Its pretty common for an ML model to lose accuracy at various points during training. But presumably that means they are worse so why do we take the last version instead of the one that had the highest accuracy?
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Hopefully someone more qualified to answer this comes by, but my understanding is that more training with lower accuracy is better simply because it likely has a better set of parameters to work from.
Imagine a ML model that tries to determine if a number is prime without doing a sieve. And your random number generator keeps accidentally churning out multiples of 2, 3 and 5. The model tries some approach but lands on “always false” as one solution and it keeps working…until it lands on a real prime and fails. Now, do you want to stop at 100% accuracy because “all numbers are probably not prime” or do you want to keep training it on data to see if it comes up with a better solution?
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