What distinguishes supervised learning from unsupervised learning?

Supervised learning is characterized by its use of labeled data, where the model is trained on a dataset that includes input-output pairs. In this framework, the system learns to map inputs to outputs based on the provided labels. The objective is to minimize the difference between the predicted outputs produced by the model and the actual labels present in the training data, allowing the model to make accurate predictions on new, unseen data.

In contrast to supervised learning, unsupervised learning operates with datasets that do not have labeled outputs. It aims to recognize patterns and structures within the data without any explicit instructions on what to predict. This method is typically used for clustering, association, or dimensionality reduction tasks.

Understanding the distinction between these two learning paradigms is crucial in machine learning, as it determines the approach that should be taken for a specific problem, including model selection, evaluation metrics, and the nature of the data being used.