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What range of values of the relative sum of sqaures error is acceptable for a good neural network? I am getting around 0.9 of the relative error for 1 model and around 0.4 for another. Are both the networks significant?

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  • $\begingroup$ Is the network being used for classification or regression ? $\endgroup$ – image_doctor Jun 28 '15 at 11:04
  • $\begingroup$ It is being used for regression! $\endgroup$ – Yash Lundia Jun 29 '15 at 12:23
  • $\begingroup$ Are you happy with both those levels of error for estimating your target function ? $\endgroup$ – image_doctor Jun 29 '15 at 12:32
  • $\begingroup$ Thats the problem! I do not know the significance of the term 'relative sum of squares error'. Are these errors out of 1? Meaning, does 0.9 mean 90% error? Or is it some other way? Please tell me the significance of the term 'relative sum of squares error'. $\endgroup$ – Yash Lundia Jun 29 '15 at 13:12
  • $\begingroup$ Also, is there some way to calculate RMSE in SPSS Neural Network analysis? Like instead of relative sum of squares error i want to calculate the RMSE of the network. $\endgroup$ – Yash Lundia Jun 29 '15 at 13:15
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Check this:

A value closer to 0 indicates that the model has a smaller random error component, and that the fit will be more useful for prediction.

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