Statistics
What is a false positive?
An example in which the model mistakenly predicted the positive class.
Card 1 of 259. Answer: An example in which the model mistakenly predicted the positive class.
An example in which the model mistakenly predicted the positive class.
Card 1 of 259. Answer: An example in which the model mistakenly predicted the positive class.
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A false positive is a case whose true label is negative but which the model predicts as positive: a legitimate email sent to spam, or a smoke alarm set off by toast. It is the FP cell of the confusion matrix. In hypothesis testing the matching error, rejecting a true null hypothesis, is a Type I error.
Its rate is measured against the actual negatives: false positive rate = FP / (FP + TN) = 1 − specificity. Example: a moderation model reviews 50,000 harmless posts and 200 abusive ones, removing 250 harmless and 150 abusive posts. The false positive rate is 250 / 50,000 = 0.5%, yet 250 of the 400 removals, 62.5%, were mistakes.
That 62.5% is the false discovery rate, FP / (FP + TP) = 1 − precision. Confusing the two is a classic interview trap: when negatives vastly outnumber positives, a tiny false positive rate can still mean most alarms are false. Raising the threshold cuts false positives at the price of more false negatives.