Performance estimate of pattern recognition tool
HI. I am creating a neural network using nprtool. I have generated the code and got the results in confusion matrix. In the end I get a perfoemace variable. I am unable to understand that what should be the value of this variable, I mean the range. Can any one please tell me that? I am appending my lines of code and my result.
% Test the Network outputs = net(inputs); errors = gsubtract(targets,outputs); performance = perform(net,targets,outputs)
My output is
- performance =
0.4772*My 2nd question is that how can i present the network generated by nprtool to new values? I don’t understand the concept.
ANSWER
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If you are using patternnet and/or nprtool the most important results are the error rates for the different classes. These are displayed in the confusion matrix. Each case is different because the number of classes may be different and the importance of each class may be different.
Unfortunately, error rates are not continuous. Therefore it is very difficult to find an acceptable objective function based on error rates to be used in a numerical optimization routine.
The standard alternative approach is to minimize MSEtrn using gradient descent and to hope that the resulting the class distribution of trn/val/tst error rates is acceptable. If not, tradeoffs can be implemented in a number of ways. One way is to use the error weight function EW, as an input to TRAIN. Another is to add noisy duplicates of small classes with high error rates because their influence on the objective function was too small.
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