What does the term "Coverage" refer to in model analysis?

The term "Coverage" in model analysis specifically refers to the proportion of verbatims in the dataset that have informative label predictions. This concept is crucial because it helps gauge how well the model is performing in terms of identifying relevant data points that have been accurately labeled. A higher coverage percentage indicates that a significant portion of the dataset is being captured and accurately classified by the model, which reflects its ability to generalize and apply learned patterns effectively.

In contrast, the other options do not align with the definition of "Coverage." The total number of labels in the dataset pertains to the structure of the dataset rather than the model's performance. The accuracy of predictions relates to how correct the model’s predictions are, but does not specify how many instances are covered. The total instances for a given label identified by the model points to a specific label’s occurrences rather than providing a broader view of predictive coverage across the dataset.