Short Definition
Supervised learning trains a model from examples that include both inputs and known target outputs.
Intuition
The model learns by comparing its prediction with an answer key. Over many examples, it adjusts until its predictions match the labels more reliably.
Technical Definition
Supervised learning estimates a function from input variables to target labels by minimizing prediction error over labeled training data.
Example
A house-price model uses previous homes and sale prices to predict the price of a new home.
Common Misunderstandings
Supervised learning does not require human labeling for every dataset, but it does require target outputs.
It is not automatically more accurate than unsupervised learning; it solves a different problem.