MATLAB's Neural Network classifier with False Positives only

Question

I have been trying to design Neural Network but have come into some issues. I am rather newbie. If that's OK, I would like to have some comments on results, I've got from training. This is simple feed forward NN with two hidden layers, two outputs and 13 inputs. The both hidden layers contain 7 neurons.

I have provided a few graphs to show my results from training a classifier. The graphs look slightly different from what I'd expect from a successful training process.

The first graphs is how the training progressed. It seems all right to me but NN stops due to validation error not minimum gradient. I don't know whether it is good news or not. On the second graph, the gradient seems to be oscillating up and down. Does it mean, NN struggled with finding an optimum structure? The ROC graph shows IMO very good results in terms of True Positive ratio and False Positive ratio. I believe higher this value, the better results. But confusion matrix is what I am worrying about the most. It states zero positive detections and all of false positives.

What do you think?

Answer 1

My opinion about your question by part. if to speak about

The first graphs is how the training progressed. It seems all right to me but NN stops due to validation error not minimum gradient. I don't know whether it is good news or not. On the second graph, the gradient seems to be oscillating up and down. Does it mean, NN struggled with finding an optimum structure?

Not necessary. It could mean, that NN found appropriate weights, and then danced around it with jumping out of global minima and then coming back to it closer or further. I can propose you to have some kind of saver of some middle stages during training in order to check later which results for ROC and confusion matrix you can get from middle stages. Some time in my life I had better results, some time worse.

The ROC graph shows IMO very good results in terms of True Positive ratio and False Positive ratio. I believe higher this value, the better results.

Hard to disagree with you on this point.

If to speak about

But confusion matrix is what I am worrying about the most. It states zero positive detections and all of false positives.

That IMHO can be considered also as good results, or even very good results because your classification is quite fine in percentage ( for majority of my cases with which I've work it was desirable result to achieve), but also confusing.