Supervised learning is the machine learning task of learning a function that maps an input to an output based on example input-output pairs.
Stuart J. Russell, Peter Norvig - «Artificial Intelligence: A Modern Approach» (2010)
In Supervised Learning, algorithms learn from labeled data.
After understanding the data, the algorithm determines which label should be given to new data based on pattern and associating the patterns to the unlabeled new data.
towardsdatascience.com/supervised-machine-learning-classification-5e685fe18a6d
In supervised learning, an algorithm is employed to learn the mapping function from the input variable (x) to the output variable (y); that is y = f(X).
The objective of such a problem is to approximate the mapping function (f) as accurately as possible such that whenever there is a new input data (x), the output variable (y) for the dataset can be predicted.
For example, when provided with a dataset about houses, and you are asked to predict their prices, that is a regression task because price will be a continuous output.