Machine Learning Times
Machine Learning Times
EXCLUSIVE HIGHLIGHTS
The Quant’s Dilemma: Subjectivity In Predictive AI’s Value
 Originally published in Forbes This is the third of a...
To Deploy Predictive AI, You Must Navigate These Tradeoffs
 Originally published in Forbes This is the second of a...
Data Analytics in Higher Education
 Universities confront many of the same marketing challenges as...
How Generative AI Helps Predictive AI
 Originally published in Forbes, August 21, 2024 This is the...
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5 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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