Supervised learning is a type of machine learning where an algorithm is trained on a labeled dataset, which means the input data is paired with corresponding output labels. The goal of supervised learning is for the algorithm to learn a mapping or relationship between the input features and the target labels. During the training phase, the algorithm makes predictions based on the input data, and the predictions are compared to the true labels. The algorithm adjusts its parameters to minimize the difference between its predictions and the actual labels, effectively learning to generalize from the training data. Once trained, the model can make predictions on new, unseen data. Supervised learning is commonly used for tasks such as classification, where the goal is to assign input data to predefined categories, and regression, where the algorithm predicts a continuous numerical value.