What does "Classification Score" mean?

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The classification score is a measure used to evaluate how well a model can identify different categories or classes within data. In simple terms, it helps to understand how accurately a model can tell one thing from another. For example, if a model looks at pictures of animals, the classification score shows how good it is at recognizing whether a picture is of a cat or a dog.

A higher classification score means that the model is better at making correct guesses, while a lower score indicates that it makes more mistakes. This score is important because it helps in choosing the right pre-trained model for tasks like image classification, ensuring that the model can perform well when looking at new images.