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- W3149999097 abstract "This paper utilizes a novel data on consumer choice under uncertainty, obtained in a laboratory experiment in order to gain substantive knowledge of individual decision-making and to test the best modeling strategy. We compare the performance of logistic regression, discriminant analysis, naive Bayes classifier, neural network, decision tree, and Random Forest (RF) to discover that the RF model robustly registers the highest classification accuracy. This model also reveals that apart from demographic and situational factors, consumer choice is highly dependent on social network effects." @default.
- W3149999097 created "2021-04-13" @default.
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- W3149999097 date "2016-01-01" @default.
- W3149999097 modified "2023-09-23" @default.
- W3149999097 title "Modeling Economic Choice under Radical Uncertainty: Machine Learning Approaches" @default.
- W3149999097 hasPublicationYear "2016" @default.
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