avoiding overfitting by introducing regularization to the linear regression model. In ridge regression, a penalty is applied to the coefficients of the independent variables in the form of the l2 norm, which is the sum of the squares of the coefficients. This penalty discourages overly large coefficients and encourages the model to spread the coefficient values more evenly across the features, effectively lowering the complexity of the model.

By controlling and shrinking the coefficients, ridge regression reduces the risk of overfitting, especially when dealing with multicollinearity or when the number of predictors is very high relative to the number of observations. Consequently, this leads to better generalization of the model on unseen data.

The other options represent different concepts. Minimizing the l1 norm is related to lasso regression, which has a different penalization mechanism aimed at producing sparse models by setting some coefficients to zero. Comparing the means of two different distributions relates to hypothesis testing and is not relevant to ridge regression. The elimination of irrelevant features is something typically handled by techniques like feature selection rather than through ridge regression, which retains all features but adjusts their influence through regularization.