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[SAP08] Models for Understanding versus Models for Prediction
Conférences invitées : COMPSTAT'08, Porto, Portugal, January 2008, pp.315-322,
motcle:
Résumé:
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.
BibTeX
| @inproceedings { | |||
| SAP08, | |||
| title | = | "{Models for Understanding versus Models for Prediction}", | |
| author | = | " G. Saporta ", | |
| booktitle | = | "{COMPSTAT'08, Porto, Portugal}", | |
| year | = | 2008, | |
| month | = | "January", | |
| pages | = | "315-322", | |
| } | |||
