What distinguishes supervised learning from unsupervised learning?

The distinction between supervised and unsupervised learning fundamentally revolves around the nature of the data used in the training process. In supervised learning, the algorithm is trained on a labeled dataset, meaning each training example is paired with an output label. This allows the model to learn the relationship between the input data and the output labels, which it then uses to make predictions on new, unseen data. This is crucial for tasks such as classification and regression, where the goal is to predict a specific outcome based on input features.

In contrast, unsupervised learning operates on datasets that do not have labeled responses. Here, the algorithm seeks to identify patterns and structures within the data without any predefined labels guiding its learning process. This can involve clustering similar data points or reducing dimensionality to uncover inherent structures.

Therefore, the key difference lies in the presence of labeled data in supervised learning, which enables the model to learn from those labels, while unsupervised learning focuses on uncovering patterns without such guidance. This foundational understanding of how data is utilized in both approaches highlights why the first option accurately captures the essence of the distinction between these two learning paradigms.