Machine Learning Times
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How To Overcome The Confidence-Killer That Destroys Most Predictive AI Projects
  Originally published in Forbes When Henry Castellanos first presented...
You Must Address These 4 Concerns To Deploy Predictive AI
 Originally published in Forbes Most predictive AI projects fail to launch into production. The...
Hybrid AI: Industry Event Signals Emerging Hot Trend
 Originally published in Forbes After decades chairing and keynoting myriad...
Predictive AI Thrives, Despite GenAI Stealing The Spotlight
 Originally published in Forbes Generative AI and predictive AI ought...
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By: Dr. John Elder, CEO and Founder, Elder Research, Inc

 (Part 2 of 11 of the Top 10 Data Mining Mistakes, drawn largely from Chapter 20 of the Handbook of Statistical Analysis and Data Mining Applications) Only out-of-sample results matter; otherwise, a lookup table would always be the best model.  Researchers at the MD Anderson medical center in Houston (almost two decades ago) used neural networks to detect cancer.  Their out-of-sample results were reasonably good, though worse than training, which is typical.  They supposed that longer training of the network would improve it – after all, that’s the way it works with doctors – and were astonished to

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