[Rachael_F]

I still don’t see how the question has enough information to decide between two of the answers. If the company is getting feedback from customers like “I lost my life savings to a scam that your spam filter didn’t catch, I’m leaving your company and getting a gmail account” and also like “I missed out on my dream job because your spam filter redirected a genuine job offer to my spam folder. I’m never using your company again, this never happened when I used gmail!” then the business goal might be to develop a spam filter that is as satisfactory to customers as gmail’s spam filter, with specific metrics (I’m making these numbers up) of at least 90% of spam are detected, and at least 99% of genuine emails are allowed to pass to customer’s inboxes. If the business goal requires reducing both false positives and false negatives relative to the current model, then tweaking the threshold won’t work. Tweaking the threshold can’t reduce both false positives and false negatives, it can only improve one at the cost of the other. I still think we need more information to answer this question.

That is assuming that I understand what “Adjust the score threshold to tune the model performance” means. I’m assuming it means changing the probability threshold for the prediction. In other words, if the model currently predicts anything with probability(spam) >.5 as spam, “adjust the score threshold” would mean change that so that the model predicts anything with probability(spam) >.4 (or any other value) as spam. That would make the model better at catching real spam, but it will also direct more genuine emails to the spam filter. If “Adjust the score threshold to tune the model performance” means something different, please explain it to me!

Thanks.