Models for Understanding Versus Models for Prediction

Gilbert Saporta ()
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Gilbert Saporta: Chaire de statistique appliquée & CEDRIC, CNAM

A chapter in COMPSTAT 2008, 2008, pp 315-322 from Springer

Abstract: Abstract According to a standard point of view, statistical modelling consists in establishing a parsimonious representation of a random phenomenon, generally based upon the knowledge of an expert of the application field: the aim of a model is to provide a better understanding of data and of the underlying mechanism which have produced it. On the other hand, Data Mining and KDD deal with predictive modelling: models are merely algorithms and the quality of a model is assessed by its performance for predicting new observations. In this communication, we develop some general considerations about both aspects of modelling.

Keywords: model choice; data mining; complexity; predictive modelling (search for similar items in EconPapers)
Date: 2008
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-7908-2084-3_26

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DOI: 10.1007/978-3-7908-2084-3_26

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