What is the difference between supervised and unsupervised learning?

Supervised learning involves training a model using labeled data, which means that the data input is paired with the corresponding correct output. This method allows the model to learn from the examples and make predictions or classifications based on new, unseen data. It is primarily used for tasks where the output is known and can be validated, such as classification and regression.

On the other hand, unsupervised learning works with unlabeled data, meaning the model attempts to identify patterns, structures, or groupings within the data without any predefined outcomes to guide the process. This approach is useful for tasks such as clustering and association where the objective is to discover inherent relationships or features in the data.

The distinction highlighted in the correct answer clarifies the fundamental difference between supervised and unsupervised learning methods regarding the type of data used—labeled versus unlabeled.