Supervised learning is a type of machine learning algorithm that uses a known data-set (called the training data-set) to make predictions.

Category of supervised learning:

**Regression:**In a regression problem, we are trying to predict results within a continuous output, meaning that we are trying to map input variables to some continuous function.**Classification:**In a classification problem, we are instead trying to predict results in a discrete output. In other words, we are trying to map input variables into discrete categories.

**Example 1:**

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.

**Example 2:**

(a) *Regression* - For continuous-response values. For example given a picture of a person, we have to predict their age on the basis of the given picture

(b) *Classification* - for categorical response values, where the data can be separated into specific “classes”. For example given a patient with a tumor, we have to predict whether the tumor is malignant or benign.