Machine Learning Times
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
  Listen to Eric Siegel, former Columbia University Professor,...
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10 years ago
It is a Mistake to…. Listen Only to the Data

 (Part 5 of 11 of the Top 10 Data Mining Mistakes, drawn from the Handbook of Statistical Analysis and Data Mining Applications) Inducing models from data has the virtue of looking at the data afresh, not constrained by old hypotheses. But, while “letting the data speak”, don’t tune out received wisdom. Experience has taught this once brash analyst that those familiar with the domain are usually more vital to the solution of the problem than the technology we bring to bear. Often, nothing inside the data will protect one from significant, but wrong, conclusions. Table 1 contains two

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