Which aspect does the F₁ score evaluate in a classification model?

The F₁ score is a harmonic mean of precision and recall, making the choice of the average of these two metrics the correct understanding of what the F₁ score evaluates. Precision measures the proportion of true positive predictions among all positive predictions, while recall measures the proportion of true positives among all actual positives. The F₁ score provides a balance between these two metrics, particularly useful in situations where there is an uneven class distribution or when one metric might be misleading if looked at in isolation.

By combining precision and recall into a single score, the F₁ score helps to understand the overall performance of the classification model more comprehensively, especially in cases where false positives and false negatives have different costs or implications. This focus on balancing precision and recall helps modelers identify models that effectively navigate the trade-offs between these aspects, making the F₁ score a critical metric for evaluation in classification tasks.