What is the primary function of the Rebalance mode?

The primary function of the Rebalance mode is to present unreviewed verbatims that are underrepresented. This mode is particularly important in machine learning and data labeling scenarios where balanced representation of data across different categories is crucial for training effective models. By focusing on unreviewed verbatims that are underrepresented, the system addresses potential biases in the dataset, helping to ensure that all possible variations and scenarios are adequately represented. This leads to a more robust and accurate model that can generalize better to unseen data.

Other options do not align with the primary purpose of Rebalance mode. For instance, presenting only high-confidence verbatims would limit the training data to the most reliable entries, potentially overlooking important edge cases that require attention. Presenting verbatims with new labels doesn’t contribute to addressing representation gaps in the data and may instead complicate the labeling process. Lastly, presenting mixed sets of verbatims can dilute the focus on those entries that require additional attention and review, which is contrary to the goal of rebalancing the dataset for improved fairness and accuracy in training.