I would like (to try) to train a NN to predict the outcome, given the initial condition.
For simplicity lets assume there are 100 input parameters which can cause either OutcomeA or OutcomeB.
Because the OutcomeB is very rare the available data is very uneven. The labeled training data contains 1,000 times more occurrences of OutcomeA than OutcomeA.
Since just guessing OutcomeA will already give a 99.9% success rate I am wondering how to adapt the training in order to compensate for the over representation of OutcomeA.
Is there a better way than to just randomly pic one OutcomeA for every OutcomeB?
I found a lot about stratifying when using train-test-splitting the data, but not the opposite.