$R_{adj}^2 = 1 - \frac{(1 - R^2)(n - 1)}{n - p - 1}$

Adjusted R-Squared is a version of R-Squared that is adjusted for the number of predictors (independent variables) in a model. As we know, $R^2$ always increases if we add more predictors. Adjusted R-Squared has an advantage over the normal R-Squared metric because it accounts for statistical shrinkage, and the normal R-Squared metric tends to hurt more when more independent variables occur in the system. This allows us to do predictor selection and can be a metric for forward or backward selection.