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I have split my dataset into training and testing. My model is fix which is give same result in every running and give a good accuracy. I want to use my neural network model coupled with genetic algorithm to select the best features from 400 features. I have done two ways first way I used my root mean square error for training as fitness function. then, after the result is identical, i tested the model another way is that i trained and tested my model so that the fitness function is the testing accuracy I find better result in second way which i got higher accuracy

my question is it logical to do the second way? what does it mean if I have two values. First one is higher accuracy than second one in training but in prediction second one is higher than first one ? which one I choose ?

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Your question is little bit hard to follow, I don't understand whether you are talking about two models or the same model being validated in two different ways - and it isn't clear what you are using the GA for.

But to answer your second question:

First one is higher accuracy than second one in training but in prediction second one is higher than first one ? which one I choose ?

Assuming that you end up with different models in the first and second approach, choose the second approach. In the first case you are getting very good training accuracy but suboptimal test accuracy - which is a classic symptom of overfitting, something to be avoided.

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  • $\begingroup$ yes same model being validated in two different ways. i want to use genetic to select the best important wavelengths from 400 wavelengths $\endgroup$ – hasan Jan 24 '18 at 22:00

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