Which of the following is NOT a step in the 'Refine and Maintain' stage?

In the 'Refine and Maintain' stage, the primary focus is on enhancing the model's accuracy and effectiveness through various activities aimed at adjusting and fine-tuning the model based on its performance metrics. To achieve this, actions like reviewing model ratings, improving entity performance, and refining label performance are essential.

Reviewing the model rating allows developers to assess how well the model performs against validation data, identifying areas for improvement. Improving entity performance focuses on enhancing the model’s ability to accurately identify and categorize relevant entities in the input data. Likewise, refining label performance involves evaluating and adjusting how different labels are applied within the model, ensuring that the model aligns perfectly with the expectations for various outputs.

On the other hand, increasing imbalance is not a valuable step in this stage. In fact, dealing with data imbalance is typically an issue to address in the earlier stages of model development. Introducing or increasing imbalance can lead to skewed results, where the model might favor certain classes over others, resulting in inaccurate predictions. Hence, it does not align with the objectives of refining and maintaining the model's performance.