Which of the following is a key characteristic of unsupervised learning?

Unsupervised learning is distinguished by its ability to identify patterns, structures, or relationships in data that is not labeled. This means that the algorithm works with inputs that do not have predefined outputs or categories. The key characteristic of unsupervised learning is centered around its focus on discovering hidden patterns or intrinsic structures from the input data.

In contrast to supervised learning, where models are trained using labeled datasets with known outcomes, unsupervised learning explores the data to find natural groupings, clusters, or associations. This makes it particularly useful for exploratory data analysis, as it can reveal insights that may not be immediately apparent or that have not yet been pre-defined by researchers.

In summary, the emphasis on working with unlabeled data and uncovering patterns without prior labeling is what makes the chosen answer the central trait of unsupervised learning.