# Does Matlab has relative square error available in Neural Network toolbox?

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Anderson am 28 Nov. 2015
Bearbeitet: Greg Heath am 5 Dez. 2015
I have seen that MSE, SSE, MAE and SAE are possible training functions of a neural network in Matlab. Does it have relative square error available?
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Greg Heath am 3 Dez. 2015
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
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Greg Heath am 5 Dez. 2015
Bearbeitet: Greg Heath am 5 Dez. 2015
Most of the time I used R2 instead of Rsq.
NEURAL NMSE 51 HITS 100 HITS
NEURAL R2 144 HITS 113 HITS
HOPE THIS HELPS.
GREG

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### Weitere Antworten (1)

Dave Behera am 2 Dez. 2015
The only error functions available in the Neural Network Toolbox are MSE, SSE, MAE and SAE. There is no function for calculating the relative square error.
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