How does supervised learning differ from unsupervised learning?

Supervised learning is a type of machine learning that relies on labeled data. In this context, labeled data refers to the input data that comes with the corresponding output or desired result, allowing the algorithm to learn the relationship between the input features and the output labels. This process of using labeled data enables the model to make accurate predictions on new, unseen data, as it has been trained on specific instances that map inputs to outputs.

In contrast, unsupervised learning involves working with data that does not have labeled outcomes. The goal is typically to uncover hidden patterns or intrinsic structures within the data, such as clustering similar data points together or identifying associations among features. Because there are no predefined labels to guide the learning, unsupervised algorithms can only derive insights based on the intrinsic properties of the input data.

In this comparison, recognizing that supervised learning is fundamentally about the availability and use of labeled data is crucial, thus making it clear why the statement regarding supervised learning requiring labeled data is accurate.