How does predictive coding function in Relativity?

Predictive coding in Relativity functions by utilizing machine learning algorithms to assess and predict the relevance of documents based on user-defined training sets. This process enables the software to analyze patterns and features in previously reviewed documents to ascertain which documents are likely to be relevant to a given legal issue or case.

As documents are reviewed and coded by human reviewers, the system learns from these decisions to enhance its understanding of what constitutes relevance. This not only streamlines the review process but also improves accuracy over time as the algorithm refines its predictions based on new inputs. Therefore, predictive coding significantly reduces the amount of manual review time required while maintaining a high level of accuracy in document selection and relevance determination.

The other options do not encapsulate the core functionality of predictive coding as effectively. While user-given parameters for manual review and random sampling may play roles in document review processes, they do not define predictive coding itself. Moreover, storing documents in a central repository does not involve predictive capabilities and is instead a baseline requirement for document management systems.