A classifier predicts the majority class for 99% of examples and reports 99% accuracy on a dataset with 1% fraud. Which evaluation is most useful next?

1. The number of features alone
2. Fraud-class precision, recall, and an appropriate precision-recall analysis
3. The artifact size alone
4. Accuracy alone with additional decimal places