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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>But presumably that means they are worse so why do we take the last version instead of the one that had the highest accuracy?
You do want the model with the highest accuracy, but you need to be careful of how you calculate “accuracy.” For example, if you’re training some ML model on pictures, but you only train on cat pictures and you test the “accuracy” of your model on even more cat pictures, your model might be great at making predictions on cat pictures, but might have poor performance when all of a sudden you show it a picture of a banana.
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