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MACHINE LEARNING · WEEK 4
Model Evaluation
42:18
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LECTURE OVERVIEW
What you need to know
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Training accuracy alone cannot tell us how well a model will perform on unseen data.
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Validation data helps compare models before the final test evaluation.
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The right evaluation metric depends on the actual problem being solved.
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Why can't we just use training accuracy?
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Training accuracy measures performance on examples the model has already seen. In this lecture, your lecturer explains that unseen validation or test data is needed to estimate how well the model generalises.
24:16
“...what we really care about is whether this carries over to data the model hasn't seen before.”
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