Variable Selection Using Random Forests (original) (raw)

Abstract

One of the main topic in the development of predictive models is the identification of variables which are predictors of a given outcome. Automated model selection methods, such as backward or forward stepwise regression, are classical solutions to this problem, but are generally based on strong assumptions about the functional form of the model or the distribution of residuals. In this pa-per an alternative selection method, based on the technique of Random Forests, is proposed in the context of classification, with an application to a real dataset.

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Authors and Affiliations

1. Dipartimento Metodi Quantitativi, Università di Brescia, c.da S. Chiara, 50, 25122, Brescia, Italy
   Marco Sandri & Paola Zuccolotto

Authors

1. Marco Sandri
2. Paola Zuccolotto

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Editors and Affiliations

1. Department of Economics Section of Statistics and Computing, University of Parma, Via Kennedy 6, 43100, Parma, Italy
   Sergio Zani, Andrea Cerioli & Marco Riani, &
2. Department of Statistics, Probability and Applied Statistics, University of Rome “La Sapienza”, Piazzale Aldo Moro 5, 00185, Roma, Italy
   Maurizio Vichi

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Sandri, M., Zuccolotto, P. (2006). Variable Selection Using Random Forests. In: Zani, S., Cerioli, A., Riani, M., Vichi, M. (eds) Data Analysis, Classification and the Forward Search. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-35978-8\_30

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• DOI: https://doi.org/10.1007/3-540-35978-8\_30
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