Genevera discusses the advantages of using randomized noise over traditional data splitting for model training and testing. She highlights how repeated randomization can reveal the robustness of discoveries, suggesting that consistent findings across different samples indicate strong signals likely to generalize well. While both methods share similarities, the challenge lies in effectively measuring the generalizability of discoveries in data splitting compared to the stability principle.
