Which type of learning uses labeled data to train the model?

Supervised learning is the type of learning that utilizes labeled data to train a model. In this approach, the training dataset comprises input-output pairs, where each input example is associated with a corresponding label or outcome. This allows the algorithm to learn the relationship between the input features and the labels, enabling it to make predictions or classify new, unseen data.

In supervised learning, the model is evaluated based on its ability to generalize from the training data to perform well on a validation or test set. Common applications include classification tasks, such as identifying whether an email is spam, and regression tasks, such as predicting house prices.

In contrast, unsupervised learning involves training models on datasets that do not contain labeled outputs, focusing instead on discovering patterns or groupings within the data. Reinforcement learning is centered around an agent that learns to make decisions through trial and error interactions with an environment, receiving rewards or penalties rather than labeled data. Semi-supervised learning combines aspects of both supervised and unsupervised learning, using a small amount of labeled data alongside a larger amount of unlabeled data.

The feature of using labeled data is what distinctly categorizes supervised learning in machine learning paradigms.