Which of the following is NOT a characteristic of unsupervised learning?

Unsupervised learning is a type of machine learning that involves training algorithms on datasets without labeled responses or outputs. This means that the model does not have any explicit guidance on what to learn or focus on. The objective of unsupervised learning is to discover the inherent structures or patterns in the data.

Finding hidden patterns is a key characteristic of unsupervised learning, as the algorithms analyze the data to identify relationships and groupings. Grouping similar data, often referred to as clustering, is also a fundamental aspect of unsupervised learning. It enables the model to categorize and cluster the data based on shared characteristics without prior knowledge of what those categories should be. Furthermore, unsupervised learning is distinguished by its ability to learn independently from the input data. This means that the model derives insights without being directed by labels.

On the other hand, using labeled datasets is not characteristic of unsupervised learning. Labeled datasets are fundamental to supervised learning, where the algorithm learns from both the input data and the corresponding output (labels) to make predictions or classifications. Thus, the presence of labels in the dataset directly defines supervised methods, distinguishing them from unsupervised techniques which operate without such labels.