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...
A University Curriculum Supplement to Teach a Business Framework for ML Deployment
    In 2023, as a visiting analytics professor...
The AI Playbook: Providing Important Reminders to Data Professionals
 Originally published in DATAVERSITY. This article reviews the new...
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10 years ago
Why Overfitting is More Dangerous than Just Poor Accuracy, Part II

 In part one, I described one problem with overfitting the data is that estimates of the target variable in regions without any training data can be unstable, whether those regions require the model to interpolate or extrapolate. Accuracy is a problem, but more precisely, the problems in interpolation and extrapolation are not revealed using any accuracy metrics and only arise when new data points are encountered after the model is deployed. This month, a second problem with overfitting is the model interpretation. Predictive modeling algorithms find variables that associate or correlate with the target variable. When models are

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