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Answer: Recall is the proportion of true positive predictions out of all positive instances, while F1 is the harmonic mean of precision and recall. In a scenario where the cost of false negatives is high, you would choose to use F1 over recall.
Recall is the proportion of true positive predictions out of all positive instances, which measures the ability of the model to find all relevant instances. F1 is the harmonic mean of precision and recall, which balances the trade-off between precision and recall. In a scenario where the cost of false negatives is high, such as in medical diagnosis, you would choose to use F1 over recall because F1 gives more importance to recall, reducing the impact of false negatives.
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In the context of binary classification, recall and F1 are commonly used evaluation metrics. Explain the difference between recall and F1, and provide a scenario where you would choose to use recall over F1 or vice versa.
A
Recall is the proportion of true positive predictions out of all positive instances, while F1 is the harmonic mean of precision and recall. In a scenario where the cost of false positives is high, you would choose to use recall over F1.
B
Recall is the proportion of true positive predictions out of all positive instances, while F1 is the harmonic mean of precision and recall. In a scenario where the cost of false negatives is high, you would choose to use F1 over recall.
C
Recall is the proportion of true negative predictions out of all negative instances, while F1 is the harmonic mean of precision and recall. In a scenario where the cost of false positives is high, you would choose to use recall over F1.
D
Recall is the proportion of true negative predictions out of all negative instances, while F1 is the harmonic mean of precision and recall. In a scenario where the cost of false negatives is high, you would choose to use F1 over recall.
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