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.
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Saporta, G. (2008). Models for Understanding Versus Models for Prediction. In: Brito, P. (eds) COMPSTAT 2008. Physica-Verlag HD. https://doi.org/10.1007/978-3-7908-2084-3_26
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DOI: https://doi.org/10.1007/978-3-7908-2084-3_26
Publisher Name: Physica-Verlag HD
Print ISBN: 978-3-7908-2083-6
Online ISBN: 978-3-7908-2084-3
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