Why when training ML models do we not take the model with the highest accuracy

Question

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?

In: 1

Answer 1

Suppose you trained to run on a particular path. Suppose you trained to be 100% proficient at running on that particular path maybe for a specific race on that path. However, now suppose as a consequence that your running skills weren’t transferable to running anywhere else. That’s what 100% accuracy would entail while training a model. If you wanted to be able to run on any other path, you would have to completely retrain from scratch, crawling on all fours.