I'm working on a task that even a 0.00001 fp rate is not acceptable, because detecting something as a positive when its not will have very bad consequences in this task, so it needs to be exactly 0 in my dataset when i use k fold, so 0 for each fold. basically my model should at least learn all the negative samples in my own dataset very well and never classify them as a positive by mistake.
but what is the best way of doing this?
so far two things came to my mind but please let me know if there is a better method :
Giving positive samples a very large weight during training
Data augmentation of positive samples, so making the positive dataset 100 time bigger or something
to sum up the question :
You are giving a binary classification task with enough balanced data, and are asked to train a deep neural model with 0 false positive rate on the given dataset, how will you do it? (input dim is around 1k-3k)