What is the ideal number of example documents needed from a large workspace when running categorization?

The ideal number of example documents needed from a large workspace when running categorization is a couple of thousand. This quantity strikes a balance between providing a diverse and representative sample of the data without becoming unmanageable.

Having a couple of thousand documents allows the categorization algorithm to effectively learn from a wide range of examples, capturing various nuances, patterns, and potential outliers present in the larger dataset. A larger sample size enhances the model's ability to generalize and perform well on unseen data, thereby improving accuracy and reliability in the categorization process.

This extensive sample enables the capturing of different contexts, terminologies, and document types that might be present in a large workspace. A few dozen or less than a hundred examples may not be sufficient to provide a comprehensive understanding of the dataset's diversity and complexity, potentially leading to inaccurate categorization outcomes. Similarly, while hundreds of examples would offer more data than a smaller sample, it may still fall short compared to the more robust dataset represented by a couple of thousand documents, reducing the model’s effectiveness in real-world applications.