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—recall the various datasets we studied in Chapter 1, A Taste of Machine Learning) 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...
