Which evaluation metric considers both false positives and false negatives?

The F1 score is a metric that harmonizes both precision and recall, providing a single measure that reflects the balance between the two. Precision focuses on the proportion of true positive results in relation to all predicted positives, while recall, on the other hand, measures the proportion of true positives among all actual positives.

By incorporating both metrics, the F1 score is particularly useful in scenarios where there is a need to balance the trade-offs between false positives and false negatives. It is calculated as the harmonic mean of precision and recall, thus ensuring that a low value in either metric results in a lower F1 score. This attribute makes the F1 score valuable in applications where one type of error (false positive or false negative) cannot be ignored, ensuring that both underlying metrics are taken into account in the evaluation of model performance.

Other metrics, such as accuracy, focus solely on the overall correctness of predictions and may not adequately represent the challenges posed by imbalanced classes. Precision and recall, while informative individually, do not directly consider both false positives and false negatives together, which is why they do not synthesize the performance in the same way the F1 score does. Hence, the F1 score offers a comprehensive assessment by encapsulating the influence of