Which type of machine learning model uses labeled data?

A supervised model is the type of machine learning model that utilizes labeled data for training. In supervised learning, the algorithm learns from a dataset that contains input-output pairs, where the input data is associated with the correct output labels. The model is trained to make predictions by finding patterns in this data, which allows it to generalize to new, unseen data with similar characteristics.

This approach contrasts sharply with unsupervised models, which do not have labeled outputs and instead focus on finding underlying structures or patterns in the data without reference to an outcome. Reinforced learning models, on the other hand, learn through interactions with an environment to maximize cumulative reward rather than relying on labeled datasets. Meta-learning models focus on learning how to learn by adapting to new learning tasks quickly, rather than operating on labeled data in a traditional sense. Hence, the emphasis on labeled data makes supervised models distinctly equipped for tasks where the goal is to predict outcomes based on known inputs.