I'm currently working on several Machine Learning Projects involving fraud detection, but I’m struggling with extremely imbalanced datasets. The model keeps ignoring the minority class because it's so small. Should I focus more on oversampling techniques like SMOTE, or is it better to adjust the cost function? I want to make sure my final project is accurate and reliable.
3 answers
Dealing with class imbalance is a rite of passage for anyone working on serious Machine Learning Projects. While SMOTE is a popular starting point, it can sometimes lead to overfitting because it creates synthetic examples based on existing noise. I’ve found that using cost-sensitive learning—where you penalize the model more for misclassifying the minority class—often yields more robust results in production. Also, make sure you aren't using "Accuracy" as your primary metric; look at the F1-Score or the Area Under the Precision-Recall Curve (AUPRC) to get the real picture of performance.
Have you tried using Ensemble methods like Balanced Random Forest to see if that handles the skewness better?
In my experience with Machine Learning Projects, simple undersampling of the majority class often works surprisingly well if you have enough data to spare.
Olivia makes a valid point. Sometimes we overcomplicate Machine Learning Projects with synthetic data when a simpler data-wrangling step can solve the bias issue without adding unnecessary noise.
Tyler, I actually tried a standard Random Forest and it failed miserably, but I haven't looked into the "Balanced" variant yet. Does that handle the under-sampling internally during the bootstrap process? I'm worried about losing too much information if I just manually remove the majority class samples, so an automated ensemble approach sounds like a much cleaner way to keep the data integrity intact for this project.