What is the recommended number of documents in the saved search for Active Learning?

The recommended number of documents in the saved search for Active Learning is aligned with optimizing the efficiency and effectiveness of the machine learning models used in this process. When working with Active Learning, it is crucial to balance the quantity of documents with the capability of the system to analyze and categorize them accurately.

A threshold of around 9 million documents is generally considered optimal because it allows sufficient data for the algorithm to learn from while preventing the system from being overwhelmed, which can lead to diminished performance or increased processing time. This figure signifies a point where the diversity of the data is also represented, ensuring that the model can learn effectively without becoming bogged down by an excessive number of documents that don’t meaningfully contribute to the training process.

Higher limits beyond this recommended maximum may introduce challenges, such as slower processing speeds and a more complex management of the data set, potentially affecting the quality of the Active Learning outcomes. Therefore, maintaining the number of documents at 9 million ensures a practical balance to achieve robust and efficient results in machine learning applications.