All ML classifiers (and algorithms) have a non-zero false positive rate. Having an error rate is baked into every ML classifier and algorithm. And its always non-zero in practice. In fact, hitting every test in some sort of test suite is likely a sign of a less accurate classifier, not a more accurate one.

Well no shit, but that doesn't mean you just trust them just because they say it works. The cost of a false accusation can be very high.

Can you give some examples of Pangram false positives? Ideally ones from before 2024, or otherwise ones from notable writers who started writing before 2024.