What is the main difference between supervised and unsupervised learning?

The main distinction between supervised and unsupervised learning lies in the use of labeled data. In supervised learning, models are trained on datasets that include input-output pairs, meaning each training example is paired with the correct output label. This allows the model to learn the relationship between inputs and outputs, making predictions based on the patterns it identifies.

In contrast, unsupervised learning involves data that does not have labeled outputs. The primary goal here is to explore the data and find underlying patterns or structures without any guidance on what those patterns might represent. This type of learning is often used for clustering, dimension reduction, and association tasks.

Thus, the statement that supervised learning relies on labeled data for predictions accurately captures the essence of how supervised algorithms function, differentiating it clearly from unsupervised learning, which operates on unlabeled data.