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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10 years ago
Recognizing and Avoiding Overfitting, Part 1

 In my last two posts I described why overfitting predictive models is dangerous beyond the most obvious problem, namely that accuracy on new data is lower than expected. In the next few posts, I’ll describe how to recognized that overfitting may be occurring, and some common approaches to remove or mitigate the effects of overfitting.  OVERVIEW Overfitting is perhaps the most common and destructive problem in predictive modeling. It is common because predictive modeling is often an inductive, data-driven exercise where the data is king, as opposed to threads of statistical modeling where the model is king (terms

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