A fraud classifier is evaluated on 10,000 transactions containing 20 fraud cases. It predicts every transaction as legitimate. Why is accuracy misleading here?

1. The number of transactions makes false negatives impossible
2. The classifier has perfect fraud recall
3. Accuracy can only be calculated for balanced datasets
4. The majority class dominates accuracy while fraud recall is zero