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When evaluating classification models, under what circumstances should the F1 score be preferred over accuracy? The F1 score is calculated as: F1 = 2 * (precision * recall) / (precision + recall).
A
The F1 score is more suitable than accuracy when the target variable has more than two categories.
B
The F1 score is recommended over accuracy when the number of actual positive instances is equal to the number of actual negative instances.
C
The F1 score should be favored over accuracy when there is a substantial imbalance between the positive and negative classes and minimizing false negatives is important.
D
The F1 score is recommended over accuracy when the target variable comprises precisely two classes.
E
The F1 score is preferable over accuracy when correctly identifying true positives and true negatives is equally critical to the business problem.
