Supervised learning is a type of machine learning where the model is trained on a labeled dataset — that is, each input is paired with a known output (also called the target or label). The goal is for the model to learn a mapping from inputs to outputs so that it can accurately predict the output for new, unseen inputs.
Typical tasks in supervised learning include classification (predicting categories, e.g. spam vs. not spam) and regression (predicting continuous values, e.g. house prices). Common algorithms include linear regression, logistic regression, decision trees, support vector machines, and neural networks.
The training process involves minimizing a loss function that measures the difference between the predicted output and the actual label. This is usually done with gradient descent or related optimization methods.
Supervised learning assumes that the data is fully labeled, which can be costly to produce. However, it remains one of the most effective and widely used paradigms in real-world applications, including image recognition, speech processing, medical diagnosis, and more. In reinforcement learning, supervised learning is sometimes used for imitation learning or in parts of hybrid systems.