The control of the learning process is done in a descriptive way by viewing the hit and error learning curve. The hit curve, which starts at 0%, reaches 100% at the end of the learning process. The error curve shows the reverse course, i.e. it starts at 100% and ends at 0%.
Three special cases are possible:
Case A: The neural net learns the response patterns and the hit curve reaches 100%, but the net does not generalize, i.e. “it has learned but has not understood anything”. In this case the error curve does not drop and remains near the 50% line.
Case B: If the questions correlate only weakly with the confirmed diagnoses, then learning is not possible and the hit and error curves remain stuck near the 50% line.
Case C: In this desirable case the hit curve increases to 100% and the error curve decreases to 0%, i.e. in this case the neural network has learned, generalized and thus “understood”.