Our results showed that the largest impact in performance estimates was the application of data augmentation to the test set: resampling_before_clustering and resampling_after_clustering tended to outperform semi_resampling and no_resampling. However, augmenting the test set might not faithfully reflect new data anymore, and could artificially inflate the performance estimates: models may specialize in discriminating between original and resampled data points instead of actives and inactives.