Supervised Learning is the machine learning paradigm in which a model is trained on labeled examples (input-output pairs) to learn a mapping from inputs to outputs​.During training, the algorithm receives a set of inputs XXX (e.g., feature vectors) and corresponding target outputs YYY (labels or continuous values), and it adjusts its parameters to predict the outputs from the inputs as accurately as possible. The end result is a function f:X→Yf: X \to Yf:X→Y that can be used to predict the output for new, unseen inputs. Common tasks under supervised learning include classification (output is a class label) and regression (output is a continuous value). For example, using a labeled dataset of images of cats and dogs, a supervised learning algorithm can train a classifier to label new images as “cat” or “dog.” Key to supervised learning is the availability of ground-truth labels: the “supervision” comes from these correct examples that the model tries to imitate. Performance is typically evaluated on a separate test set to ensure the model generalizes well. Popular algorithms in this category range from linear models and decision trees to complex neural networks – but all share the common approach of learning from example pairs​.