Answer / Prateek Kumar Srivastava

False positive refers to a situation where a positive result is incorrectly predicted by the model when the actual outcome is negative. For example, a spam filter might mark a non-spam email as spam (false positive). A false negative occurs when a model incorrectly predicts a negative result for a positive case. In our example, a spam filter might miss an actual spam email (false negative). True positives are correctly predicted positives, while true negatives are correctly predicted negatives.

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