It's a big topic. But 5/12 false positives tells you nothing about accuracy. You also need to measure the number of true negatives and false negatives and what not. Accuracy tends to be worthless for most use cases when the event being predicted is rare. If you're predicting a 1 in a million event always predicting NO gives you a 99.9999% accurate model.

Many medical tests are 99%+ accurate, but the false positive rate, meaning the probability that you don't have the disease given a false positive test is still extremely high. Like 90%+. Bayes law fucks us all.