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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.
