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4 cards from this section. Quick, good the night before a test.
Define accuracy in the context of model evaluation.
Think about how you would measure performance overall.
What does precision measure?
Focus on what percentage of positive predictions were actually correct.
What is the primary focus of precision in model evaluation?
Think about how many of the positive predictions are right.
True or False: Recall is more important than precision when missing a positive case has severe consequences.
Consider examples like disease detection.
You have a predictive model that classifies emails as spam or not. Out of 100 emails, 40 were identified as spam, but 10 of these were not spam. Also, there are 20 spam emails that were not identified by the model. Calculate the accuracy, precision, recall, and F1 score of the model.
Apply the formulas for accuracy, precision, and recall to find the F1 score.
In a given dataset, you are asked to evaluate two models. Model A yields a precision of 90% and a recall of 60%. Model B presents with precision of 80% and recall of 80%. Which model would you recommend, and why?
Evaluate if you prioritize correctness in alerts or capturing all relevant cases.
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