You built a spam email classifier with these results: Pr…
Questions
Yоu built а spаm emаil classifier with these results: Pred: Nоt Spam Pred: Spam Actual: Nоt Spam 180 20 Actual: Spam 10 90 Answer: Calculate the accuracy of the model. Calculate the precision and recall for detecting spam. In this context, is it worse to have a false positive (legitimate email marked as spam) or false negative (spam not caught)? Explain why and how you might adjust the model NOTE: It is sufficient to show the proper formula with values. You do not need to calculate the actual result.
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