What is the purpose of a 'Label Suggestion' in the training process?

The purpose of 'Label Suggestion' in the training process is to quickly apply relevant labels while training. This feature enhances the efficiency of the labeling process by automatically suggesting appropriate labels based on the context and characteristics of the data being analyzed. By leveraging pre-trained models or algorithms that recognize patterns, 'Label Suggestion' allows data scientists and machine learning practitioners to streamline their work, reducing the time and effort involved in manually labeling data. This ultimately accelerates the training process and helps in building more accurate machine learning models.

Other options, such as predicting outcomes for datasets, verifying existing labels, or creating new datasets, do not align with the function of 'Label Suggestion'. While they are important aspects of the machine learning workflow, they serve different purposes and are not the primary focus of this feature.