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title: Supervised Learning
---
## Supervised Learning

In supervised learning, we know what the correct output should be. Supervised learning problems can be categorized into regression and classification. A regression problem is where you map input to a continuous output. A classification problem, in contrast, is where you map (group) inputs into discrete categories.

### Regression

Given data about used cars such as their mileage, you can predict their market prices. Since price is a continuous variable, this is a regression problem. In another example, Microsoft released a web app that predicts age based on picture. Again, as age is continuous rather than discrete or categorical, this is also a regression problem.  

### Classification

The regression problems above can be turned into classification problems. Suppose you want to look for a used car less than X dollars. Then the output would be if the used car fits the price that you set. Similarly, age prediction can be a classification problem if we are looking to predict if a submitted picture belongs to someone under 18, and therefore should not be allowed to buy cigarettes.

###  Discussion points:
  
- What is special about supervised learning?
- In what scenario would you use it in?
- Caveats or traps to think about?
- What are some example models?

#### 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.

#### Suggested Reading:

- https://en.wikipedia.org/wiki/Supervised_learning
- https://stackoverflow.com/a/1854449/6873133