Which of the following metrics can be used to find a suitable balance between precision and recall in a model?

The F1 Score is a harmonic mean of precision and recall, making it a valuable metric for situations where you want to balance these two aspects of model performance. Precision measures the accuracy of the positive predictions, while recall indicates how well the model captures all relevant instances (true positives). The F1 Score takes both precision and recall into account, providing a single metric that reflects the trade-off between them. It is particularly useful in scenarios where the class distribution is imbalanced, as it can avoid giving misleading impressions of model performance that could result from solely relying on metrics like accuracy.

Accuracy, while a common metric, does not provide insights into the balance between precision and recall and could be misleading in cases where one class significantly outnumbers another. ROC AUC is useful for understanding the trade-off between true positive rate and false positive rate but does not directly address precision and recall. Log Loss measures the performance of a classification model based on the probabilities of predictions, not specifically balancing precision and recall.