What best describes a false negative in classification?

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In a binary classification task, outcomes fall into four categories: true positives, true negatives, false positives, and false negatives. A false negative happens when the actual outcome is positive, but the model predicts negative. In other words, a positive case is missed because the prediction is negative. This is often a concern in screening or diagnostic contexts because it means a real positive is ignored, hindering the model’s ability to identify positives.

The described scenario matches this idea: a negative prediction for something that is actually positive. The other possibilities describe different errors or neutral results: a positive prediction that is actually negative is a false positive; a correct positive prediction is a true positive; and a prediction with no relation to the outcome isn’t a recognized classification outcome.