C) Improvement in accuracy during training? Ah, the classic 'bettering yourself' approach. I wonder if the model gets a gold star at the end of the day.
B) The percentage of incorrect predictions? Sounds like a good old-fashioned way to measure how well the model is doing. Gotta keep it simple, am I right?
A) Ah, the good old 'Loss' metric - it's like a secret language that only the AI wizards can understand. I'd say it's the difference between the model's accuracy at the start and end of training, like a grand reveal of how much it's learned.
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