Which of the following describes supervised learning?

Supervised learning is a type of machine learning where the algorithm is trained using labeled data, which means that the input data is paired with the correct output labels. This approach allows the model to learn the relationship between the input features and the output labels, enabling it to make predictions on new, unseen data based on the patterns learned during training.

In this context, the focus is on using the labeled data effectively to improve the model's accuracy in predicting outcomes. For instance, if the model is trained on a dataset where emails are labeled as "spam" or "not spam," it learns to categorize new emails based on these learned relationships.

The other options do not accurately define supervised learning. Unlabeled data describes unsupervised learning, while ignoring feedback from past predictions refers more to methods that do not adapt based on previous performance, which is generally not how supervised learning operates. Classification methods, like spam detection, can be included in supervised learning, but those methods alone do not encompass the full definition of supervised learning. Thus, the description of a method that trains on labeled data for predictions encapsulates the core concept of supervised learning perfectly.