Answer / Rani Katare

The F1 score is a weighted average of precision and recall, providing a single metric to evaluate the performance of binary classification models. It is defined as the harmonic mean of precision (P) and recall (R), with a range between 0 and 1, where 1 indicates perfect accuracy. The formula for F1 score is F1 = 2PR/(P + R). F1 score is useful when both precision and recall are important, as it balances their contributions. It can be used to compare the performance of different classification models or to evaluate the effectiveness of various tuning parameters.

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