Sum of the Squared Residuals
More commonly known as the Residual Sum of Squares or RSS...
The Sum of Squared Residuals is the total, across every data point in a dataset, of the squared difference between the ground-truth target value (the actual observed outcome from your training labels) and the model’s predicted output for that input.
Keep in mind that a Residual = ObservedVal - PredictedVal
Formula
\[
\mathrm{SSR} = \sum_{i=1}^{n} \bigl(y_i - \hat{y}_i\bigr)^2
\]