If a classifier predicts the positive class perfectly but struggles with the negative class, the ________ might still be high due to the imbalance.
- AUC-ROC
- Accuracy
- F1 Score
- True Positive Rate
If a classifier predicts the positive class perfectly but struggles with the negative class, the Accuracy might still be high due to class imbalance. Accuracy can be misleading in imbalanced datasets because it doesn't account for the unequal distribution of classes. F1 Score and AUC-ROC are more robust metrics in such cases.
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