Predicting Product Choice with Symbolic
Regression and Classification
Philip Truscott and Michael F. Korns
Abstract Market researchers often conduct surveys to measure how much value
consumers place on the various features of a product. The resulting data should
enable managers to combine these utility values in different ways to predict the
market share of a product with a new configuration of features. Researchers
assess the accuracy of these choice models by measuring the extent to which the
summed utilities can predict actual market shares when respondents choose from
sets of complete products. The current paper includes data from 201 consumers
who gave ratings to 18 cell phone features and then ranked eight complete cell
phones. A simple summing of the utility values predicted the correct product on
the ranking task for 22.8 % of respondents. Another accuracy measurement is to
compare the market shares for each product using the ranking task and the estimated
market shares based on summed utilities. This produced a mean absolute difference
between ranked and estimated market shares of 7.8 %. The current paper applied two
broad strategies to improve these prediction methods. Various evolutionary search
methods were used to classify the data for each respondent to predict one of eight
discrete choices. The fitness measure of the classification approach seeks to reduce
the Classification Error Percent (CEP) which minimizes the percent of incorrect
classifications. This produced a significantly better fit with the hit rate rising from
22.8 to 35.8 %. The mean absolute deviation between actual and estimated market
shares declined from 7.8 to 6.1 % (p. <0.01). A simple language specification will
be illustrated to define symbolic regression and classification searches.
Keywords Abstract regression grammars • Genetic algorithms • Symbolic
regression • Classification • Non-linear regression
P. T r u s c o t t ()
Southwest Baptist University, Bolivar, MO 65613, USA
e-mail:[email protected]
M.F. Korns
Analytic Research Foundation, 2240 Village Walk Drive Suite 2305, Henderson,
NV 89052, USA
e-mail:[email protected]
© Springer International Publishing Switzerland 2016
R. Riolo et al. (eds.),Genetic Programming Theory and Practice XIII,
Genetic and Evolutionary Computation, DOI 10.1007/978-3-319-34223-8_12
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