Is it necessary to operate with a large number of documents in the workspace when running categorization?

Operating with a large number of documents in the workspace when running categorization is essential because it ensures that there is a diverse range of examples to work from. Having a variety of documents allows the categorization process to more accurately represent the different nuances and variations within the dataset, helping to improve the robustness of the model being trained.

Diversity in examples helps the model learn to recognize patterns across different scenarios, making it more effective in classifying new, unseen documents. A larger dataset can also minimize the impact of bias, as it captures a wider spectrum of content and variations, leading to better generalization of the categorization results.

In scenarios where the categorization task involves identifying subtle differences or multiple categories, extensive data can significantly enhance the performance and reliability of the categorization system. This principle emphasizes the importance of data quantity and diversity in support of effective categorization processes.