What is the primary purpose of labeling data in supervised learning?

The primary purpose of labeling data in supervised learning is to teach the model the correct relationships between inputs and outputs. In a supervised learning framework, a model is provided with a dataset that includes input features and corresponding output labels. This structure allows the model to learn and make predictions based on the relationships it identifies from the labeled examples.

For instance, in a dental context, if the task is to identify dental diseases from X-ray images, the images (inputs) would be labeled with the corresponding diagnoses (outputs). By training on this labeled data, the model learns the features and patterns that correlate with specific conditions, enabling it to make accurate predictions when presented with new, unseen data in the future.

Labeling data is crucial because it provides context and meaning to the input features, allowing the algorithms to build a comprehensive understanding of how to respond to similar inputs. This clarity is what distinguishes supervised learning from unsupervised learning, where no labels are provided and the model tries to find patterns without explicit instructions.