What does supervised learning primarily involve?

Supervised learning primarily involves using labeled data to train a model so it can make predictions about outcomes. In this paradigm, the model learns from input-output pairs, where each input has a corresponding correct output label. This enables the model to establish a mapping from inputs to outputs, allowing it to predict outcomes for new, unseen data based on learned patterns.

For instance, in a supervised learning task such as image classification, a dataset might consist of images labeled with their corresponding categories (e.g., dog, cat, car). The model analyzes these labeled images during training and learns to recognize features associated with each category, thus improving its accuracy in predicting categories for new images it hasn't encountered before.

The other options represent different concepts in machine learning. Training on unlabeled data describes unsupervised learning, where models identify patterns without explicit guidance. Clustering is a technique used in unsupervised learning to group data points into categories based on similarities. Reducing dimensions pertains to techniques like Principal Component Analysis (PCA) that simplify datasets by reducing the number of input variables to make analysis easier while preserving important information. Each of these approaches serves different purposes and relies on distinct types of data and methodologies.