Confusion Matrix

Category: science

A performance measurement tool for ML classification problems, showing true positives, true negatives, false positives, and false negatives.

The matrix is the "accountability report." It shows exactly where the AI is making mistakes. If the model is flagging "Legitimate" claims as "Fraudulent" (False Positives), the matrix makes that error clear so you can refine the model’s thresholds.

Common Examples

• The confusion matrix revealed that our fraud-detection engine was being too aggressive, resulting in a 15% false positive rate.
• After reviewing the confusion matrix, we recalibrated the decision threshold to balance the precision-recall trade-off.