What is the F-1 Score a measure of?

The F-1 Score is a metric commonly used to evaluate the performance of a classification model, particularly in scenarios where the distribution of classes is imbalanced. It is the harmonic mean of precision and recall, providing a single score that captures both aspects.

Precision measures how many of the predicted positive cases are true positives, while recall measures how many of the actual positive cases were identified by the model. By combining these two metrics, the F-1 Score offers a more nuanced assessment of a model's performance, especially when one measure alone may not fully capture its effectiveness. High precision with low recall, or vice versa, can lead to misleading assessments of model performance. The F-1 Score seeks to balance these two competing metrics, making it especially useful in many real-world classification tasks, where the goal is not just to be correct when predicting a positive case but also to ensure that we are identifying as many positive cases as possible.

Thus, the F-1 Score is critical in evaluating the balance between precision and recall, which reflects the model's ability to not only make accurate predictions but also to detect positives effectively.