Which metric provides the weighted average of precision and recall?

The F1 score is the metric that provides a weighted average of precision and recall. It is particularly useful in scenarios where there is an uneven distribution of classes, making it important to consider both false positives and false negatives. Precision measures the accuracy of positive predictions, while recall assesses the ability of a model to find all the relevant cases in a dataset.

The F1 score balances these two metrics by calculating their harmonic mean, which tends to emphasize the lower of the two values, ensuring that if one is significantly lower, it will impact the F1 score considerably. This is especially beneficial in cases where the cost of false negatives is high, as it seeks to maintain a good balance between correctly identifying true positives and minimizing false positives.

Other metrics, such as accuracy, focus solely on correctly predicted instances out of the total instances and do not differentiate between the types of errors being made. Recall and precision scores individually represent only one aspect of model performance and do not combine both perspectives into a single score like the F1 score does. Therefore, the F1 score is the most comprehensive measure when precision and recall are both important considerations in evaluating a model's performance.