What distinguishes supervised learning from unsupervised learning?

The distinguishing factor between supervised learning and unsupervised learning lies in the nature of the training data, specifically the presence of labeled outputs. In supervised learning, the model is trained on a dataset that includes both input features and their corresponding output labels. This facilitates the creation of a predictive model that can learn the relationship between the inputs and outputs, enabling it to make predictions or classifications based on new, unseen data.

In contrast, unsupervised learning deals with datasets that lack labeled outputs. Instead, the model attempts to find patterns, groupings, or structures within the data itself, such as clustering or association. The absence of labeled data means that there is no explicit guidance on what the outputs should be, which fundamentally changes the nature of how the model learns and processes information. Thus, the focus on labeling outputs is what fundamentally differentiates supervised learning from unsupervised learning.