What is "data labeling" in machine learning?

Data labeling in machine learning refers specifically to the process of annotating data with relevant tags, which is crucial for supervised learning. Supervised learning relies on labeled datasets to train models effectively; the labels provide the model with information about the input data, allowing it to understand the type of output it should predict. For example, in image classification tasks, each image may need to be labeled with the specific objects it contains, such as "cat" or "dog." This annotated data helps the model learn patterns and relationships within the data, making it capable of making accurate predictions on unseen data.

The other options discuss different processes that are not directly related to the concept of data labeling. Segregating unstructured data from structured data involves organizing data into different categories but does not involve annotation. Creating backups for machine learning datasets is related to data management and preservation rather than labeling. Lastly, generating public access data sets pertains to data sharing and accessibility, which is separate from the labeling process that focuses specifically on preparing data for training models.