F1-score

The F1-score is a metric used to evaluate the accuracy of a binary classification model, which categorizes items into one of two values, like "yes" or "no". It combines two other metrics: precision and recall.12

• Precision measures the proportion of a model's positive predictions that were actually correct.
• Recall measures the proportion of actual positive cases that the model correctly identified.

The F1-score is calculated as the harmonic mean of precision and recall, giving equal weight to both. It ranges from 0 to 1, where 1 signifies a perfect model and 0 indicates a complete failure in either precision, recall, or both. While useful, the F1-score can be misleading as it doesn't specify whether precision or recall is the weaker component.12