In this chapter, we studied two methods for modeling numeric data. The first method, linear regression, involves fitting straight lines to data. The second method uses decision trees for numeric prediction. The latter comes in two forms: regression trees, which use the average value of examples at leaf nodes to make numeric predictions; and model trees, which build a regression model at each leaf node in a hybrid approach that is, in some ways, the best of both worlds.

We used linear regression modeling to calculate the expected medical costs for various segments of the population. Because the relationship between the features and the target variable are well-described by the estimated regression model, we were able to identify certain demographics, such as smokers and the obese, who may need to be charged higher insurance rates to cover the higher-than-average medical expenses.

Regression trees and model trees were used to model the subjective quality of wines from measureable characteristics...