How do Data Scientists handle imbalanced datasets in classification problems?

All answers to this question.

First, stop using accuracy as your metric. For fraud detection, you should look at Precision, Recall, and the F1-Score. Specifically, the Area Under the Precision-Recall Curve (AUPRC) is much more informative than ROC-AUC for imbalanced sets. Technically, you can use SMOTE (Synthetic Minority Over-sampling Technique) to create synthetic examples, but be careful not to leak information from your test set. Often, using cost-sensitive learning, where you assign a higher weight to the minority class in your algorithm, yields the best results.

   Answered 2024-06-12 by Elizabeth Hall

Have you tried using ensemble methods like Balanced Random Forest or EasyEnsemble which are specifically designed to handle these types of skewed distributions internally?

   Answered 2024-06-18 by Thomas Harris

• Thomas, I haven't tried Balanced Random Forest yet! I was mostly sticking to standard XGBoost. Does the balanced version of the algorithm handle the sampling during the bootstrap phase for each tree? If so, that sounds much more efficient than manually adjusting the dataframe before training. I'll look into the imbalanced-learn library to see how to implement that effectively this week.

     Commented 2024-06-22 by Kevin Moore

Sometimes the best approach is to treat it as an anomaly detection problem rather than a classification problem. Isolation Forests work great for this.

   Answered 2024-06-28 by Susan Young

• That is a smart alternative, Susan. Using an unsupervised approach like Isolation Forests can often find outliers that a supervised model might miss entirely.

     Commented 2024-07-05 by Elizabeth Hall