Relative squared error is a regression evaluation metric that compares a model's total squared error with the squared error of a baseline that predicts the average target value. It emphasizes larger errors more heavily than relative absolute error.
In regression , an evaluation metric that is the sum of all squared absolute errors divided by the sum of squared distances between correct label values and the average of all correct label values.