The type of machine learning that focuses on training a model using labeled data is known as supervised learning. In supervised learning, the algorithm learns from input-output pairs, where each input is associated with a specific output label. This approach helps the model to generalize patterns and make predictions on unseen data based on the learned relationships. It's widely used in applications such as image recognition, natural language processing, and fraud detection, where accurate outputs are critical for model effectiveness.