Data Science for Marketing Analytics

6. More Tools and Techniques for Evaluating Regression Models

Overview

This chapter explains how to evaluate various regression models using common measures of accuracy. You will learn how to calculate the
Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE), which are common measures of the accuracy of a regression model. Later, you will use Recursive Feature Elimination (RFE) to perform feature selection for linear models. You will use these models together to predict how spending habits in customers change with age and find out which model outperforms the rest. By the end of the chapter, you will learn to compare the accuracy of different tree-based regression models, such as regression trees and random forest regression, and select ...