How do I handle imbalanced datasets in classification problems for Data Science?

All answers to this question.

First, stop using Accuracy as your metric; it’s completely misleading for imbalanced data. You should focus on the Precision-Recall curve and the Area Under the Precision-Recall Curve (AUPRC). To address the data itself, you can use SMOTE (Synthetic Minority Over-sampling Technique) to create synthetic examples of the minority class, or try cost-sensitive learning by increasing the penalty for misclassifying a fraud case. In my last project, we used an Ensemble approach with Balanced Random Forest, which specifically samples the majority class to match the minority class in each bootstrap sample. This led to a much better F1-score than standard algorithms.

   Answered 2025-08-25 by Jennifer Garcia

Is it better to use under-sampling of the majority class or over-sampling of the minority class in a dataset this skewed? Does under-sampling run the risk of losing too much valuable information from the 99.5% of normal transactions?

   Answered 2025-08-27 by Richard Moore

• Richard, that’s exactly the trade-off. Under-sampling is faster but risky if your dataset is small. For 0.5% fraud, I prefer a hybrid approach: SMOTE for slight over-sampling and then a light under-sampling of the majority class. This balances the data without creating a massive, redundant dataset that slows down training.

     Commented 2025-08-28 by David Anderson

Try using Anomaly Detection algorithms like Isolation Forest or One-Class SVM instead of standard classification. Sometimes it's easier to find "the weird stuff" than to classify a rare event.

   Answered 2025-08-29 by Nancy Hall

• Great point, Nancy. When the classes are that imbalanced, treating it as an outlier detection problem often yields much more robust results than trying to force a binary classifier to learn.

     Commented 2025-08-30 by Jennifer Garcia