Tolerance False Positive Rate

For an anomaly detection skill there are two possible test outcomes: a positive outcome (the image is correctly classified as an anomaly) and a negative outcome (the image is not an anomaly but was predicted as one). This negative outcome is referred to as a False Positive. Tolerance False Positive Rate (for a skill) = (False Positive Images/Total Validation Images) * 100 It is expressed as a percentage.