In supervised learning, we are given an input (some data set) and an output. There is some relationship between the input and output.

Supervised learning problems are classified into:

- regression and
- classification problems.

In a regression problem, we try to predict results by mapping input variables to some continuous output function.

*e.g.,* trying to predict housing prices given a set of house sizes and their respective prices

In a classification problem, we try to predict results by mapping input variables into discrete categories.

*e.g.,* trying to predict whether a tumor is malignant or benign given a patient with a tumor