Imbalanced data means the classes in your dataset are not represented equally. One category has a lot of samples, while the other has very few. For example, imagine a medical dataset where 95% of patients are healthy and only 5% have a rare disease, that’s clearly imbalanced.
The issue is that models trained on such data tend to learn the “easy pattern,” which is predicting the majority class every time. This makes the accuracy look high, but the model is actually useless for detecting the minority class, which is often the most important one.
To handle this, I use techniques like oversampling the minority class (SMOTE), undersampling the majority class, using class-weighted algorithms, or choosing models that naturally handle imbalance better. I also focus more on metrics like F1-score, recall, and precision rather than plain accuracy.
In my experience, dealing with imbalance is less about forcing the data to look “perfect” and more about making sure the model pays attention to what truly matters. A little extra care here can dramatically improve real-world predictions, especially when the minority class carries the real risk or value.
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