What is the maximum recommended number of example documents for categorization?

The maximum recommended number of example documents for categorization is 50,000. Having a larger dataset of example documents is beneficial because it provides a more comprehensive representation of various categories and helps improve the accuracy and reliability of categorization models. A higher volume of examples allows machine learning algorithms to better understand patterns, nuances, and the diversity within the data, which is crucial for training effective classification systems.

Moreover, with a larger sample size, the model can capture edge cases and less common scenarios that might not be represented in smaller datasets. This ultimately leads to improved performance in real-world applications, where data can vary significantly. Categories can also become more well-defined with a wider array of examples, allowing for more nuanced differentiation between them.

While smaller sample sizes may be easier to manage and quicker to process, they typically do not provide the level of detail and accuracy needed for robust categorization, which is why the recommendation leans towards using a much larger dataset whenever possible.