I learned that when creating neural networks the go to was to overfit and then to regularize. However I am now in a situation where, when I make the model more complex (more layers, more filters, ...) my scores become worse.
I am training a CNN to predict pollution 6 hours in advance. The input I give to my model is the pollution of the past 18 hours.
Can I safely say that because there probably is a lot of noise in this data that, that is the reason when increasing my complexity, my model becomes worse?