In the context of supervised learning, what do labels represent?

In supervised learning, labels play a crucial role as they represent the outcomes or categories associated with the data. Labels provide the target values that the model aims to predict. For instance, in a classification task, labels could denote different classes, such as 'spam' or 'not spam' in an email filtering model. In a regression task, the label would be a continuous value that the model is trying to predict based on given input features.

The relationship between features and labels is foundational in supervised learning. Features are the input variables used to make predictions, while labels are the results we are attempting to learn from these features. By training on a dataset containing features and their corresponding labels, the model learns to associate the input data with the correct output, thereby enabling it to make predictions on unseen data in the future.

Understanding the distinction between labels and other components of the model, such as the parameters that control the model or the raw input data, is essential for grasping how supervised learning operates.