Machine Learning Times
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Survey: Machine Learning Projects Still Routinely Fail to Deploy
 Originally published in KDnuggets. Eric Siegel highlights the chronic...
Three Best Practices for Unilever’s Global Analytics Initiatives
    This article from Morgan Vawter, Global Vice...
Getting Machine Learning Projects from Idea to Execution
 Originally published in Harvard Business Review Machine learning might...
Eric Siegel on Bloomberg Businessweek
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9 years ago
It is a Mistake to…. Extrapolate

 (Part 8 (of 11) of the Top 10 Data Mining Mistakes, drawn from the Handbook of Statistical Analysis and Data Mining Applications) Modeling “connects the dots” between known cases to build up a plausible estimate of what will happen in related, but unseen, locations in data space. Obviously, 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”, as discussed in the next installment.) But, there are other types of extrapolations that are equally dangerous. We tend to learn too much from

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