What does setting Optimize Training Set to Yes on a conceptual index remove from the training data source search?

Setting Optimize Training Set to Yes on a conceptual index is intended to enhance the quality and relevance of the training data by filtering out certain elements that may not contribute effectively to the training process. In this context, one major aspect it targets is lists that contain a significant amount of numbers.

The rationale behind this is that datasets populated predominantly with numerical data can often carry less contextual meaning compared to text-rich datasets. In legal and eDiscovery scenarios, for example, the focus is usually on the substance of the communication or evidence—which is more text-based—rather than on numeric data that may not provide insightful context for learning algorithms. By removing these numbers-heavy lists, the training process can concentrate on more relevant textual information, ultimately improving the model’s ability to recognize and analyze patterns effectively.

The other options involve types of content that might not align with the primary goal of optimizing the training set. Lengthy email conversations could potentially be informative; lists of words from the dictionary may not inhibit training but are not typically relevant to the context; and system files often do not provide pertinent insights for most legal analytics scenarios.