[goking]

There’s no loss in false positives as the company still gains money for false negative predictions.

That is true, but the question isn’t formulated like that. “The model has high accuracy on the training dataset but low on the test dataset. ” So i will give more extreme example ->

9990 negative and 10 positive samples. The model can simply predict all to be negative (99.9 accurate) and have 10 false negatives. But if the test data is 5000 positive and 5000 negative the accuracy will fall to 50%. In that case, we should minimize FN and should be more tolerant of false positives.(Even if it guessed positive and it is not – it is ok,because real data might contain much much more)

“Tweak the cost function in such a way that the impact of false negatives on cost value is higher than false positives.” – I undestand this as mininimze the false negatives.

The current example is not so extreme but clearly much more negatives than positives.