Machine Learning Times
EXCLUSIVE HIGHLIGHTS
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...
Hybrid AI Emerges To Tame LLMs – And Not A Moment Too Soon
 Originally published in Forbes The great potential of LLMs is...
AGI Is Infeasible. Instead, Pursue Superhuman Adaptable Intelligence
  Originally published in Forbes On a recent episode of the...
SHARE THIS:

6 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

This content is restricted to site members. If you are an existing user, please log in on the right (desktop) or below (mobile). If not, register today and gain free access to original content and industry news. See the details here.

Comments are closed.