Improved customer choice predictions using ensemble methods

Abstract

In this paper various ensemble learning methods from machinelearning and statistics are considered and applied to the customerchoice modeling problem. The application of ensemble learningusually improves the prediction quality of flexible models likedecision trees and thus leads to improved predictions. We giveexperimental results for two real-life marketing datasets usingdecision trees, ensemble versions of decision trees and thelogistic regression model, which is a standard approach for thisproblem. The ensemble models are found to improve upon individualdecision trees and outperform logistic regression.Next, an additive decomposition of the prediction error of amodel, the bias/variance decomposition, is considered. A modelwith a high bias lacks the flexibility to fit the data well. Ahigh variance indicates that a model is instable with respect todifferent datasets. Decision trees have a high variance componentand a low bias component in the prediction error, whereas logisticregression has a high bias component and a low variance component.It is shown that ensemble methods aim at minimizing the variancecomponent in the prediction error while leaving the bias componentunaltered. Bias/variance decompositions for all models for bothcustomer choice datasets are given to illustrate these concepts.brand choice;data mining;boosting;choice models;Bias/Variance decomposition;Bagging;CART;ensembles