Machine Learning Times
Machine Learning Times
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Three Best Practices for Unilever’s Global Analytics Initiatives
    This article from Morgan Vawter, Global Vice...
Getting Machine Learning Projects from Idea to Execution
 Originally published in Harvard Business Review Machine learning might...
Eric Siegel on Bloomberg Businessweek
  Listen to Eric Siegel, former Columbia University Professor,...
Effective Machine Learning Needs Leadership — Not AI Hype
 Originally published in BigThink, Feb 12, 2024.  Excerpted from The...
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4 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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