I am using KNIME MLP Neural Network learner (If you are not familiarized with KNIME think of that like a package which implements Neural Network to a set of data).
The thing is you can tune the number of hidden layers, the number of iterations and neurons per layers, so here comes the first question: How to know a priori values for these parameters? For instance, for Random Forest 500 trees is supposed to be a standard to test its performance in the first stages.
Second, I know in general add more layers and/or neurons does not ensure any enhance in the prediction, sometimes even the opposite happen. Someone could give some theoric insights respect this topic; why by adding more layers the prediction might get deteriorated?
Thanks in advance!