I'm working in a neural network with BackPropagation. The network has 6 inputs, 1 hidden layer (6 neurons on that layer) and 1 output. I train the network with algorithms "Levenberg-Marquardt" and "Bayesian Regularization". So, the idea is can "predict" a result but the results are not the right ones according to the table with the historical data.
To stop the training, for the moment, I look the "regression plot", the "Mean squared Error" and "Regression R Values", wich have the "ideal values" but still the results are not accurates and are not even close with datas who "doesn't exist" in the table with the historical data.
What graphic should I look at to know the network is not overfitting or is correctly trained?
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