Introduction

The following is Wikipedia's definition of supervised learning:

"Supervised learning is the machine learning task of inferring a function from labeled training data."

There are two types of supervised learning algorithms:

• Regression: This predicts a continuous valued output, such as a house price.
• Classification: This predicts a discreet valued output (0 or 1) called label, such as whether an e-mail is a spam or not. Classification is not limited to two values (binomial); it can have multiple values (multinomial), such as marking an e-mail important, unimportant, urgent, and so on (0, 1, 2...). 

We are going to cover regression in this chapter and classification in the next.

We will use the recently sold house data of the City of Saratoga, CA, as an example to illustrate the steps of supervised learning in the case of regression:

1. Get the labeled data:
   
   • How labeled data is gathered differs in every use case. For example, to convert paper documents into a digital format, documents can...