Answer-first summary for fast verification
Answer: 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.
The F1 score is particularly useful in scenarios with significant class imbalance, where reducing false negatives is crucial. It balances precision and recall, making it ideal for situations like medical diagnosis or fraud detection, where the cost of missing a positive case (false negative) is high. Accuracy, on the other hand, is more appropriate when the costs of false positives and false negatives are similar.
Author: LeetQuiz Editorial Team
Ultimate access to all questions.
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.
No comments yet.