Answer / Pawan Kumar Gupta

F1 score is a performance metric used to evaluate the accuracy of binary classification models. It combines precision (true positives / total predicted positives) and recall (true positives / actual positives) into a single score that ranges from 0 to 1. F1 score is useful for situations where both false positives and false negatives have a significant impact on the performance of the model, such as in spam filtering or medical diagnosis.

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