I have 50,000 DNA samples from a biobank. Only 2,200 samples have the label (disease) that I want to work with for supervised learning in a neural network.
So my question is, how many of those non-diseased samples do I need to include in my training data? If the diseased samples are my cases, how big should my control group be? Twice as big? The same size? What would a good ratio be?
The feature space is insanely big, so I am worried about training times. It feels unnecessary to include all the other 48,000 controls.
And yes, yes I know it will depend on the accuracy that I am seeing in the model, but what’s a good starting ratio?