Hi @KFrank!
First of all, thank you for your reply.
The thing that was not allowing me to interprete my problem as a classification one was that my threshold is not 0.5, but definitely smaller (let’s suppose 0.1)
But thanks to your words I have understand the right point of view.
Can I ask you how, since my classes are unbalanced, can I train my NN? When I have a label equal to 0.9 for example - or any value greater than the threshold - should I round it to 1? Or should I differently weight somehow the 2 terms belonging to the BCELoss?
The fact is that if I have a label equal to 0.5, the BCE Loss will not decrease to zero by its definition of I leave the label unchanged. But also, I think that I should differentiate a label equal to 0.5 with respect to one equal to 0.9