What is the difference between training data and test data in supervised machine learning?

In supervised machine learning, training data and test data play distinct but complementary roles in the model development process. Training data is the dataset used to train the machine learning model, allowing it to learn the underlying patterns and relationships within the data. This involves adjusting the model's parameters based on the input-output pairs present in the training data.

Test data, on the other hand, is a separate dataset that the model has not seen during the training phase. It is utilized to evaluate the model's performance and effectiveness in making predictions or understanding new, unseen data. This evaluation helps determine how well the model is likely to perform in real-world scenarios, where it encounters data it hasn't encountered before.

The distinction captured in the correct answer precisely reflects this relationship—training data is essential for the learning process, and test data serves as a metric for the effectiveness of that learning, allowing practitioners to gauge the model's accuracy and generalization capabilities.