Rare-event oversampling is proposed for a classifier. Which implementation best preserves credible evaluation?

1. Oversample before splitting so synthetic relatives appear in training and test.
2. Remove all negative examples from training and evaluation.
3. Apply the sampling strategy to training only and evaluate on an independent representative distribution.
4. Change the test class ratio to 50/50 and report it as the production prevalence.