What type of machine learning is primarily used when models learn from labeled training data?

Supervised learning is the type of machine learning where models learn from labeled training data. In this approach, the algorithm is provided with input-output pairs, meaning that for each example in the training dataset, the desired output is known. The model learns to make predictions by finding patterns in the data that correlate with the labels provided. This enables the model to generalize from the training data to unseen data, allowing for tasks such as classification and regression.

For instance, in a supervised learning scenario, if the task is to classify emails into "spam" or "not spam," the model is trained on a set of emails that are already labeled as such. The algorithm analyzes the features from these labeled examples to establish a mapping between the input data (e.g., email text) and the corresponding output (e.g., spam indicator).

This learning approach is foundational in many AI applications, making it crucial for tasks where the output is known and the objective is to predict or classify new instances. In contrast, other types of machine learning, like reinforced learning and unsupervised learning, do not involve labeled datasets in the same way—reinforced learning focuses on learning from rewards and penalties, while unsupervised learning deals with identifying patterns or groupings in data without