The relative (i.e., NORMALIZED) square error is the ratio of the mean-square-error of the model, MSE, to the mean-square-error of the NAIVE CONSTANT-OUTPUT MODEL, MSE00. To minimize the mse of the latter model, the constant output is just the target mean. Correspondingly, MSE00 is just the average target variance.
MSE00 = mean(var(target',1))
Since
MSE = mse(target-output);
NMSE = MSE/MSE00 % "N"ormalized, or relative, mse.
The coefficient-of-variation or Rsquared, Rsq (See WKIPEDIA)
Rsq = 1 - NMSE
is the fraction of target variance that is "explained by the model.
I have zillions of posts in both the NEWSGROUP and ANSWERS using the above variables. In some of them I have gone into more detail than I have here.
Hope this helps.
Thank you for formally accepting my answer
Greg
