Whenyou apply oversampling, the minority class is expanded to match the size of the majority class. Here’s how it works I understand neither what problem unbalanced classes pose, nor how oversampling is supposed to address these problems. In my opinion, unbalanced data do not pose a problem at all. One should model class membership probabilities, and these may be small. Dataoversampling is a technique applied to generate data in such a way that it resembles the underlying distribution of the real data. 7 SMOTE Variations for Oversampling Image by Author. The imbalanced dataset is a problem in data science. The problem happens because imbalance often leads to modeling performance issues. To mitigate the imbalance problem, we can use the oversampling method. Know your SMOTE ways to oversampledyourdata. Cornellius Yudha Wijaya's avatar.Youdon’t want the prediction model to ignore the minority class, right? That is why there are techniques to overcome the imbalance problem — Undersampling and Oversampling. When I think of "bloated" in this context it means the star is much bigger than it ought to be given the conditions. Usually a problem with focus. The issue with this extreme oversampling is that the star simply has a very large diameter, measured in pixels.
