Abstract
The aim of this contribution is to illustrate the role of statistical models and, more generally, of statistics, in choosing a Data Mining model. After a preliminary introduction on the distinction between Data Mining and statistics, we will focus on the issue of how to choose a Data Mining methodology. This well illustrates how statistical thinking can bring real added value to a Data Mining analysis, as otherwise it becomes rather difficult to make a reasoned choice. In the third part of the paper we will present, by means of a case study in credit risk management, how Data Mining and statistics can profitably interact.
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Giudici, P. (2005). Data Mining Model Comparison. In: Maimon, O., Rokach, L. (eds) Data Mining and Knowledge Discovery Handbook. Springer, Boston, MA. https://doi.org/10.1007/0-387-25465-X_31
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DOI: https://doi.org/10.1007/0-387-25465-X_31
Publisher Name: Springer, Boston, MA
Print ISBN: 978-0-387-24435-8
Online ISBN: 978-0-387-25465-4
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