When I deal with class imbalance in a dataset, the first thing I do is understand how severe the imbalance is, by checking class distributions and looking at metrics like the ratio of minority to majority classes. Once I know the extent, I choose the best strategy based on the problem and data size.
If the dataset is small, I often use resampling techniques, like oversampling the minority class with methods such as SMOTE or undersampling the majority class to balance the data. When the dataset is large, I prefer using class weights in algorithms like logistic regression, random forests, or XGBoost so the model gives more importance to the minority class without losing information.
I also make sure to use evaluation metrics that work well with imbalanced data, like precision, recall, F1-score, or the AUC-ROC curve, instead of just accuracy, which can be misleading.
In some cases, data scientists like me experiment with ensemble methods or anomaly detection approaches when the imbalance is extreme. The goal is always to help the model learn meaningful patterns rather than being biased toward the majority class.
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