I'm working on a credit card fraud detection model where only 0.1% of the transactions are actually fraudulent. My model has 99.9% accuracy, but it's failing to catch any actual fraud! What are the best techniques for handling this kind of class imbalance so the model actually learns the minority class?
3 answers
This is a classic "Accuracy Paradox." In fraud detection, accuracy is a useless metric because the model can just predict "not fraud" every time and be right 99.9% of the time. You need to look at Precision, Recall, and the F1-Score instead. To fix the data itself, try SMOTE (Synthetic Minority Over-sampling Technique), which creates "fake" minority samples to balance the classes. Alternatively, you can use "cost-sensitive learning" where you penalize the model more heavily for missing a fraud case than for a false alarm. These methods force the algorithm to pay attention to the rare cases.
Cynthia, between over-sampling and under-sampling, which one do you find leads to less "overfitting" in a production environment?
You should also try the Precision-Recall curve instead of the ROC curve. It gives a much better picture of performance on imbalanced data.
Spot on, Heather. The PR curve focuses specifically on the minority class, which is exactly what Aaron needs for his fraud detection model.
Patrick, that’s a sharp question. Over-sampling (like SMOTE) carries a higher risk of overfitting because you're creating synthetic data. Under-sampling is safer for overfitting but you risk throwing away valuable information from the majority class. Personally, I prefer using ensemble methods like Balanced Random Forest. These algorithms handle the sampling internally across different "trees," which usually results in a much more robust model that generalizes well to new, unseen transactions.