Variables importance in questionnaire data on advertising

摘要

In this article, we deal with the problem of measuring the importance of features, that determine the purchase of the product after being exposed to an advertisement. We use an algorithm called Monte Carlo feature selection, which is based on multiple usage of decision trees, to achieve a ranking of variables from the questionnaire data. Our data generation process relies on low-involvement during the advertisement watching phase and the comparison of advertised products is based on purchase in a virtual shop.