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Machine Learning: It is a Mistake to Lack Relevant Data

Tags: mistake

In his Top 10 Data Mining Mistakes Dr. John Elder shares lessons learned from more than 20 years of data science consulting experience. Avoiding these mistakes are cornerstones to any successful analytics project. In this blog about Mistake #0 you will learn that the less probable the interesting event is, the more data it takes to obtain enough to generalize a model to unseen cases, and why some projects probably should not proceed until enough critical data is gathered to make them worthwhile.



This post first appeared on Elder Research Data Science & Machine Learning Blog, please read the originial post: here

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Machine Learning: It is a Mistake to Lack Relevant Data

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