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 isn’t about making the data look flawless, it’s about guiding the model to focus on the signals that actually matter. With a bit of extra care, the model’s real-world performance improves a lot, especially when the minority class is the critical one. This is why data scientists often spend extra time tuning these scenarios instead of relying on raw accuracy.
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