A supervised machine-learning model transforms its input data into meaningful outputs, a process that is “learned” from exposure to known examples of inputs and outputs. It is trained rather than explicitly programmed.
It’s presented with many examples relevant to a task, and it finds statistical structures in these examples that eventually allow the system to come up with rules for automating the task.
Classical supervised machine learning algorithms range from Decision Trees to Naive Bayes to Support Vector Machines and so on.