R2 score is a criteria for linear model.

$$R^2=1-\frac{SSE}{SST}$$

where, $SSE$ is the residual sum of squares

$$SSE=\sum _{i=1}^n({\hat{y}}_i-y_i{)}^2$$

$SSR$ is the regression sum of squares

$$SSR=\sum _{i=1}^n({\hat{y}}_i-{\bar{y}}_i{)}^2$$

$SST$ is the total sum of squares

$$SST=\sum _{i=1}^n(y_i-{\bar{y}}_i{)}^2=SSE+SSR$$

$R^2$ in $[0,1]$, the closer $1$, the better the model.

$R^2$ can be calculated from

$$R^2=1-\frac{{\sum }_{i=0}^n(y_i-{\hat{y}}_i{)}^2/n}{{\sum }_{i=0}^n(y_i-{\bar{y}}_i{)}^2/n}=1-\frac{MSE}{Var}$$