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3 cards from this section. Quick, good the night before a test.
What does precision measure in machine learning?
Recall the formula for precision.
Calculate the precision if there are 40 true positives and 10 false positives.
Use the precision formula for calculation.
What does precision indicate in an AI model?
Think about what portion of predicted positives are correct.
True or False: A higher precision is always better.
Consider impact on real-world applications.
A model predicts 200 instances as positive, of which 150 are actual positives, but 50 are false positives. If the model is used in a fraud detection scenario, discuss the implications of this precision level.
Consider the potential consequences of false positives and customer trust.
In a different model where high precision is maintained over time, what could you suggest as maintenance practices to ensure this precision stays high?
Think about how real-world changes can affect model performance.
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