And if you want to increase accuracy you just add more tests

I wrote an ai that classifies spam emails with 99.9% accuracy.

Our test set contained 1000 emails, 999 aren't spam.

The algorithm:

All odd numbers are prime: 1 is prime, 3 is prime, 5 is prime, 7 is prime, 9 is experimental error, 11 is prime, and so on, I don't have funding to check all of them, but it suggests an avenue of productive further work.

It's been a fat minute since I last did any programming outside of batch scripts and AHK... I'm struggling to understand how it's not returning false for 100% of the tests

How many primes are there before 1 and 2^31. IIRC prime numbers get more and more rare as the number increases. I wouldn't be surprised if this would pass 99% of tests if tested with all positive 32 bit integers.

It even passes over 100% of tests!

Edit: I can't read floats.

Ah yes, my favorite recurring lemmy post! It even has the same incorrect test output.

Last time I saw this I did a few calculations based on comments people made:
https://l.sw0.com/comment/32691 (when are we going to be able to link to comments across instances?)

• There are 9592 prime numbers less than 100,000. Assuming the test suite only tests numbers 1-99999, the accuracy should actually be only 90.408%, not 95.121%
• The 1 trillionth prime number is 29,996,224,275,833. This would mean even the first 29 trillion primes would only get you to 96.667% accuracy.

In response to the question of how long it would take to round up to 100%:

• The density of primes can be approximated using the Prime Number Theorem: 1/ln(x). Solving 99.9995 = 100 - 100 / ln(x) for x gives e^200000 or 7.88 × 10^86858. In other words, the universe will end before any current computer could check that many numbers.