r2

$R^2$ looks at how much variance in the target vector is explained by the features. It is the ratio between the variance in the predictions vs true values and the variance in the target vector.

$R^2=1-\frac{\sum (y_i-{\hat{y}}_i{)}^2}{\sum (y_i-\bar{y})},$ where

• $y_i$ is the true value
• ${\hat{y}}_i$ is the predicted value
• $\bar{y}$ is the mean of the true values