What is the term for data that is labeled to train machine learning models?

The term for data that is labeled to train machine learning models is training data. This type of data is crucial in supervised machine learning, where the model learns patterns and relationships from the labeled examples. By using training data, which contains input-output pairs, the machine learning model can adjust its parameters to minimize the difference between its predictions and the actual labels.

In contrast, raw data refers to unprocessed information that has not been categorized or labeled, making it unsuitable for training without preprocessing. Validation data is a separate dataset used to tune model hyperparameters and ensure that the model generalizes well to new, unseen data. Test data is another distinct set of data used to evaluate the model's performance after training and validation. Therefore, since training data is specifically for the purpose of training the model with labeled input, it is the correct answer.