Another useful quantitative measure of a model's performance is the so-called Mean Squared Error (MSE), which is simply the averaged value of the SSE cost that we minimized to fit the linear regression model. The MSE is useful to compare different regression models or for tuning their parameters via grid search and cross-validation, as it normalizes the SSE by the sample size:
