Which of the following describes supervised learning?

Supervised learning is characterized by the use of labeled data, where each training example includes both input features and the corresponding output label. This enables the model to learn the relationships between the input data and the labels, allowing it to make informed predictions on unseen data. The learning process occurs with guidance from the labeled outputs, which means the model receives feedback on its predictions during training. Consequently, this structured approach is particularly effective for tasks such as classification and regression, where the goal is to predict outcomes based on previously observed data.

The other options don't accurately capture the essence of supervised learning. For instance, the first option refers to a scenario where models learn without labeled outputs, which is specifically a characteristic of unsupervised learning. The second option describes identifying data patterns without supervision, again pointing towards unsupervised learning. Lastly, the statement that supervised learning only applies to classification problems is misleading; supervised learning also encompasses regression tasks, where the goal is to predict continuous values. Hence, the most accurate description of supervised learning is that models are trained on labeled data to make predictions.