Adjusted R-Squared is a version of R-Squared ($R^2$) that is adjusted for the number of predictors (independent variables) in a model. Its advantage over $R^2$ is that it accounts for statistical shrinkage. The normal $R^2$ statistic tends to increase when more independent variables occur in the system; i.e., we cannot use it to compare models with different numbers of predictors. The adjusted R-Squared can be negative, but it is usually not. It is always lower than or equal to the $R^2$.

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

Where $p$ is the number of predictors and $n$ is the sample size.