When running categorization, how many documents may need to serve as examples in a workspace of several million documents?

The correct choice is that a couple of thousand documents may need to serve as examples in a workspace of several million documents when running categorization. This number is significant because it provides a balanced representation of the various categories present in the dataset, allowing the machine learning model to learn patterns effectively.

Having a few thousand examples offers sufficient diversity and complexity for the model to learn from. It allows for capturing the nuances and variations within each category, which is crucial for improving the accuracy of the categorization process. Additionally, using a relatively larger sample, such as a couple of thousand, helps mitigate the risk of overfitting, ensuring that the model generalizes well to unseen data rather than just memorizing the examples provided.

The other options suggest either too few examples or excessively large numbers. A few dozen would likely not provide enough data for the model to capture the necessary category characteristics adequately. A few hundred might be too limiting as well, potentially missing vital variations that could impact categorization. Suggesting several hundred thousand would be impractical in terms of processing, requiring substantially more computational resources without necessarily yielding proportionate benefits in model performance compared to using a couple of thousand well-chosen examples.