What differentiates supervised learning from unsupervised learning?

Supervised learning is characterized by the use of labeled data to train models. In this approach, each training example is paired with an output label, allowing the algorithm to learn the relationship between the input features and the corresponding outcomes. This structured dataset enables the model to make predictions or classifications on new, unseen data based on the learned relationships.

In contrast, unsupervised learning does not utilize labeled data. Instead, it focuses on identifying patterns, relationships, or structures in datasets where the outcomes are not known. Therefore, the requirement for labeled data in supervised learning clearly sets it apart from unsupervised learning, making this choice the correct answer.

Other options do not accurately capture the fundamental differences. For instance, while it can be true that unsupervised learning may involve less computational complexity for certain tasks, it is not a general rule that designates it as consistently faster. Additionally, supervised learning encompasses both classification and regression tasks, making the statement about it being only for classification incorrect. Lastly, unsupervised learning specifically aims to minimize human intervention by discovering patterns autonomously, which does not align with the premise provided in that option. This reinforces the clear distinction based on the use of labeled data in supervised learning.