Machine Learning Times
Machine Learning Times
EXCLUSIVE HIGHLIGHTS
Effective Machine Learning Needs Leadership — Not AI Hype
 Originally published in BigThink, Feb 12, 2024.  Excerpted from The...
Today’s AI Won’t Radically Transform Society, But It’s Already Reshaping Business
 Originally published in Fast Company, Jan 5, 2024. Eric...
Calculating Customer Potential with Share of Wallet
 No question about it: We, as consumers have our...
A University Curriculum Supplement to Teach a Business Framework for ML Deployment
    In 2023, as a visiting analytics professor...
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6 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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