Haha, yeah, using the original dataset for both training and evaluation would be the easy way out. But I guess the exam wants to test our understanding of model validation and making sure we don't overfit the data.
Yeah, I agree with Barbra. C seems like the best option here. Although, I do wonder why we can't just use the original dataset for both training and evaluation. Wouldn't that be simpler?
Hmm, I think the answer might be C. We're evaluating the model, so we should use the testing dataset rather than the training dataset. That way we can see how the model performs on data it hasn't seen before.
I'm not sure about this question. It seems like it's asking about splitting the dataset, but I'm not clear on the difference between features, labels, training, and testing datasets. I'll have to think through this carefully.
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