Greetings. Say you have a model with 70% accuracy for your use case:
- What are you supposed to do with the other 30%?
- What can you say to customers that got the 30%?
- I suppose every industry and every company will have their own standards, but among actual data science professionals, is this percentage ‘good enough’ to take into production?
- Perhaps that score can be compared to humans performing the same real world task. How much better than humans should it be, and at what cost in pre-training, domain adaptation, and task finetuning compared to the cost of wages & benefits?
I never see anyone talking about these issues in any detail or with any real world experience to back it up. Instead, I see a lot of “who’s biggest” flexing, which is already tiresome. Thanks for sharing your insight.