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Jev Fever: Predictive AI Is Superhot Again
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  EDITOR’S NOTE: This article is adapted from the...
The Data Disconnect: A Key Challenge for Machine Learning Deployment
  EDITOR’S NOTE: This article is excerpted from The...
A Brief History of Why Machine Learning Projects Stall
  EDITOR’S NOTE: This is the preface to The...
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7 years ago
Re-examining Model Evaluation: The CRISP Approach

 The performance of prediction models can be judged using a variety of methods and metrics. Some years ago, I was challenged to arrive at a set of rules that would provide both the analyst and marketer guidance as to how to evaluate results of a predictive modeling exercise. “What?” you ask.  “Just look into a standard textbook, and a whole host of criteria is readily available.”  These provide value to a more quantitative oriented manager, but to the novice marketer, these evaluation tools can be intimidating. After all, a ROC curve, a  Kolmogorov Smirnov test, or a  Root

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