Machine Learning Times
Machine Learning Times
Elon Musk Predicts Artificial General Intelligence In 2 Years. Here’s Why That’s Hype
 Originally published in Forbes, April 10, 2024 When OpenAI’s...
Survey: Machine Learning Projects Still Routinely Fail to Deploy
 Originally published in KDnuggets. Eric Siegel highlights the chronic...
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...

5 years ago
The Loss of Inference

    For more from this writer, Stephen Chen, see his session, “The Perils of Prediction” at PAW Business, June 19, 2019, in Las Vegas, part of Mega-PAW. The burgeoning field of Data Science / Machine Learning borrows heavily from Statistics but bastardizes it. For example, “dummy variable” becomes “one-hot encoding”, “independent variables” become “features”. This shift in nomenclature results in a loss of methodological meaning that was inherent in the original names; for instance, a casual Google search on the “auto-mpg” dataset will throw out many how-to pages, almost all of which treat the variables as “features”

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