Supervised Learning

A machine learning technique where the goal is to learn a mapping from inputs to outputs based on example input-output pairs. This process involves analyzing labeled training data—each example consisting of an input paired with the correct output—to infer a function that can predict outputs for new, unseen inputs. The effectiveness of supervised learning hinges on the algorithm’s ability to generalize from the training data to novel situations effectively. This generalization is crucial for the algorithm to accurately assign class labels to unseen instances, making supervised learning a foundational approach for tasks such as classification and regression, where predicting precise outcomes based on historical data is essential.