Pretty new to machine learning and would like to know what is the difference in model accuracy between using single multi-output NN and multiple single-output NNs all used in tandem (OvA and OvO)?
Eg. Say there is a problem where there is a set of input medical diagnosis codes
Xd with sample vectors like
[dx1, ..., dx5]
and a set of "correct" medical diagnosis codes that the the input codes commonly need to be changed to
Yd with sample vectors also like
[dy1, ..., dy5]
and I'd like to train a NN to predict all of the correct diagnoses.
The way I see it, there are two options, a single NN with multiple outputs each trained on one of the different elements of the answer vectors
yd OR using multiple NN models all used in tandem each with one output trained on one of the different elements of the answer vectors
In my inexperienced view, the single multi-output NN would be better since the inputs and back-propagations have more interaction with each other, but I really don't know. Could somebody help me out and explain the differences, if any? Thanks.