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3 cards from this section. Quick, good the night before a test.
Define precision in the context of model evaluation.
Think about what it means for a model to predict a positive outcome.
What is the formula for calculating precision?
Look for the terms used in the definition.
What does precision measure in a model?
Remember, it focuses only on the positives.
False positives contribute to which evaluation metric?
Consider how inaccurate predictions impact metrics.
You developed a model that predicts a health condition. If it correctly identifies 50 out of 70 actual cases as positive but wrongly identifies 20 healthy individuals as positive, calculate the model's precision and discuss the implications of this result.
Use the precision formula to calculate.
If a model has a high precision rate but a low recall rate, what might this indicate about the model's performance? Give an example of what this could look like in a real-world context.
Think about how precision and recall interact with one another.
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