What is one key feature of the Low Confidence mode?

One key feature of Low Confidence mode is that it presents data requiring new label creation. This mode is particularly useful in situations where the model encounters instances that fall outside the pre-defined categories or labels it has been trained to recognize with high confidence. By identifying these instances, Low Confidence mode helps practitioners understand where their current labeling may be inadequate and where new categories might need to be established.

This capability is crucial for continuous improvement of machine learning models, as it allows for the expansion of the dataset and ensures that the model can adapt to new patterns or classes that emerge from the data. It essentially acts as a feedback mechanism for data enrichment and model training, helping to refine and enhance the overall performance of the model.

The other options, while valuable in their contexts, do not specifically address the primary function of Low Confidence mode in relation to advancing label creation.