In a binary decision, the positive result means the condition being tested is present. A false positive occurs when the system reports that result for an example whose correct label is negative. Its practical cost depends on the task.
Changing a decision threshold can alter the balance between false positives and false negatives, which are missed positive cases. A useful comparison considers both kinds of mistake rather than assuming one threshold suits every application.
ELI5
A false positive is a mistaken yes. The system says it found what it was looking for, but it did not.
For example, a filter might label an ordinary receipt as spam. That is a false positive for the spam category. Missing a real spam message would be the opposite kind of mistake, a false negative.
Is a false positive always harmful?
Its importance depends on the task. It might waste review time, hide a useful message or trigger an unnecessary response.
Can reducing false positives create other errors?
Yes. A stricter threshold can reduce false positives while causing the system to miss more genuine positive cases.
