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4 cards from this section. Quick, good the night before a test.
What does accuracy measure in an AI model?
Think about what it means to be correct in predictions.
What is precision used for?
Focus on the positive predictions.
What does accuracy measure?
Accuracy looks at the total predictions.
Is a precision of 0.9 always better than a recall of 0.6?
Consider the impact of missing actual positives.
A spam detection model identifies 70 spam emails correctly out of 100 total emails, but also wrongly marks 10 non-spam emails as spam. Calculate its accuracy, precision, recall, and F1 score, and discuss the implications of the results.
Remember to use the appropriate formulas for each metric.
Consider an AI model in a medical field. If it shows high precision but low recall, what would you conclude about it? Discuss possible recommendation adjustments.
Think about what this means for patient diagnosis.
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