Describe the Recall and F1 score as evaluation metrics, including their formulas and the scenarios where each might be particularly useful. How do these metrics help in evaluating the performance of a classification model?

Simulated

Recall measures the model's ability to predict positive instances correctly, while F1 score is the harmonic mean of precision and recall, balancing both false positives and false negatives.

46.2%

Recall is used when the cost of false negatives is high, and F1 score is used when both precision and recall are important.

53.8%

Both Recall and F1 score are primarily used in regression models.

0.0%

Recall and F1 score are identical and interchangeable in all scenarios.

0.0%

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Describe the Recall and F1 score as evaluation metrics, including their formulas and the scenarios where each might be particularly useful. How do these metrics help in evaluating the performance of a classification model?