Answer Posted / Renu Gangwani

The F1 score is a metric that combines precision and recall to provide a single value indicating the accuracy of a binary classification model. It is calculated as the harmonic mean of precision and recall, ensuring both are given equal weightage. F1 score can be used to evaluate and compare the performance of multiple classification models on the same dataset.

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