What is the recommended limit for example documents to increase categorization accuracy?

The recommended limit for example documents to enhance categorization accuracy is based on the principle that a larger dataset provides more diverse and representative examples for the machine learning model to learn from. The use of a substantial number of examples, such as 50,000, allows for better pattern recognition and more nuanced understanding of the data, which is crucial in complex tasks like categorization.

When a model is trained on a larger set of examples, it can more effectively identify variations in the data, adapt to different contexts, and improve overall accuracy. A limit of 50,000 allows for a comprehensive training experience, which leads to a model that is more robust and capable of generalizing well to new, unseen documents.

In contrast, smaller example limits, like 50, 500, or even 5,000, may not provide enough data for the model to learn effectively, thereby resulting in diminished accuracy in categorization tasks. These smaller datasets may lead to overfitting, where a model learns the specifics of the training examples too well, but fails to perform adequately on new data. Therefore, the recommended limit of 50,000 example documents is key to achieving high categorization accuracy and ensuring that the model can perform reliably across a diverse set of scenarios