Which type of data is primarily used for training supervised learning models?

In the context of supervised learning, the primary type of data used for training models is labeled data. Labeled data contains input-output pairs where each input is associated with a specific outcome or target variable. This allows the model to learn the relationship between the input features and the corresponding labels during the training process. By providing both the features and their correct labels, the model can adjust its parameters based on the errors it makes in predicting the outputs, ultimately improving its accuracy.

Labeled data is crucial for supervised learning because it enables the algorithm to discern patterns and associations that it can apply when presented with new, unseen data. Without labeled data, the model would not have a reference point to learn from, which would hinder its performance and ability to generalize. This characteristic distinguishes supervised learning from other approaches, such as unsupervised learning, which relies on unlabeled data to find underlying structures or groupings in the data without explicit guidance on the outcomes.