False negatives occur when a predictive model incorrectly predicts a negative outcome for a case that is actually positive. This term is crucial in understanding the effectiveness of predictive analytics, especially in fields like healthcare, fraud detection, and marketing, where missing a positive case can lead to significant consequences. False negatives can greatly affect decision-making processes and the reliability of forecasts, as they represent missed opportunities to identify true positive instances.
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