Supervised learning is the machine learning task of learning a function that maps an input to an output based on example input-output pairs. It infers a function from labeled training data consisting of a set of training examples, for every example in the training data set, the correct answer is already given.
There are two types of supervised learning problems:
In a regression problem, the supervised learning algorithm is trying to predict results within a continuous output, meaning that it is trying to map input variables to some continuous function.
Given data about the size of houses on the real estate market, try to predict their price. Price as a function of size is a continuous output, so this is a regression problem.
In a classification problem, the supervised learning algorithm is instead trying to predict results within a discrete output, meaning that it is trying to map input variables to discrete categories.
Classification - Given a patient with a tumor, we have to predict whether the tumor is malignant or benign.