Now let's turn our attention to a regression problem. As I'm sure you can recite in your sleep by now, regression is all about predicting continuous outcomes rather than predicting discrete class labels.

# Using regression models to predict continuous outcomes

# Understanding linear regression

The easiest regression model is called **linear regression**. The idea behind linear regression is to describe a target variable (such as Boston house pricing) with a **linear combination** of features.

To keep things simple, let's just focus on two features. Let's say we want to predict tomorrow's stock prices using two features: today's stock price and yesterday's stock price. We will denote today's stock...