What indicates a successful completion of a project using active learning methodology?

A successful completion of a project using active learning methodology is characterized by a low percentage of documents coded as not relevant. This outcome suggests that the active learning process has effectively trained the model to accurately discern which documents are pertinent to the project goals. In active learning, the objective is to minimize the number of irrelevant items by continuously refining the model based on the most informative data that has been reviewed.

When the percentage of documents coded as not relevant is low, it indicates that the active learning system has successfully prioritized and identified relevant information. This efficiency is critical, as the primary aim of employing active learning is to enhance the relevance and quality of results derived from the dataset.

The other options could imply certain levels of productivity or efficiency but do not directly indicate the success of the active learning process in terms of relevance evaluation. For instance, simply reviewing a high number of documents or having no relevant documents left in the discard pile may not directly reflect the model's effectiveness in filtering through information. Similarly, completion time being within the predicted timeframe is beneficial but does not inherently relate to the quality of the work concerning relevance discernment.