What is the F1-score?

The F1-score is indeed a measure that combines precision and recall, making it particularly valuable in situations where there is an uneven class distribution or when the costs of false positives and false negatives differ significantly. Precision indicates the accuracy of the positive predictions made by the model (how many correctly predicted positive instances there are out of all predicted positive instances), while recall measures the model's ability to capture all actual positive instances (how many correctly predicted positive instances there are out of all true positive instances in the dataset).

By calculating the F1-score, you obtain a single metric that balances these two critical aspects of model performance. This is done through the formula:

[ \text{F1-score} = 2 \times \frac{{\text{Precision} \times \text{Recall}}}{{\text{Precision} + \text{Recall}}} ]

This balance makes the F1-score particularly useful in scenarios such as binary classification problems where class distribution can skew the absolute performance metrics.

While the other options touch upon different concepts—averages of predictions, assessments limited to false positives, or indicating data size—they do not capture the essence of what the F1-score is designed to represent in the context of model evaluation.