I'm working on classification of
delay / (no delay) / negative delay for airplanes ( 2 or 3 labels). I'm also playing with idea to model no delay, delay and large delay.
I noticed that there are cases where I can quite well train
neural network form scikit that it makes little mistake for either delay or negative delay in the training set. From my little experience my gut is telling me it is nonsense to use two/three models for each label. What if they disagree on the label? Could I then pick the one with highest probability or something?
I dont think it is a good approach, but cannot find any articles explaining it.
I'd like to check, is it non-sense to have multiple models in multi-label case or not? If yes or no, why?