Which type of machine learning (ML) utilizes labeled data for training?

Supervised learning is the type of machine learning that specifically uses labeled data for training. In this process, the model learns to map inputs to the correct output based on the provided labels. During training, the algorithm analyzes the labeled dataset, which contains input-output pairs, allowing it to recognize patterns and relationships within the data. This approach is foundational in various applications, such as image classification, spam detection, and medical diagnosis, where the goal is to predict an outcome based on input data.

In contrast, other types of learning, such as reinforcement learning, involve training through a system of rewards and penalties without the need for labeled data. Similarly, circuit learning and algorithmic learning do not pertain directly to the concept of using labeled datasets, making supervised learning the clear and appropriate choice for this question.