Which metric provides a single score that balances the trade-off between precision and recall?

  • F1 Score
  • Accuracy
  • ROC AUC
  • Log Loss
The F1 Score is a metric that balances the trade-off between precision and recall. It is especially useful when dealing with imbalanced datasets or when you want to find a balance between correctly identifying positive cases (precision) and capturing all positive cases (recall). The F1 Score is the harmonic mean of precision and recall. It is a suitable choice for evaluating models when both precision and recall are essential.
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