1.3.1. MSE (L2 Loss)

Mean Squared Error (L2 Loss)

The Mean Squared Error (MSE) is the average squared loss across a dataset,
$MSE=\frac{1}{n}{\sum }_{i=1}^n(y_i-{\hat{y}}_i{)}^2$
where $y_i$ is the actual value, and $\widehat{y_i}$ is the predicted value for a given observation $i$.