Answer-first summary for fast verification
Answer: The F1 score should be favoured over accuracy when there is a substantial imbalance between the positive and negative classes and minimizing false negatives is important.
The F1 score is particularly valuable in scenarios with significant class imbalance, where minimizing false negatives is crucial. It harmonizes precision and recall, focusing on both false negatives (low recall) and false positives (low precision). This makes the F1 score more appropriate than accuracy in critical applications like medical diagnosis or fraud detection, where the consequences of false negatives are severe. The other options do not accurately reflect the advantages of the F1 score over accuracy in such contexts.
Author: LeetQuiz Editorial Team
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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 target variable comprises precisely two classes.
C
The F1 score is preferable over accuracy when correctly identifying true positives and true negatives is equally critical to the business problem.
D
The F1 score is recommended over accuracy when the number of actual positive instances is equal to the number of actual negative instances.
E
The F1 score should be favoured over accuracy when there is a substantial imbalance between the positive and negative classes and minimizing false negatives is important.
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