Regression is used for what purpose in supervised learning?

Regression is utilized in supervised learning primarily to predict numerical values based on input variables. In this context, regression algorithms analyze the relationship between dependent and independent variables to create a model that can forecast continuous outcomes. For example, such models might be employed to predict house prices based on features like size, location, and number of bedrooms.

Unlike classification tasks, where the goal is to categorize data into distinct classes, regression focuses on understanding and modeling the relationships among numeric inputs to produce a fluid range of outputs. This makes it a powerful tool in scenarios where quantifiable predictions are necessary.

The other options involve methods or strategies that serve different purposes and are outside the scope of regression analysis within supervised learning. Clustering, for instance, pertains to unsupervised learning and involves grouping similar data points without prior labels, while enhancing visual representation and exploring patterns without labels are techniques that help in understanding or visualizing data rather than directly predicting numerical outcomes.