What does Coverage Mode in a review queue help improve?

Coverage Mode in a review queue is designed to enhance the training of the model with relevant documents. This feature allows the system to ensure that the model is exposed to a wide range of document types and categories during its learning phase. By incorporating a broad selection of documents, Coverage Mode helps to train the model effectively, allowing it to learn patterns and characteristics that are representative of the data it will encounter in real-world scenarios.

The training of the model is crucial because it directly impacts how well it performs in categorizing documents in the future. When the training data is more diverse and representative, the model can improve its generalization capabilities, leading to better performance when it encounters new documents.

While other options like speed of document retrieval, overall accuracy in document categorization, and visibility of all document types are important aspects in managing a review queue, they do not specifically address the primary objective of Coverage Mode, which focuses on enhancing the learning process of the model through exposure to relevant documents.