A binary classifier reports 99% accuracy on a dataset with 1% positives but predicts every example as negative. Which conclusion is correct?

1. More negative test examples prove that positive detection works.
2. The classifier is necessarily production-ready because accuracy exceeds 95%.
3. The model has perfect positive-class recall.
4. Accuracy hides the failure; inspect positive-class precision, recall, and the confusion matrix.