It Is a Mistake to... Extrapolate


In his Top 10 Data Mining Mistakes John Elder shares lessons learned from 20 years of data science consulting experience. Avoiding these mistakes are cornerstones to any successful analytics project.

Models – and especially nonlinear ones — are very unreliable outside the bounds of any known data. Boundary checks are the very minimum protection against “over-answering”. But, there are other types of extrapolations that are equally dangerous.

In this paper about Mistake #7 you will learn how extrapolating conclusions from your modeling efforts can be fraught with danger. 

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