Use Case Fraud Detection Pipeline

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Use Case: Fraud Detection Pipeline

Complete fraud detection: diagnose imbalance → SMOTE → model comparison → confusion matrix + ROC/AUC.

Pipeline Steps

1. Diagnose imbalance with class_distribution — 99.5% legit, 0.5% fraud
2. SMOTE to balance training data — Smote::new(5, 42).fit_resample()
3. Compare models: Logistic Regression vs Random Forest vs Gradient Boosting
4. Evaluate: ConfusionMatrix, precision/recall/F1 (recall is king for fraud)
5. ROC/AUC: threshold-independent ranking evaluation
6. Cross-validate: 5-fold stratified for reliability
7. With vs Without SMOTE: demonstrate recall improvement

Key Lessons

Lesson	Why
Accuracy lies	99.5% accuracy = 0% fraud caught
Recall is king	Missing fraud costs money
SMOTE train only	Never contaminate test data
AUC > accuracy	Threshold-independent evaluation

Algorithms Used

Algorithm	ix Crate	Role
SMOTE	ix-supervised	Balance data
Gradient Boosting	ix-ensemble	Primary classifier
Random Forest	ix-ensemble	Comparison
Confusion Matrix + ROC/AUC	ix-supervised	Evaluation
Cross-validation	ix-supervised	Reliability

Related

• Full tutorial
• SMOTE docs