A false negative happens when the classifier misses the true category. For instance, labeling a return from a 'damaged in shipping' situation as 'customer damage.' False negatives hide true return drivers, potentially causing merchants to overlook critical issues. Balancing false positive and false negative rates depends on whether catching all cases or minimizing irrelevant alerts is more important.
← Glossary
False Negative
When the AI fails to identify the correct return reason and assigns an incorrect one.
Related terms
- False Positive — When the AI incorrectly assigns a return reason that doesn't match the actual reason.
- Confidence Score — A number between 0 and 1 indicating how certain the AI model is about its classification decision.
- Model Accuracy — The percentage of classifications the AI model gets correct across all categories.
- AI Classifier — A machine learning model that automatically categorizes inputs—like return reasons or customer feedback—into predefined groups.