Only 1% of examples belong to the positive class. A small random split leaves almost no positives in validation. What should the data engineer evaluate?

1. Move all positive examples into training and none into evaluation.
2. Measure only majority-class accuracy and ignore the missing positives.
3. A stratified split that preserves class representation where compatible with temporal and group constraints.
4. Duplicate validation positives into training to improve scores.