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A team is developing guidelines on when to use various evaluation metrics for classification problems. The team needs to provide input on when to use the F1 score over accuracy. Which of the following suggestions should the team include in their guidelines?
Correct Answer: C
The F1 score is the harmonic mean of precision and recall and is particularly useful in situations where there is a significant imbalance between positive and negative classes. When there is a class imbalance, accuracy can be misleading because a model can achieve high accuracy by simply predicting the majority class. The F1 score, however, provides a better measure of the test's accuracy in terms of both false positives and false negatives. Specifically, the F1 score should be used over accuracy when: There is a significant imbalance between positive and negative classes. Avoiding false negatives is a priority, meaning recall (the ability to detect all positive instances) is crucial. In this scenario, the F1 score balances both precision (the ability to avoid false positives) and recall, providing a more meaningful measure of a model's performance under these conditions. Reference: Databricks documentation on classification metrics: Classification Metrics
