What happens during the 'Prune / Reorganize' phase of model training?

During the 'Prune / Reorganize' phase of model training, the process focuses on refining the dataset to enhance the model’s accuracy and efficiency. This involves removing redundant or less useful labels, as well as merging similar ones to create a more streamlined and coherent set of categories. This pruning and reorganizing ensure that the model learns from the most relevant and distinctive examples, improving its ability to classify and predict effectively with the remaining data.

Such an approach allows for better generalization of the model, as it reduces noise and confusion that might arise from too many similar labels or overly diverse categories. By optimizing the label structure, the model can focus on learning meaningful patterns without being overwhelmed by excessive granularity in its classifications. This phase is crucial for model training as it directly impacts the quality of predictions made by the AI after deployment.

The other choices either refer to actions that do not align with the objectives of pruning and reorganizing or involve aspects that occur at different stages of the training process. Therefore, focusing on the removal and merging of labels highlights the essential purpose of this phase in refining the dataset for better training outcomes.