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.