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4 cards from this section. Quick, good the night before a test.
What does recall measure?
Think about what happens in terms of True Positives.
What is the formula for recall?
Recall involves True Positives and places them in relation with False Negatives.
What does recall specifically measure in a model evaluation?
Think of how well the model detects true positives.
True or False: Recall can be negatively impacted in imbalanced datasets.
Consider how classes are represented in the dataset.
A model identifies 100 true positives out of 150 actual positives, while also mistakenly labeling 20 negatives as positives. Calculate recall, and discuss why it might be crucial to improve this number in a healthcare setting.
Use the recall formula and think about its implications in healthcare.
Discuss the trade-offs between precision and recall in fraud detection. How might a focus on improving recall impact precision?
Reflect on the balance these metrics represent and the context of their application.
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