Side-by-side confusion matrix comparison across 5 ML models for fraud detection on highly imbalanced data. Models include Logistic Regression, LOF, Isolation Forest, Random Forest and XGBoost. XGBoost achieved 85 true fraud detections with only 13 false negatives.
