Machine Learning Times
Machine Learning Times
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AI Success Depends On How You Choose This One Number
 Originally published in Forbes, March 25, 2024. To do...
Elon Musk Predicts Artificial General Intelligence In 2 Years. Here’s Why That’s Hype
 Originally published in Forbes, April 10, 2024 When OpenAI’s...
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...
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7 years ago
Feature Engineering vs. Machine Learning in Optimizing Customer Behavior

 The debate on this topic is not a new one. What is the secret sauce in yielding improved modelling performance?  Is it the inputs, features or variables of a given predictive model or is it the specific mathematics that is used alongside these inputs or features? Historically, practitioners including myself, have tended to argue that it is the inputs or the feature engineering component which yield the most value when building models. In fact, I wrote a paper several years ago which was published in the “Journal of Marketing Analytics” –May, 2013 entitled “Is predictive analytics for marketers

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