In the previous chapter, we learned that training data is used to estimate the parameters of a model in supervised learning problems. Observations of explanatory variables and their corresponding response variables comprise training data. The model can be used to predict the value of the response variable for values of the explanatory variable that have not been previously observed. Recall that the goal in regression problems is to predict the value of a continuous response variable. In this chapter, we will examine simple linear regression, which can be used to model a linear relationship between one response variable and one feature representing an explanatory variable.
Suppose you wish to know the price of a pizza. ...
