False Positive Rate (FPR) is a metric in binary classification that measures the proportion of negative instances that are incorrectly classified as positive. It is defined as FPR = FP / (FP + TN), where FP is the number of false positives and TN is the number of true negatives. It’s also known as the fall-out. The FPR is basically 1 – specificity. In the context of a ROC (Receiver Operating Characteristic) curve, FPR is plotted on the X-axis (with TPR or recall on the Y-axis) to show the trade-off between catching positives and avoiding false alarms. For example, in a medical test for a disease, a false positive is a healthy person diagnosed as sick; the FPR then is the fraction of healthy people that wrongly test positive. Managing FPR is critical in applications where false alarms have a cost (like flagging legitimate transactions as fraud).