In machine learning, which of the following best describes a labeled dataset?

A labeled dataset is characterized by the inclusion of outcomes or labels that correspond to each input instance within the dataset. This means that for every example in the dataset, a specific target value or category is predefined, allowing machine learning algorithms to learn the relationship between the input features and the associated outputs. This structured approach is essential for supervised learning tasks, where the goal is to predict labels for new, unseen data based on the learned patterns from the labeled examples.

In contrast, datasets that are disorganized or unstructured do not have associated labels, which is not representative of a labeled dataset. Similarly, a collection of random data points lacks any meaningful correlation to a supervised learning task, as there are no defined outcomes for the inputs. Finally, datasets used for unsupervised learning intentionally contain no labels, as the aim is to identify patterns or groupings within the data without specific target outcomes. Thus, the most accurate depiction of a labeled dataset is one where outcomes are provided for each input instance.