IMO this technique is often used when people are confused about binary model outputs. Many data scientists think a model *needs* to only return 0/1's for predictions, when they should have the model return probabilities, and then they use those probabilities in downstream decision making. A common mistake is to not take the model output from SMOTE and re-calibrate to actual proportions in data, https://andrewpwheeler.com/2020/07/04/adjusting-predicted-probabilities-for-sampling/.