What are false positives?
False positives are the cases a classifier labels as positive when they are actually negative. A fraud model that declines a legitimate purchase, a spam filter that quarantines a real customer email, and an inspection camera that rejects a good part have all produced false positives. In statistics the same mistake is called a Type I error. False positives occupy one cell of the confusion matrix and feed two metrics: precision, which falls as they rise, and the false positive rate, which measures them against all actual negatives.
Why false positives matter
False positives are the visible cost of a model. Each one usually triggers human work, a blocked transaction, or an annoyed customer. A regional retailer that switched on a card-fraud model saw declined transactions jump on launch day. Most of the declines were regular customers making ordinary purchases at a new store location the model had never seen, and store managers asked to turn the model off within a week.
The fix is a business decision as much as a technical one. The team must decide how many false positives the review process and the customer base can absorb, then set the confidence threshold to match. Every reduction in false positives is paid for with more false negatives, so the two must be weighed together. Tracking false positives by segment, such as store, card type, or time of day, usually reveals a few concentrated causes that better features or more representative training data can fix.