I am working on a Data Science project involving financial transactions, and my target variable is extremely skewed. Only 0.1% of the data represents actual fraud. When I train my Random Forest model, the accuracy is 99.9%, but it fails to catch any actual fraud cases. What are the best strategies to handle this imbalance and improve the recall for the minority class?
3 answers
Accuracy is definitely a trap in cases like this. You need to stop looking at accuracy and start focusing on the Precision-Recall curve or the Area Under the Precision-Recall Curve (AUPRC). For fraud detection, I highly recommend using SMOTE (Synthetic Minority Over-sampling Technique) to create synthetic examples of the minority class. However, don't just oversample; you should also try "Cost-Sensitive Learning" by increasing the penalty for misclassifying a fraud case in your loss function. This forces the model to prioritize the minority class during the training phase, which usually leads to much better real-world performance.
Have you tried using Anomaly Detection algorithms like Isolation Forest or One-Class SVM instead of a traditional classifier?
I usually suggest using XGBoost with the scale_pos_weight parameter. It’s a very quick way to tell the model to pay more attention to the positive class.
I agree with Kelly. Adjusting the weight directly in the boosting algorithm is often more efficient than generating synthetic data with SMOTE.
That’s a valid point, Gregory. In scenarios where fraud is this rare, treating it as an outlier detection problem rather than a classification problem can often yield more robust results. I've found that Isolation Forests are particularly good at handling high-dimensional data without requiring the intensive balancing that Random Forests do.