What is the process of adding descriptive tags to data, especially for supervised learning, called?

The process of adding descriptive tags to data, particularly in the context of supervised learning, is known as data labeling. This process involves assigning meaningful labels to different sets of data points, which enables models to learn from specific examples during training. Each label acts as a target for the algorithm, allowing it to understand what the correct output should be for a given input.

In supervised learning, where the model is trained using input-output pairs, accurate data labeling is crucial because it directly impacts the performance and accuracy of the machine learning model. Well-labeled data helps algorithms to generalize better and make informed predictions on unseen data.

While data categorization, data identification, and data annotation may seem related, they do not precisely capture the essence of the tagging process that is foundational to the supervised learning paradigm. Data categorization often refers to the broader task of organizing data into categories, data identification deals more with recognizing elements within datasets, and data annotation can sometimes involve more complex contextual or additional information, which may or may not include labeling. Thus, data labeling is specifically tailored to the needs of supervised learning.