Resampling in Supervised Fraud Detection Models

This workflow shows the performances of four different decision tree models for fraud detection, each one trained on a different training set. One training set represents the original data with highly unbalanced target classes. The three others have been resampled to make the target classes balanced. The resampling methods that we demonstrate in this workflow are SMOTE oversampling, Bootstrap oversampling, and Bootstrap undersampling.

External resources

• Scoring Metrics eBook
• Resampling Imbalanced Data and Its Limits