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2 cards from this section. Quick, good the night before a test.
What does accuracy measure in AI models?
Think about the total predictions versus correct predictions.
Define precision in terms of a prediction model.
Focus on how correct positives are out of all predicted positives.
What is accuracy?
Remember, it's a straightforward measure!
True or False: The F1 score is used when you only care about recall.
Think about why balance is important.
An AI model predicts whether an email is spam or not. If it correctly identifies 80 out of 100 spam emails, but also classifies 10 legitimate ones as spam, what are the precision and recall?
Remember the formulas for each.
If a model has 90% accuracy but only identifies 50% of actual positive cases, why might this be problematic?
Consider the implications in real-world scenarios.
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