Classifying Output of a Network

Question

I made a network that predicts either 1 or 0. I'm now working on the ROC Curve of that network where I have to find the TN, FN, TP, FP. When the output of my network is >= 0.5 with desired output of 1, I classified it under True Positive. And when it's >=0.5 with desired output of 0, I classified it under False Positive. Is that the right thing to do? Just wanna make sure if my understanding is correct.

Answer 1

It all depends on how you are using your network as the True/False Positive/Negative is just a form of analysing results of your classification, not the internals of the network. From what you have written I assume, that you have a network with one output node, which can yield values in the [0,1]. If you use your model in the way, that if this value is bigger then 0.5 then you assume the 1 output and 0 otherwise, then yes, you are correct. In general, you should consider what is the "interpretation" of your output and simply use the definition of TP, FN, etc. which can be summarized as follows:

         your network
truth      1     0
    1     TP    FN
    0     FP    TN

I refered to "interpretation" as in fact you are always using some function g( output ), which returns the predicted class number. In your case, it is simply g( output ) = 1 iff output >= 0.5. but in multi class problem it would be probably g( output ) = argmax( output ), yet it does not have to, in particular - what about "draws" (when two or more neurons have the same value). For calculating True/False Positives/Negatives you should always only consider the final classification. And as a result, you are measuring the quality of the model, learning process as well as this "interpretation" g.

It should also be noted, that concept of "positive" and "negative" class is often ambiguous. In problems like detection of some object/event it is quite clear, that "occurence" is a positive event and "lack of" is negative, but in many others - like for example gender classification there is no clear interpretation. In such cases one should carefully choose used metrics, as some of them are biased towards positive (or negative) examples (for example precision do not consider neither true nor false negatives).