Model Evaluation Metrics

Unlike many other machine learning problems, fraud detection cannot rely only on accuracy.

Several evaluation metrics are used to assess model performance.

Accuracy

Measures the proportion of correctly classified transactions.

Precision

Indicates how many transactions predicted as fraudulent were actually fraudulent.

High precision reduces false alarms.

Recall

Measures how many actual fraudulent transactions were successfully detected.

High recall is extremely important because missing fraudulent transactions can result in financial loss.

F1-Score

The harmonic mean of precision and recall.

It provides a balanced evaluation when working with imbalanced datasets.

ROC-AUC Score

Measures the model's ability to distinguish between genuine and fraudulent transactions across different probability thresholds.

These metrics provide a much clearer understanding of fraud detection performance than accuracy alone.