What is the role of a label in a supervised learning model?

In supervised learning, the role of a label is to serve as the output that the model tries to predict based on the input data. During the training phase, the model learns the relationship between the input features and the corresponding labels. For instance, in a task where the goal is to classify images of animals, the images themselves are the input data (features), while the animal names (e.g., "cat," "dog") are the labels representing the correct classes that the model should learn to predict. The model uses this labeled data to adjust its internal parameters and improve its prediction accuracy over time.

This concept is fundamental to supervised learning, where having a reliable set of labels allows the model to evaluate its predictions and iteratively improve based on the difference between its predictions and the actual labels. Thus, the label functions as the target variable that guides the learning process.